Plant Phenomics studies the phenotypic dynamics that form plant phenotypes by systematically measuring traits using phenotyping methods from the quantum to the ecosystem levels. It has emerged as an interdisciplinary field advancing sensing, computation, and plant biology. However, Plant Phenomics has lacked a unifying framework that integrates its community's core concepts from the formal and life sciences. Central to this framework is the definition of the phenome as a set of phenes that govern phenotypic dynamics across all spatial and temporal scales of biological and ecological organization and in interaction with the environment. This paradigm moves beyond gene-centric views and recognizes the equal importance of all spatial and temporal scales in forming plant phenotypes, advancing Plant Phenomics as a data-driven discipline and its emerging profession, the plant phenomicist.
Why it matches plant phenotyping methods植物フェノミクスの概念・枠組みを扱うレビューであり、植物形質の系統的測定とフェノタイピング手法を主題としているため、方法論レビューとして適格。
abstractPlant Phenomics studies the phenotypic dynamics that form plant phenotypes by systematically measuring traits using phenotyping methods from the quantum to the ecosystem levels.
Published17 Aug 2026Growth hormone & IGF research : official journal of the Growth Hormone Research Society and the International IGF Research SocietyCited by 0 · OpenAlex ↗
Background ecombinant human growth hormone (rhGH) has been used for four decades under a largely population-based dosing paradigm, yet growth response varies substantially among children with apparently similar auxological phenotypes. Advances in genomics, GH-insulin-like growth factor-1 (IGF-1) axis biomarkers, mathematical and machine-learning (ML) prediction models, and digital health technologies now allow individualized characterization of growth disorders, forming the basis of an emerging precision-endocrinology paradigm. Objectives (1) to synthesize evidence on monogenic and polygenic genetic determinants of pediatric growth faltering and/or short stature and their diagnostic yield; (2) to evaluate GH-IGF-1 axis biomarkers and pharmacogenetic markers, including the IGF-1/IGFBP-3 molar ratio as a likely better indicator of bioactive IGF-1 than IGF-1 alone, together with mathematical/ML prediction models, for individualizing rhGH therapy; and (3) to appraise artificial intelligence (AI) and digital-health tools, bone-age algorithms, facial-recognition phenotyping, adherence-prediction models, and smartphone growth-monitoring, as instruments for operationalizing precision endocrinology in children and adolescents with growth disorders. Methods This is a systematic review employing narrative synthesis (a systematic scoping review). Reporting explicitly followed the PRISMA Extension for Scoping Reviews (PRISMA-ScR) checklist rather than the PRISMA 2020 statement for systematic reviews and meta-analyses, because the heterogeneous outcome metrics across genetic, diagnostic-accuracy, prediction-model, and AI/digital-health studies preclude meta-analytic pooling of a single quantitative effect size; PRISMA-ScR is the methodologically appropriate reporting framework for a review mapping evidence across such conceptually distinct domains. PubMed/MEDLINE was searched for English-language, pediatric (0-18 years) studies published between 2000 and 2026. Two-stage screening (title/abstract, then full text) was performed. Quality was appraised using design-appropriate tools: an adapted Newcastle-Ottawa Scale for genetic-association studies, QUADAS-2 for diagnostic-accuracy biomarker studies, PROBAST/TRIPOD-informed criteria for prediction-model and ML studies, and reference-standard/external-validation criteria for AI-imaging studies. Sixty-two studies were retained for qualitative synthesis. Results Monogenic defects (SHOX, ACAN, NPR2) and exome-sequencing panels explain a meaningful minority (approximately one-quarter) of previously "idiopathic" short stature, while genome-wide association studies and polygenic scores capture a substantial share of the remaining heritable variance, with polygenic risk scores achieving areas under the receiver-operating-characteristic curve up to 0.84 for predicting adult short stature. The IGF-1/IGFBP-3 M ratio outperforms IGF-1 alone for diagnosing GH deficiency (sensitivity 87.5%, specificity 83.0%), reflecting the greater bioavailability of free, unbound IGF-1 relative to that carried in the ternary IGF-1/IGFBP-3/acid-labile-subunit complex. GH-receptor exon-3 (d3) pharmacogenetic variants and machine-learning models (random forest, transcriptomic classifiers) improve prediction of individual rhGH response beyond classical mathematical models. AI-based bone-age algorithms achieve near-radiologist accuracy with reduced inter-observer variability, computer-aided facial-phenotyping tools show comparable diagnostic accuracy for syndromic short-stature disorders such as Noonan and Turner syndrome, and connected-device/ML adherence-monitoring and smartphone growth-tracking tools objectively detect suboptimal adherence and growth faltering earlier than conventional clinic-based surveillance; network meta-analyses of once-weekly long-acting rhGH formulations further suggest that reduced injection burden can translate into modestly improved height outcomes relative to daily rhGH. These findings are synthesized into a Precision-Medicine Cascade, a practice-oriented framework showing how genotype, biomarker, and digital data streams can be layered onto routine auxological assessment to guide same-visit clinical decisions on diagnostic work-up, dosing, and monitoring frequency. Conclusion Converging genetic, biomarker, computational, and digital-health evidence supports a feasible, evidence-grounded trajectory toward individualized therapy, including rhGH and emerging growth-plate-targeted agents, in pediatric growth disorders. The Precision-Medicine Cascade proposed here offers pediatric endocrinologists an immediately applicable framework for integrating these tools into everyday practice. However, current tools remain adjunctive rather than replacement for clinical judgment, and prospective, ethnically diverse validation of integrated precision-endocrinology pathways is required before routine adoption.
Why it matches plant phenotyping methods植物ではなく小児成長障害を対象とするため通常の植物フェノタイピング索引には不適合だが、提示基準上の「plant」要件を満たさないため本来は除外。ただしレビュー自体はAI画像・成長モニタリング等のヒト表現型手法を中心的に扱う。
Sustainable crop improvement is urgently needed to ensure global food security, particularly for developing and densely populated countries. The integration of artificial intelligence (AI) and machine learning (ML) into crop science tri typing is reshaping the conventional agriculture practices into an era of high-throughput phenotyping (HTPP) data-driven modern agriculture. AI tools accelerate data generation, mining, imputation, storage, transfer, and optimal decision-making within agricultural systems. AI tools are paving the way for modern plant breeding strategies by uncovering genetic variability and bridging the genotype-to-phenotype (G2P) gap, thus enabling the future of predictive breeding. Plant genetic gains or phenotype (P), by and large, depend on the genotype (G), environment (E), and their interaction (GEI). This review will provide a comprehensive overview of the historical background, current status, and prospects for integrating AI and ML tools in agricultural tri-typing, encompassing genotyping, phenotyping, and envirotyping. We explore AI-driven tools for genome analysis, HTPP platforms, and environmental data integration, emphasizing how these technologies overcome persistent bottlenecks in predictive breeding. Furthermore, this review will offer the reader key insight into modern trends, including the paradigm shift in phenomics patent filings, global distribution of HTP phenomics facilities, the publications volume and related research over the last two decades, and individual institutions currently leading or prospectively will lead the world in plant phenomics. Similar to plant phenotyping, we also try to address the integration and application of AI/ML algorithms in plant genotyping and envirotyping. Supplementary information The online version contains supplementary material available at 10.1007/s11032-026-01705-1.
Why it matches plant phenotyping methods植物フェノタイピング、ハイスループットフェノタイピング、AI/MLツールを中心に扱う包括的レビューであり、方法論レビューとして該当する。
abstractThis review will provide a comprehensive overview of the historical background, current status, and prospects for integrating AI and ML tools in agricultural tri-typing, encompassing genotyping, phenotyping, and envirotyping.
Crop stress develops through a sequence that begins with molecular and biophysical perturbation, progresses through physiological dysfunction, and only later becomes visually apparent. Precision agriculture therefore requires sensors that can shorten the interval between stress onset and actionable diagnosis while preserving spatial context. This critical narrative review examines the complementary roles of nanosensors, plant-wearable and implantable electronics, proximal sensing, unmanned aerial vehicles, satellite remote sensing, and geographic information systems in early crop-stress detection. Literature published from 2000 to 1 June 2026 was selected through live searches of accessible scholarly indexes, DOI registries, publisher records, institutional repositories, and citation networks, with foundational studies retained where necessary. The evidence shows that nano-enabled interfaces can measure early biochemical, ionic, volatile, electrical, and microclimatic signals at high temporal resolution, whereas geospatial technologies reveal the distribution, persistence, and management relevance of stress across canopies and fields. Optical nanotube sensors, surface-enhanced Raman probes, electrochemical microneedles, ion-selective wearables, and flexible leaf sensors have demonstrated biologically meaningful signals before visible symptoms in controlled or pilot field settings. Yet most remain constrained by sparse sampling, crop-specific calibration, bio-interface effects, power and communication burdens, uncertain durability, and limited agronomic validation. Geospatial methods are operationally more mature, particularly thermal and multispectral imaging for water stress and hyperspectral imaging for disease and nutrient-related changes, but they often infer stress through non-specific proxies that are confounded by canopy structure, atmosphere, soil background, phenology, and co-occurring stresses. The strongest future architecture is therefore not a contest between nanoscale and landscape-scale sensing. It is a multiscale system in which physiologically specific plant sensors anchor and interpret spatial imagery, while remote sensing directs where high-specificity measurements and interventions are most valuable. Progress depends on prospective field trials, reference measurements, uncertainty-aware data fusion, interoperability, lifecycle safety assessment, and decision thresholds linked to economic and agronomic outcomes.
Why it matches plant phenotyping methods植物ストレス状態の検出に用いるナノセンサー、ウェアラブルセンサー、熱・マルチスペクトル・ハイパースペクトル画像などを中心に批判的に統合した方法レビューであり、単なる農業応用紹介ではなく、センサー性能、校正、検証、データ融合を論じている。
abstractThis critical narrative review examines the complementary roles of nanosensors, plant-wearable and implantable electronics, proximal sensing, unmanned aerial vehicles, satellite remote sensing, and geographic information systems in early crop-stress detection.
Traditional breeding is approaching its efficiency limit in addressing global food security challenges, climate change, and multi-dimensional information integration. Artificial intelligence (AI) and digital phenotyping have become core driving forces in data-driven breeding. This review systematically elaborates the evolutionary roadmap from AI breeding 1.0 , which relies on traditional phenotypic data, to the emerging paradigm of AI breeding 2.0 . We clarify the core differences between these two paradigms, dissect the “data divide” in the transition process, and summarize the integrated technical framework underlying AI breeding 2.0 . The central argument is that the core limitation of AI breeding 1.0 lies in data inadequacy rather than constraints on algorithmic capacity. The transition to AI breeding 2.0 relies on standardized high-throughput digital phenotyping, multi-modal data integration, and closed-loop data-centric breeding systems. Finally, we propose four priority actions to promote the widespread adoption of data-driven breeding and provide an actionable roadmap for enhancing global crop improvement efforts.
Why it matches plant phenotyping methods植物デジタルフェノタイピングをAI育種の中核技術として体系的に論じるレビューであり、フェノタイピング手法・データ統合・技術枠組みが中心です。
abstractArtificial intelligence (AI) and digital phenotyping have become core driving forces in data-driven breeding.
Purpose: This paper investigated automated physical quality assessment of harvested seeds. Design/Methodology/Approach: This study provides an extensive review of computer vision-based two-dimensional (2D) and three-dimensional (3D) deployments for the physical inspection of harvested seeds and grains. For this purpose, a total of 75 peer-reviewed articles published between 2022 and 2025 were identified from scientific databases, including Scopus, Web of Science, IEEE Xplore, and ScienceDirect. These articles were based on seed quality assessment, image processing, and artificial intelligence. The selected articles were systematically analysed according to different stages of the processing pipeline, including data acquisition, preprocessing, segmentation, feature extraction, and classification. Research Limitation: This review is restricted to physical quality assessment of harvested seeds, excluding chemical, biochemical, and nutritional parameters. It references 75 peer-reviewed articles published between 2022 and 2025. Findings: This study identified technical problems related to variations in seed samples, hardware setups, segmentation, feature selection, and classification. These problems significantly affect the performance of automated systems. Based on a critical examination of the present automated systems, this paper highlighted the scope for future research. Practical Implication: An advanced, future-ready system can address the need for integrated imaging methods and effective data processing. Social Implication: The adoption of automated seed inspection systems provides assurance of food security. Originality/ Value: This paper identified critical gaps such as the absence of a unified processing framework, the lack of cross-species generalisation, and the limited adoption of explainable AI.
Why it matches plant phenotyping methods収穫種子の物理品質を画像から評価する2D/3Dコンピュータビジョン手法を体系的にレビューしており、植物形質取得法が中心である。
abstractThis study provides an extensive review of computer vision-based two-dimensional (2D) and three-dimensional (3D) deployments for the physical inspection of harvested seeds and grains.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Background: Rice breeding requires faster development of high-yielding, climate-resilient, resource-efficient, and high-quality cultivars for production systems exposed to environmental variability and increasing input constraints. Genomic selection offers an opportunity to predict breeding value before extensive field evaluation, although its effectiveness depends on the integration of genomic, phenotypic, and environmental information. Methods: This narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding. Results: Genome-wide marker data can support early ranking of breeding materials for grain yield, grain quality, disease resistance, drought tolerance, salinity tolerance, and nutrient-use efficiency. Prediction performance is influenced by trait architecture, marker density, training-population size, genetic relatedness between training and candidate populations, phenotypic data quality, and genotype-by-environment interaction. Red-green-blue, multispectral, hyperspectral, thermal, and light detection and ranging platforms can generate temporal traits associated with plant architecture, biomass, water status, nutrient status, and stress responses, which may improve prediction under suitable population and validation designs. Speed-breeding systems shorten generation intervals and facilitate rapid advancement, recurrent selection, and recycling of superior parental lines. Conclusions: Integrated breeding pipelines that combine genomic prediction, high-throughput phenotyping, environmental data, and speed breeding can improve selection efficiency and shorten rice improvement cycles. Wider adoption will require affordable technology platforms, standardized data systems, multi-environment validation, breeder capacity development, and collaborative data-sharing frameworks for smart and greener agriculture.
Why it matches plant phenotyping methods高スループット表現型解析をゲノム選抜との統合という方法論的主題の一部として批判的にレビューしており、各種画像・センサープラットフォームと形質抽出を扱うため、表現型手法レビューに該当する。
abstractThis narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding.
Agriculture plays a pivotal role in ensuring global food security, economic stability, and sustainable development. Plant diseases significantly reduce agricultural productivity, resulting in substantial economic losses and threatening food supply worldwide. Early and accurate identification of plant leaf diseases enables timely intervention, minimizes crop damage, and enhances agricultural yield. Traditional disease diagnosis relies heavily on visual inspection by agricultural experts, making the process labor-intensive, subjective, and unsuitable for large-scale deployment. Recent advances in artificial intelligence, particularly deep learning, have transformed plant disease diagnosis by enabling automatic feature extraction and highly accurate image-based classification. This review presents a comprehensive analysis of recent developments in deep learning techniques for plant leaf disease identification. Various convolutional neural network (CNN) architectures, including AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, MobileNet, Inception, and Xception, are critically reviewed along with modern transformer-based models such as Vision Transformer (ViT), Swin Transformer, and hybrid CNN–Transformer frameworks. The paper also examines transfer learning strategies, object detection methods including YOLO and Faster R-CNN, and semantic segmentation approaches such as U-Net and DeepLabV3+. Publicly available benchmark datasets, including PlantVillage, PlantDoc, AI Challenger, Cassava Leaf Disease, and Rice Leaf Disease datasets, are discussed in terms of dataset diversity, annotation quality, and practical applicability. Furthermore, image preprocessing techniques, data augmentation methods, evaluation metrics, and deployment considerations for mobile and edge devices are comprehensively reviewed. The paper identifies current research challenges, including dataset imbalance, environmental variability, model interpretability, computational complexity, and limited real-world generalization. Finally, emerging research directions such as explainable artificial intelligence, federated learning, multimodal learning, self-supervised learning, lightweight architectures, and edge AI are discussed to provide future research opportunities. This review serves as a valuable resource for researchers, practitioners, and agricultural technologists interested in developing robust, scalable, and intelligent plant disease identification systems.
Why it matches plant phenotyping methods植物葉の病害状態を画像から識別する深層学習手法を中心に、モデル、データセット、評価、展開を体系的にレビューしており、植物表現型計測手法のレビューに該当する。
abstractThis review presents a comprehensive analysis of recent developments in deep learning techniques for plant leaf disease identification.
Abstract Detecting crop diseases early and responding promptly is vital for protecting agricultural productivity. It also helps maintain the quality and quantity of yields and reduces the risk of disease transmission to humans and livestock. Effective disease management is therefore critical to ensuring both global and local food security. However, traditional methods often based on visual inspection and delayed human judgment, are typically insufficient for identifying diseases at an early stage. Recent developments in Artificial Intelligence (AI) and the Internet of Things (IoT) offer new opportunities to address these challenges. By integrating IoT sensor networks with AI techniques such as machine learning and deep learning, it becomes possible to monitor plant health in real time and detect diseases with greater accuracy. This review explores the strengths and limitations of current AI-enabled IoT solutions in agriculture. It highlights how these systems leverage large-scale data and advanced image processing to outperform conventional methods in terms of speed, precision, and efficiency. Such improvements can significantly reduce crop losses and support more sustainable agricultural practices. Finally, the paper reviews key research trends, identifies current challenges, and outlines future directions in the field. It emphasizes the transformative potential of smart agriculture in advancing plant disease management and promoting environmentally responsible food production. This systematic review was conducted in strict accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, synthesizing a final selection of 152 peer-reviewed papers. The overarching aim is to critically evaluate and map literature published between 2015 and 2026 using a systematic approach that addresses the integration of the IoT, AI, Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks (CNN), sensor technologies, and sustainable agricultural practices in the context of plant disease detection.
Why it matches plant phenotyping methods植物病害の症状・健康状態をAI、画像処理、IoTセンサーで検出する手法を主題とした系統的レビューであり、植物フェノタイピング手法のレビューに該当する。
titleA comprehensive review of artificial intelligence and Internet of Things integration based plant disease detection for sustainable agriculture
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Plant phenomics has undergone rapid development over the past two decades, driven by advances in imaging, robotics, artificial intelligence and data analysis. Whilst field phenotyping is increasingly operational and scalable, the relevance of controlled-environment (CE) phenotyping is questioned because of concerns regarding the limited transferability of results to agricultural conditions. This Expert View first addresses the limitations and risks of using CE as surrogate of outdoor conditions. However, we argue that CE enables the disentangling of interacting environmental drivers allowing causal analysis of plant responses to multiple abiotic and biotic stresses. CE platforms also provide access to complex traits that are difficult or impossible to measure in the field whilst providing a robust framework in combination of field approaches to interpret and predict field performance. We further discuss contexts where CE remains indispensable, including quarantine and biosafety regulations together with emerging opportunities for agricultural innovation. Whilst limitations of CE systems are acknowledged, including issues of extrapolation, pot effects, environmental realism, and the indispensable need for rigorous envirotyping, we conclude that CE phenotyping should be regarded as an enabling analytical framework that complements and strengthens field phenomics for crop adaptation research under climate change.
Why it matches plant phenotyping methods管理環境フェノタイピングとフィールドフェノミクスの役割・限界・分析枠組みを論じる専門的レビューであり、植物表現型計測の方法論が中心です。
titleWhy bother with controlled-environment phenotyping when field phenomics is already up and running?
Plant disease phenotyping underpins resistance breeding, epidemiology and crop-loss management, yet it remains a recognised bottleneck. This review asked whether the two metrics that dominate the discipline, the disease severity index (DSI) and the area under the disease progress curve (AUDPC), adequately represent disease as a temporally unfolding process, and what the evidence says about dynamic alternatives. Reporting followed PRISMA 2020 and the Synthesis Without Meta-analysis (SWiM) guideline. Web of Science Core Collection, Scopus, PubMed and a Google Scholar grey-literature sweep were searched for records published between January 2020 and December 2025, retrieving 1,192 records; 874 remained after de-duplication, 128 full texts were assessed and 31 studies met the eligibility criteria. Citation chasing added 24 foundational works, giving 55 included studies. Records were dual-screened (Cohen's kappa = 0.86), appraised with an adapted Mixed Methods Appraisal Tool, and synthesised using vote counting by direction of effect, an evidence map and structured cross-study comparison; meta-analysis was inappropriate because outcomes were not commensurable. Thirty studies (54.5%) represented disease at a single assessment and eight (14.5%) collapsed the epidemic into one integrated area, whereas only twelve (21.8%) retained the full trajectory. Across six outcome domains, all 29 study-level comparisons favoured the temporally richer method and none reported a null or negative result, an asymmetry indicating probable reporting bias. Certainty was high for visual-assessment findings, moderate for sensing and dynamic modelling, and low for field-realised genetic gain. The phenotyping bottleneck has migrated from data acquisition to data representation.
Why it matches plant phenotyping methods植物病害フェノタイピング手法の動的評価を中心に、既存指標と代替手法を体系的に比較した方法論レビューである。
titleA Systematic Review of Dynamic Disease Phenotyping in Plant Pathology
Main conclusion This review highlights that integrating physiological, molecular, imaging, and AI-based approaches enables early and reliable detection of graft incompatibility, improving rootstock-scion selection, orchard sustainability, fruit productivity, and long-term tree performance. One of the most serious problems in fruit growing is the breaking, weakening, or dying of the tree at the graft union, either within a short period of time or after 10-15 years. This condition is often triggered by environmental factors; however, it is certainly not solely caused by environmental conditions. This problem is defined as graft incompatibility. Graft incompatibility refers to the failure of successful anatomical and physiological integration between a rootstock and a scion, primarily due to biochemical, molecular, and genetic mismatches that impair vascular reconnection and long-term stability of the graft union. Graft incompatibility remains a significant constraint in fruit tree production, resulting in reduced longevity, yield, and quality of orchards. This review integrates recent advancements in physiological, molecular, and technological approaches for the early detection of graft incompatibility, with special emphasis on Prunus species such as sweet cherry. Physiological and biochemical markers, including phenolic accumulation, antioxidant enzyme activities, and isozyme patterns, serve as early indicators of incompatibility. At the molecular level, transcriptomic, metabolomic, and epigenetic analyses have revealed differentially expressed genes (DEGs) and post-translational modifications associated with stress signaling, vascular reconnection, and callus formation. Imaging-based non-destructive technologies such as micro-CT, MRI, terahertz, and hyperspectral imaging now allow real-time visualization of graft-union structures without damaging plant tissues. The integration of artificial intelligence and machine learning with multi-omics datasets and imaging tools offers unprecedented potential for predictive diagnosis and compatibility assessment. Collectively, these multidisciplinary advances are reshaping the detection and management of graft incompatibility, enabling faster, more reliable, and sustainable rootstock-scion selection in fruit tree breeding.
Why it matches plant phenotyping methods果樹の接ぎ木不親和性という植物状態の早期検出法を、画像・生理・分子・AI技術の観点から体系的にレビューしており、フェノタイピング手法が中心である。
abstractThis review integrates recent advancements in physiological, molecular, and technological approaches for the early detection of graft incompatibility
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Drought is recognized as the primary abiotic stress limiting global crop productivity and poses a significant threat to food security. Consequently, the genetic improvement of drought tolerance has become a priority for modern plant breeding. Developing resilient cultivars requires a fundamental understanding of the physiological, biochemical, and molecular mechanisms that plants employ to counteract water deficits. This review provides a comprehensive analysis of drought-induced effects across various developmental stages in legumes, detailing the signaling networks that facilitate stress perception and response. Furthermore, we evaluate the experimental parameters and methodologies frequently used to assess drought tolerance, weighing their respective advantages and limitations. Finally, we analyze the revolutionary role that high-throughput phenotyping could play in stress assessment and precision breeding.
Why it matches plant phenotyping methodsマメ科植物の乾燥耐性評価に用いる実験パラメータ・方法論をレビューし、高スループット表現型解析の役割も論じるため、植物フェノタイピング方法のレビューが中心である。
abstractFurthermore, we evaluate the experimental parameters and methodologies frequently used to assess drought tolerance, weighing their respective advantages and limitations.
High-throughput phenotyping (HTP) has advanced rapidly in recent decades, driven by technological developments across research and agricultural frameworks. Despite its success in measuring traits, it remains underutilized in field crop breeding programs. While the most critical and labor-intensive selection is conducted on heterozygous single plants or small plots at early stages, most phenotyping research focuses on stable genotypes grown in large plots. Here, we reconsider how HTP can be effectively integrated into breeding by accounting for methodologies, scale-related constraints, and technological limitations. Our focus remains on self-pollinated field crops, the predominant global food source. In light of climate change and food security needs, improving the integration of breeding and phenomics can accelerate genetic and technological advances in developing elite varieties.
Why it matches plant phenotyping methods圃場作物育種における高スループット表現型解析の方法論、規模制約、技術的限界を中心に論じるレビューであり、植物フェノタイピング手法の統合が主題である。
abstractHere, we reconsider how HTP can be effectively integrated into breeding by accounting for methodologies, scale-related constraints, and technological limitations.
Artificial intelligence (AI) has emerged as a transformative tool for plant health monitoring, offering new opportunities for scalable, timely, and data-driven pest and disease management in agriculture. This review provides a comprehensive synthesis of AI-based methods for pest and plant disease detection, systematically organizing existing literature across sensing modalities, learning paradigms, and deployment scales. We distinguish between population-level pest monitoring, plant-centric visual inspection, and field-scale surveillance, as well as between post-symptomatic disease recognition and pre-symptomatic detection enabled by spectral imaging technologies. Beyond summarizing recent advances, this work places strong emphasis on critical analysis, discussing fundamental limitations related to data scarcity, domain shift, generalization under field conditions, and the challenge of disentangling biotic from abiotic stress factors. The review further examines the distinction between correlation-driven AI predictions and causal disease understanding, positioning AI as a complementary decision-support tool alongside established diagnostic methods. Building on these insights, we outline key future research directions, including multimodal sensor fusion, explainable and trustworthy AI, edge-based deployment for real-time monitoring, and the development of foundation models for unified agricultural intelligence. This review aims to serve as both an accessible entry point and a critical reference for advancing AI-driven plant health management.
Why it matches plant phenotyping methods植物の病害・害虫状態を画像・スペクトルなどで検出するAI手法を対象としたレビューであり、植物状態の取得・推定手法が中心である。
titleA Review on Artificial Intelligence Methods for Plant Disease and Pest Detection
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Field / plotCell / cellular structureWhole plant / canopy / plot / fieldGrowth / development / phenologyStress response / tolerance
Abiotic stress tolerance has been significantly weakened in modern crops during the domestication process. Regaining tolerance has become a critical task in light of current climate trends and their impact on global food security. Abiotic stress tolerance is an extremely complex trait and is conferred at various levels of plant functional organization and developmental stages, with regulatory mechanisms operating across multiple scales, from individual cells to tissues and the entire plant. The emergence of advanced molecular tools such as single-cell RNA sequencing and spatial omics technologies has revolutionized the field, advancing our understanding of plant responses to hostile environments. However, the implementation of this knowledge in crop breeding programmes is handicapped by the lack of appropriate phenotyping platforms. Here, we argue that current phenotyping methods may be excellent tools for functional validation of previously discovered traits but have limited predictive value in stress biology. We also propose that bridging the mismatch between omics technologies and phenotyping is the only way to account for cell-specific operation of key genes conferring stress tolerance and implementing them in breeding programmes. Some practical examples using cell-based phenotyping tools such as fluorescence dyes or electrophysiological methods are given, and current limitations and prospects of cell-based phenotyping are discussed.
Why it matches plant phenotyping methods細胞ベースの植物フェノタイピング手法を扱い、蛍光色素や電気生理学的方法の例、限界、展望を論じる方法論レビューである。
abstractSome practical examples using cell-based phenotyping tools such as fluorescence dyes or electrophysiological methods are given, and current limitations and prospects of cell-based phenotyping are discussed.
Abstract Faba bean ( Vicia faba L.) is a widely cultivated legume in temperate regions, valued for human consumption, animal feed, hay production, and as a cover crop. Improving faba bean productivity requires accurate characterization of morphological and physiological traits that govern crop performance and yield. Although conventional phenotyping methods are available, they are often constrained by high cost, limited accuracy, and insufficient spatial and temporal coverage. In recent years, high-throughput phenotyping (HTP) approaches have shown potential to overcome these limitations. HTP integrates sensors, unoccupied aerial and ground vehicles, and imaging systems to enable rapid, and non-destructive monitoring of crop traits at high spatiotemporal resolution. Despite increasing adoption of HTP, no study has comprehensively compared and synthesized these methods for faba beans, therefore is the goal of this study with emphasis on advanced sensing and data analytics including machine learning (ML) and deep learning (DL). A systematic evaluation of 24 peer-reviewed articles from an initial pool of 381 publications between 2015 and 2025, identified research trends, performance benchmarks, and integration challenges with HTP-based faba bean characterization. Substantial increase in faba bean HTP studies has been noted after 2021, with ML approaches dominating current applications (37.5%). Most studies have relied on small datasets, single-season experiments, and limited environmental variability, restricting model robustness and scalability. Such limitations, research gaps, and future directions are also outlined to support reliable, and scalable phenotyping for improved faba bean production.
Why it matches plant phenotyping methodsソラマメの高スループット表現型解析手法を体系的に比較・評価し、センサー、画像、UAV・地上車両、データ解析の性能と課題を扱う方法論レビューである。
titleHigh-Throughput Phenotyping of Faba Bean Crop Using Remote Sensing Technologies and Data Analytics: A Systematic Assessment of Status and Trends Over Past Decade
Field / plotMultimodalLiDAR / point cloudMultispectral / hyperspectralWhole plant / canopy / plot / fieldArchitecture / morphology / geometry
Crop phenotyping serves as a fundamental basis for crop breeding, precision cultivation, and smart agriculture. In recent years, it has evolved toward multi-modal integration and multi-scale coordination. This paper analysed indoor and outdoor phenotyping platforms across diverse application scenarios, and reviewed sensing technologies including RGB imaging, multi-spectral imaging, hyperspectral imaging, thermal imaging, fluorescence imaging, LiDAR, and nuclear magnetic resonance (NMR). The applications of these technologies were summarized in capturing crop morphological traits, physiological status and biochemical components. The phenotyping acquisition methods and intelligent analytical techniques were also analyzed at different scales such as plant cells, tissues and organs, individual plants, population plot and field. Additionally, the advancements were explored in high-throughput phenotyping technologies and their integration with crop gene function analysis, providing a reference for future phenotyping research.
Why it matches plant phenotyping methods作物表現型センシング技術・装置、取得法、解析技術、プラットフォームを主題とする包括的レビューであり、表現型手法が中心です。
abstractThis paper analysed indoor and outdoor phenotyping platforms across diverse application scenarios, and reviewed sensing technologies including RGB imaging, multi-spectral imaging, hyperspectral imaging, thermal imaging, fluorescence imaging, LiDAR, and nuclear magnetic resonance (NMR).
In modern agriculture, artificial intelligence (AI) is doing excellent work in crop monitoring, crop disease detection, crop yield prediction, and crop stress assessment. Various techniques such as deep learning, generative models, vision transformers, explainable AI (XAI), multimodal fusion, etc., have helped in building intelligent crop analysis. This paper provides the comparative crop analysis of various crop species, data modes, and environmental conditions for the review of benchmark studies for the current framework, experimental methodologies, and datasets. The important challenges and open issues identified are limited field datasets, class imbalance, dataset bias, high computational complexity, privacy concerns, etc. Based on these, we suggested future work that can include foundation models, digital twin techniques, federated learning, multimodal frameworks, and interpretability architecture. This review provides a review for creating reliable, scalable, and sustainable AI-driven crop analysis systems. In addition to that, the survey seeks to give researchers and AI practitioners a comprehensive analysis of the current situation.
Why it matches plant phenotyping methods作物の病害・収量・ストレス評価を対象に、AI手法、データモード、ベンチマーク、実験方法、データセットを体系的にレビューしており、植物表現型取得・推定手法のレビューが中心です。
abstractThis paper provides the comparative crop analysis of various crop species, data modes, and environmental conditions for the review of benchmark studies for the current framework, experimental methodologies, and datasets.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
RiceGrowth / development / phenologyStress response / tolerance
The reliance on traditional or manual phenotyping creates significant operational bottlenecks in rice breeding due to its resource-intensive and time-consuming nature. This review focused on the significance of high-throughput phenotyping (HTP) as a promising technology that enables rapid, accurate, and non-destructive phenotyping of large populations. HTP has great potential to accelerate rice breeding by revolutionizing phenomics. This review examined the various applications of HTP in rice research, phenomics, and breeding. The use of HTP in rice has been substantiated through a range of cutting-edge technologies, such as drones, imaging systems, and sensor networks, that facilitate precise monitoring of key traits at various growth stages, assessment of responses to biotic and abiotic stresses, and the identification of genes or quantitative trait loci (QTLs) associated with essential characteristics. Also, this review discussed HTPs' contributions to current rice breeding programs and documented notable challenges in scaling them. This review offers insights into optimizing HTP strategies to advance rice research, phenomics, and rice breeding.
Why it matches plant phenotyping methodsイネのハイスループット表現型解析技術の応用、技術、課題を中心に扱うレビューであり、植物フェノタイピング手法レビューに該当する。
abstractThis review focused on the significance of high-throughput phenotyping (HTP) as a promising technology that enables rapid, accurate, and non-destructive phenotyping of large populations.
Why it matches plant phenotyping methods植物の水分状態を測定する古典的方法（重量法、プレッシャーチャンバー、サイクロメトリ等）を中心に原理・手順・検証用途をレビューしており、植物生理形質のフェノタイピング方法レビューに該当する。
abstractIn Part 1 of a two-part review, we provide insights into using leaf water content as a reliable proxy for assessing water status and synthesize classical, destructive methods for measuring plant water status, encompassing gravimetric techniques, Scholander pressure chamber and psychrometric techniques.
Why it matches plant phenotyping methods植物の水分状態を非破壊・遠隔センシングで測定する手法を体系的に比較・評価したレビューであり、植物フェノタイピング手法が中心です。
abstractThis second of a two-part review synthesizes recent advances in non-destructive approaches for measuring plant water status, evaluating their principles, applications and limitations.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Agriculture plays a crucial role in the development of countries whose economies rely heavily on food production. In the face of climate change and growing global population, plant breeders are challenged to adopt more efficient crop improvement strategies. The advances in artificial intelligence (AI), particularly in large-scale data integration, analysis, and pattern recognition, have revolutionized several scientific disciplines, including plant breeding. In this review, we provide a comprehensive survey of the potential of AI tools in plant breeding with four key objectives: (i) revolutionizing high-throughput phenotyping, (ii) exploring AI-driven breeding methodologies beyond traditional approaches, (iii) optimizing breeding pipelines through improved modelling of genotype × environment × management interactions, and (iv) highlighting the limitations of AI in plant breeding and future directions. Case studies published during the past two decades illustrate successful implementations of AI-powered phenotyping and breeding frameworks for major traits across diverse crop species. Furthermore, AI tools show great promise in refining crop traits at the molecular level by increasing the accuracy and precision of emerging fields including gene editing and genomic selection. We emphasize the importance of interdisciplinary collaboration to maximize the benefits of AI in plant breeding programs and to support the sustainable and food-secure future. This review bridges the gap between AI and agricultural applications, offering a roadmap for researchers, industry professionals, and policymakers to harness information fusion and computational models for advancing precision agriculture. It will serve as a valuable resource for future plant breeding, accelerating crop improvement from phenotyping to genomic selection and breeding decision support.
Why it matches plant phenotyping methodsAIを用いた植物のハイスループット・デジタルフェノタイピングを主要対象として扱うレビューであり、フェノタイピング手法の方法論的総説に該当する。
abstractIn this review, we provide a comprehensive survey of the potential of AI tools in plant breeding with four key objectives: (i) revolutionizing high-throughput phenotyping
Strawberry greenhouse cultivation is increasingly supported by sensing technologies, artificial intelligence (AI), and decision-support infrastructure, but their horticultural value depends on whether heterogeneous measurements can be translated into biologically meaningful crop states and practical management decisions. This review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation. The reviewed studies show substantial progress in measuring and interpreting vegetative, reproductive, fruit-quality, stress-related, and environmental crop states through imaging, spectral, environmental, root-zone, and modeling approaches. However, much of the literature still emphasizes measurement accuracy, model performance, or infrastructure capability, whereas fewer studies validate whether AI-derived outputs improve crop response, management decisions, workflow, resource use, or production outcomes. The review therefore distinguishes sensing technologies for data acquisition and measurement from AI-based methods for interpretation and prediction, and examines how crop-state information can be connected to practical greenhouse decision making. It also compares established decision technologies, including expert systems, model predictive control, digital twins, and closed-loop coordination, with supervised agentic coordination as bounded decision-support concepts rather than as evidence of unrestricted autonomous control. Future work should emphasize phenotype-to-action validation, domain-aware benchmarking, and supervised deployment studies that connect model outputs with decision rules, crop outcomes, operational constraints, and grower oversight. By grounding sensing technologies and AI-based interpretation methods in crop-response validation, strawberry greenhouse systems can progress toward supervised, crop-state-driven decision support.
Why it matches plant phenotyping methods温室イチゴのフェノタイピング、マルチモーダルセンシング、AIによる作物状態解釈を中心に整理する方法論レビューであり、植物状態の取得・推定手法が主題。
abstractThis review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation.
Global food security requires crop improvement strategies that can respond to population growth, climate variability and increasing constraints on agricultural resources. Conventional plant breeding has contributed substantially to crop productivity, yet long selection cycles and dependence on extensive field evaluation can limit the rate of genetic gain. This review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding, with emphasis on their combined contribution to selection accuracy and breeding efficiency. Key genomic approaches discussed include whole-genome sequencing, reference and pan-genome resources, genome-wide association studies, genomic selection and CRISPR-Cas-based genome editing. The review also examines high-throughput phenotyping platforms, including controlled-environment systems, ground-based robots, UAV-based remote sensing and root phenotyping tools. Machine learning approaches, ranging from random forest and support vector machines to convolutional neural networks, recurrent networks, transformers and explainable artificial intelligence, are considered in relation to genomic prediction, image analysis and breeding decision support. Multi-omics integration, data management, FAIR principles and an integrated genomics-phenomics-ML breeding pipeline are reviewed as enabling components for practical deployment. Crop-specific examples from wheat, rice, maize, soybean and legumes illustrate the potential and constraints of these technologies. The review further identifies key challenges, including phenotyping bottlenecks, genotype-environment interaction, data governance, model interpretability and regulatory uncertainty.
Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、ハイスループット計測プラットフォーム、画像解析、機械学習、UAV・ロボット・根系計測などをレビューしているため。
abstractThis review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding
Crop diseases cause 20-40% of food losses every year and cause economic damage of more than USD 220 billion per year on a global scale. The basic problems in precision agriculture remain the same as early and accurate disease detection. Hyperspectral imaging (HSI) is a type of imaging technique that captures hundreds of contiguous wavelengths of the electromagnetic spectrum spanning from 400 to 2500 nm that has been found to be a useful non-destructive diagnostic tool that can detect the subtle biochemical differences that occur in plant tissue before symptoms are visible. The paper critically summarizes and reviews the literature from 2000 to 2024, especially focusing on the application of AI and machine learning (ML) for HSI-based crop disease detection. A total of 48 primary studies are reviewed and grouped into five thematic categories: (1) spectral vegetation index methods, (2) classic machine learning classifiers, (3) deep learning architectures, (4) attention and transformer mechanisms and (5) disease severity quantification. Based on this review, four gaps in the literature are identified: (1) lack of comparison of classical and deep learning models on the same splits of the same datasets, (2) underutilisation of the SWIR-2 spectral range (>2000 nm) for the discrimination of diseases, (3) lack of integrated spatial mapping of the disease severity from spectral index fusion, and (4) lack of lightweight deep learning spectral-only architectures for field deployment in resource-constrained environments. These gaps together form a promising research program based on this AI approach to automated crop disease detection, and the experimental research work reported in our companion paper is fueled by these gaps.
Why it matches plant phenotyping methods植物病害の症状・重症度をハイパースペクトル画像とAIで推定する手法を対象とした体系的レビューであり、植物フェノタイピング手法のレビューが中心。
abstractHyperspectral imaging (HSI) is a type of imaging technique that captures hundreds of contiguous wavelengths of the electromagnetic spectrum spanning from 400 to 2500 nm that has been found to be a useful non-destructive diagnostic tool that can detect the subtle biochemical differences that occur in plant tissue before symptoms are visible.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Plant diseases result in the estimated loss of 20–40% of the world’s crop production annually, amounting to more than $220 billion in economic losses and threatening food security for a rapidly expanding world population. While the conventional methods for detecting plant diseases rely on visual inspection of the symptoms, they are resource-consuming. For effective plant disease detection at a pre-mature stage, hyperspectral imaging (HSI) represents a paradigm shift in technology. It can be used to obtain subtle spectral signatures outside the visible spectrum, which enables pre-symptomatic and highly specific plant disease diagnosis. Concurrently, deep learning (DL) has become the prevalent analytical paradigm for decoding the complex and high-dimensional data that HSI produces. This paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026. We first set out the biological and physical principles by which HSI is uniquely suited to detecting plant–pathogen interactions in the absence of visible symptoms. We then present a detailed taxonomy of deep learning architectures for Vision Imaging and HSI data, ranging from basic 1D and 3D convolutional neural networks (CNNs) to hybrid models with attention mechanisms and, most recently, vision transformers, which have achieved greater robustness to real-world conditions. There is currently a major and consistent “lab-to-field” performance gap. A critical analysis of various studies reveals a persistent and significant performance gap between models that perform well on controlled lab datasets (ranging from 95 to 99%) and field-collected data (typically 70–85%). This paper also addresses the practical gap of environmental variability, image noise, and the domain gap between the controlled environment and the real dataset. Finally, this review concludes by providing strategic research recommendations and a roadmap, highlighting that the future of the field is contingent upon not only architectural innovation but also a holistic approach, with robustness, scalability, affordability, and interpretability as the main focus to bring the proven potential of HSI-DL systems from the lab to the field, ultimately contributing to global food security.
Why it matches plant phenotyping methods植物病害の症状・状態をハイパースペクトル画像と深層学習で推定する手法を中心に扱うレビューであり、植物フェノタイピング手法レビューに該当する。
abstractThis paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026.
Driven by artificial intelligence, multi-source sensing, agricultural robots and big data technologies, global agriculture is rapidly upgrading from precision agriculture and agriculture 4.0 to agriculture 5.0. Artificial intelligence has evolved from a single diagnostic tool to an intelligent system that integrates the “perception-decision-execution” process throughout. It is widely applied in crop phenotype analysis, remote sensing monitoring, yield prediction, and autonomous operation of intelligent equipment, etc. This article takes the framework of “intelligent perception-cognitive decision-autonomous execution” to systematically review the core technologies, typical applications, and frontier directions of agricultural artificial intelligence. It focuses on introducing the progress of key technologies such as three-dimensional phenotype, hyperspectral remote sensing, multimodal fusion, and causal machine learning, as well as their value in improving resource utilization efficiency, enhancing climate resilience, and supporting field precision management. At the same time, it points out that current agricultural AI still faces practical bottlenecks such as insufficient generalization ability of models, scarce data and high annotation costs, difficulties in edge deployment, barriers in multi-source data integration, and weak interpretability and engineering reliability. Future research will focus on the construction of closed-loop autonomous farms, the collaboration of agricultural large models and intelligent agents, the construction of data centers and AI and data infrastructure, and the development of green and low-cost AI research. This will provide support for the technological innovation and industrialization implementation of agricultural artificial intelligence.
Why it matches plant phenotyping methods農業AIの総説であり、作物表現型解析を明示的に扱い、三次元表現型・ハイパースペクトルリモートセンシング・マルチモーダル融合などの表現型取得技術を主要テーマとしてレビューしている。
abstractIt is widely applied in crop phenotype analysis, remote sensing monitoring, yield prediction, and autonomous operation of intelligent equipment, etc.
The detection technology of crop diseases and pests is transitioning from single sensor monitoring to intelligent perception and multimodal fusion. This paper follows the PRISMA 2020 standard and systematically reviews the relevant core literature. This paper systematically summarizes the development history of spectral sensing technology and analyzes the physical mechanisms of hyperspectral and multispectral imaging in early identification of crop diseases. The focus is on the architectural evolution of deep learning models, including lightweight convolutional neural networks (CNNs), vision transformers (ViTs) with long-range dependency modeling capabilities, and the efficient computing state space model Mamba. In addition, the research progress of spatial spectral joint learning, heterogeneous data fusion, and vision-language models (VLMs) in improving system robustness and interpretability are introduced. By synthesizing the integrated applications of UAV remote sensing, Internet of Things (IoT) edge computing and intelligent robots in staple and cash crops, this paper summarizes the implementation of the integrated system of perception, decision-making and execution. To address the issues of insufficient cross-domain generalization ability and uneven allocation of computing resources in existing models, this paper provides perspectives on the future development of agricultural artificial intelligence (AI) towards foundation model-driven, edge-intelligent collaboration, and green sustainable direction, which can provide theoretical reference for engineering applications in the field of intelligent plant protection.
Why it matches plant phenotyping methods作物病害の画像・スペクトル観測による植物の病徴・病害状態推定を中心に、検出技術と計算手法を体系的にレビューしており、植物フェノタイピング手法のレビューに該当する。
abstractThis paper systematically summarizes the development history of spectral sensing technology and analyzes the physical mechanisms of hyperspectral and multispectral imaging in early identification of crop diseases.
Unmanned aerial vehicle (UAV) based remote sensing has emerged as a disruptive technology for detecting crop water stress (CWS) in real time, precisely and at low cost offering significant advancements over conventional approaches. The study examined the red green blue (RGB), multispectral (MSP), hyperspectral (HSP), thermal image sensors integrated with UAVs, which offers a high-spatial and temporal resolution of physiological indicators such as chlorophyll content and canopy cover, canopy temperature, stomatal conductance. The study highlights that in spring maize, random forest (RF) models using UAV-derived MSP and thermal indices with leaf area index (LAI) performed well (R² > 0.575, root mean square error (RMSE)
Why it matches plant phenotyping methodsUAV搭載センサーによる作物の水ストレスや生理形質のモニタリング技術をレビューしており、表現型取得法が中心である。
titleRecent trends in crop water stress monitoring using remote sensing technologies: A review
ABSTRACT Improving genetic gain and optimizing agronomic practices are fundamental to ensuring global food security. Phenotyping plays a pivotal role in accelerating genetic gain, and recent advancements in high-throughput technologies—particularly those based on remote sensing—have significantly enhanced phenotyping capacities. However, the widespread adoption of these innovations has been constrained by high operational and infrastructure costs. In response, affordable yet accurate phenotyping solutions are emerging, enabling large-scale deployment beyond elite research settings. The integration of artificial intelligence (AI) to process and analyze phenotypic data and the open-source nature of the supporting software, combined with the synergistic power of information and communication technologies, is accelerating the development of scalable and cost-effective phenotyping systems. An example of the application capabilities associated with AI is the development in recent years of smartphone-based phenotypic platforms that process information in the cloud. Additionally, the potential use of satellites as future phenotyping platforms promises to further reduce costs and expand access. These low-cost solutions encompass a broad array of components, including sensors, platforms, data processing pipelines, and decision-support tools, collectively ushering in a new era of accessible phenotyping.
Why it matches plant phenotyping methods低コスト植物フェノタイピング技術を総説し、センサー、プラットフォーム、データ処理、AI解析、スマートフォンおよび衛星による測定系を中心に扱っているため、方法論レビューとして適格。
abstractPhenotyping plays a pivotal role in accelerating genetic gain, and recent advancements in high-throughput technologies—particularly those based on remote sensing—have significantly enhanced phenotyping capacities.
Abstract Tomato diseases significantly affect crop productivity and food security, necessitating accurate and timely detection methods. This paper presents a systematic and analytical review of deep learning approaches for tomato disease detection and severity assessment, based on 76 research studies. Existing methods are categorized into classification, detection, segmentation, and emerging multi-task frameworks. The analysis shows that convolutional neural networks achieve high accuracy on controlled datasets but exhibit limited generalization in real-world conditions. Advanced architectures, including transformer-based and hybrid models, improve performance but increase computational complexity. A key finding is the limited focus on disease severity assessment, which remains underexplored despite its importance for precision agriculture. The review identifies major challenges, including dataset limitations, lack of standardized benchmarks, and deployment constraints. Future directions emphasize multi-task learning, real-world dataset development, lightweight models, and explainable AI. This study provides a foundation for developing robust and practical tomato disease detection systems.
Why it matches plant phenotyping methodsトマト病害の検出・重症度評価という植物状態の画像推定手法を、研究・データセット・課題の観点から体系的にレビューしており、フェノタイピング手法のレビューが中心である。
abstractThis paper presents a systematic and analytical review of deep learning approaches for tomato disease detection and severity assessment, based on 76 research studies.
Photosynthesis is the fundamental biological process that introduced oxygen into Earth's atmosphere and continues to power life, from the earliest single-celled organisms to entire global ecosystems. Yet, measuring photosynthesis across scales has been challenging because traditional techniques have not transcended scales. The emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales, representing a new age for optical remote sensing to study photosynthesis and shaping the decades of satellite SIF research. Here, focusing on spatiotemporal scales, we review the mechanisms that drive the relationship between SIF and photosynthesis. Remotely sensed SIF is modulated by biological drivers, environmental drivers, the interaction between biological and environmental drivers, and the viewing geometry. Studying fluorescence at small scales provides the ecophysiological understanding needed to disentangle the biological and environmental drivers of SIF at larger scales. Leveraging progress in satellite SIF, future research should focus on cross-scale mechanistic understanding of the drivers of SIF and using SIF as a metric for plant function beyond photosynthesis.
Why it matches plant phenotyping methods植物の光合成・機能を推定するリモートセンシング手法（SIF）を中心に、その機構とスケール間利用をレビューしており、植物フェノタイピング手法のレビューに該当する。
abstractThe emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales
Crop diseases continue to pose a serious danger to agricultural productivity worldwide, resulting in large losses in crop quality, yield, and economic value. For large-scale farming, traditional disease detection techniques, which mostly rely on specialist knowledge and manual examination, are frequently laborious, subjective and ineffective. Deep learning (DL), a branch of artificial intelligence, has become a potent method for automated and precise crop disease prediction because to developments in information technology. With an emphasis on image-based analysis and data-driven modelling, this paper provides a thorough overview of current advancements in deep learning-based methods for crop disease diagnosis and prediction. Convolutional Neural Networks (CNNs), one type of deep learning architecture, have shown exceptional performance in reliably diagnosing different crop illnesses and extracting complicated characteristics from plant photos. The detection accuracy has been further enhanced by advanced versions like ResNet, VGGNet, and EfficientNet, which frequently surpass 95 percentage under controlled circumstances. Precision agriculture techniques have been improved by the real-time monitoring and early disease identification made possible by the integration of deep learning with Internet of Things (IoT) devices, remote sensing technologies, and drone-based imaging systems. Despite these developments, a number of problems still exist, such as the requirement for sizable labelled datasets, high processing demands, overfitting problems, and restricted model generalisation in practical settings. This paper identifies these drawbacks and explores possible remedies, such as explainable deep learning methods, data augmentation, and transfer learning. Future research will focus on integrating intelligent decision-support systems, scalable deployment approaches, and edge computing. All things considered, deep learning-based crop disease prediction systems have enormous potential to revolutionise contemporary agriculture by facilitating early intervention, enhancing crop health management, and encouraging sustainable farming methods.
Why it matches plant phenotyping methods植物画像から病害状態を推定する深層学習手法を中心に扱うレビューであり、植物フェノタイピング手法レビューに該当する。
abstractWith an emphasis on image-based analysis and data-driven modelling, this paper provides a thorough overview of current advancements in deep learning-based methods for crop disease diagnosis and prediction.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Global changes in agricultural and environmental systems will necessitate new crop research methodologies in the future years to ensure more effective use of natural resources and food security. The progress in next-generation sequencing has led to the emergence of multi-omics techniques as successful crop improvement strategies. Multi-omics studies using high-throughput techniques have been critical in understanding growth, senescence, yield, and biotic and abiotic stress responses in an array of crops. When multi-omics provide a high-resolution map of the molecular frameworks governing stress responses, advanced deep phenotyping systems can utilize advanced sensors to quantify dynamic physiological and morphological traits non-destructively. The systematic integration of these multi-layered datasets through association mapping and machine learning frameworks allows for the identification of superior alleles and regulatory hubs. Currently, the non-invasive imaging methods have effectively incorporated computer vision, machine learning, and deep learning components of AI. The use of machine learning and deep learning have progressively increased the effectiveness of data gathering and analysis. The supervised, unsupervised, and deep learning architectures have become effective tools for overcoming the genotype-to-phenotype gap, enabling more accurate predictions of yield and stress tolerance. Despite challenges related to data dimensionality, high infrastructure costs, and the need for standardized protocols, the convergence of these fields offers a robust architecture for predictive breeding. By linking microscopic molecular shifts to macroscopic field performance, integrated strategies accelerate the discovery of adaptive traits and the delivery of high-yielding, climate-smart cultivars. This review examines the revolutionary potential of combining deep phenotyping and multi-omics data for developing a thorough, high-throughput crop improvement strategy that can revolutionize crop breeding.
Why it matches plant phenotyping methods深層フェノタイピングと非破壊センサー・画像解析を中心に、作物形態・生理形質の高スループット計測とマルチオミクス統合を論じる方法論的レビューである。
abstractThis review examines the revolutionary potential of combining deep phenotyping and multi-omics data for developing a thorough, high-throughput crop improvement strategy that can revolutionize crop breeding.
Phenotypic traits represent the observable outcomes of interactions between plant genetic architecture and environmental conditions and form the foundation for understanding plant growth, adaptation, and productivity. With the rapid advancement of high-throughput phenotyping and phenomics platforms, trait evaluation has evolved from isolated measurements toward integrative, data-driven frameworks capable of capturing structural, physiological, and functional plant responses across spatial and temporal scales. However, the expanding diversity of measurable traits and analytical approaches has created a need for a coherent conceptual framework that organizes phenotypic traits in a biologically meaningful and experimentally practical manner. This review addresses this gap by synthesizing existing knowledge and presenting a comprehensive classification of plant phenotypic traits based on measurement strategy, including direct and surrogate traits, as well as biological organization, developmental stage, functional relevance, environmental responsiveness, and genetic control. Particular emphasis is placed on the growing role of surrogate image-derived traits as scalable proxies for complex physiological processes, enabling rapid, non-destructive phenotyping across large populations. By integrating traditional trait concepts with modern imaging technologies, sensor systems, and data analytics, the review highlights how multidimensional trait classification strengthens genotype-phenotype interpretation, improves experimental design, and accelerates precision breeding efforts. Ultimately, this synthesis provides a unified perspective linking phenomics technologies with plant biology, offering a conceptual and methodological foundation for advancing crop improvement and developing climate-resilient agricultural systems.
Why it matches plant phenotyping methods植物フェノタイピングおよびフェノミクスの測定戦略、画像由来形質、センサー、データ解析を体系化する方法論的レビューであり、方法論が中心です。
abstractThis review addresses this gap by synthesizing existing knowledge and presenting a comprehensive classification of plant phenotypic traits based on measurement strategy
Abstract Fusarium head blight (FHB) of wheat ( Triticum aestivum L.) is primarily caused by the fungal pathogen Fusarium graminearum . This disease can cause significant economic loss due to decreasing yield, reducing seed quality, and the production of deoxynivalenol (DON); therefore, resistance to the disease is a primary concern for breeders. Phenotyping methods largely depend on the resistance mechanism being evaluated, but traditional approaches are often time‐consuming, subjective, and largely inaccurate. This review explores and contrasts traditional and modern phenotypic methods for various FHB resistance components. Digital image‐based phenotyping spans low‐cost RGB (red, green, blue) (i.e., Bayer array) sensors, multispectral sensors, and hyperspectral sensors. Across these sensors, approaches using spectral indices or deep learning have shown strong promise for detecting and classifying infection in both wheat spikes and kernels. Hyperspectral imaging has been largely explored and can be used to accurately estimate infection in spikes and kernels, as well as estimate DON content in the grain, using spectral indices or models input with specific wavebands. However, waveband‐specific approaches do not generalize well to new data, and hyperspectral imaging is significantly more resource‐intensive than RGB or multispectral cameras, limiting its practicality for most breeding programs. Phenotypic approaches using spectral indices and/or deep learning on digital images show the most potential for use in wheat breeding, due to their scalability and low cost. However, the widespread adoption of these techniques will depend on standardized imaging protocols, robust generalization across diverse genotypes, and effective integration into breeding pipelines.
Why it matches plant phenotyping methodsコムギ赤かび病抵抗性の表現型取得手法を、従来法からRGB・マルチスペクトル・ハイパースペクトル画像解析まで比較・レビューしており、フェノタイピング手法が中心である。
abstractThis review explores and contrasts traditional and modern phenotypic methods for various FHB resistance components.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Plant phenotyping captures the integrated structural and functional traits of crops across cellular, tissue, organ, whole-plant, and population scales. It represents the outward expression of genotype-environment interactions and provides essential technological support for precision breeding, smart agriculture, and sustainable crop production. As farming shifts from experience-based to data-driven decision-making, the efficient acquisition and integrated analysis of phenotypic information at multiple spatial scales has emerged as a major research frontier at the intersection of agronomy, plant science, and agricultural engineering.
Why it matches plant phenotyping methods植物フェノタイピングの多尺度取得・統合解析を主題とするレビューであり、方法論分野を直接扱っている。
titleAdvancing multi-scale plant phenotyping for precision agriculture and sustainable crop production.
Papaya (Carica papaya L.) is a crop of great economic importance. However, factors such as low genetic variability, abiotic stresses, and technological limitations compromise fruit productivity and quality. In this context, the use of modern tools, such as digital phenotyping, combined with genetic improvement, has stood out in the search for more productive cultivars with improved postharvest quality. The objective of this study was to conduct a comprehensive literature review on papaya cultivation, addressing its main agronomic aspects, as well as postharvest evaluation methods, with an emphasis on digital image-based phenotyping techniques applied to fruit quality analysis. The study was based on scientific publications selected from the Scopus, SciELO, and Google Scholar databases, mainly covering the period from 2014 to 2024. Studies related to papaya cultivation, genetic improvement, digital phenotyping, sensory analysis, and physicochemical evaluation of fruits were included, as well as classical references relevant to the topic. The literature indicates that papaya breeding depends on the exploitation of genetic variability present in germplasm banks, aiming at the development of cultivars with superior agronomic traits and improved fruit quality. Digital phenotyping stands out as an efficient tool for collecting and analyzing phenotypic data, allowing greater precision, cost reduction, and optimization of the selection process. The integration of digital phenotyping with physicochemical and sensory analyses enables the identification of genotypes with higher productive potential and fruits with better consumer acceptance.
Why it matches plant phenotyping methodsデジタル画像ベースの植物表現型解析を果実品質評価に適用する方法を中心に扱うレビューであり、植物フェノタイピング手法レビューとして適格。
abstractThe objective of this study was to conduct a comprehensive literature review on papaya cultivation, addressing its main agronomic aspects, as well as postharvest evaluation methods, with an emphasis on digital image-based phenotyping techniques applied to fruit quality analysis.
Plant diseases are a major problem worldwide, affecting crop yield and quality and impacting food security. Early identification and accurate diagnosis of plant diseases is crucial to minimizing crop damage and maximizing eco-friendly farming practices. Traditional methods of plant disease detection include manual diagnosis by experts, which is time consuming to arrive at the final diagnosis and is prone to human errors. In recent years, Machine Learning (ML) and Deep Learning (DL) algorithms have been used for the automatic detect plant diseases, with no intervention needed by experts. These algorithms are used to detect diseases by analysing images of plant stems, leaves and other parts, differentiating between healthy and diseased plants. Step by step procedures are used to diagnose plant diseases by employing techniques such as image pre-processing, feature extraction, and classification to enhance image quality for accurate disease classification and prediction. This paper details the application of various existing ML and DL algorithms in the detection, classification and prediction of plant diseases, where image-based techniques are also adopted for accurate and swift diagnosis. Performance metrics such as sensitivity, accuracy, and specificity elaborate on the efficacy of ML and DL algorithms, acting as reliable tools for accurate plant disease detection. The outcomes of this study illustrate that these ML and DL models are well suited for the identification and classification of plant diseases.
Why it matches plant phenotyping methods植物画像から病害状態を検出・分類する機械学習手法を体系的に扱うレビューであり、植物フェノタイピング手法が中心です。
abstractThis paper details the application of various existing ML and DL algorithms in the detection, classification and prediction of plant diseases, where image-based techniques are also adopted for accurate and swift diagnosis.
Vegetable crops are the backbone of global food and nutritional security, yet they remain among the most vulnerable agricultural commodities to climate change. Heat, drought, salinity, flooding and escalating biotic pressures now represent existential threats to vegetable productivity, quality and accessibility, particularly in developing nations. This review examines how the synergistic deployment of genomics and phenomics can accelerate the development of climate-resilient vegetable cultivars. The objective is to critically synthesize current advances in genomic tools—including next-generation sequencing (NGS), genome-wide association studies (GWAS), CRISPR/Cas9 genome editing and genomic selection—alongside high-throughput phenotyping (HTP) technologies such as multispectral imaging, thermal sensors and drone-based platforms, and to evaluate their integration as a strategic framework for vegetable breeding. Key findings demonstrate that the integration of genomics and phenomics enables high-resolution genotype-to-phenotype (G→P) mapping, significantly reduces breeding cycle duration and improves selection accuracy for complex, multigenic stress-adaptive traits across major vegetable crops including tomato, pepper, cucumber, lettuce, spinach and Brassica species. Specific discoveries include the identification of heat-tolerance QTL in tomato via GWAS-phenomics integration, CRISPR-mediated improvement of drought signaling in pepper and genomic selection models achieving prediction accuracies of 0.65 for heat stress indices in lettuce. The review also identifies critical limitations including high phenotyping costs, data integration challenges, lack of standardized protocols, regulatory hurdles for genome-edited cultivars and limited capacity in low-income countries. These findings have direct policy implications: so in order to unlock the transformative potential of genomics-phenomics integration, smallholder farmers in climate-vulnerable regions will not have access to improved cultivars. Instead, national agricultural research programs and international funding bodies should prioritize investment in affordable phenotyping infrastructure, open-access pan-genome databases, harmonized data ontologies, and regulatory frameworks that facilitate the deployment of genome-edited vegetables.
Why it matches plant phenotyping methods植物フェノタイピング技術（マルチスペクトル画像、熱センサー、ドローンプラットフォーム等）をゲノミクスとの統合という観点から批判的にレビューしており、方法論が中心的である。
abstractThis review examines how the synergistic deployment of genomics and phenomics can accelerate the development of climate-resilient vegetable cultivars.
Abstract This study presents a systematic review of artificial intelligence applications, particularly machine learning and deep learning models, for okra leaf disease detection and diagnosis. Due to the limited number of okra-specific studies, a related study on leaf diseases of other crops was included for comparative analysis. Following the PRISMA framework, 28 peer-reviewed articles published between 2015 and 2025 were examined. The results show that convolutional neural networks dominate the current study, accounting for nearly 70% of all reviewed models. Architectures such as MobileNet, ResNet, and InceptionV3 consistently achieved accuracies above 90%. Okra-focused studies reported performance ranging from 87% to 98.63% (mean = 94.5%) but were constrained by small or imbalanced datasets. In contrast, studies on other crops achieved accuracies between 77% and 99.84% (mean = 96%), supported by substantially larger datasets. The review further identifies limited adoption of explainable AI, vision transformers, and federated learning approaches. Key study gaps include dataset scale, environmental integration, real-world validation, and reproducibility. The study recommends developing large-scale okra-specific datasets, integrating agronomic variables, deploying lightweight, mobile-ready architectures, and strengthening the adoption of explainable and federated learning frameworks to enable scalable, field-deployable diagnostic systems.
Why it matches plant phenotyping methods植物病害の症状を画像から検出・診断するAI手法を体系的にレビューしており、植物の病態を直接推定する方法論が中心です。
abstractThis study presents a systematic review of artificial intelligence applications, particularly machine learning and deep learning models, for okra leaf disease detection and diagnosis.
Abstract Water stress is a major limiting factor for crop productivity worldwide, and its impacts are intensifying due to climate variability and increasing water scarcity. This review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment and supporting precision water management. We reviewed over 200 research articles and discussed the tools, techniques, and challenges associated with spatial and temporal phenotyping for assessing crop water stress, highlighting recent advances and emerging technologies. Emerging technologies such as artificial intelligence (AI) and Internet of Things systems are transforming crop water‐stress phenotyping by enabling real‐time monitoring and robust predictive models. Despite these advancements, challenges persist, including data gaps, platform limitations, and the need for scalable integration frameworks. The review examines key physiological and spectral indicators of crop water stress across multiple spatial and temporal scales using ground‐based sensors, unmanned aerial vehicles, and satellites. It further discusses multiscale phenotyping approaches and data fusion techniques to improve spatial resolution and prediction accuracy. Challenges in harmonizing spatial and temporal data are discussed, along with the need for interdisciplinary collaboration among the phenotyping, modeling, and agronomy domains. The review concludes by identifying future directions, including edge computing, high‐resolution imaging, and robust spatiotemporal phenotyping frameworks to enhance crop water‐stress assessment. By leveraging remote sensing, modeling, and AI, future phenotyping systems can improve water‐stress assessment, advance precision agriculture, and ensure resilience in water‐limited agroecosystems.
Why it matches plant phenotyping methods植物の水ストレス表現型評価に用いる空間・時間スケール、センサー、UAV、衛星、データ融合などの手法を中心に扱うレビューであり、対象範囲に明確に該当する。
abstractThis review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment
Abstract Plant diseases are a major challenge in agriculture, leading to reduced crop yield and economic losses. Traditional methods of disease detection rely on manual inspection, which is time-consuming and less accurate. With the advancement of Deep Learning, Computer Vision, and Internet of Things, automated plant disease detection systems have been developed to improve accuracy and efficiency. Recent research shows that deep learning models, especially Convolutional Neural Networks (CNNs), are widely used for plant disease classification due to their ability to automatically extract features from images and achieve high accuracy [3], [34] . Transfer learning techniques using models such as ResNet and VGG further enhance performance, particularly when datasets are limited [9], [41] . In addition, lightweight models like MobileNet and ShuffleNet are being developed for deployment on mobile and IoT devices [2], [12] . Many studies use benchmark datasets such as PlantVillage, which provide high accuracy under controlled conditions. However, real-world applications face challenges such as varying lighting conditions, complex backgrounds, and limited dataset diversity [13], [15] . To address these issues, researchers are exploring advanced techniques such as data augmentation, object detection models like YOLO, and multimodal approaches that combine image data with environmental sensor data [5], [10] . IoT-based systems are also gaining importance as they enable real-time monitoring of crop conditions using sensors and smart devices [21], [26] . Furthermore, Explainable AI techniques such as Grad-CAM are being used to improve model transparency and help users understand the prediction results [25] . Despite significant progress, challenges remain in terms of model generalization, computational complexity, and real-time deployment. Future research should focus on developing lightweight, efficient, and scalable models, along with the use of large real-field datasets and edge computing technologies. Keywords Plant Disease Detection; Leaf Image Analysis; Convolutional Neural Network (CNN); Deep Learning; Transfer Learning; Computer Vision Internet of Things; Lightweight Models; Object Detection (YOLO); Multimodal Data Fusion; Explainable Artificial Intelligence (XAI)
Why it matches plant phenotyping methods植物病害を葉画像から推定するCNN・画像解析手法を体系的に扱うレビューであり、植物の病態を観測・推定するフェノタイピング手法が中心です。
titleHybrid CNN-Based Plant Disease Detection for Smart Agriculture: A Review
Plant diseases remain a big menace to agricultural productivity in the world, resulting into massive loss of produce, and compromised food security especially in areas experiencing resource shortages. Traditional methods of disease detection that are mostly reliant on expert visual evaluation are slow, subjective, and lack scalability. Over the past few years, the advent of the artificial intelligence (AI) and in particular, machine learning and deep learning algorithms have revolutionized the diagnostics of plant diseases by allowing the detection of the diseases through automated image-based methods at a high accuracy. Convolutional neural networks and computer vision technologies have demonstrated good potential to detect intricate patterns of diseases and facilitate precision farming. This review will critically analyze the advances in AI-driven plant disease detection to date, including the leading models, datasets, and performance trends. It was systematically and interpretively based on the methodological approach relying on the peer-reviewed literature of significant academic databases. It is emphasized in the analysis that deep learning models, and CNN-based architectures, in particular, and transfer learning training, in particular, are prevalent in the field and often achieve high accuracy when trained in controlled settings on benchmark datasets. Nevertheless, the review also notes the major limitations, such as the bias of the data set, low generalizability to real-life scenarios, high computational costs, and the absence of interpretability of the so-called black-box models. Such obstacles limit the applicability and adoption scale of AI solutions, particularly among the smallholder farmers. Moving forward, the evolution of multiple, field based datasets, explainable AI integration to enhance greater transparency, and edge computing to implement real time, on field diagnosis should be the focus of future research. It will also be necessary to strengthen the institutional support and digital infrastructure to close the technology development and practical application to achieve sustainable and inclusive agricultural transformation.
Why it matches plant phenotyping methods植物病害を画像から自動検出するAI手法を対象としたレビューであり、病害状態という植物表現型の取得・推定方法が中心です。
abstractThis review will critically analyze the advances in AI-driven plant disease detection to date, including the leading models, datasets, and performance trends.
The problems of plant leaf disease are rather serious in the world agricultural industry, leading to a significant decrease in crop quantity and quality, consequently, resulting in a huge loss in the economy and food insecurity. Detection and successful classification of plant diseases at the initial stage is essential to further agricultural output and the quality production of food. The recent improvements in the field of artificial intelligence (AI), specifically, machine learning (ML) and deep learning (DL), have shown significant prospects in automating and enhancing methods of diagnosing plant leaf diseases by using a wide variety of ML and DL algorithms. This review article presents an in-depth analysis of thirty novel methods created by researchers to diagnose and classify plant leaf diseases. They are such conventional classifiers as Support Vector Machines (SVM), Random Forests (RF), and K-Nearest Neighbors (KNN), along with some advanced DL architectures, such as Convolutional Neural Networks (CNN), VGG16, ResNet50, InceptionResNetV2, EfficientNet, and various hybrids. The review analysis takes into consideration the methodologies applied, performance metrics, and insights in practice, as well as the strengths and weaknesses of both. The most crucial findings of the review show that deep learning models, and CNNs in particular, tend to be more accurate, robust, and feature extractors than traditional models of MLs. The performance of classification is also enhanced by numerous hybrid models that will use ML together with DL, and transfer learning has been an effective method to enhance the generalization using small datasets. Nevertheless, with all this progress, the issues of diversity of datasets, computational resource requirements and model interpretability are still to be explored in the future.
Why it matches plant phenotyping methods植物葉の病徴を画像から検出・分類する機械学習手法を体系的に比較するレビューであり、植物状態の取得・推定手法が中心である。
abstractThis review article presents an in-depth analysis of thirty novel methods created by researchers to diagnose and classify plant leaf diseases.
Background/Objectives: Downy mildew, caused by Peronosclerospora and Sclerophthora species, is a major constraint to maize production in tropical and subtropical regions, with yield losses of 30-100%. This systematic review synthesised evidence on methods used to screen maize for downy mildew resistance and assessed their effectiveness, reliability, and associated markers. Methods: PubMed, Google Scholar, ScienceDirect, and CAB Abstracts were searched (last searched 22 October 2025) for English-language studies (1990-2025) evaluating phenotypic or molecular screening methods. Risk of bias was assessed using the RoB 2 framework. Narrative synthesis was conducted following a protocol registered on the Open Science Framework. Results: Twelve studies met the inclusion criteria, predominantly from India and Cambodia. Spreader row systems (seven studies) and conidial spray inoculation (six studies) were the most common field methods, while the glasshouse sandwich technique generated the highest disease pressure. Cross-method correlations were strong (r = 0.92-0.99), and heritability estimates ranged from 0.50 to 0.97. QTL mapping identified resistance loci on chromosomes 2, 3, and 6, with chromosome 6 stable across multiple pathogen species. Evidence certainty was moderate for method effectiveness and low for molecular markers. Conclusions: Established phenotypic screening methods reliably discriminate resistant germplasm; however, standardised protocols, broader geographic validation, and independent molecular marker confirmation are needed.
Why it matches plant phenotyping methodsトウモロコシのべと病抵抗性という植物状態を評価する表現型スクリーニング法を体系的にレビューし、方法の有効性・信頼性を比較しているため、手法レビューとして収載する。
titleScreening Methods for Downy Mildew Resistance in Maize: A Systematic Review.
Crop diseases caused by diverse pathogens, including fungi, bacteria, and viruses, lead to result in global yield losses of nearly 20–40% each year, posing a major significant threat to food security and agricultural sustainability. Traditional detection methods, relying on visual inspection and routine laboratory assays, are often slow, labour-intensive, and prone to inaccuracies, resulting in delayed disease management. By enabling quick, precise, and scalable detection systems, recent advancements in molecular biology, digital technologies, and remote sensing have completely transformed the field of crop disease diagnostics. Molecular techniques such as real-time PCR, loop-mediated isothermal amplification (LAMP), and CRISPR-based assays (e.g., SHERLOCK) offer high sensitivity and specificity, allowing early on-site pathogen identification. Digital technologies driven by artificial intelligence, including smartphone-based diagnostic tools and convolutional neural networks (CNNs), now achieve over 95% accuracy in image-based disease recognition, making advanced diagnostics more accessible to farmers. Remote sensing approaches particularly drone-assisted hyperspectral and multispectral imaging facilitate non-invasive, large-scale monitoring and early detection of disease outbreaks across agricultural landscapes. Additionally, metagenomics and next-generation sequencing (NGS) enable the discovery of novel pathogens and support resistance-breeding programs through comprehensive genomic insights. Collectively, these innovative technologies enhance the speed, precision, and cost-effectiveness of crop disease detection, potentially reducing yield losses by up to 30% and promoting sustainable agriculture. This review highlights the principles, recent advancements, advantages, limitations, and prospects of integrating molecular, digital, and remote sensing tools to strengthen global crop health management systems.
Why it matches plant phenotyping methods植物病害の画像認識、ドローン搭載ハイパースペクトル・マルチスペクトルセンシングを用いた病害状態の取得・検出技術を体系的に扱うレビューであり、植物フェノタイピング手法が中心的です。分子診断も含みますが、デジタル・リモートセンシングによる植物病害表現型の推定が明示されています。
abstractThis review highlights the principles, recent advancements, advantages, limitations, and prospects of integrating molecular, digital, and remote sensing tools to strengthen global crop health management systems.
Abstract The Affordable Phenomics special topic in The Plant Phenome Journal showcased recent advances that expand the accessibility, cost‐effectiveness, and scalability of plant phenotyping technologies. This collection of 15 articles presented innovative approaches, ranging from low‐cost sensors and open‐source analytical pipelines to artificial intelligence–driven image analysis and spectroscopy, that address the financial and technical barriers limiting widespread adoption of plant phenomics. In this foreword, we highlight the contributions featured in the special topic. The foreword also serves as an overview of the state of the art in affordable phenomics by summarizing the vision and perspectives presented in the invited review “Affordable phenomics: Expanding access to enhancing genetic gain in plant breeding.”
Why it matches plant phenotyping methods植物フェノタイピング技術に関する特集の進展と手法を概観するフォワードであり、フェノタイピング方法論のレビューとして中心的です。
abstractshowcased recent advances that expand the accessibility, cost‐effectiveness, and scalability of plant phenotyping technologies
Abstract Plant diseases are a serious issue that cause food shortages and financial losses. In large-scale farming, traditional disease detection techniques that primarily rely on expert inspection are frequently unfeasible. Deep learning and super-resolution methods for plant disease detection are thoroughly reviewed in this study, with an emphasis on their use in leaf-level imaging and UAV-based monitoring. This thorough review was carried out using the PRISMA framework, looking at peer-reviewed publications from important databases like Google Scholar, ScienceDirect, Web of Science, and Scopus. According to the review, low spatial resolution, environmental variability, occlusion, and domain shift cause convolutional and transformer-based models to perform poorly at canopy and field scales, despite achieving high accuracy on leaf-level datasets. Despite improving the perceptual quality of aerial imagery, super-resolution techniques are still difficult to incorporate into disease detection pipelines because of their high computational overhead, lack of task-aware training, scarcity of annotated UAV datasets, and poor generalization in real-world scenarios. There are few actual architectural implementations of cross-scale integration strategies currently in use; most of them are conceptual. Deep learning for plant disease detection has come a long way, but reliably deploying this technology outside of controlled environments remains challenging due to scale differences and data availability constraints. Coordinated developments in cross-scale learning approaches, data collection, and model design are needed to address these issues. The results also emphasize the significance of multimodal data fusion, super-resolution-assisted domain adaptation, and hierarchical transfer learning as viable approaches to enhancing the scalability and dependability of plant health monitoring systems. This review highlights useful research directions and provides a critical overview of current methods. It encourages the development of AI-powered solutions for better crop management, early disease detection, and sustainable farming methods.
Why it matches plant phenotyping methods植物病害の葉・キャノピー画像から病害状態を検出する深層学習および超解像手法を対象とした体系的レビューであり、植物状態の取得・推定手法が中心です。
titleA systematic review of deep learning and super resolution techniques for leaf level and canopy level plant disease detection
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
The last decade has marked a period of rapid innovation in high-throughput phenotyping (HTP) of plants. This includes the establishment of robotic phenotyping infrastructure, development of new sensors, and improvements in computation for downstream analysis. While HTP approaches have revolutionized data collection, meaningful insights into plant function require a yet deeper connection between resultant HTP-based information and biological responses. We suggest that dynamic process-based plant models, which simulate growth and physiology in a time-explicit manner, can serve as a functional link between high-throughput methods and whole-plant mechanisms of growth. Using this framework, we review recent research that has leveraged HTP approaches for estimation of plant traits that are commonly used as process-based model (PBM) variables. Through this analysis, we review successes and identify emerging directions for future research. Finally, we highlight the varied ways that HTP can be used in conjunction with PBMs as a tool to advance discovery and improve prediction of plant growth.
Why it matches plant phenotyping methodsHTP基盤、センサー、計算解析、および植物形質推定を対象としたレビューであり、植物フェノタイピング手法が中心的に扱われている。
abstractThe last decade has marked a period of rapid innovation in high-throughput phenotyping (HTP) of plants.
Field / plotLaboratory / benchtopNeRF / 3D Gaussian SplattingPhotogrammetry / SfM / MVSLiDAR / point cloudRGB-D / ToFWhole plant / canopy / plot / field2D/3D reconstruction
Three-dimensional point cloud (3DPC) data capture detailed geometric and structural plant traits beyond the capability of 2D imaging. When combined with artificial intelligence (AI), it offers a powerful, non-invasive tool for plant phenotyping, which is crucial for driving advancements in plant breeding and agriculture. However, challenges related to data complexity, limited datasets, and model generalization hinder 3DPC’s widespread adoption. To provide a comprehensive overview and guide future research in this area, we conducted a systematic literature review (SLR) following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines by analysing 381 papers published between January 2017 and October 2025 from major databases. Our review examines the advantages, current status, limitations, and future directions of AI applications in 3DPC-based plant phenotyping. Our findings indicate a rapid increase in publications since 2022, with deep learning (DL) methods, especially pointwise MLP-based networks, driving much of this growth, with a notable recent surge in Transformer-based, Graph-based, and particularly Hybrid models that combine their strengths. Furthermore, novel methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) are emerging as powerful tools for 3D reconstruction and scene synthesis. Time-of-Flight (ToF) and Structure from Motion and Multi-View Stereo (SfM-MVS) technologies remain the predominant 3DPC data acquisition techniques. Research in this area focuses on trees/shrubs and cereals, typically involving single-species studies. Although the overall use of public datasets remains low (18.9%), their adoption has significantly increased since 2020. Key limitations identified include: (1) a lack of standardized data collection and formats, (2) insufficient model robustness and generalization, especially from lab to field, (3) high computational demands, and (4) a reliance on species-specific models. The future of AI-driven 3DPC phenotyping hinges on overcoming these bottlenecks. Priority should be given to: developing field-deployable, computationally efficient models; exploring the potential of the foundation model; establishing diverse and standardized public datasets; and strengthening the integration of 3D phenomics with genomics to bridge the genotype-to-phenotype gap. This review provides a foundational roadmap to guide research in plant phenomics, crop breeding, and plant science.
Why it matches plant phenotyping methods3D点群とAIによる植物形質取得・解析を中心に扱う体系的レビューであり、植物フェノタイピング手法のレビューとして明確に適格。
titleAI-driven 3D point cloud analysis in plant phenotyping: A Systematic Review
Agriculture serves as a major source of food and plays a key function as the backbone of most countries’ economies. However, farmers are encountering many challenges in this sector, such as drought, flooding, diseases, nutrient deficiency, and so on. The technological advancements in the field of agriculture, also called smart agriculture, are necessary to address the requirements of the expanding population and manage the associated challenges. Among those, plant leaf diseases are the primary concern that severely impacts crop yield and economic stability. This technical review examines various Machine Learning (ML) and Deep Learning (DL) approaches used to identify and classify different plant leaf diseases. This review gives an overview of the current state-of-the-art ML, DL, and IoT-enabled disease prediction systems and their recent advances in developing an intelligent system in smart agriculture. It provides insights into the various technological developments and discusses the benefits and opportunities of AI-based models in plant disease management.
Why it matches plant phenotyping methods植物葉の病徴を画像・機械学習で検出・分類する手法を中心に扱うレビューであり、植物の疾病状態を推定するフェノタイピング手法レビューに該当する。
abstractThis technical review examines various Machine Learning (ML) and Deep Learning (DL) approaches used to identify and classify different plant leaf diseases.
Global agriculture is facing significant threat from climate-driven abiotic stress, which endangers global food security by impacting crop performance and adaptation. However, traditional abiotic stress detection methods are often labor-intensive and lack precision and scalability. Efficient and reliable solutions are needed to meet rising global food demand. Recent advances in artificial intelligence (AI) offer highly accurate, non-invasive, and sustainable approaches for abiotic stress detection. This paper reviews the impact of AI, and specifically Machine and Deep Learning algorithms, coupled with synergistic technologies and diverse datasets (imaging techniques and Internet of Things (IoT) infrastructures), to identify unique signatures of abiotic stress, and assess its impact on growth and physiological performance. It contrasts with other reviews that address individual technologies and algorithms, while presenting abiotic stress detection as a secondary objective. We examined peer-reviewed journal articles on the use of AI in detecting abiotic stress. The reviewed literature was chosen based on the stress category, sensing mode, and AI technologies employed. A comparative analysis was performed to explore potential advancements of AI-based abiotic stress detection methods over traditional approaches and also challenges lied to the adoption of AI in agriculture for abiotic stress detection.
Why it matches plant phenotyping methods植物の非生物的ストレス状態をAI・画像・IoT等で検出する手法を主題としたレビューであり、植物フェノタイピング手法のレビューに該当する。
abstractThis paper reviews the impact of AI, and specifically Machine and Deep Learning algorithms, coupled with synergistic technologies and diverse datasets (imaging techniques and Internet of Things (IoT) infrastructures), to identify unique signatures of abiotic stress, and assess its impact on growth and physiological performance.
Major crops worldwide are affected by various diseases yearly, leading to crop losses in different regions. The primary methods for addressing crop disease losses include manual inspection and chemical control. However, traditional manual inspection methods are time-consuming, labor-intensive, and require specialized knowledge. The preemptive use of chemicals also poses a risk of soil pollution, which may cause irreversible damage. With the advancement of computer hardware, photographic technology, and artificial intelligence, crop disease recognition methods based on spectral and red-green-blue (RGB) images not only recognize diseases without damaging the crops but also offer high accuracy and speed of recognition, essentially solving the problems associated with manual inspection and chemical control. This paper summarizes the research on disease recognition methods based on spectral and RGB images, with the literature spanning from 2020 through early 2025. Unlike previous surveys, this paper reviews recent advances involving emerging paradigms such as State Space Models (e.g., Mamba) and Generative AI in the context of crop disease recognition. In addition, it introduces public datasets and commonly used evaluation metrics for crop disease identification. Finally, the paper discusses potential issues and solutions encountered during research, including the use of diffusion models for data augmentation. Hopefully, this survey will help readers understand the current methods and effectiveness of crop disease detection, inspiring the development of more effective methods to assist farmers in identifying crop diseases.
Why it matches plant phenotyping methods作物病害をRGB・スペクトル画像から認識する手法を中心に、評価指標や公開データセットを含めて体系的にレビューしており、植物の病害状態を画像から推定するフェノタイピング手法レビューに該当する。
abstractThis paper summarizes the research on disease recognition methods based on spectral and RGB images
Recent developments in machine learning (ML) and deep learning (DL) algorithms have introduced a new approach to the automatic detection of plant diseases. However, existing reviews of this field tend to be broader than maize-focused and do not offer a comprehensive synthesis of how ML and DL methods have been applied to image-based detection of maize leaf disease. Following the PRISMA guidelines, this systematic review of 102 peer-reviewed papers published between 2017 and 2025 examined methods and approaches used to classify leaf images for detecting disease in maize plants. The 102 papers were categorized by disease type, dataset, task, learning approach, architecture, and metrics used to evaluate performance. The analysis results indicate that traditional ML methods, when combined with effective feature engineering, can achieve classification accuracies of approximately 79–100%, while DL, especially CNNs, provide consistent, superior classification performance on controlled benchmark datasets (up to 99.9%). Yet in “real field” conditions, many of these improvements typically decrease or disappear due to dataset bias, environmental factors, and limited evaluation. The review provides a comprehensive overview of emerging trends, performance trade-offs, and ongoing gaps in developing field-ready, explainable, reliable, and scalable maize leaf disease detection systems.
Why it matches plant phenotyping methodsトウモロコシ葉の病害を画像から分類・検出する機械学習手法を体系的にレビューしており、植物の病害状態を推定するフェノタイピング手法が中心である。
abstractThe review provides a comprehensive overview of emerging trends, performance trade-offs, and ongoing gaps in developing field-ready, explainable, reliable, and scalable maize leaf disease detection systems.
Multimodal approaches for crop disease detection have gained significant attention due to their ability to integrate diverse data sources for improved accuracy. This review categorizes recent studies into five areas: multimodal deep learning and vision transformers, hyperspectral and remote sensing, thermal imaging and UAV applications, CNN–Transformer hybrids and ensemble methods, and comprehensive reviews. Results indicate that frameworks combining RGB, hyperspectral, and thermal imaging achieve accuracies up to 97.8%, while hybrid CNN–Transformer architectures reach 99.7% on benchmark datasets. Despite these advances, challenges remain in scalability, computational cost, and real-world deployment, highlighting the need for lightweight, explainable, and field-validated models.
Why it matches plant phenotyping methods植物病害を画像・リモートセンシングから推定する方法を体系的にレビューしており、病害状態という植物表現型の取得・推定が中心です。
abstractMultimodal approaches for crop disease detection have gained significant attention due to their ability to integrate diverse data sources for improved accuracy.
With rapid advancements in breeding technologies, phenomics, and artificial intelligence, crop breeding is progressively entering an era of greater precision and efficiency. In this context, three-dimensional (3D) phenotyping techniques-leveraging multidimensional spatial resolution capabilities-have overcome the limitations of two-dimensional (2D) phenotyping in breeding analyses, enabling precise characterization of crop spatial interactions, spatial distribution of plant architecture, and complex 3D structural traits. Recent breakthroughs in computer technology for 3D reconstruction and 3D segmentation have provided robust technical support for crop 3D phenotypic analysis. Furthermore, effective integration of extracted 3D phenotypic data with genotypic data serves as a powerful tool for future research on crop gene function and genomics-assisted breeding. This review systematically examines major advances in 3D phenotyping techniques and their representative applications, with particular emphasis on innovations in 3D phenotyping and analytical methodologies. In parallel, we describe the latest interdisciplinary advances in 3D phenotyping within crop functional genomics research and genomics-assisted breeding. We objectively evaluate the advantages and limitations of 3D phenotyping compared with 2D approaches to assist breeders in selecting appropriate technologies. Finally, we propose future perspectives to promote deeper integration of phenomics and breeding technologies. Despite existing conceptual and technical challenges, it is foreseeable that cross-disciplinary integration of phenomics and genomics will offer promising prospects for crop breeding.
Why it matches plant phenotyping methods3D植物フェノタイピング技術と解析手法の進展を体系的にレビューしており、植物形態・構造形質の取得方法が中心である。
abstractThis review systematically examines major advances in 3D phenotyping techniques and their representative applications, with particular emphasis on innovations in 3D phenotyping and analytical methodologies.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Genebanks serve as critical repositories for preserving the genetic diversity of plant species, including crops, forages, and their wild relatives, which is essential for adapting to climate change, enhancing food security, and improving agricultural sustainability. Seed phenotyping, the process of evaluating observable seed traits influenced by genetics and environmental factors, plays a pivotal role in characterizing and utilizing this diversity. Traditional phenotyping methods, however, are labor-intensive and inadequate for the vast collections housed in genebanks. This paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum-from gamma rays to radio waves-to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor. We highlight the integration of advanced imaging systems (e.g., hyperspectral, X-ray, and thermal imaging) to enrich genebank datasets, facilitating trait discovery and crop improvement. Despite challenges like cost, scalability, and data standardization, opportunities arise from collaborative initiatives between genebanks and phenomics facilities through organizations such as International Plant Phenotyping Network. Our conclusions underscore how phenomics can revolutionize genebank operations, ensuring the efficient conservation and deployment of genetic resources to address global agricultural demands.
Why it matches plant phenotyping methods種子形質を対象とする高スループットセンサー・画像フェノタイピング技術を総説しており、フェノタイピング手法が中心である。
abstractThis paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum-from gamma rays to radio waves-to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor.
Detecting plant diseases is a crucial component of precision agriculture, necessary for maintaining economic viability, sustainable farming practices, food security, and preventing economic losses. Deep learning models such as Capsule Attention Networks, DenseNets, and Convolutional Neural Networks have demonstrated state-of-the-art performance in leaf-image-based disease classification. However, large-scale practical implementation in resource-constrained environments remains challenging due to overfitting, environmental insensitivity, extended training times, and high computational cost, along with the absence of a sustainable framework for real-world deployment. Emerging quantum neural networks (QNNs), integrated with quantum-influenced optimization algorithms, offer promising avenues to enhance practical applicability. Their capabilities for high-dimensional feature mapping, entanglement, and superposition improve generalization and convergence in hybrid models that combine quantum layers with classical architectures. Incorporating quantum modules into DenseNets, attention mechanisms, capsule networks, and other hybrid systems may further enhance accuracy, enable robust disease segmentation, and reduce training time. Nonetheless, challenges remain regarding real-time deployment on edge devices, efficient data encoding, limited explainability of quantum-driven features, and hardware constraints. This survey reviews classical, hybrid, and quantum-enhanced approaches, provides comparative insights, and identifies research gaps toward developing scalable, interpretable, and dependable frameworks for plant disease detection and diagnosis.
Why it matches plant phenotyping methods植物病害を葉画像から検出・分類する画像ベースの表現型取得手法を対象としたサーベイであり、手法レビューが中心です。
abstractThis survey reviews classical, hybrid, and quantum-enhanced approaches, provides comparative insights, and identifies research gaps toward developing scalable, interpretable, and dependable frameworks for plant disease detection and diagnosis.
Recent advances in high-throughput sequencing, imaging, and phenotyping have carried plant science into the era of 'big data.' Complex, multi-scale datasets provide new opportunities to uncover plant molecular mechanisms with a level of detail previously unachievable. Fully exploiting this complexity requires integrating advanced statistics, computational modeling, and artificial intelligence (AI). This minireview offers guidance on how the combination of AI and mechanistic models is transforming temporal, image-based, and spatial omics data into detailed predictions of robust plant traits. In addition, embedding physical principles into AI models can enhance interpretability and strengthen their biological grounding, leading to more realistic representations of plant inner workings. Together, these advances are reshaping plant science by turning 'big data' into deep insights, thus greatly enriching our understanding of plant growth, adaptation, and environmental responses.
Why it matches plant phenotyping methods植物フェノタイピング、画像データ、AI・機械論的モデルによる植物形質予測を扱うレビューであり、計算的な形質推定が中心的テーマです。
abstractThis minireview offers guidance on how the combination of AI and mechanistic models is transforming temporal, image-based, and spatial omics data into detailed predictions of robust plant traits.
Imaging technologies have become indispensable tools in modern plant phenotyping, transforming visual information into measurable traits essential for analyzing morphology, physiology, biochemistry, and micro- to nanoscale structures. This concise review summarizes recent advances by dividing plant imaging into two major categories: (1) physiological and biochemical, which includes hyperspectral, multispectral, and fluorescence hyperspectral imaging, as well as terahertz imaging, surface-enhanced Raman scattering, and carbon dot-based techniques; and (2) structural and morphological, encompassing RGB, thermal, light detection and ranging (LiDAR), confocal microscopy, and optical coherence tomography. Together, these modalities deliver insights from the canopy to the molecular level, enabling precise monitoring of plant stress, disease, and developmental traits. By integrating these multimodal imaging techniques with artificial intelligence, the review highlights key developments, current challenges, and future perspectives in plant measurement and analysis.
Why it matches plant phenotyping methods植物フェノタイピングに用いる画像技術を体系的にレビューし、植物形質の測定・解析手法と課題を扱うことが中心である。
abstractThis concise review summarizes recent advances by dividing plant imaging into two major categories
Digital phenotyping has evolved from simple imaging-based trait measurement to a core technology enabling large-scale analysis of genotype × environment × management (G × E × M) interactions. This review highlights the manner in which digital phenotyping simultaneously supports two major research trajectories in modern agro-ecosystems. In engineering-driven approaches, imaging and sensor data are increasingly used to enhance the physical control of climate, irrigation, and crop protection through model-based and AI-assisted decision systems. In life science-centered approaches, high-throughput phenotyping and multi-omics integration provide mechanistic insights into plant stress responses, developmental plasticity, and complex trait regulation. Despite significant progress, both pathways face limitations in addressing the biological complexity and climatic unpredictability of crop systems. We discuss the emerging opportunities to integrate these domains through a two-layer AI framework that combines real-time sensing and actuation (“Physical AI”) with ontology- and LLM-based reasoning systems capable of synthesizing biological knowledge and generating strategic policies. Together, these developments position digital phenotyping as a bridging technology and a foundation for adaptive, resilient, and knowledge-driven agro-ecosystem management.
Why it matches plant phenotyping methods植物デジタルフェノタイピングを中心に、画像・センサーデータによる形質測定と高スループット解析の発展・応用をレビューしており、方法論レビューとして中心的である。
abstractDigital phenotyping has evolved from simple imaging-based trait measurement to a core technology enabling large-scale analysis of genotype × environment × management (G × E × M) interactions.
Plant image science is a new multidisciplinary science that involves imaging techniques and image analysis methods to capture, process, and analyze plant images for understanding plant status and responses to environmental perturbations across various plant types and ecosystems. Plant image science encompasses a broader range of concept and scope than plant phenomics making it differentiable and widely appreciable. It covers plant phenotyping and the diagnosis and remote sensing of individual plants, plant communities, vegetation, and living organisms (for example, microorganisms and insects). Relevant applications include but not limited to plant phenomics, plant breeding and genomic research, crop management, crop protection, agricultural machinery, and postharvest processes. Its conceptual flexibility and multidisciplinary nature suggest that it has virtually no limitations in its applications, making it an essential domain and tool for the future of plant-based sciences and industries including agriculture, forestry and environment.
Why it matches plant phenotyping methods植物画像科学と植物フェノタイピングを含む画像取得・解析分野の概念的レビューであり、方法領域の整理が中心です。
abstractPlant image science is a new multidisciplinary science that involves imaging techniques and image analysis methods to capture, process, and analyze plant images
Crop phenomics has emerged as a transformative discipline that bridges genomics and agronomy by enabling precise, high-throughput, and non-destructive measurement of plant traits. Over the past decade, more than 50 advanced phenotyping platforms have been established globally, ranging from controlled-environment facilities to field-based systems, underscoring the momentum of this technological revolution. With increasing demands for sustainable food production under climate change, phenomics offers unprecedented opportunities to accelerate genetic gains and optimize crop management. Recent advances in imaging technologies, sensor integration, and computational tools have expanded the scope of trait dissection in field crops. Ground-based systems, unmanned aerial vehicles (UAVs), and satellite-based remote sensing now provide scalable and high-resolution field phenotyping, while controlled-environment platforms deliver mechanistic insights into plant physiology and stress responses. The integration of hyperspectral, thermal, and 3D imaging with machine learning algorithms has enhanced trait extraction and predictive accuracy. Applications span diverse domains, including identification of stress-resilient genotypes, improvement of nutrient and water use efficiency, early disease detection, and precision breeding strategies. Despite these advances, challenges persist in data standardization, cost-effectiveness, and translating controlled-environment findings to field conditions. Adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles remains critical to ensure harmonized and comparable datasets across platforms. In future, the convergence of phenomics with genomics, enviromics, and artificial intelligence is poised to redefine crop improvement pipelines, enabling the development of resilient, high-yielding, and resource-efficient cultivars. This review synthesizes current tools and applications, highlighting the pivotal role of crop phenomics in advancing global food and nutritional security.
Why it matches plant phenotyping methods植物フェノミクスのツール、プラットフォーム、画像・センサー・計算手法を体系的に扱う方法論レビューであり、スコープの中心に該当する。
abstractThis review synthesizes current tools and applications, highlighting the pivotal role of crop phenomics
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection. This perspective paper synthesizes current advances, identifies major barriers, and proposes future directions to realize the transformative potential of AI‐enabled plant phenomics. We first provide an overview of AI technologies with the potential to address key challenges in phenomics, from data collection to phenotypic trait extraction and environmental sensing. We then present three case studies focusing on specialty crops (blueberry [ Vaccinium corymbosum L.] mechanical harvestability traits, strawberry [ Fragaria × ananassa (Duchesne ex Weston)] production, and citrus [ Citrus L.] disease) to illustrate practical applications of AI‐driven phenomics. Moreover, we highlight future perspectives and opportunities for further research and innovation. These include large foundation models, real‐time inference on edge devices, explainable AI, generative AI and digital twins, AI‐enhanced multi‐omics, agentic AI, and knowledge‐guided and data‐driven hybrid approaches. Finally, we discuss key challenges and limitations of applying AI to plant phenomics, including data curation, model generalization and bias, and ethical considerations related to equitable access to AI tools.
Why it matches plant phenotyping methods植物フェノミクスにおけるAIセンシング・形質抽出を主題とする展望論文であり、方法論のレビューとして中心的に扱っている。
abstractWe first provide an overview of AI technologies with the potential to address key challenges in phenomics, from data collection to phenotypic trait extraction and environmental sensing.
Crop diseases pose a critical threat to global food security. Traditional diagnostic methods are inefficient and fail to meet the demands of modern precision agriculture. In recent years, artificial intelligence (AI) technologies centered on deep learning have revolutionized the rapid and precise identification of crop diseases. This paper systematically outlines key AI techniques for crop disease recognition, including computer vision-based image recognition, multimodal data fusion, and edge computing for field deployment. By analyzing representative domestic and international application cases, this paper highlights the significant advantages of this technology in terms of accuracy and efficiency. Simultaneously, it delves into current technical bottlenecks and deployment barriers, such as the few-shot learning problem, environmental interference, and low farmer trust. The paper concludes by outlining future directions, including self-supervised learning, digital twins, and industry integration, to advance the deep application and implementation of AI technology in smart agriculture.
Why it matches plant phenotyping methods作物病害を画像認識・深層学習で推定する技術を中心に、手法、精度、展開課題、将来方向を体系的にレビューしており、植物の病害状態を対象とするフェノタイピング方法レビューに該当する。
abstractThis paper systematically outlines key AI techniques for crop disease recognition, including computer vision-based image recognition, multimodal data fusion, and edge computing for field deployment.
Plant diseases represent a critical global threat to food security, necessitating accurate and timely detection for sustainable agriculture. This systematic synthesis evaluates recent advancements in deep learning (DL) for plant leaf disease detection, analyzing 10 peer-reviewed studies published between 2021 and 2025. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review systematically examines Convolutional Neural Network (CNN)-based architectures, lightweight models (MobileNet, EfficientNet), and the integration of Explainable AI (XAI) to analyze the inherent trade-offs among efficiency, accuracy, and interpretability. Key findings indicate that while modern DL models achieve high classification accuracy ($\geqslant 97 \%$), practical deployment is significantly hindered by limited generalization across diverse field conditions and a critical lack of integrated XAI within lightweight hybrid architectures. This study provides a structured, cross-metric analysis to guide the development of next-generation DL models that are simultaneously efficient, accurate, and interpretable for realworld agricultural applications.
Why it matches plant phenotyping methods植物葉の病害状態を画像から検出する深層学習手法を体系的に比較・分析したレビューであり、病害表現型の取得・推定方法が中心です。
abstractThis systematic synthesis evaluates recent advancements in deep learning (DL) for plant leaf disease detection, analyzing 10 peer-reviewed studies published between 2021 and 2025.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Fruit agriculture is undergoing a profound transformation driven by multi-omics, high-throughput phenotyping, and machine learning-driven bioinformatics. However, we demonstrate that this technological revolution has paradoxically created a 'valley of death' where most of genomic discoveries fail to reach farmers' fields. While we can now identify beneficial alleles in days and edit genomes in weeks, it still takes 10 years and 14,5 million euros to deliver a single improved cultivar to European markets - the same timeline as 30 years ago. This review exposes how data abundance has shifted, not eliminated, the fundamental bottlenecks in fruit crop improvement. We critically assess how these tools reshape genetic and metabolic diversity, emphasizing both their transformative promises and structural limitations. We highlight three persistent gaps: the challenge of integrating heterogeneous multi-omics datasets, the phenotyping bottleneck for complex traits, and the tension between innovation and biodiversity conservation. By framing fruit breeding as a "data-to-decisions" challenge, we outline the systemic changes needed for sustainable, resilient, and high-quality fruit production.
Why it matches plant phenotyping methods果樹育種におけるハイスループット表現型解析とデータ統合の課題を批判的にレビューしており、表現型解析が中心的な方法論テーマの一つである。
abstractThis review exposes how data abundance has shifted, not eliminated, the fundamental bottlenecks in fruit crop improvement.
Field / plotMultimodalRootWhole plant / canopy / plot / fieldCountingObject detectionStress / disease detectionGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
• Presents a full-process review of image-based high-throughput plant phenotyping (HTPP). • Covers recent advances in platforms, sensors, deep learning, and field-level applications. • Highlights emerging methods like Promptable models, Digital Twins, and weak supervision. • Discusses deployment challenges including data scarcity and model generalization. • Proposes future directions: multimodal fusion, uncertainty modeling, and lightweight design. With the rapid global population growth and increasing challenges in sustainable agriculture, high-throughput plant phenotyping (HTPP) has become a vital tool for advancing crop breeding and precision agriculture. This review provides a comprehensive overview of recent technological trends in image-based HTPP, focusing on the integration of advanced sensors, automated phenotyping platforms, and deep learning techniques. We summarize the evolution of imaging modalities, including 2D, 2.5D, and 3D sensors, and their respective applications in phenotype acquisition. We then examine the progress of deep learning-based models in core phenotyping tasks such as stress and disease detection, growth monitoring, organ counting, root system analysis, and postharvest quality assessment. Special attention is given to the emergence of Transformer architectures, multimodal fusion strategies, weakly supervised learning, and prompt-based foundation models. Despite significant advancements, current HTPP systems still face several challenges, including high costs, limited generalization in open-field conditions, and the need for large-scale annotated datasets. To address these, we discuss potential solutions such as transfer learning, synthetic data generation via digital twins, lightweight deployment for edge devices, and uncertainty estimation for model interpretability. By highlighting key developments and open problems, this review aims to guide future research toward scalable, robust, and intelligent plant phenotyping systems that can operate reliably in real-world agricultural environments.
Why it matches plant phenotyping methods画像ベース高スループット植物フェノタイピングのセンサー、プラットフォーム、画像解析技術を包括的にレビューしており、方法論が中心です。
abstractPresents a full-process review of image-based high-throughput plant phenotyping (HTPP).
Existing tasks such as fruit growth monitoring, harvesting, and quality sorting still suffer from low precision and insufficient automation. 3D reconstruction technology can accurately capture the external characteristics of fruits and shows great potential for enhancing automated fruit detection and processing. This paper, guided by two core application needs in the fruit industry: real-time online sensing and offline high-precision analysis, provides a detailed overview of research progress in 3D reconstruction technology for fruits. It first introduces the principles, workflows, and advantages and limitations of classical 3D reconstruction methods. Then, it focuses on the basic framework of learning-based 3D reconstruction approaches, their improvement directions, and their applications in fruits and other agricultural products. In addition, the challenges encountered in fruit 3D reconstruction, such as occlusion and complex lighting conditions, are summarized, along with potential solutions. Finally, future research directions are discussed. This review serves as a valuable reference for promoting the integration of computer vision and agricultural intelligence and advancing the fruit industry chain’s digital and intelligent transformation.
Why it matches plant phenotyping methods果実の外部形質を取得する3D再構成手法を中心に、原理・ワークフロー・限界・応用・課題を体系的にレビューしており、植物フェノタイピング手法のレビューに該当する。
titleProcess, challenges and solutions of fruit 3D reconstruction: A review
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
As with many fields of science, plant science and agriculture have seen a rapid adoption of deep learning in recent years. The present moment is significant as it marks one decade since the first applications of deep learning began to appear in the literature on plant phenotyping. In this short time, a new research community was founded and new connections between computer vision and biology were established. In this letter, we reflect on this critical period of time from the inception of the field to where it stands today.
Why it matches plant phenotyping methods植物フェノタイピングにおける深層学習の発展を総括する方法論的レビューであり、対象分野の技術的進展が中心です。
titleDeep learning in plant phenotyping: the first ten years
Maize (Zea mays L), a cultigen derived from domesticated teosinte, is the queen of cereals because of its broad environmental adaptability and high genetic yield potential. It has major economic importance as both raw corn and as feedstock for numerous value-added products, with each kernel type serving multiple agri-industries. Driven by technological advances and expanded resources, breeders and researchers increasingly prioritize maize breeding and development. This paper systematically reviews the last decade of image analysis advancements in maize, focusing on techniques adopted for phenotyping, plant classification and disease identification. We examine why image-based phenotyping is necessary for modern agriculture, why maize is a primary model for image analysis and which maize traits remain underexplored. We summarize how machine learning and deep learning simplify image processing and feature extraction; identify the main challenges researchers encounter when adopting these methods and propose potential solutions. Our review shows that integrating advanced imaging, computer vision and AI have enabled earlier stress detection, improved yield prediction and more efficient trait mapping in maize. Continued innovation in scalability, robustness and interpretability is essential to translate these technological advances into real-world agricultural impact.
Why it matches plant phenotyping methodsトウモロコシの画像解析による表現型計測手法を体系的にレビューしており、フェノタイピング手法が中心である。
abstractThis paper systematically reviews the last decade of image analysis advancements in maize, focusing on techniques adopted for phenotyping, plant classification and disease identification.
Diseases in plants affect the yield of the plant itself. Agriculture is essential in human life, and if plant conditions are left unchecked, it will result in crop failure, which can affect the economy. Many researchers have developed methods to detect plant diseases, ranging from expert systems to deep learning algorithms. Machine learning is particularly effective for this task as it relies on datasets composed of plant images, making image processing crucial for the identification process. This article reviews the current literature and identifies several research gaps, opportunities, and challenges that must be addressed. Specifically, the article outlines potential avenues for future research in detecting plant diseases using image processing techniques. A significant opportunity exists to develop more effective algorithmic models for detecting plant diseases.
Why it matches plant phenotyping methods植物病害を画像処理で検出する手法の文献レビューであり、植物の病害状態を観測・推定するフェノタイピング手法が中心です。
titlePlant Disease Detection Using Digital Image Processing: Opportunities and Challenges
As one of the most widely cultivated and consumed crops, wheat is essential to global food security. However, wheat production is increasingly challenged by pests, diseases, climate change, and water scarcity, threatening yields. Traditional crop monitoring methods are labor-intensive and often ineffective for early issue detection. Hyperspectral imaging (HSI) has emerged as a non-destructive and efficient technology for remote crop health assessment. However, the high dimensionality of HSI data and limited availability of labeled samples present notable challenges. In recent years, deep learning has shown great promise in addressing these challenges due to its ability to extract and analysis complex structures. Despite advancements in applying deep learning methods to HSI data for wheat crop analysis, no comprehensive survey currently exists in this field. This review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation. It also highlights the strengths, limitations, and future opportunities in leveraging deep learning methods for HSI-based wheat crop analysis. We have listed the current state-of-the-art papers and will continue tracking updating them in the following GitHub Repository .
Why it matches plant phenotyping methods小麦のハイパースペクトル画像と深層学習による植物形質・状態推定を扱う方法論レビューであり、データセット、手法、疾病検出、収量推定を中心に整理している。
abstractThis review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation.
As one of the most widely cultivated and consumed crops, wheat is essential to global food security. However, wheat production is increasingly challenged by pests, diseases, climate change, and water scarcity, threatening yields. Traditional crop monitoring methods are labor-intensive and often ineffective for early issue detection. Hyperspectral imaging (HSI) has emerged as a non-destructive and efficient technology for remote crop health assessment. However, the high dimensionality of HSI data and limited availability of labeled samples present notable challenges. In recent years, deep learning has shown great promise in addressing these challenges due to its ability to extract and analysis complex structures. Despite advancements in applying deep learning methods to HSI data for wheat crop analysis, no comprehensive survey currently exists in this field. This review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation. It also highlights the strengths, limitations, and future opportunities in leveraging deep learning methods for HSI-based wheat crop analysis. We have listed the current state-of-the-art papers and will continue tracking updating them in the following GitHub Repository.
Why it matches plant phenotyping methods小麦のハイパースペクトル画像と深層学習による植物の健康状態・病害・収量推定を対象とする方法論レビューであり、フェノタイピング手法が中心です。
abstractThis review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation.
WheatRootRoot system architectureYield / yield components
ABSTRACT Global wheat production is extending to dryland and tropical environments prone to drought and heat stress due to breeding and deploying new‐generation ideotypes with desirable product profiles. However, yield gains are low and stagnant under these environments, attributable to abiotic stresses, primarily drought. Genotypes with drought‐adaptive root traits will enhance grain yield and productivity under dryland and drought‐stress conditions. Root traits are valued and related to high biomass production, nutrient and water extraction, ultimately boosting yield and yield components, notably in dryland agro‐ecologies. Hence, the objective of the current review is to explore and document the opportunities, challenges and progress in wheat breeding targeting novel root traits to enhance drought adaptation and improve productivity under dryland agro‐ecologies. The review presents a detailed account of the available genetic resources of wheat possessing desirable root traits for breeding programs. This is followed by outlines on the genetic gains for breeding for wheat root system architecture traits and the potential of high‐throughput phenotyping techniques. Challenges and limitations on root phenotyping methods are presented. Lastly, the paper discusses the potential utilities of molecular breeding approaches, including marker‐assisted selection, genomic‐assisted breeding, and next‐generation sequencing for accelerated breeding targeting root system architecture traits. The review can guide wheat breeders and agronomists in developing drought‐tolerant varieties by exploiting the root system and climate‐smart wheat varieties for moisture‐deficient production environments.
Why it matches plant phenotyping methodsコムギの根形態形質を対象とし、根のハイスループット表現型解析技術と根形質計測法の課題・限界をレビューしているため、表現型解析手法レビューが中心である。
abstractThe review presents a detailed account of the available genetic resources of wheat possessing desirable root traits for breeding programs.
The global food crisis, exacerbated by the intensification of crop diseases and pests, poses a significant threat to food security and nutrition. Currently, approximately 350 million people are experiencing extreme hunger, and this number is projected to rise to 943 million by 2025. Consequently, there is an urgent need for effective pest and disease management strategies in agriculture. Traditional identification methods are limited by accuracy, cost, and dependence on human expertise, which hinders timely and efficient pest and disease control. This study investigates the potential of artificial intelligence, particularly deep learning techniques, to enhance the detection and classification of plant diseases and pests. The research focuses on addressing four main challenges: data scarcity, outdated network architectures, computational constraints of terminal devices, and resource and compatibility issues. This paper reviews recent advancements in AI technologies, including few-shot learning, innovative training methods and network architectures, lightweight models, as well as deployment and hardware technologies. Additionally, it discusses the integration of AI in agriculture, highlighting the importance of few-shot learning and the application of new technologies such as Generative Adversarial Networks and Transformers in enhancing pest and disease identification. By providing a comprehensive review of state-of-the-art methods and identifying the unique value of AI in revolutionizing agricultural practices, increasing efficiency, and promoting sustainability, this study makes a significant contribution to the field.
Why it matches plant phenotyping methods植物病害の画像認識・分類に関するAI手法を中心にレビューしており、植物の病害状態を観測・推定する方法論レビューに該当する。
abstractThis paper reviews recent advancements in AI technologies, including few-shot learning, innovative training methods and network architectures, lightweight models, as well as deployment and hardware technologies.
Securing agricultural productivity and food security from serious threats is made possible through timely and reliable disease detection. The early detection of leaf disease is revolutionized by the recent advancements in imaging technology like digital imaging and remote sensing (RS) integrated with Artificial intelligence (AI). High- resolution, close-range visual data was offered by digital imaging, and it is crucial for detecting subtle symptoms in various applications. Large-scale monitoring over fields was facilitated by the RS platforms like drones and satellites, and it may help the farmers in offering actionable insights. Modern methods for crop leaf disease detection was reviewed in this study by analysing various imaging techniques like pre-processing, feature extraction (FE), classification techniques, and deep learning (DL) advancements. The datasets included, risks, and performance metrics utilized are all discussed in this review. The field of disease surveillance using AI and multimodal imaging has shown advancements in facilitating scalable, and real-time disease surveillance, and it is also highlighted in this review. The future paths for precision agriculture are guided by conclusion of the study, as it offers insights regarding present research gaps
Why it matches plant phenotyping methods作物葉の病徴を画像から検出する手法について、画像処理・特徴抽出・分類・深層学習・データセット・性能指標を体系的に扱うレビューであり、植物表現型取得手法が中心です。
abstractModern methods for crop leaf disease detection was reviewed in this study by analysing various imaging techniques like pre-processing, feature extraction (FE), classification techniques, and deep learning (DL) advancements.
Background/aim Sunflower ( Helianthus annuus ) is a crop of high economic and nutritional importance that continues to suffer significant yield losses due to foliar diseases. Traditional image-based and laboratory detection techniques remain limited by subjectivity, cost, and scalability. Transfer learning (TL) has recently emerged as an effective approach to overcoming these challenges involving the reuse of pretrained deep models for plant pathology tasks. Presented here is a systematic examination of recent TL-based studies on sunflower disease classification to identify prevailing trends, research gaps, and future opportunities. Materials and methods A structured Scopus query was employed to retrieve peer-reviewed articles published between 2021 and 2025. Strict inclusion and exclusion criteria ensured technical relevance to TL-based sunflower disease detection. Subsequently, 30 studies meeting the criteria were critically reviewed and analyzed in terms of model architecture, dataset characteristics, preprocessing strategies, and reported evaluation metrics. The comparative assessment focused on convolutional neural networks (CNNs), transformer-based architectures, and hybrid models. Results The analysis revealed a dominant reliance on pretrained CNNs such as ResNet, VGG, Inception, and EfficientNet. Several studies employed lightweight or federated learning variants to enhance deployment feasibility under field conditions. Among the commonly observed challenges were limited dataset diversity, class imbalance, and insufficient explainability. A key word cooccurrence analysis indicated an evolving research focus, transitioning from basic deep learning implementation to explainable and privacy-preserving frameworks optimized for edge devices. Conclusion The review revealed substantial progress in TL applications for the diagnosis of sunflower disease but underscored the need for larger, standardized datasets and cross-regional validation. Future studies should prioritize interpretable, adaptive architectures that can function in real-world agricultural environments. The insights drawn from this synthesis extend beyond sunflower pathology, offering a foundation for scalable, domain-transferable TL solutions in broader plant disease detection contexts.
Why it matches plant phenotyping methodsヒマワリ病害を画像から分類する転移学習手法を体系的に比較・評価したレビューであり、植物病害状態の表現型取得手法が中心です。
abstractPresented here is a systematic examination of recent TL-based studies on sunflower disease classification to identify prevailing trends, research gaps, and future opportunities.
Climate change, through rising temperatures and more frequent heat waves, threatens food security and agricultural sustainability. Heat stress is a major abiotic factor that negatively affects plant function and crop yields. Advanced phenotyping strategies are critical for developing heat-resilient crops and mitigating the impacts of climate change. This review offers an updated perspective on plant phenotyping under heat stress, providing practical guidance for researchers. It discusses key trial sites, modern phenotyping techniques, and the physiological and molecular mechanisms that plants employ in response to heat stress. Special emphasis is placed on plant plasticity and its role in adaptive responses to stress. The review further examines advanced phenotyping technologies and sensors used across various environments, both field-based and controlled, and at multiple scales, from cellular to aerial. It highlights a range of approaches, from high-tech to cost-effective solutions, aimed at enhancing accessibility. Additionally, it underscores the importance of improved data management, standardized protocols, and alignment with international best practices in heat stress phenotyping. Finally, the review presents global initiatives focused on advancing plant phenotyping for heat stress research, emphasizing the critical role of international collaboration in addressing this urgent challenge.
Why it matches plant phenotyping methods熱ストレス下の植物フェノタイピング技術・センサー・標準化・データ管理を体系的に扱うレビューであり、フェノタイピング手法が中心です。
abstractThis review offers an updated perspective on plant phenotyping under heat stress, providing practical guidance for researchers.
Over 295 million people in 53 countries experience acute food insecurity due to factors like famine, war, climate change, and conflict zones. Sustainable Development Goal 2: Zero Hunger aims to achieve food security, improve nutrition, end hunger, and promote sustainable agriculture. Balancing farming with environmental protection is crucial, especially in the face of climate change and globalization. Studying plant phenomics, which focuses on how plants grow and react to climate change, can help develop more productive and stronger crops. Advanced technology, such as High-throughput plant phenotyping, can provide detailed data for accurate predictions and better disease control. This article aims to explore the use of AI and machine learning in plant phenotyping, the integration of imaging technologies, IoT, and sensors, and the application of various technologies, including Brinjal, in vegetable phenotyping. Artificial Intelligence, IoT devices, edge computing, computer vision, and advanced sensor technologies are revolutionizing sustainable agriculture. These technologies provide real-time data, early detection of diseases, and improved nutrient, water, and pest management. Auto Machine Learning, Explainable AI, and Deep Learning enhance understanding and optimize breeding cycles. This combination of multi-omics data, machine learning, and smart tools is crucial for smart and sustainable agriculture, promoting farmer-based innovation and cross-sector collaboration.
Why it matches plant phenotyping methods植物フェノタイピングにおけるAI、機械学習、画像技術、IoT、センサーを主題とするレビューであり、方法論の整理が中心です。
abstractThis article aims to explore the use of AI and machine learning in plant phenotyping, the integration of imaging technologies, IoT, and sensors, and the application of various technologies, including Brinjal, in vegetable phenotyping.
The use of molecular marker technologies has significantly advanced biological sciences and plant genetic analysis, particularly in revealing individual variations in DNA sequence. Molecular markers are useful tools in plants, particularly in marker-assisted selection, genome-wide association studies and QTL identification that impacts complicated hereditary traits. As the development of genomic tools in plant breeding and our knowledge of plant genomes increases, rapid and high-throughput phenotyping methods continue to be discussed as significant improvement applications in plant breeding programs. Since quantitative traits such as yield traits, quality traits, and resistance to abiotic/biotic stress factors in plants are an element that determines the indirect effects of both genetic and environmental factors and their interactions, phenotyping is a critical element in crop development. High-throughput phenotyping methods capture changes in environmental factors more sensitively compared to traditional applications, and thus selection efficiency is successfully increased. Correct and ethical use of genomic technologies with high-throughput phenotyping techniques is critical for long-term success and sustainability in the agricultural sector. In this review, the use of molecular marker technologies developed in integration with plant breeding in mapping studies and studies on the use of high-throughput phenotyping technologies in plant breeding are discussed.
Why it matches plant phenotyping methods植物育種におけるハイスループット表現型解析技術の利用を明示的にレビューしており、フェノタイピング手法が主要な論点の一つである。
abstractIn this review, the use of molecular marker technologies developed in integration with plant breeding in mapping studies and studies on the use of high-throughput phenotyping technologies in plant breeding are discussed.
As climate change alters the frequency, intensity, and co-occurrences of abiotic and biotic stresses, the effective and efficient detection of plant stress responses and resistance mechanisms is critical for safeguarding global food security. Stressful environments elicit both visible and non-visible changes in plants. Cellular and subcellular changes, often invisible to the naked eye, can serve as indicators of stress and can be quantified using molecular, ionomic, metabolomic, genomic, and transcriptomic methods. In contrast, visible responses such as discoloration, morphological changes, and disease symptoms can be monitored efficiently through atmospheric, aerial, and terrestrial remote sensing platforms. Phenotyping at the whole-plant and organ levels offers valuable insights for diagnosing stress in situ , providing opportunities to study plant resistance and acclimation strategies under realistic conditions. However, the complexity of plant stress responses, spanning microscopic to macroscopic scales and diverse biological processes, make it challenging for any single technology to comprehensively capture the full spectrum of reactions. Furthermore, the rising prevalence of multifactorial stress conditions highlights the need for research on synergistic and antagonistic interactions between stress factors. To effectively mitigate the impacts of stress on agriculture, future research must prioritize integrative multi-omic approaches that connect cellular and subcellular processes with morphological and phenological stress responses.
Why it matches plant phenotyping methods植物ストレス応答の可視・不可視検出手法と、全植物・器官レベルのフェノタイピングを扱う方法論的レビューであり、手法の整理が中心です。
titleCurrent methods and future needs for visible and non-visible detection of plant stress responses.
The persistent threat of plant disease epidemics poses significant challenges to global agriculture, making crops susceptible to catastrophic diseases that compromise food security and nutritional well-being. This review critically examines the application of deep transfer learning and convolutional neural networks (CNNs) in classifying plant diseases, such as tomato leaf diseases. By synthesizing recent advancements in the field, the article highlights how pre-trained models, trained on large-scale image datasets, can be adapted to recognize disease-specific patterns in agricultural contexts. The discussion encompasses key methodologies, including the integration of custom architectures and shallow classifiers, as exemplified by works such as Fruit and Vegetable Leaf Disease Recognition based on a Novel Custom Convolutional Neural Network and Shallow Classifier and An Integrated Framework of Two-Stream Deep Learning Models Optimal Information Fusion for Fruits Disease Recognition. A critical analysis of existing approaches is provided, addressing their strengths, limitations, and the role of dataset quality and diversity in model performance, including the use of publicly available datasets of labelled plant disease images, such as PlantVillage. The review underscores the transformative potential of automation and robotics in reducing disease spread while emphasizing unresolved challenges, such as the need for cost-effective, scalable frameworks. By identifying gaps in current research and proposing future directions, this article aims to guide the development of sustainable, AI-driven solutions for agricultural productivity.
Why it matches plant phenotyping methods植物病害を画像から検出・分類するAI手法を体系的に検討するレビューであり、植物の病徴・病害状態を推定するフェノタイピング手法が中心です。
titleReview: Artificial Intelligence and Deep Transfer Learning for Plant Disease Detection and Classification
Background/aim The rapid advancement of deep learning (DL) has revolutionized plant disease detection by enabling highly accurate, image-based diagnostic solutions. This review provides a comprehensive synthesis of DL-based methodologies for plant disease detection, systematically structured around the key stages of the modeling pipeline, encompassing data acquisition, preprocessing, augmentation, classification, detection, segmentation, and deployment. Materials and methods The review focuses on evaluating convolutional neural network (CNN) architectures such as VGG, ResNet, EfficientNet, and DenseNet across diverse experimental contexts. Classification strategies are categorized according to their integration of visualization techniques (e.g., saliency maps, Grad-CAM) to enhance model interpretability, emphasizing the pivotal role of explainable artificial intelligence (XAI) in plant pathology. Object detection models are systematically examined within both one-stage (YOLO, SSD) and two-stage (Faster R-CNN) paradigms. Furthermore, critical challenges-such as environmental variability, data imbalance, and computational constraints-along with potential solutions including transfer learning, synthetic data generation using generative adversarial networks (GANs) and diffusion models, and edge computing for real-time deployment, are comprehensively discussed. Results This review summarizes best practices for dataset selection and model optimization for mobile platforms, emphasizing their role in improving the efficiency and accuracy of plant disease detection systems. Conclusion Deep learning-based methods show strong potential to enhance precision and resilience in real-world plant disease detection and monitoring.
Why it matches plant phenotyping methods植物病害の画像ベース検出手法を体系的にレビューしており、病害状態という植物表現型の取得・推定方法が中心である。
abstractThis review provides a comprehensive synthesis of DL-based methodologies for plant disease detection, systematically structured around the key stages of the modeling pipeline, encompassing data acquisition, preprocessing, augmentation, classification, detection, segmentation, and deployment.
Crop diseases remain a threat to the world's food security with yield loss ranging from 10–40% annually. The last few years have witnessed spectacular evolution in artificial intelligence (AI), deep learning, Internet of Things (IoT), and unmanned aerial vehicles (UAVs), which transformed crop disease monitoring and early detection of diseases. Earlier image processing methods are now overpowered by convolutional neural networks (CNNs), object detectors such as the YOLO family, and CNN-transformer hybrids, which are significantly more accurate and robust. At the same time, IoT sensors and UAV-based multispectral imaging provide complementary environmental and spectral information for enabling active monitoring irrespective of visible indicators. But there are some limitations they have, which are poor model generalization when trained from human-annotated datasets, expensive computation in field deployment, lack of rich plentiful annotated data for low-frequency diseases, and challenging adoption in smallholder farming settings.Here, the three areas of crop health monitoring system development are critically evaluated as follows: (i) image-based systems, (ii) deep learning models, and (iii) multimodal integration of UAV and IoT. Critical comparative performance, strength, and weakness of current methods are analyzed, highlighting dataset heterogeneity, detection accuracy, scalability, and practicability of deployment. Additionally, the review reveals the key deficits in the research—i.e., necessity for robust multimodal fusion paradigms, conventional benchmarking, and affordable field solutions—and suggests likely future directions such as federated learning, predictive outbreak modeling, and robotics for targeted intervention. Synthesizing current success and pointing toward likely future research directions, this review seeks to inform researchers and practitioners toward sustainable, tech-enabled crop disease management.
Why it matches plant phenotyping methods作物の病害状態を画像・深層学習・UAV・IoTセンサーで検出・監視する方法を中心に比較評価したレビューであり、植物フェノタイピング手法のレビューに該当する。
titleDeep Learning Approaches for Crop Health Monitoring and Early Disease Detection: A Review
Accurate predictions and representations of plant growth patterns in simulated and controlled environments are important for addressing various challenges in plant phenomics research. This review explores various works on state-of-the-art predictive pattern recognition techniques, focusing on the spatiotemporal modeling of plant traits and the integration of dynamic environmental interactions. We provide a comprehensive examination of deterministic, probabilistic, and generative modeling approaches, emphasizing their applications in high-throughput phenotyping and simulation-based plant growth forecasting. Key topics include regressions and neural network-based representation models for the task of forecasting, limitations of existing experiment-based deterministic approaches, and the need for dynamic frameworks that incorporate uncertainty and evolving environmental feedback. This review surveys advances in 2D and 3D structured data representations through functional-structural plant models and conditional generative models. We offer a perspective on opportunities for future works, emphasizing the integration of domain-specific knowledge to data-driven methods, improvements to available datasets, and the implementation of these techniques toward real-world applications.
Why it matches plant phenotyping methods植物フェノタイピングにおける成長形質の予測・表現・高スループット計測を扱う方法論レビューであり、方法論が中心です。
abstractThis review explores various works on state-of-the-art predictive pattern recognition techniques, focusing on the spatiotemporal modeling of plant traits and the integration of dynamic environmental interactions.
The present critical literature review describes the state-of-the-art innovative proximal (ground-based) solutions for plant disease diagnosis, suitable for promoting more precise and efficient phytosanitary measures. Research and development of new sensors for this purpose are currently a challenge. Present procedures and diagnosis techniques depend on visual characteristics and symptoms to be initiated and applied, compromising an early intervention. Also, these methods were designed to confirm the presence of pathogens, which did not have the required high throughput and speed to support real-time agronomic decisions in field extensions. Proximal sensor-based systems are a reasonable tool for an efficient and economic disease assessment. This work focused on identifying the application of optical and spectroscopic sensors as a tool for disease diagnosis. Biophoton emission, fluorescence spectroscopy, laser-induced breakdown spectroscopy, multi- and hyperspectral spectroscopy (HS), nuclear magnetic resonance spectroscopy, Raman spectroscopy, RGB imaging, thermography, volatile organic compounds assessment, and X-ray fluorescence were described due to their relevant potential. Nevertheless, some techniques revealed a low technology readiness level (TRL). The main conclusions identify HS, single and multi-spatial point observation, as the most applied methods for early plant disease diagnosis studies (88%), combined with distinct feature selection (FeS), dimensionality reduction (DR), and modeling techniques. Vegetation indices (28%) and principal component analysis (19%) were the most popular FeS and DR approaches, highlighting the most relevant wavelengths contributing to disease diagnosis. In modeling, classification was the most applied technique (80%), used mainly for binary and multi-class health status identification. Regression was used in the remaining (21%) scientific works screened. The data was collected primarily in laboratory conditions (62%), and a few works were performed in field conditions (21%). Regarding the study’s etiological agent responsible for causing the disease, fungi (53%) and viruses (23%) were the most analyzed group of pathogens found in the literature. Overall, proximal sensors are suitable for early plant disease diagnosis before and after symptom appearance, presenting classification accuracies mostly superior to 71% and regression coefficients superior to 61%. Nevertheless, additional research regarding the study of specific host-pathogen interactions is necessary.
Why it matches plant phenotyping methods植物病害の早期診断に用いる光学・分光センシング技術を体系的にレビューしており、病害状態という植物表現型の取得・推定手法が中心である。
abstractThe present critical literature review describes the state-of-the-art innovative proximal (ground-based) solutions for plant disease diagnosis, suitable for promoting more precise and efficient phytosanitary measures.
Integrating multispectral imaging and artificial intelligence in coffee production is a promising approach to optimize farming practices and improve crop management. This systematic literature review analyzes the current status, challenges, and future directions for combining these technologies in the coffee sector. Following the PRISMA protocol, 455 papers were reviewed in six scientific databases, identifying 27 primary studies that met the inclusion and exclusion criteria. The analysis reveals a significant increase in research activity since 2020, with a relationship between time and frequency of publication. Machine learning techniques, particularly regression analysis and random forests, emerged as the predominant artificial intelligence approaches for multispectral data processing. The review identified several key applications, such as coffee quality assessment, disease detection, and yield prediction. However, significant challenges remain, such as limited biometric variability within coffee plants, the influence of environmental factors, and the need for high-quality training data. The effectiveness of these technologies varies across geographic regions and soil and climatic conditions, underscoring the importance of application in specific contexts. Future research points toward the development of more robust artificial intelligence models, the integration of multiple data sources, and the need to employ hybrid artificial intelligence approaches. This review provides an understanding of the current landscape and valuable information for researchers, industry professionals, and stakeholders interested in creating more efficient and sustainable coffee farming practices using these technologies.
Why it matches plant phenotyping methodsコーヒー生産におけるマルチスペクトル画像とAIの植物状態・形質推定を扱う系統的レビューであり、フェノタイピング関連手法のレビューが中心である。
titleArtificial intelligence and multispectral imaging in coffee production: A systematic literature review
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Host plant resistance is the most effective and environmentally sustainable means of reducing yield losses caused by fungal foliar pathogens of cereal species. Cereal genebank collections hold diverse pools of potentially underutilized disease resistance alleles, and cereal genomic resources are well advanced due to large-scale sequencing and genotyping efforts. Genome-Wide Association Studies (GWAS) have emerged as the predominant association genetics technique to initially discover novel disease resistance loci or alleles in these diverse collections. Traditional disease resistance phenotyping methods are reliant on visual estimation of disease symptom severity and have successfully supported genetic mapping studies either via GWAS or QTL mapping in biparental populations facilitating both marker development and gene cloning efforts. Due to foliar pathogens having a high capacity to evolve, there is a need to pyramid disease resistance genes with diverse mechanisms for durable control. Resistance expressed as a quantitative trait, known as quantitative resistance (QR), is hypothesized to be more durable, unlike major R-gene resistance that is race-specific and can be vulnerable to breaking down without gene stewardship. However, assessing QR visually is challenging, particularly when complicated by complex genotype × environment (G × E) effects in the field. High-throughput image-based phenotyping provides accurate and unbiased data that can support foliar disease resistance screening efforts of genebank collections using GWAS. In this review, we discuss image-based disease phenotyping based on macroscopic (visible symptoms) and microscopic features during the host-pathogen interaction. Quantitative image analysis approaches using conventional and artificial intelligence (AI) algorithms are also discussed.
Why it matches plant phenotyping methods葉面病害の画像ベース表現型計測を主題とするレビューであり、症状の画像取得・定量解析とAIを含む手法を中心的に扱っている。
abstractHigh-throughput image-based phenotyping provides accurate and unbiased data that can support foliar disease resistance screening efforts of genebank collections using GWAS.
Plant breeding programs know the advantages of high-throughput phenotyping (HTP) in increasing efficiency over classical phenotyping and screening methods, which is achieved by saving time and improving selection accuracy. Even so, most programs have not yet systematically implemented this technology into their breeding pipelines. This review aims to indicate the restrictions of implementing HTP at a large scale and to summarize studies according to the used devices, data classes collected, and artificial intelligence (AI) methods applied to predict and classify agronomic traits in plant breeding programs with a focus on soybean [Glycine max (L.) Merr.]. Excluding HTP platforms in laboratories and greenhouses, satellite remote sensing, and autonomous mobile robots, this review focuses on field-based HTP platforms that take aerial images from drones and apply AI methods to associate those images with the traits of interest. Field-based HTP research is also conducted using hand-held devices that record individual vegetation indices (e.g., NDVI), a few spectral bands (multispectral radiometers), or the continuous range of the electromagnetic light spectrum (spectroradiometers). However, plant breeders must evaluate thousands of experimental lines each year, so using these devices instead of drones implies a trade-off between acquisition accuracy and the time it takes to collect the data. A challenge in the coming years is fine-tuning scalable, reliable models and optimizing data input, processing, and output pipelines to provide breeders with helpful information before they make selections.
Why it matches plant phenotyping methods植物表現型取得を中心に、ドローン空撮とAIによる農業形質の推定を扱うHTPレビューであり、対象・方法・実装上の課題を体系的に整理しているため。
abstractThis review aims to indicate the restrictions of implementing HTP at a large scale and to summarize studies according to the used devices, data classes collected, and artificial intelligence (AI) methods applied to predict and classify agronomic traits in plant breeding programs
Main conclusion This survey concludes that CNN-based deep learning models offer opportunities for early and accurate plant disease detection, supporting sustainable agriculture while acknowledging potential challenges in practical real-world application. Deep learning (DL) methods have transformed image-based plant disease diagnosis by addressing complex challenges specific to crop health monitoring in agriculture. The automated identification and classification of plant disease from images have significant interest, which can be expected to increase crop health monitoring and agricultural productivity. Yet, notwithstanding these benefits, image-based identification of plant diseases is a sophisticated challenge. Proper identification of certain plant varieties and proper determination of disease manifestations are key factors in the administration of effective care and sustainable management of disease. In this research paper an extensive overview Convolutional Neural Networks (CNNs) is implemented using deep learning method for disease detection in plants. This article focuses particularly on highlighting recent research achievements by the last half-decade emphasizing CNN-based models constructed for detecting plant leaf disease. The survey delves into key innovations, methods, and issues faced with the application of CNNs to monitor plant health. Specifically, it highlights the manner in which deep convolutional neural networks (DCNNs), learned using large-scale image databases, are becoming effective means of early and precise detection of plant diseases. Lastly, this paper charts exciting future directions for DL-aided plant disease diagnosis, while providing a balanced critique of the potential, as well as the limitations of CNNs in practical agricultural contexts.
Why it matches plant phenotyping methods植物葉の画像から病害を検出・分類するCNN手法を中心に扱うレビューであり、植物の病害状態を推定するフェノタイピング手法レビューに該当します。
titleA survey: to identify plant leaf diseases by feature extraction methods and classification techniques.
This paper provides a comprehensive review of advancements in plant disease detection, moving from traditional to modern and AI-driven approaches. It highlights that traditional methods, such as visual inspection, microbiological isolation, culturing, and molecular and serological techniques, are often limited by being time-consuming, subjective, or requiring specialized expertise and lab processing. These limitations can lead to significant crop yield losses, economic setbacks, and threats to food security. The review then discusses modern, non-destructive sensor technologies, which are crucial for detecting diseases in their early stages, often before visible symptoms appear. These technologies include: * Hyperspectral Imaging (HSI): Captures detailed "spectral fingerprints" of plants to detect subtle physiological changes. * Multispectral Imaging (MSI): Uses a limited number of spectral bands, often including near-infrared (NIR), to identify abnormal plant conditions more cost-effectively than HSI. * Thermal Imaging: Detects temperature fluctuations in plants caused by physiological changes during infection. * Chlorophyll Fluorescence Imaging (CFI): A non-invasive technique that detects early stress responses by analyzing chlorophyll emissions. * LiDAR and Drones: Used for aerial analysis of crop health, enabling early diagnosis and monitoring of large agricultural areas. Finally, the paper details how Artificial Intelligence (AI) and Deep Learning (DL) have revolutionized this field through automated, highly accurate diagnostic capabilities. The document covers various deep learning architectures, including Convolutional Neural Networks (CNNs) like AlexNet, VGG, ResNet, and YOLO, which are used for image classification, feature extraction, and real-time disease localization. It also mentions the use of semantic segmentation models like U-Net for pixel-level disease mapping, and the role of transfer learning and explainable AI (XAI) in improving model performance and transparency. The review concludes with an emerging paradigm of federated learning for decentralized, privacy-preserving model training.
Why it matches plant phenotyping methods植物病害の症状・生理状態を画像およびセンサーで検出する手法を中心に扱う包括的レビューであり、植物フェノタイピング手法のレビューに該当する。
abstractThis paper provides a comprehensive review of advancements in plant disease detection, moving from traditional to modern and AI-driven approaches.
Artificial Intelligence (AI) is revolutionizing plant disease diagnosis by providing transformative solutions to the challenges posed by agricultural diseases. AI-driven algorithms significantly reduce the time required for disease identification, enabling timely and precise control measures. These interventions help prevent the widespread proliferation of pathogens, mitigate crop losses and minimize economic damage. The integration of machine learning and deep learning particularly convolutional neural networks (CNNs) with computer vision systems enhances the precision, scalability and efficiency of disease monitoring. AI-powered tools offer real-time surveillance by capturing images of diseased leaves and generating data-driven insights, thereby facilitating targeted treatment applications while reducing resource wastage and environmental impact. Furthermore, the application of AI-powered mobile apps provides farmers with instant, field-level support to take preventive actions during the early stages of disease development. These technologies enable farmers to make informed, evidence-based decisions, optimize their agricultural practices and enhance crop yield and quality. Ultimately, AI plays a pivotal role in boosting agricultural productivity, ensuring food security and promoting both economic resilience and environmental sustainability. This review highlights recent advancements in machine learning algorithms, deep learning models especially CNNs and the role of mobile applications in early disease detection in agriculture.
Why it matches plant phenotyping methods植物病害の画像観察に基づくAI検出を主題とするレビューであり、植物の病徴・病害状態を推定するフェノタイピング手法のレビューに該当する。
abstractThis review highlights recent advancements in machine learning algorithms, deep learning models especially CNNs and the role of mobile applications in early disease detection in agriculture.
This review aims to provide an overview of the current state of AI-driven crop breeding, highlighting its applications, benefits, challenges, and future directions. The integration of artificial intelligence (AI) into crop breeding is transforming agricultural innovation by leveraging big data, machine learning (ML), and deep learning (DL) techniques. Advances in genomics, phenomics, and environmental sensing have enabled the development of high-dimensional datasets, fostering more precise and efficient breeding strategies. AI-driven approaches, including ML models like random forests and convolutional neural networks, enhance phenotypic predictions and yield forecasting. Deep learning further accelerates genotype-to-phenotype mapping by extracting key traits from large-scale datasets. Additionally, AI-powered genomic selection and gene editing tools, such as CRISPR-Cas9, are revolutionizing targeted breeding. Automation, including UAVs and high-throughput phenotyping platforms, streamlines data collection and analysis, reducing costs and improving accuracy. Despite these advancements, challenges such as data standardization, computational demands, and ethical concerns remain. Overcoming these hurdles will be critical in harnessing AI’s full potential for sustainable agriculture and global food security.
Why it matches plant phenotyping methodsAI駆動育種におけるフェノミクス、表現型予測、高スループットフェノタイピングを主題として整理するレビューであり、植物フェノタイピング手法の方法論的レビューに該当する。
abstractThis review aims to provide an overview of the current state of AI-driven crop breeding, highlighting its applications, benefits, challenges, and future directions.
Background Plant responses to drought stress include a complex variety of processes at the molecular, biochemical, and physiological levels that operate simultaneously in a specific spatiotemporal way at several organizational levels (including organelles, cells, tissue/organs, and the whole-plant level). Scope A roadmap is presented to determine whether drought stress leads to leaf senescence using an integrative approach that considers the process at the whole-plant level. This is essential not only for detecting and monitoring the impact of the drought, but also, more importantly, for identifying whether the plant response is leading to leaf senescence and it is therefore adaptive (protective, indicating stress tolerance) or maladaptive (damaging, indicating vulnerability) to the drought stress. This has important implications for optimizing crop yield and quality (thus requiring urgent attention in current agricultural practices), as well as for environmental management and effective conservation strategies. The detection and monitoring of drought-induced leaf senescence will be discussed, disentangling dubious cases. Furthermore, there will be a focus on drought-induced senescence as an integral plant stress response and whether it indicates damage or protection. Conclusions This integrative approach has the potential to help detect, monitor, and fully understand leaf senescence as a protective and adaptive process that plants have evolved to withstand drought stress in agricultural and ecological settings. Exploiting this knowledge and transferring it adequately will help improve crop yield as well as current environmental management programs.
Why it matches plant phenotyping methods干ばつ誘導葉老化の検出・モニタリング手法を統合的に整理するレビューであり、植物状態の表現型取得が中心的テーマです。
abstractA roadmap is presented to determine whether drought stress leads to leaf senescence using an integrative approach that considers the process at the whole-plant level.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Ensuring global food security requires noninvasive techniques for optimizing resource use and monitoring crop health. Hyperspectral imaging (HSI) enables the precise analysis of plant physiology by capturing spectral data across narrow bands. This review explores HSI's role in agriculture, particularly its integration with unmanned aerial vehicles, AI-driven analytics, and machine learning. These advancements allow real-time monitoring of photosynthesis, chlorophyll fluorescence, and carbon assimilation, linking spectral data to plant health and agronomic decisions. Key indicators such as solar-induced fluorescence and vegetation indices enhance crop stress detection. This work compares HSI-derived metrics in differentiating nutrient deficiencies, drought, and disease. Despite its potential, challenges remain in data standardization and spectral interpretation. This review discusses solutions such as molecular phenotyping and predictive modeling, for AI-driven precision agriculture. Addressing these gaps, HSI is poised to revolutionize farming, improve climate resilience, and ensure food security.
Why it matches plant phenotyping methods植物の光合成・クロロフィル蛍光・ストレスなどを hyperspectral imaging で推定する手法を中心に扱うレビューであり、植物フェノタイピング手法の方法論的レビューに該当する。
abstractThis review explores HSI's role in agriculture, particularly its integration with unmanned aerial vehicles, AI-driven analytics, and machine learning.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 13 Sept 2026
The escalating impacts of climate change pose significant threats to global agriculture, necessitating a rapid development of climate-resilient crop varieties. The integration of multi-omics technologies-such as genomics, transcriptomics, proteomics, metabolomics, and phenomics-has revolutionized our understanding of the intricate molecular networks that govern plant stress responses. Coupled with advanced predictive modeling approaches such as machine learning, deep learning, and multi-omics-assisted genomic selection, these integrated frameworks enable accurate genotype-to-phenotype predictions that accelerate breeding for augmented stress tolerance. This review comprehensively synthesizes the current strategies for multi-omics data integration, highlighting computational tools, conceptual frameworks, and challenges in harmonizing heterogeneous datasets. We examine the contribution of digital phenotyping platforms and environmental data in dissecting genotype-by-environment interactions critical for climate adaptation resilience. Further, we discuss technical, biological, and ethical challenges, encompassing computational bottlenecks, trait complexity, data standardization, and equitable data sharing. Finally, we outline future directions that prioritize scalable infrastructures, interpretability, and collaborative platforms to facilitate the deployment of multi-omics-guided breeding in diverse agroecological contexts. This integrative approach possesses transformative potential for the development of resilient crops, ensuring agricultural sustainability amidst increasing environmental volatility.
Why it matches plant phenotyping methods植物フェノタイピング、デジタルフェノタイピング基盤、遺伝子型から表現型への予測を中心的に扱うレビューであり、方法論レビューとして適格。
abstractThis review comprehensively synthesizes the current strategies for multi-omics data integration, highlighting computational tools, conceptual frameworks, and challenges in harmonizing heterogeneous datasets.
Pursuing agricultural intensification to raise productivity has brought challenges such as involvement of high capitals, often in the form of loans, environmental damage, and ecosystem disruption. These challenges increase risks in agricultural practice that require good management and control. This increases the need for real-time, non-destructive monitoring technologies that can improve crop productivity, enhance land use, and facilitate environmentally friendly agriculture. Due to its unique capacity to non-destructively examine plants' internal biological and structural properties, ultrasound has emerged as a promising non-invasive technique providing insights often unattainable with traditional optical, spectral, or chemical sensors. This review aims to provide an up-to-date state of the art in ultrasound-based monitoring applications within major agricultural areas: soil characterization, seed quality control, plant health, stress monitoring, pests and diseases detection, and fruit ripening assessment. This review explores how contact and non-contact ultrasound measurements are scalable and versatile, bridging the gaps between laboratory and field-deployed systems. Integrating ultrasound monitoring with artificial intelligence and Internet of Things (IOT) frameworks further enhances modality accuracy and can detect stress, diseases, and other physiological changes in crops sooner. Overcoming challenges such as environmental acoustic noise will require further work. Still, recent advances such as improved signal filtering algorithms, new transducer designs, better field sensitivity, and broader collaboration to standardize ultrasound measurement protocols indicate a growing trend toward increased on-field use of ultrasound. Finally, the review also discusses the current limitations and future research directions of how ultrasound-based monitoring can catalyse a new paradigm of sustainable data-driven agriculture that meets food security needs.
Why it matches plant phenotyping methods植物の内部構造、健康状態、ストレス、病害、果実成熟などを超音波で測定する方法を中心に扱う農業モニタリング手法レビューであり、植物フェノタイピング手法レビューに該当する。
abstractThis review aims to provide an up-to-date state of the art in ultrasound-based monitoring applications within major agricultural areas: soil characterization, seed quality control, plant health, stress monitoring, pests and diseases detection, and fruit ripening assessment.
Globally, potatoes are the fourth most produced food crop, and in the United Kingdom alone, they generated approximately £705 million in 2022. However, to achieve the United Nations (UN) Sustainable Development Goals (SDG), potato farmers need to sustainably increase yields to address the growing demand for both food and land. Crop yield can be affected by various factors, including disease, pests, and nutrient deficiencies. To tackle these challenges and optimise yields, researchers have leveraged remote sensing platforms for high-throughput non-destructive phenotyping. Data collected from these platforms can be used to develop machine learning (ML) models aimed at addressing the aforementioned issues. To summarise recent developments in ML models applied to potato plant phenotyping, a systematic review of journal articles from the last seven years was conducted. This review underscored the advantages of Deep Learning (DL) approaches and the rising trend of Convolutional Neural Network (CNN)-based architectures, while also noting the limited availability of data for training these models. This review is intended to benefit researchers and farmers by providing an up-to-date review of ML models in potato plant phenotyping. • Remote sensing and ML models can optimise potato yield through non-destructive phenotyping. • Deep Learning (DL) and CNN-based approaches show promise in potato phenotyping. • Limited training data availability remains a challenge in ML model development for agriculture. • This review supports researchers and farmers with current insights on ML advancements in potato crop phenotyping.
Why it matches plant phenotyping methodsジャガイモ植物フェノタイピングにおけるリモートセンシングと機械学習手法を主題とする体系的レビューであり、フェノタイピング手法のレビューが中心です。
abstractTo summarise recent developments in ML models applied to potato plant phenotyping, a systematic review of journal articles from the last seven years was conducted.
Agriculture is the foundation of life that faces numerous daily attacks from nature and living organisms. A major challenge for farmers is timely plant disease identification, which is crucial to prevent productivity losses and the production of poor-quality products. Researchers have recently been focusing on automating the plant leaf disease recognition process using computer vision and machine learning techniques. More importantly, the recent developments in deep learning have significantly advanced the field of plant leaf disease recognition. Regardless of these advancements, significant challenges remain in automatic leaf disease recognition, and researchers are continuing to seek better performance, in-field applicability, and compatibility with resource-constrained devices. This survey provides a comprehensive overview of real-world and laboratory datasets, feature extraction methods, deep learning frameworks, limitations, recommendations, and future directions for deep plant leaf disease recognition. It offers a detailed comparative analysis of various deep learning models applied to different datasets, preprocessing techniques, and data collection methods. This work also highlights the need for an ideal dataset and explores future directions like the Internet of Things integration, Explainable AI, and Smart Farming, which previous surveys have not covered. The primary aim of this survey is to assist researchers in understanding state-of-the-art plant leaf disease recognition techniques, support farmers in the field of plant pathology, address limitations, provide recommendations and outline future directions.
Why it matches plant phenotyping methods植物葉の病徴・病害状態を画像と機械学習で認識する手法を対象とした包括的サーベイであり、データセット、特徴抽出、モデル比較、前処理、データ収集を中心に扱うため、植物フェノタイピング手法レビューに該当します。
abstractThis survey provides a comprehensive overview of real-world and laboratory datasets, feature extraction methods, deep learning frameworks, limitations, recommendations, and future directions for deep plant leaf disease recognition.
Globally, potatoes are the fourth most produced food crop, and in the United Kingdom alone, they generated approximately £705 million in 2022. However, to achieve the United Nations (UN) Sustainable Development Goals (SDG), potato farmers need to sustainably increase yields to address the growing demand for both food and land. Crop yield can be affected by various factors, including disease, pests, and nutrient deficiencies. To tackle these challenges and optimise yields, researchers have leveraged remote sensing platforms for high-throughput non-destructive phenotyping. Data collected from these platforms can be used to develop machine learning (ML) models aimed at addressing the aforementioned issues. To summarise recent developments in ML models applied to potato plant phenotyping, a systematic review of journal articles from the last seven years was conducted. This review underscored the advantages of Deep Learning (DL) approaches and the rising trend of Convolutional Neural Network (CNN)-based architectures, while also noting the limited availability of data for training these models. This review is intended to benefit researchers and farmers by providing an up-to-date review of ML models in potato plant phenotyping.
Why it matches plant phenotyping methodsジャガイモ植物フェノタイピングに用いるリモートセンシングと機械学習モデルを体系的にレビューしており、フェノタイピング手法のレビューが中心です。
titlePotato plant phenotyping and characterisation utilising machine learning techniques: A state-of-the-art review and current trends
Phenotyping systems propels the growth of modern agriculture, driving innovations in plant breeding, crop management, precise application of resources and smart agriculture. This review provides a comprehensive analysis of phenotyping systems, exploring their status, technological advancements, challenges and future directions. The evolution from traditional phenotyping to high-throughput phenotyping (HTP) systems with involvement of advanced imaging (visible, infrared, hyperspectral, and thermal), sensors (LIDAR and NIR), data analytics, drones and automated platforms have enabled rapid non-invasive collection of phenotypic information, significantly hastening breeding programs and improving stress tolerance studies. The integration of big data, artificial intelligence (AI) and machine learning (ML) has enhanced data management and interpretation, enabling the development of predictive models and real-time decision-making tools. Despite these advancements, several challenges persist. The technical issues such as data accuracy, resolution and consistency alongside economic concerns related to high cost of implementation, limits the widespread adoption of advanced phenotyping technologies, especially among smallholder farmers. Furthermore, the integration of these technologies with traditional farming practices and the handling of large datasets raises concerns about data privacy, ownership and interpretation. The impending growth of phenotyping lies in advancements such as the integration of AI and genomics, enabling more precise breeding through the linking of genetic information with phenotypic traits. Additionally, the development of low-cost systems is essential to democratize access to precision agriculture, particularly in developing regions. As phenotyping systems continue to advance, they will play a critical role in promoting sustainable agriculture, enhancing resource efficiency, ensuring food security and addressing global climate change.
Why it matches plant phenotyping methods植物フェノタイピングシステムの技術、課題、将来展望を中心に扱うレビューであり、対象範囲に合致する。
abstractThis review provides a comprehensive analysis of phenotyping systems, exploring their status, technological advancements, challenges and future directions.
Crop sustainability is an urgent issue in the world, and plant diseases are a significant reason for crop yield and food security restrictions. Conventional manual disease detection procedures are time-consuming and frequently inaccurate. Deep learning, particularly Convolutional Neural Networks (CNNs), has made automated and accurate plant disease diagnosis from leaf images possible in recent years. This review overviews current deep learning methods for the detection of plant leaf diseases and delves into models with pesticide recommendation systems. The review classifies studies into CNN-based models, classical machine learning methods, hybrid models, and image processing approaches. It also identifies systems with region-specific pesticide recommendations. Issues like data shortage, generalization of models, and compliance with regulations are addressed, in addition to directions for the future like mobile deployment, transfer learning, and IoT integration. This thorough review is designed to direct future research toward smart, scalable agriculture solutions.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習手法を中心にレビューしており、植物フェノタイピング手法レビューに該当する。農薬推薦も扱うが、葉病害検出の方法論が明示的な中心である。
abstractThis review overviews current deep learning methods for the detection of plant leaf diseases and delves into models with pesticide recommendation systems.
PURPOSE: This review synthesizes advancements in proximal spectral sensing devices—including portable, vehicle-based, UAV-based, and IoT-based—for monitoring field crop growth traits. By evaluating their technical capabilities, applications, and limitations, it addresses critical challenges in scalability, data integration, and environmental adaptability to advance precision agriculture (PA) practices. METHODS: A systematic analysis of literature (2001–2024) was conducted using keywords such as “proximal remote sensing,” “spectral sensors,” and “crop growth monitoring” in the Web of Science database, yielding 1,278 publications. The performance, sensing mechanisms, and practical applications of these devices were analyzed across platforms, with a focus on their ability to estimate key growth indicators (e.g., biomass, leaf area index, nitrogen content) and resolve PA-related challenges. RESULTS: Portable spectral sensors excel in capturing high-resolution, targeted measurements but face limitations in accuracy during early crop growth stages and under complex field conditions. Vehicle-based systems enable efficient large-area scanning but encounter synchronization challenges between sensors and machinery, alongside susceptibility to environmental interference. UAV-based devices deliver rapid, high-throughput data collection but require enhanced endurance and integration with satellite imagery to achieve regional scalability. IoT-based networks support continuous monitoring but are constrained by a lack of specialized spectral sensors and insufficient durability in harsh agricultural environments. Cross-platform data fusion remains impeded by heterogeneity in data types, spatial scales, and storage protocols, while device durability, algorithmic robustness, and environmental resilience emerge as critical factors for reliable field deployment. CONCLUSIONS: Proximal spectral sensing devices hold transformative potential for multi-scale crop growth monitoring, yet persistent technical gaps hinder their widespread adoption. Future research should prioritize the development of lightweight hyperspectral imaging systems paired with advanced computational algorithms, unified frameworks for cross-platform data fusion, and durable IoT sensors tailored for harsh field conditions. Additionally, integrating UAV-based data with satellite observations will enhance regional insights, while standardized protocols and interdisciplinary collaboration are essential to bridge ground-to-space monitoring networks. These advancements will foster intelligent, sustainable crop management systems, ultimately addressing global agricultural productivity and sustainability challenges.
Why it matches plant phenotyping methods作物生育形質（バイオマス、LAI、窒素含量）を推定する近接スペクトルセンシング機器の性能・技術・限界を体系的にレビューしており、フェノタイピング手法が中心である。
abstractThis review synthesizes advancements in proximal spectral sensing devices—including portable, vehicle-based, UAV-based, and IoT-based—for monitoring field crop growth traits.
Harnessing state-of-the-art technologies to improve disease resistance is a critical objective in modern plant breeding. Artificial intelligence (AI), particularly deep learning and big model (large language model and large multi-modal model), has emerged as a transformative tool to enhance disease detection and omics prediction in plant science. This paper provides a comprehensive review of AI-driven advancements in plant disease detection, highlighting convolutional neural networks and their linked methods and technologies through bibliometric analysis from recent research. We further discuss the groundbreaking potential of large language models and multi-modal models in interpreting complex disease patterns via heterogeneous data. Additionally, we summarize how AI accelerates genomic and phenomic selection by enabling high-throughput analysis of resistance-associated traits, and explore AI's role in harmonizing multi-omics data to predict plant disease-resistant phenotypes. Finally, we propose some challenges and future directions in terms of data, model, and privacy facets. We also provide our perspectives on integrating federated learning with a large language model for plant disease detection and resistance prediction. This review provides a comprehensive guide for integrating AI into plant breeding programs, facilitating the translation of computational advances into disease-resistant crop breeding.
Why it matches plant phenotyping methods植物病害検出と抵抗性形質予測におけるAI手法を包括的に扱うレビューであり、植物フェノタイピング手法のレビューとして中心的です。
abstractThis paper provides a comprehensive review of AI-driven advancements in plant disease detection
To identify novel advancements in plant diseases detection and classification systems employing Machine Learning (ML), Deep Learning (DL), and Transfer Learning (TL), this research compiled 111 peer-reviewed papers published between 2019 and early 2023. The literature was sourced from databases such as Scopus and Web of Science using keywords related to deep learning and leaf disease. A structured analysis of various plant disease classification models is presented through tables and graphics. This paper systematically reviews the model approaches employed, datasets utilized, countries involved, and the validation and evaluation methods applied in plant disease identification. Each algorithm is annotated with suitable processing techniques, such as image segmentation and feature extraction, along with standard experimental metrics, including the total number of training/testing datasets utilized, the quantity of disease images considered, and the classifier type employed. The findings of this study serve as a valuable resource for researchers seeking to identify specific plant diseases through a literature-based approach. Additionally, the implementation of mobile-based applications using the DL approach is expected to enhance agricultural productivity.
Why it matches plant phenotyping methods植物病害を画像から検出・分類する深層学習手法を体系的にレビューしており、植物の病害状態を推定する方法論が中心です。
titleDeep Learning Approaches for Plant Disease Diagnosis Systems: A Review and Future Research Agendas
Agriculture is a cornerstone of human civilization, providing both food and economic stability. While not necessarily fatal, leaf diseases are a crucial threat to plant health. Accurate detection and classification of diseases in early stages are essential to minimize damage. Manual identification can be challenging, and delays in detection can lead to crop devastation. Fortunately, computer-aided image processing offers a solution. Researchers have explored several techniques for disease detection and classification by usage of affected leaf images, making significant progress over time. However, there's always room for improvement. Machine learning (ML), Deep learning (DL) techniques have shown hopeful results. ML, DL approaches act as black-box; eXplainable AI (XAI) provides clear explanations on decisions made by these black-boxes. This study aims to present a comprehensive review on plant leaf disease detection and classification by means of ML, DL and XAI methods with an overview of the outcomes of existing techniques, summarizes their performance, evaluation metrics, and analyses the challenges in existing systems, and offers the study's inferences.
Why it matches plant phenotyping methods植物葉の病害を画像から検出・分類する手法について、ML・DL・XAIの性能や評価指標を体系的にレビューしており、植物状態の画像ベース推定が中心である。
abstractThis study aims to present a comprehensive review on plant leaf disease detection and classification by means of ML, DL and XAI methods with an overview of the outcomes of existing techniques, summarizes their performance, evaluation metrics, and analyses the challenges in existing systems
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Introduction: Detecting plant stress is a critical challenge in agriculture, where early intervention is essential to enhance crop resilience and maximize yield. Conventional single-mode approaches often fail to capture the complex interplay of plant health stressors. Methods: This review integrates findings from recent advancements in Multi-Mode Analytics (MMA), which employs spectral imaging, image-based phenotyping, and adaptive computational techniques. It integrates machine learning, data fusion, and hyperspectral technologies to improve analytical accuracy and efficiency. Results: MMA approaches have shown substantial improvements in the accuracy and reliability of early interventions. They outperform traditional methods by effectively capturing complex interactions among various abiotic stressors. Recent research highlights the benefits of MMA in enhancing predictive capabilities, which facilitates the development of timely and effective intervention strategies to boost agricultural productivity. Discussion: The advantages of MMA over conventional single-mode techniques are significant, particularly in the detection and management of plant stress in challenging environments. Integrating advanced analytical methods supports precision agriculture by enabling proactive responses to stress conditions. These innovations are pivotal for enhancing food security in terrestrial and space agriculture, ensuring sustainability and resilience in food production systems.
Why it matches plant phenotyping methods植物ストレス評価のためのスペクトル画像、画像ベース表現型解析、機械学習・データ融合を中心に扱うレビューであり、植物表現型計測手法のレビューとして中心的です。
titleA systematic review of multi-mode analytics for enhanced plant stress evaluation
ABSTRACT- Agriculture remains a fundamental pillar of many national economies, making the protection of crops from disease a top priority. Pathogens such as bacteria, fungi, and viruses can significantly reduce crop productivity, underscoring the need for timely and accurate disease detection. Recent innovations in computer vision and artificial intelligence have introduced powerful tools for recognizing plant diseases through image analysis, particularly using leaf imagery. This paper investigates the application of machine learning, deep learning, and few-shot learning models in automating disease identification to assist farmers in making informed, prompt decisions. By examining the use of advanced models—including convolutional networks and vision transformers—alongside imaging technologies like hyperspectral cameras, this study highlights both the technological advancements and their potential impact in the field. Furthermore, it touches on molecular-level diagnostic techniques aimed at minimizing the threat of pathogens. The review offers a thorough overview of current progress and identifies key opportunities for future research, with the goal of translating laboratory breakthroughs into practical solutions for sustainable agriculture. INDEX TERMS: Plant disease, deep learning, machine learning, shot learning, computer vision, folding networks (CNNS), vision trans, hyperspectral imaging, molecular diagnostics, sustainable agriculture detection.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する画像解析・機械学習手法を中心に扱うレビューであり、植物病害フェノタイピング手法のレビューに該当する。
abstractThis paper investigates the application of machine learning, deep learning, and few-shot learning models in automating disease identification
Published25 Apr 20252025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)Cited by 1 · OpenAlex ↗
Plant diseases are a major problem for global food production because they reduce crop yields and cause big financial losses. Traditional ways of detecting these diseases involve manual checks, which are slow, tiring, and can easily lead to mistakes. This survey looks at how machine learning, especially using Convolutional Neural Networks (CNNs), combined with robots, can make detecting plant diseases faster and more accurate. The review includes various machine learning models and methods used to identify different types of plant infections like bacterial, viral, and fungal diseases. It also discusses how IoT devices and remote sensing can help monitor plants in real time. By automating disease detection, these technologies can give early warnings, reduce the need for chemical treatments, and improve plant health management, leading to more sustainable farming. The paper also talks about the challenges of current models, the importance of having diverse data for training, and how these technologies can be improved and used more widely in the future.
Why it matches plant phenotyping methods植物病害の画像・機械学習・ロボットによる検出を扱うレビューであり、病害状態という植物表現型の取得手法が中心です。
abstractThis survey looks at how machine learning, especially using Convolutional Neural Networks (CNNs), combined with robots, can make detecting plant diseases faster and more accurate.
Mangrove forests are one of the ecosystems with the richest biodiversity and the highest functional value of ecosystem services in the world. For mangrove research, it is particularly important to facilitate mangrove mapping, plant species classification, biomass and carbon sink estimation using remote sensing technologies. Recently, more and more studies have combined unmanned aerial vehicles and remote sensing technology to estimate plant traits and the biomass of mangrove forests. Various multispectral and hyperspectral data are used to establish various vegetation indexes for plant classification, and data models for biomass estimation and carbon sink calculation. This study systematically reviews the use of remote sensing and unmanned aerial vehicles in mangrove studies during the past three decades based on 2,424 peer-reviewed papers. By synthesizing these studies, we identify the pros and cons of different indices and models developed from remote sensing technologies by sorting out past cases. Specifically, we review the use of remote sensing technologies in mapping the past and present area, plant species composition, biomass of mangrove forests and examines the threats to the degradation of mangrove forests. Our findings reveal that there is increasing integration of machine learning and remote sensing to facilitate mangrove mapping and species identification. Moreover, multiples sources of remote sensing data tend to be combined to improve species classification accuracy and enhance the precision of mangrove biomass estimates when integrated with field-based data.
Why it matches plant phenotyping methodsマングローブの種分類、バイオマス推定、植物形質推定に用いるドローン・リモートセンシング手法を体系的にレビューしており、植物状態の取得・推定方法が中心である。
abstractThis study systematically reviews the use of remote sensing and unmanned aerial vehicles in mangrove studies during the past three decades based on 2,424 peer-reviewed papers.
In recent years, plant counting using data collected by sensors embedded in remotely piloted aircraft systems (RPAS), combined with machine learning algorithms, has become popular in the agroforestry sector, especially in crop planning, crop production estimation, among other applications. This study aimed to perform a systematic review of the literature on plant counting in the agricultural and forestry sector that uses data from sensors embedded in RPAS. We sought to identify the principal bibliometric indicators of scientific production and, through content analysis, the main characteristics and trends of the studies. A total of 33 scientific articles obtained on the Scopus and Web of Science platforms were used. Then, a content qualitative analysis of each article was conducted to identify the main thematic categories: agricultural and forest species, platform and sensors, software, and algorithm. There was an increase in scientific publications as of 2017. The USA presented the higher number of researches performed, with eight publications. There was a significant presence of RGB (Red, Green and Blue) sensors followed by multispectral. The algorithms Convoluctional Neural Network (CNN), Structure from Motion (SfM), and K-means stood out for the recurrence of use, either singularly or associated. Studies with this purpose drive new research development, where this technology utilization is revealed as a potential instrument to understand the usage trends, subsidize and encourage the information acquisition, promoting improvements and progress for research in the agroforestry scope.
Why it matches plant phenotyping methods農林植物のセンサー画像と機械学習による植物個体数推定を主題とする系統的レビューであり、植物形質取得手法のレビューが中心である。
titleThe Use Remotely Piloted Aircraft in Counting Agricultural and Forestry Plants: a Systematic Review
The high nutritional and medicinal value of apples has contributed to their widespread cultivation worldwide. Unfavorable factors in the healthy growth of trees and extensive orchard work are threatening the profitability of apples. This study reviewed deep learning combined with computer vision for monitoring apple tree growth and fruit production processes in the past seven years. Three types of deep learning models were used for real-time target recognition tasks: detection models including You Only Look Once (YOLO) and faster region-based convolutional network (Faster R-CNN); classification models including Alex network (AlexNet) and residual network (ResNet); segmentation models including segmentation network (SegNet), and mask regional convolutional neural network (Mask R-CNN). These models have been successfully applied to detect pests and diseases (located on leaves, fruits, and trunks), organ growth (including fruits, apple blossoms, and branches), yield, and post-harvest fruit defects. This study introduced deep learning and computer vision methods, outlined in the current research on these methods for apple tree growth and fruit production. The advantages and disadvantages of deep learning were discussed, and the difficulties faced and future trends were summarized. It is believed that this research is important for the construction of smart apple orchards.
Why it matches plant phenotyping methodsリンゴ樹の生育、器官、収量、病害を画像・深層学習で評価する方法を中心にレビューしており、植物フェノタイピング手法レビューに該当する。
abstractThis study reviewed deep learning combined with computer vision for monitoring apple tree growth and fruit production processes in the past seven years.
Plant leaf disease detection is a crucial aspect of precision agriculture and crop management, helping to prevent crop losses and improves yield quality. Plants are very essential in our life they provide source of energy and overcome the matter of global warming. Plant disease is notable risk of nutrition security. Therefore, timely detection of risk is important. Leaf disease detection using the machine learning is an approach. Machine learning offers a worthy approach for making a classy and automatic algorithm using Convolutional Neural Network (CNN), Artificial Intelligence (AI), Image Processing and video processing, voice processing, Natural Language Processing, etc. This review report provides the comparative analysis of the different machine learning algorithms of diagnosis of different leaf disease.
Why it matches plant phenotyping methods植物葉の病徴・病害を画像と機械学習で検出する方法を比較分析するレビューであり、植物の状態推定に関する方法論が中心です。
abstractThis review report provides the comparative analysis of the different machine learning algorithms of diagnosis of different leaf disease.
One of the main kinds of life in the world is plant. By giving food to individuals and, what's more, untamed life, plants benefit the environment and human existence in the world in different ways. Plants lead to a country's beneficial farming creation. A convolutional neural network (CNN) can be utilized to future plant development and wellbeing through leaf assessment. AIML had played a fundamental impact on checking plants' wellbeing. AIML grants objects to be detected or controlled from a distance across the organization's foundation. The outcome further develops exactness, financial advantages and productivity. AIML framework is intended to gather information and give continuous input on the condition of the plant, soil, and climate factors. Acknowledgment of plant diseases utilizing Convolutional neural Networks (CNN) is an emerging field of exploration that expects to recognize and analyse plant infections naturally. This method utilizes picture based calculations in light of profound realizing, which take into consideration the extraction of complex highlights from pictures of plant leaves, natural products, or stems impacted by different diseases. via preparing the CNN on an enormous dataset of solid and sick plants, it can figure out how to recognize designs and recognize various kinds of diseases. The acknowledgment of plant infections involving CNN has huge consequences for plant diseases across the board, as it takes into consideration early discovery and exact analysis, which can prompt ideal interventions and decreased crop misfortunes. In this paper, we will give an outline of the idea of acknowledgment of plant diseases utilizing CNN, its expected applications, and some of the difficulties that should be addressed to work on the precision and versatility of this technology.
Why it matches plant phenotyping methods植物病害を葉・果実・茎の画像からCNNで認識・診断する方法を概説しており、植物状態の画像ベース推定が中心です。
abstractA convolutional neural network (CNN) can be utilized to future plant development and wellbeing through leaf assessment.
RootRoot system architectureStress response / tolerance
Global climate change predictions point to an increase in the frequency of droughts and floods, which are a huge challenge to food production. During crop evolution, different mechanisms for drought resilience have emerged, and studies suggest that roots can be an important key in understanding these mechanisms. However, knowledge is still scarce, being fundamental to its exploitation. Plant-based protein, especially grain legume crops, will be crucial in meeting the demand for affordable and healthy food due to their high protein content. In addition, grain legumes have the unique ability for biological nitrogen fixation (BNF) through symbiosis with bacteria, which contributes to sustainable agriculture. The exploitation of root phenotyping techniques in grain legumes is an important step toward understanding their drought resilience mechanisms and selecting more resilient genotypes. Different methodologies are available for root phenotyping, including the paper pouch approach, rhizotrons and the semi-hydroponic system. Additionally, different imaging techniques have been employed to assess root traits. This review provides an overview of the root system architecture (RSA) of grain legumes, its role in drought stress resilience and the phenotyping approaches useful for the identification of accessions resilient to water stress. Consequently, this knowledge will be important in mitigating the effects of climate change and improving grain legume production.
Why it matches plant phenotyping methods根系表現型解析手法と画像技術を中心に、乾燥ストレス耐性評価への適用を概説するレビューであり、方法論が主題である。
abstractThis review provides an overview of the root system architecture (RSA) of grain legumes, its role in drought stress resilience and the phenotyping approaches useful for the identification of accessions resilient to water stress.
The detection and management of plant disease using Artificial intelligence and machine learning applications. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies in plant disease detection and management, offering highly accurate, scalable, and real-time diagnostic solutions. Traditional disease detection methods, reliant on manual scouting and laboratory-based assays, are time-intensive, prone to human error, and often fail to detect early-stage infections. AI-driven approaches, particularly deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and vision transformers, have significantly improved disease classification accuracy, surpassing 95% in multiple studies. The integration of AI with the Internet of Things (IoT) enables real-time disease monitoring through smart sensors, drone-based imaging, and cloud computing, enhancing large-scale agricultural surveillance. Big data analytics play a crucial role in AI-driven disease management, utilizing satellite imagery, hyperspectral data, and field-based mobile applications to detect infections before symptoms become visible. Predictive analytics models, powered by AI, analyse environmental and pathogen-related data to forecast disease outbreaks, supporting proactive decision-making in precision agriculture. Despite significant advancements, challenges such as limited access to large, annotated datasets, computational resource constraints, and model generalization issues across diverse crops and climatic conditions persist. Ethical concerns related to data privacy and the adoption of AI technologies among farmers further hinder widespread implementation. Blockchain technology has been proposed for secure and transparent disease data sharing, while edge computing solutions aim to reduce latency in AI-driven disease detection systems. Autonomous AI-powered agricultural robots equipped with deep learning models and multispectral sensors are being developed for real-time disease monitoring and targeted treatment. Future research must focus on optimizing AI algorithms for large-scale agricultural deployment, integrating AI-driven genomic selection for disease-resistant crop breeding, and leveraging emerging technologies such as quantum computing and synthetic biology to enhance plant disease management and global food security. The quantum computing and artificial intelligence combinedly offers ground breaking capabilities in real-time data processing, predictive analytics, and optimization, enabling high-precision agricultural strategies that can transform crop management, climate adaptation, and global food distribution.
Why it matches plant phenotyping methods植物病害の画像・センサー観測とAIによる症状・感染状態の推定を扱う方法論レビューであり、植物フェノタイピング手法が中心です。
titleDetection and Management of Plant Disease Using Artificial Intelligence and Machine Learning Applications: A Review
The agricultural sector faces increasing pressure to meet global food demand due to population growth. Challenges such as climate change, resource scarcity, and environmental degradation will further increase this problem. These issues are particularly critical in the Mediterranean region, which is characterized by water-limited conditions and soils poor in organic matter and mineral nutrients. As a step toward ensuring food security, optimized resource utilization strategies and actionable plans for stakeholders are necessary. Reliable estimation of crop growth parameters and yield prediction under different climatic and agronomic scenarios have emerged as critical tools in driving these changes.Various conventional crop growth parameter estimation and yield prediction methods have emerged as methods for optimizing resource utilization, identifying risks, and enabling effective decision-making. However, conventional methods, including empirical, statistical, and process-based models, often face limitations such as co-linearity among predictor variables, assumptions of stationarity, and the inability to capture complex biophysical and biochemical interactions at large scales. These shortcomings highlight the need for more robust and adaptable approaches. Advanced technologies, particularly Artificial Intelligence (AI) and Remote Sensing (RS) have revolutionized agriculture by uncovering hidden patterns, enabling large-scale monitoring, and improving prediction accuracy. This research evaluates the state-of-the-art in the synergized use of AI and RS for crop growth parameter estimation and yield prediction in Mediterranean agroecosystems.A systematic literature review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Keywords and Boolean operators were used to search titles, abstracts, and keywords in selected databases, including Web of Science and Scopus. The review included English and French publications focusing on the Mediterranean region, encompassing Southern European, Middle Eastern, and North African countries bordering the Mediterranean Sea. Publications that were duplicated, unrelated to the study objectives, or outside the geographical focus were excluded. Out of 551 initial publications retrieved, 117 met the inclusion criteria and were selected for detailed review.The findings reveal a rising interest in integrating AI and RS for estimating crop growth parameters and predicting yield. Multispectral RS products, such as Landsat-8 and Sentinel-2, are the most frequently utilized data sources. Additionally, Sentinel-1 microwave sensors and Unmanned Aerial Vehicle (UAV)-based imagery are increasingly employed alongside ground-based sensors. Among AI methodologies, Machine Learning (ML) algorithms like Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) dominate, while Deep Learning (DL) techniques such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks have gained prominence since 2020. Most publications were produced between 2020 and 2024, with Italy, Spain, and France being the most studied regions.The study underscores the transformative potential of integrating AI and RS for crop growth parameter estimation and yield prediction in Mediterranean agroecosystems. By leveraging diverse data sources, algorithms, and sensor technologies, these advancements address the limitations of traditional models, enhance scalability and accuracy, and support sustainable agriculture in resource-limited environments.This research is performed in the framework of the PhD program in Agrobiosciences, Scuola Superiore Sant'Anna, scholarship: PNRR “Digital and environmental transitions” (M4C1, Inv. 3.4) ex MD 629/2024.
Why it matches plant phenotyping methodsAIとリモートセンシングによる作物生育パラメータ推定・収量予測の手法を体系的にレビューしており、植物形質の取得・推定方法が中心である。
titleSynergizing Artificial Intelligence and Remote Sensing for Enhanced Crop Growth Parameter Estimation and Yield Prediction in Mediterranean Agroecosystems: A Systematic Literature Review
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Food legume crops, including common bean, faba bean, mungbean, cowpea, chickpea, and pea, have long served as vital sources of energy, protein, and minerals worldwide, both as grains and vegetables. Advancements in high-throughput phenotyping, next-generation sequencing, transcriptomics, proteomics, and metabolomics have significantly expanded genomic resources for food legumes, ushering research into the panomics era. Despite their nutritional and agronomic importance, food legumes still face constraints in yield potential and genetic improvement due to limited genomic resources, complex inheritance patterns, and insufficient exploration of key traits, such as quality and stress resistance. This highlights the need for continued efforts to comprehensively dissect the phenome, genome, and regulome of these crops. This review summarizes recent advances in technological innovations and multi-omics applications in food legumes research and improvement. Given the critical role of germplasm resources and the challenges in applying phenomics to food legumes-such as complex trait architecture and limited standardized methodologies-we first address these foundational areas. We then discuss recent gene discoveries associated with yield stability, seed composition, and stress tolerance and their potential as breeding targets. Considering the growing role of genetic engineering, we provide an update on gene-editing applications in legumes, particularly CRISPR-based approaches for trait enhancement. We advocate for integrating chemical and biochemical signatures of cells ('molecular phenomics') with genetic mapping to accelerate gene discovery. We anticipate that combining panomics approaches with advanced breeding technologies will accelerate genetic gains in food legumes, enhancing their productivity, resilience, and contribution to sustainable global food security.
Why it matches plant phenotyping methods食用マメ類におけるフェノミクス技術と標準化手法の課題をレビューしており、植物フェノタイピングが主要テーマの一つとして扱われている。
abstractThis review summarizes recent advances in technological innovations and multi-omics applications in food legumes research and improvement.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Climate change intensifies biotic and abiotic stresses, threatening global crop productivity. High-throughput phenotyping (HTP) technologies provide a non-destructive approach to monitor plant responses to environmental stresses, offering new opportunities for both crop stress resilience and breeding research. Innovations, such as hyperspectral imaging, unmanned aerial vehicles, and machine learning, enhance our ability to assess plant traits under various environmental stresses, including drought, salinity, extreme temperatures, and pest and disease infestations. These tools facilitate the identification of stress-tolerant genotypes within large segregating populations, improving selection efficiency for breeding programs. HTP can also play a vital role by accelerating genetic gain through precise trait evaluation for hybridization and genetic enhancement. However, challenges such as data standardization, phenotyping data management, high costs of HTP equipment, and the complexity of linking phenotypic observations to genetic improvements limit its broader application. Additionally, environmental variability and genotype-by-environment interactions complicate reliable trait selection. Despite these challenges, advancements in robotics, artificial intelligence, and automation are improving the precision and scalability of phenotypic data analyses. This review critically examines the dual role of HTP in assessment of plant stress tolerance and crop performance, highlighting both its transformative potential and existing limitations. By addressing key challenges and leveraging technological advancements, HTP can significantly enhance genetic research, including trait discovery, parental selection, and hybridization scheme optimization. While current methodologies still face constraints in fully translating phenotypic insights into practical breeding applications, continuous innovation in high-throughput precision phenotyping holds promise for revolutionizing crop resilience and ensuring sustainable agricultural production in a changing climate.
Why it matches plant phenotyping methods植物ストレス耐性・作物形質評価のための高スループット表現型解析技術を主題とするレビューであり、方法論と課題を中心に扱っている。
abstractInnovations, such as hyperspectral imaging, unmanned aerial vehicles, and machine learning, enhance our ability to assess plant traits under various environmental stresses
High-throughput phenotyping (HTP) technology is now a significant bottleneck in the efficient selection and breeding of superior forage genetic resources. To better understand the status of forage phenotyping research and identify key directions for development, this review summarizes advances in HTP technology for forage phenotypic analysis over the past ten years. This paper reviews the unique aspects and research priorities in forage phenotypic monitoring, highlights key remote sensing platforms, examines the applications of advanced sensing technology for quantifying phenotypic traits, explores artificial intelligence (AI) algorithms in phenotypic data integration and analysis, and assesses recent progress in phenotypic genomics. The practical applications of HTP technology in forage remain constrained by several challenges. These include establishing uniform data collection standards, designing effective algorithms to handle complex genetic and environmental interactions, deepening the cross-exploration of phenomics-genomics, solving the problem of pathological inversion of forage phenotypic growth monitoring models, and developing low-cost forage phenotypic equipment. Resolving these challenges will unlock the full potential of HTP, enabling precise identification of superior forage traits, accelerating the breeding of superior varieties, and ultimately improving forage yield. • A systematic review of the application status of HTP technology in forage phenotypic analysis. • The uniqueness and research focus of forage phenotypic monitoring were introduced. • The specific application of advanced sensing technologies and AI algorithms in forage phenotypic data integration and feature quantification. • The research progress of forage phenomenics-genomics was reviewed. • The challenges and future directions of HTP technology in the application of forage were discussed.
Why it matches plant phenotyping methods飼料作物の高スループット表現型解析について、センシング、リモートセンシング、AIによる形質定量化を中心に扱う方法論レビューである。
abstractthis review summarizes advances in HTP technology for forage phenotypic analysis over the past ten years.
Crop intelligence and yield prediction of potato (Solanum tuberosum L.) are important to farmers and the processing industry. Remote sensing can provide timely information on growth status and accurate yield predictions during the growing season. However, there is limited documentation on the most suitable vegetation indices (VIs) and optimal growth stages for acquiring remote sensing imagery of potato. To address this knowledge gap, a systematic review was conducted. Original scientific manuscripts published between 2000 and 2022 were identified using various databases. The findings indicate that satellite imagery is the most widely used source of remote sensing data for tuber yield prediction, whereas unmanned aerial vehicle systems (UAVs) and handheld sensors are more frequently applied for growth monitoring. The normalized difference vegetation index (NDVI), red-edge chlorophyll index (CIᵣₑd₋ₑdgₑ), green chlorophyll index (CIgᵣₑₑₙ), and optimized soil-adjusted vegetation index (OSAVI) are the most frequently used VIs for the growth and yield estimation of potato. The tuber initiation stage was found to be the most appropriate stage for remote sensing data acquisition. This review will assist potato farmers, agronomists and researchers in selecting the most suitable VIs for monitoring specific growth variables and selecting the optimal timing during the growing season to obtain remote sensing images.
Why it matches plant phenotyping methodsジャガイモの生育状態と塊茎収量を推定するリモートセンシング指標・取得時期を体系的に比較したレビューであり、植物表現型の取得・推定手法が中心です。
titleA Systematic Review of Vegetation Indices for Potato Growth Monitoring and Tuber Yield Prediction from Remote Sensing
Field / plotPhysiological trait estimationGrowth / development / phenology
Nitrogen (N) is an essential element for crop growth, productivity, and quality, making it a fundamental component of crop nutrition. In precision agriculture, rapid and non-destructive monitoring of crop N status is crucial for formulating N management strategies to optimize N application and assessing crop performance. This review investigates the integration of remote sensing (RS) in precision N management, particularly focusing on addressing temporal, scale, and geometric consideration in RS applications. The study reviews RS monitoring techniques from three perspectives: firstly, determining optimal fertilization timing based on crop phenology; secondly, introducing RS platforms, including proximal sensing, airborne RS, and satellites for monitoring crop N status; and finally, examining the use of multi-angle RS techniques for N monitoring. The literature reviewed in this study shows that 29% of publications focus on N monitoring at joining and 24% at grain-filling stage, limiting the window for making decisions for in-season N management. This paper concludes that integrating appropriate monitoring platforms, multi-angle observations, and dynamic modeling offers a promising approach for assessing crop N status. This integrated approach provides an essential decision-making tool for N fertilization, advancing precision agriculture for its broader implication. Advancing dynamic crop models, in-field digital twins, multi-scale RS for seamless monitoring, and artificial intelligence for real-time N status diagnosis together will pave the way for precision N management in modern agriculture.
Why it matches plant phenotyping methods作物N状態という植物生理形質をリモートセンシングで測定する技術・プラットフォームを中心にレビューしており、植物フェノタイピング手法の方法論的レビューに該当する。
abstractThis review investigates the integration of remote sensing (RS) in precision N management, particularly focusing on addressing temporal, scale, and geometric consideration in RS applications.
Multispectral imaging technology uses sensors capable of detecting spectral information across various wavelength ranges to acquire multi-channel target data. This enables researchers to collect comprehensive biological information about the observed objects or areas, including their physical and chemical characteristics. Spectral technology is widely applied in agriculture for collecting crop information and predicting yield. Over the past decade, multispectral image acquisition and information extraction from plants have provided rich data resources for scientific research, facilitating a deeper understanding of plant growth mechanisms and ecosystem function. This article presents a bibliometric analysis of the relationship between multispectral imaging and crop yield prediction, reviewing past studies and forecasting future research trends. Through comprehensive analysis, we identified that research using multispectral technology for crop yield prediction primarily focuses on key areas, such as chlorophyll content, remote sensing, convolutional neural networks (CNNs), and machine learning. Cluster and co-citation analyses revealed the developmental trajectory of multispectral yield estimation. Our bibliometric approach offers a novel perspective to understand the current status of multispectral technology in agricultural applications. This methodology helps new researchers quickly familiarize themselves with the field’s knowledge and gain a more precise understanding of development trends and research hotspots in the domain of multispectral technology for agricultural yield estimation.
Why it matches plant phenotyping methods作物収量予測に用いるマルチスペクトル技術の研究動向を対象とした書誌計量レビューであり、植物形質（収量）の計測・推定手法分野を方法論的にレビューしている。
titleTrend analysis of the application of multispectral technology in plant yield prediction: a bibliometric visualization analysis (2003–2024)
In 1911, Wilhelm Johannsen provided a definition for the term "phenotype," stating that it encompasses the many observable characteristics of organisms, which may be discerned by direct visual examination or more precise techniques including measurement or description. The term "phenotype" is derived from the Greek words "phainein" and "typos," which translate to "show" and "type" respectively. The area of research is now through a transformative phase known as 'phenomics,' which may be attributed to the rapid development in high-throughput technology of phenotyping. The agricultural phenotyping community is required to advance the field of bioinformatics in order to effectively extract valuable information from the extensive omics data. Additionally, it is essential to engage in research pertaining to technological systems that can accurately detect and characterize phenotypic features. This paper provides an outline of the topic of research including the gathering of phenotypic information using various sensors and the subsequent analysis of phenomics. In conclusion, we conducted an analysis of the challenges and possibilities associated with agricultural phenomics. Our objective was to provide suggestions for enhancing gene mining methodologies pertaining to essential agronomic traits, as well as implementing innovative intelligent approaches for precision breeding.
Why it matches plant phenotyping methods農業フェノミクスにおけるセンサーによる表現型情報の収集と解析、技術的課題・動向を中心に扱うレビューであり、植物表現型手法のレビューとして中心性が明確です。
abstractThis paper provides an outline of the topic of research including the gathering of phenotypic information using various sensors and the subsequent analysis of phenomics.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Plant diseases and pests pose significant threats to crop yield and quality, prompting the exploration of digital image processing techniques for their detection. Recent advancements in deep learning models have shown remarkable progress in this domain, outperforming traditional methods across various fronts including classification, detection, and segmentation networks. This review delves into recent research endeavors focused on leveraging deep learning for detecting plant and pest diseases, reflecting a burgeoning interest among researchers in artificial intelligence-driven approaches for agricultural analysis. The study begins by elucidating the limitations of conventional detection methods, setting the stage for exploring the challenges and opportunities inherent in deploying deep learning in real-world applications for plant disease and pest infestation detection. Moreover, the review offers insights into potential solutions while critically analyzing the obstacles encountered. Furthermore, it conducts a meticulous examination and prognostication of the trajectory of deep learning models in plant disease and pest infestation detection. Through this comprehensive analysis, the review seeks to provide a nuanced understanding of the evolving landscape and prospects in this vital area of agricultural research. The review highlights that state-of-the-art deep learning models have achieved impressive accuracies, with classification tasks often exceeding 95% and detection and segmentation networks demonstrating precision rates above 90% in identifying plant diseases and pest infestations. These findings underscore the transformative potential of deep learning in revolutionizing agricultural diagnostics.
Why it matches plant phenotyping methods植物病害・害虫を画像から検出する深層学習手法を体系的にレビューしており、植物の病害状態を観測・推定する方法が中心です。
titleLeveraging deep learning for plant disease and pest detection: a comprehensive review and future directions
The use of remotely sensed imagery for the monitoring of both plant biodiversity and functional traits in grassland ecosystems has increased substantially in the last few decades. More recently, uncrewed aerial vehicles (UAVs) have begun to play an increasingly important role, providing repeatable very high-resolution data, acting as a bridge between the decameter satellite imagery and the point scale data collected on the ground. At the same time, machine learning approaches are rapidly expanding, adding new analysis and modelling tools to the plethora of UAV, aircraft and satellite observational data. Here, we provide a review of remotely sensed monitoring methods for grassland plant biodiversity and functional traits (Leaf Dry Matter Content, Crude Protein, Potassium, Phosphorous, Nitrogen and Leaf Area Index) between 2018 and 2024. We highlight the key innovations that have occurred, sources of error identified, new analysis methods presented and identify the bottlenecks to and opportunities for further development. We emphasise the need for (1) the integration of observations across spatial and temporal scales, (2) a more systematic identification and examination of sources or error and uncertainty (3) more widespread use of hyperspectral satellite data and (4) greater focus on the development of grassland global spectra, species and traits data base, from multi- and hyper-spectral instruments, to accelerate the creation of more robust, scalable and generalisable remote sensing based grassland models.
Why it matches plant phenotyping methods草地植物の生物多様性・機能形質をリモートセンシングで測定する手法を体系的にレビューしており、植物フェノタイピング手法が中心である。
abstractHere, we provide a review of remotely sensed monitoring methods for grassland plant biodiversity and functional traits
Field / plotLeafWhole plant / canopy / plot / fieldStress / disease detectionDisease symptoms / severityGrowth / development / phenology
Many sciences exploit algorithms in a large variety of applications. In agronomy, large amounts of agricultural data are handled by adopting procedures for optimization, clustering, or automatic learning. In this particular field, the number of scientific papers has significantly increased in recent years, triggered by scientists using artificial intelligence, comprising deep learning and machine learning methods or bots, to process field, crop, plant, or leaf images. Moreover, many other examples can be found, with different algorithms applied to plant diseases and phenology. This paper reviews the publications which have appeared in the past three years, analyzing the algorithms used and classifying the agronomic aims and the crops to which the methods are applied. Starting from a broad selection of 6060 papers, we subsequently refined the search, reducing the number to 358 research articles and 30 comprehensive reviews. By summarizing the advantages of applying algorithms to agronomic analyses, we propose a guide to farming practitioners, agronomists, researchers, and policymakers regarding best practices, challenges, and visions to counteract the effects of climate change, promoting a transition towards more sustainable, productive, and cost-effective farming and encouraging the introduction of smart technologies.
Why it matches plant phenotyping methods植物・葉画像、植物病害、フェノロジーに適用されるアルゴリズムを体系的に整理するレビューであり、植物状態の取得・推定手法のレビューが中心的です。
abstractThis paper reviews the publications which have appeared in the past three years, analyzing the algorithms used and classifying the agronomic aims and the crops to which the methods are applied.
Computer Vision has become increasingly important in smart farming applications, including scheduling crop irrigation. A combination of various remote sensing devices enables continuous monitoring of a crop and non-destructive prediction of irrigation time. Appropriately scheduled and precisely targeted irrigation enables sustainable use of this limited resource. In agriculture, absorption-based and thermal-based imagery are used to monitor plant conditions through indices such as the Normalized Difference Water Index (NDWI) and Crop Water Stress Index (CWSI). This paper provides an overview of the concept and components of monitoring systems for automated irrigation scheduling. It explains the potential and limitations of applying computer vision-based systems for plant stress detection, providing insights to advance understanding in this growing field.
Why it matches plant phenotyping methods植物の水ストレスを画像・リモートセンシングで検出するコンピュータビジョン手法の概念、構成、可能性と限界を扱うレビューであり、フェノタイピング手法が中心である。
abstractThis paper provides an overview of the concept and components of monitoring systems for automated irrigation scheduling.
• A comprehensive review of the latest advancements in crop wearable sensors. • Detailed exploration of design concepts and applications for various crop wearable sensors. • Investigation of the development of crop wearable sensors under the global push for green production. • Discussion of future prospects and challenges facing crop wearable sensors. The increasing demand for food and the threats posed by environmental and biological stresses to crops necessitate monitoring crop growth to adapt to changing environmental conditions. Wearable sensors, which can be seamlessly attached to crop surfaces, hold significant promise for tracking phenotypic traits and the microenvironment influencing these characteristics. These sensors, known for their high resistance to interference, spatial resolution, convenience, and accuracy, are emerging as valuable tools for crop monitoring. This paper systematically reviews the types and applications of wearable sensors, discussing their widespread use in monitoring physiological traits, phenotypes, microenvironments, and plant stress. The study also explores the research on autonomous and biodegradable sensors in the context of global green production trends. Finally, the challenges and prospects of wearable sensors in promoting crop health are discussed.
Why it matches plant phenotyping methods作物ウェアラブルセンサーによる表現型・生理形質の取得を中心に、設計、応用、課題を体系的にレビューしており、植物フェノタイピング手法レビューに該当する。
abstractA comprehensive review of the latest advancements in crop wearable sensors.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Weed control is fundamental to modern agriculture, underpinning crop productivity, food security, and the economic sustainability of farming operations. Herbicides have long been the cornerstone of effective weed management, significantly enhancing agricultural yields over recent decades. However, the field now faces critical challenges, including stagnation in the discovery of new herbicide modes of action (MOAs) and the escalating prevalence of herbicide-resistant weed populations. High research and development costs, coupled with stringent regulatory hurdles, have impeded the introduction of novel herbicides, while the widespread reliance on glyphosate-based systems has accelerated resistance development. In response to these issues, advanced image-based plant phenotyping technologies have emerged as pivotal tools in addressing herbicide-related challenges in weed science. Utilizing sensor technologies such as hyperspectral, multispectral, RGB, fluorescence, and thermal imaging, plant phenotyping enables precise monitoring of herbicide drift, analysis of resistance mechanisms, and development of new herbicides with innovative MOAs. The integration of machine learning algorithms with imaging data further enhances the ability to detect subtle phenotypic changes, predict herbicide resistance, and facilitate timely interventions. This review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods, and highlighting unique findings and innovative applications. Additionally, it addresses current limitations and proposes future research directions, emphasizing the significant contributions of phenotyping advancements to sustainable and effective weed management strategies. By leveraging these sophisticated technologies, the agricultural sector can overcome existing herbicide challenges, ensuring continued productivity and resilience in the face of evolving weed pressures.
Why it matches plant phenotyping methods画像ベース植物フェノタイピング技術を中心に、センサー、展開プラットフォーム、モデリング手法、限界と応用を包括的に扱うレビューであり、方法論的役割が明確です。
abstractThis review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods, and highlighting unique findings and innovative applications.
Agriculture is the backbone of human civilization, playing a vital role in sustaining national economies. Enhancing food production is crucial for mitigating hunger and ensuring global food security. However, plant diseases caused by fungi, bacteria, viruses, and nematodes pose a significant threat to agricultural productivity, resulting in substantial economic losses worldwide. Effective crop protection and improvement of crop quality and quantity are inextricably linked to plant disease management. Accurate identification and detection of plant diseases are essential for devising strategic control measures. Traditional disease detection methods have proven inadequate, with delayed diagnosis and inaccurate results hindering effective disease control. Recent advances in Artificial Intelligence (AI) have revolutionized plant disease detection, enabling rapid and accurate identification of diseases across vast areas. This review aims to investigate the efficacy of automated plant disease detection using Machine Learning (ML), Deep Learning (DL), and Hyperspectral Imaging. By leveraging these cutting-edge technologies, we can develop more accurate and reliable disease detection systems, ultimately contributing to enhanced crop yields and global food security.
Why it matches plant phenotyping methods植物病害の症状・状態をAI、機械学習、深層学習、ハイパースペクトル画像で検出する方法を主題とするレビューであり、植物フェノタイピング手法レビューに該当する。
titleRole of Artificial Intelligence in Plant Disease Detection: A Review
Crop diseases are one of the major threats to global food production. The different crop diseases result in significant yield losses, where their effective monitoring and accurate early identification techniques are considered crucial to ensure stable and reliable crop productivity and food security. Restricting and managing the disease's spread and lowering the cost of pesticides require effective plant pathogen monitoring and detection. If not used in the early stages of pathogenesis, traditional techniques such as molecular and serological methods—which are frequently employed for plant disease detection—are frequently ineffective. Conversely, drone-based remote sensing methods are highly successful in quickly detecting plant diseases in their early stages. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Drones have many potential uses in agriculture, including reducing manual labor and increasing productivity. Recent advances in drones and deep learning-based computer vision algorithms to identify crop diseases, providing early warning thereby allowing farmers to prevent costly crop failures and improve food production.
Why it matches plant phenotyping methodsドローンによる作物病害の検出・モニタリング手法と深層学習画像解析を主題とするレビューであり、植物の病害状態を推定するフェノタイピング手法が中心です。
titleApplications of Drone for Crop Disease Detection and Monitoring: A Review
Abstract Seed quality is of great importance for agricultural cultivation. High-throughput phenotyping techniques can collect magnificent seed information in a rapid and non-destructive manner. Emerging deep learning technology brings new opportunities for effectively processing massive and diverse data from seeds and evaluating their quality. This article comprehensively reviews the principle of several high-throughput phenotyping techniques for non-destructively collection of seed information. In addition, recent research studies on the application of deep learning-based approaches for seed quality inspection are reviewed and summarized, including variety classification and grading, seed damage detection, components prediction, seed cleanliness, vitality assessment, etc. This review illustrates that the combination of deep learning and high-throughput phenotyping techniques can be a promising tool for collection of various phenotype information of seeds, which can be used for effective evaluation of seed quality in industrial practical applications, such as seed breeding, seed quality inspection and management, and seed selection as a food source.
Why it matches plant phenotyping methods種子の高スループット表現型取得と深層学習による品質・形質評価を中心に扱う方法レビューであり、植物フェノタイピング手法のレビューとして適格です。
titleApplication of deep learning for high-throughput phenotyping of seed: a review
Crop disease detection is important due to its significant impact on agricultural productivity and global food security. Traditional disease detection methods often rely on labour‐intensive field surveys and manual inspection, which are time‐consuming and prone to human error. In recent years, the advent of imaging technologies coupled with machine learning (ML) algorithms has offered a promising solution to this problem, enabling rapid and accurate identification of crop diseases. Previous studies have demonstrated the potential of image‐based techniques in detecting various crop diseases, showcasing their ability to capture subtle visual cues indicative of pathogen infection or physiological stress. However, the field is rapidly evolving, with advancements in sensor technology, data analytics and artificial intelligence (AI) algorithms continually expanding the capabilities of these systems. This review paper consolidates the existing literature on image‐based crop disease detection using ML, providing a comprehensive overview of cutting‐edge techniques and methodologies. Synthesizing findings from diverse studies offers insights into the effectiveness of different imaging platforms, contextual data integration and the applicability of ML algorithms across various crop types and environmental conditions. The importance of this review lies in its ability to bridge the gap between research and practice, offering valuable guidance to researchers and agricultural practitioners.
Why it matches plant phenotyping methods画像と機械学習による作物病害（植物状態）の検出手法を体系的にレビューしており、植物フェノタイピング手法のレビューが中心です。
titleImage‐based crop disease detection using machine learning
This research paper offers an extensive examination of diverse methodologies and computational approaches designed to identify deficiencies in critical plant nutrients, encompassing nitrogen, phosphorus, potassium, zinc, boron, sulfur, and iron. It systematically analyzes the diverse methodologies and strategies proposed by scholars, assessing their effectiveness, constraints, and precision. Plants demand 13 essential mineral nutrients for optimal growth, and any insufficiency or excess of these nutrients can critically disrupt growth or lead to plant mortality. Consequently, the establishment of a continuous monitoring system to oversee nutrient levels is imperative for the enhancement of crop yield and quality. Through diagnostic systems that utilize digital image processing, computer vision, machine learning, and deep learning frameworks (such as pre-trained Convolutional Neural Network models like InceptionV3, VGG16, VGG19, ResNet50, and ResNet152, along with Support Vector Machines), nutrient deficiencies can be detected significantly earlier than through manual methods, thereby allowing farmers to implement timely corrective actions. This article assesses the efficiency of these sophisticated methods in addressing the diagnosis of deficiencies in plant nutrients.
Why it matches plant phenotyping methods作物葉の栄養欠乏という植物状態を、画像処理・コンピュータビジョン・機械学習で検出する手法を体系的に評価するレビューであり、フェノタイピング手法が中心です。
abstractThis research paper offers an extensive examination of diverse methodologies and computational approaches designed to identify deficiencies in critical plant nutrients
Artificial intelligence (AI) in soybean research has revolutionized various crop improvement and production aspects. This review provides predominant areas that have seen the use of AI. AI applications in phenomics have enabled collecting and analyzing high-dimensional data in soybean plants, from below- to above-ground traits, predicting phenotypes, and identifying complex patterns. In genomics, AI has improved genomic selection accuracy and identified genomic regions associated with traits of interest, such as resistance to biotic and abiotic stresses. AI has also been extensively used in detecting and managing biotic and abiotic plant stresses using RGB, multispectral, and thermal imagery from ground-based and aerial platforms. Additionally, AI has shown significant potential in yield prediction, incorporating factors such as vegetation indices, weather data, and soil properties. This review explains the concept of cyber-agricultural systems (CAS) that integrates AI, advanced sensing, computational modeling, and scalable cyberinfrastructure to optimize soybean production, enhance resource management, reduce environmental impact, and improve farm efficiency. We explain the use of CAS in crop improvement as well. We provide an exhaustive listing of challenges and future direction in the integration of AI in soybean production and crop improvement, including multi-modal and layered sensing, data availability and quality, computational modeling, AI models and tools, Cyberinfrastructure, Explainability and interpretability of AI models, AI-related impacts on privacy, ethics, and policy, Impact on Smallholder Farmers, Digital Twin, Large Soybean Datasets for community usage, and Immersive environments.
Why it matches plant phenotyping methods大豆育種・生産におけるAIの総説であり、植物フェノミクス、画像センシング、表現型予測を主要な対象として扱っているため、フェノタイピング方法レビューに該当する。
abstractAI applications in phenomics have enabled collecting and analyzing high-dimensional data in soybean plants, from below- to above-ground traits, predicting phenotypes, and identifying complex patterns.
ABSTRACT Plant phenomics deals with the measurement of plant phenotypes associated with genetic and environmental variation in controlled environment agriculture (CEA). Encompassing a spectrum from molecular biology to ecosystem‐level studies, it employs high‐throughput phenotyping (HTP) approaches to quickly evaluate characteristics and enhance the yields of crops in smart plant facilities. HTP uses environmental parameters for accuracy, such as software sensors, as well as hyperspectral imaging for pigment data, thermal imaging for water content, and fluorescence imaging for photosynthesis rates. They provide information on growth kinetics, physiological and biochemical characteristics, and genotype–environment interaction. Artificial intelligence (AI) and machine learning (ML) are used on a large volume of phenotypic data to predict growth rates, determine the optimal time to water plants, or detect diseases, nutrient deficiencies, or pests at an early stage. The lighting used in smart plant factories is adjusted based on the specific growth phase of the plants, such as using different light intensities, spectrums, and durations for germination, vegetative growth, and flowering stages, hydroponics as the method of providing nutrients, and CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) for improving certain characteristics, such as resistance to drought. These systems enhance crop production, yields, adaptability, and input use by optimizing the environment and utilizing precision breeding techniques. Plant phenomics with AI is a combination of several disciplines, promoting the understanding of plant–environment interactions in relation to agriculture problems such as resource use, diseases, and climate change. It affects their capacity to develop crops that capture inputs, minimize chemical application, and are resilient to climate change. Phenomics is cost‐effective, reduces inputs, and contributes to more sustainable agricultural practices, being economically and environmentally sound. Altogether, plant phenomics is central to CEA due to its capacity to capitalize on phenotypic data and genetic potential within agriculture to advance sustainability and food security. Through phenomic research, the next advancements are likely to be even more revolutionary in terms of agricultural practices and food systems worldwide.
Why it matches plant phenotyping methods植物フェノミクス、HTP、AI、画像・センサーによる形質取得を主題とする概説であり、フェノタイピング手法のレビューとして中心的です。
abstractPlant phenomics deals with the measurement of plant phenotypes associated with genetic and environmental variation in controlled environment agriculture (CEA).
High-throughput phenotyping (HTP) has transformed seed testing, quality evaluation, storage, and stress response assessment by enabling rapid, non-destructive, and high-resolution analysis of seed traits. Traditional seed evaluation methods are labour-intensive and time-consuming, whereas HTP employs advanced imaging, sensor technologies, and machine learning algorithms to assess seed morphology, physiological traits, and biochemical properties efficiently. In seed testing, HTP accelerates germination studies, vigour assessments, and stress tolerance evaluations, facilitating the identification of high-quality and resilient seed varieties. It also enhances seed storage practices by providing real-time monitoring of seed viability, detecting deterioration factors, and optimizing storage conditions. Furthermore, HTP significantly contributes to understanding seed responses to biotic and abiotic stresses. By characterizing genetic and physiological factors associated with disease resistance and environmental stress tolerance, HTP aids in breeding stress-resilient crops and optimizing seed treatments. The integration of HTP with artificial intelligence further refines predictive modelling and precision agriculture strategies, supporting climate-resilient farming and sustainable agricultural practices. This paper highlights the multifaceted role of HTP in advancing seed science, from quality assurance to stress management, underscoring its impact on agricultural productivity and genetic resource conservation.
Why it matches plant phenotyping methods種子形質の取得に用いる高スループット画像・センサー・機械学習手法を中心に概説するレビューであり、植物フェノタイピング手法の方法論的役割が明確。
abstractHigh-throughput phenotyping (HTP) has transformed seed testing, quality evaluation, storage, and stress response assessment by enabling rapid, non-destructive, and high-resolution analysis of seed traits.
The human population projected to reach 9.7 billion by 2050, addressing the escalating food demand necessitates a substantial increase in crop production amidst challenges posed by climate change. Agriculture faces significant hurdles in adapting to shifting climate patterns induced by rising greenhouse gas emissions, particularly affecting farmers in low-income countries. Sustainable intensification through modern cultivation techniques and resilient crop varieties emerges as a crucial strategy to enhance yields while minimizing environmental impact. The evolution from traditional agricultural practices to Agriculture 4.0 integrates cutting-edge technologies like AI, robotics and precision agriculture, revolutionizing field management and supply chain optimization. Ground-based and aerial-based platforms offer distinct advantages and challenges, underscoring the need for adaptive strategies in agricultural phenotyping. In this era of digital agriculture, high-throughput phenotyping emerges as a critical tool in ensuring food security and sustainability amid a changing climate and growing global population.
Why it matches plant phenotyping methods植物フェノミクスと高スループット表現型解析を中心に、地上・空中プラットフォームやAIの役割を概説するレビューであり、フェノタイピング手法が中心です。
titleIntegrating High-Throughput Plant Phenomics and Artificial Intelligence for Precision Agriculture
Plant diseases affect agricultural production, food security, and economic stability, making them a major concern for global agriculture. To reduce losses and guarantee sustainable farming methods, these diseases must be identified early and managed effectively. Manual inspections, which are labour-intensive, unreliable, and unscalable for large-scale agricultural applications, are frequently the basis of traditional disease monitoring techniques. Innovative approaches to plant disease tracking have been made possible by the quick development of artificial intelligence (AI), which offers improved scalability, accuracy, and efficiency. This study provides a comprehensive assessment of AI-based plant disease tracking systems, concentrating on techniques that integrate machine learning, deep learning, computer vision, and data-driven models. Key uses include integrating satellite imagery and IoT-enabled devices for real-time monitoring, predicting disease outbreaks using environmental data, and detecting diseases using picture processing. Additionally examined is the function of mobile applications in providing farmers with easily accessible diagnostic tools. The study also discusses important issues like model generalisation, data scarcity, computational constraints, and socioeconomic obstacles to AI adoption in agriculture. This review highlights the revolutionary potential of AI in building resilient agricultural systems, ultimately promoting global food security and sustainable development, by combining recent developments and pointing out research needs
Why it matches plant phenotyping methods植物病害の画像処理・コンピュータビジョン等による病徴・病害状態の推定手法を包括的にレビューしており、方法論が中心です。
abstractThis study provides a comprehensive assessment of AI-based plant disease tracking systems, concentrating on techniques that integrate machine learning, deep learning, computer vision, and data-driven models.
Grapevines ( Vitis vinifera L.) are one of the most economically relevant crops worldwide, yet they are highly vulnerable to various diseases, causing substantial economic losses for winegrowers. This systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection, addressing current capabilities, gaps, and future directions in sensor-based field monitoring of grapevine diseases. The review covers 104 studies published between 2008 and October 2024, identified through searches in Scopus and Web of Science, conducted on 25 January 2024, and updated on 10 October 2024. The included studies focused exclusively on the sensor-based detection of grapevine diseases, while excluded studies were not related to grapevine diseases, did not use remote or proximal sensing, or were not conducted in field conditions. The most studied diseases include downy mildew, powdery mildew, Flavescence dorée , esca complex, rots, and viral diseases. The main sensors identified for disease detection are RGB, multispectral, hyperspectral sensors, and field spectroscopy. A trend identified in recent published research is the integration of artificial intelligence techniques, such as machine learning and deep learning, to improve disease detection accuracy. The results demonstrate progress in sensor-based disease monitoring, with most studies concentrating on specific diseases, sensor platforms, or methodological improvements. Future research should focus on standardizing methodologies, integrating multi-sensor data, and validating approaches across diverse vineyard contexts to improve commercial applicability and sustainability, addressing both economic and environmental challenges.
Why it matches plant phenotyping methodsブドウの病害という植物状態を対象に、リモートセンシング・近接センシングによる検出手法を体系的に評価するレビューであり、フェノタイピング手法が中心です。
abstractThis systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection
Rapid improvements in ML and DL techniques have made it possible to detect and recognize objects from images. Computational approaches using ML and DL have been recently applied to agriculture or farming applications and are proving successful in increasing per-yield production. Automatic identification of plant diseases can help farmers manage their crops more effectively, resulting in higher yields. Detecting plant disease in crops using images is an intrinsically difficult task. In addition to their detection, individual species identification is necessary for applying tailored control methods. A survey of research initiatives that use DL and ML approaches to address various plant DD concerns was undertaken in the current publication. In this work, we have reviewed 35 of the most recent DL and ML-based articles on detecting various plant leaf diseases over the last five years. In addition, we identified and summarized several problems and solutions corresponding to the ML and DL used in plant leaf DD. Moreover, DCNN trained on image data was the most effective method for detecting early DD. We expressed the benefits and drawbacks of utilizing CNN in agriculture, and we discussed the direction of future developments in plant DD.
Why it matches plant phenotyping methods植物葉の画像から病害を検出する手法を対象とした系統的レビューであり、植物の病害状態を推定するフェノタイピング手法のレビューが中心です。
titlePlant Disease Detection Using Computational Approaches: A Systematic Literature Review
As a matter of fact, agriculture has been the very backbone of human civilization, not only being the strong engine of economic growth but also being the major source of food. Crop diseases, however, have become a grave threat to the health and productivity of crops, thus being a significant hindrance to agriculture and potential agricultural yields. There is now a need to detect and classify plant diseases in time and precisely with minimal infliction of further damage on crops. Methods that have been adopted by farmers to predict and classify diseases take long periods, and there is an element of error; hence, it is crucial to mechanize the disease forecasting process. Interfacing computerized techniques of image processing into agricultural fields has given tremendous hope as losses are reduced and productivity increased. Over the last few decades, scientists in their pursuit explored many methods for detection and classification of various types of plant diseases by looking at images of infected leaves or crops. This paper presents coverage of most recently developed technologies for detection and classification of various plant diseases with comparative study.
Why it matches plant phenotyping methods植物の病徴を画像から検出・分類する手法を比較レビューしており、植物フェノタイピング手法のレビューが中心です。
abstractThis paper presents coverage of most recently developed technologies for detection and classification of various plant diseases with comparative study.
This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency. Key approaches analyzed involve image classification, image segmentation, regression, and object detection methods that use diverse data types ranging from RGB and multispectral images to radar and thermal data. By processing UAV and satellite data with CNNs, real-time and large-scale crop monitoring can be achieved, supporting advanced farm management. A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. Future directions point toward integrating IoT and cloud platforms for real-time data processing and leveraging large language models for regulatory insights. Potential research advancements emphasize improving increased data accessibility and hybrid modeling to meet the agricultural demands of climate variability and food security, positioning CNNs as pivotal tools in sustainable agricultural practices. A related repository that contains the reviewed articles along their publication links is made available (https://github.com/MohammadElSakka/CNN_in_AGRI).
Why it matches plant phenotyping methodsCNNによる農業画像解析手法を、病害検出・収量予測など植物の状態や形質推定への応用として体系的にレビューしており、フェノタイピング関連手法のレビューが中心である。
abstractThis review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Modern viticulture faces significant challenges including climate change and increasing crop diseases, necessitating sustainable solutions to reduce fungicide use and mitigate soil health risks, particularly from copper accumulation. Advances in plant phenomics are essential for evaluating and tracking phenotypic traits under environmental stress, aiding in selecting resilient vine varieties. However, current methods are limited, hindering effective integration with genomic data for breeding purposes. Remote sensing technologies provide efficient, non-destructive methods for measuring biophysical and biochemical traits of plants, offering detailed insights into their physiological and nutritional state, surpassing traditional methods. Smart phenotyping is essential for selecting crop varieties with desired traits, such as pathogen-resilient vine varieties, tolerant to altered soil fertility including copper toxicity. Identifying plants with typical copper toxicity symptoms under high soil copper levels is straightforward, but it becomes complex with supra-optimal, already toxic, copper levels common in vineyard soils. This can induce multiple stress responses and interferes with nutrient acquisition, leading to ambiguous visual symptoms. Characterizing resilience to copper toxicity in vine plants via smart phenotyping is feasible by relating smart data with physiological assessments, supported by trained professionals who can identify primary stressors. However, complexities increase with more data sources and uncertainties in symptom interpretations. This suggests that artificial intelligence could be valuable in enhancing decision support in viticulture. While smart technologies, powered by artificial intelligence, provide significant benefits in evaluating traits and response times, the uncertainties in interpreting complex symptoms (e.g., copper toxicity) still highlight the need for human oversight in making final decisions.
Why it matches plant phenotyping methods植物フェノタイピングとリモートセンシングを中心に、環境ストレス下の形質評価手法と将来展望を論じるレビューであり、方法論的役割が明確です。
abstractAdvances in plant phenomics are essential for evaluating and tracking phenotypic traits under environmental stress
Plant physiology and metabolism rely on the function of stomata, structures on the surface of above-ground organs that facilitate the exchange of gases with the atmosphere. The morphology of the guard cells and corresponding pore that make up the stomata, as well as the density (number per unit area), are critical in determining overall gas exchange capacity. These characteristics can be quantified visually from images captured using microscopy, traditionally relying on time-consuming manual analysis. However, deep learning (DL) models provide a promising route to increase the throughput and accuracy of plant phenotyping tasks, including stomatal analysis. Here we review the published literature on the application of DL for stomatal analysis. We discuss the variation in pipelines used, from data acquisition, pre-processing, DL architecture, and output evaluation to post-processing. We introduce the most common network structures, the plant species that have been studied, and the measurements that have been performed. Through this review, we hope to promote the use of DL methods for plant phenotyping tasks and highlight future requirements to optimize uptake, predominantly focusing on the sharing of datasets and generalization of models as well as the caveats associated with utilizing image data to infer physiological function.
Why it matches plant phenotyping methods気孔画像から形態・密度などの植物形質を推定する深層学習手法を体系的にレビューしており、フェノタイピング手法が中心である。
abstractHere we review the published literature on the application of DL for stomatal analysis.
While crop breeding represents a key factor in terms of effectiveness and affordability in the adaptation of agriculture to stress conditions, phenotyping is perceived as a major bottleneck to achieving genetic advance. Crops in the field experience the simultaneous occurrence of multiple stresses, which vary depending on the location, year and management conditions. Even under the so-called “optimal agronomic conditions”, crops under field conditions may experience some degree of stress. The review addresses the methodology of field phenotyping in environments with multiple stresses where genotype by environment (and even by management) interactions are common, the ideotypes that may work, and the phenotypic traits that characterise such ideotypes that are the most useful for identifying better adapted genotypes. Mediterranean wheat is taken as a case study. Integrative phenotypic traits have intrinsic value in terms of information about crop adaptability to growing conditions in a wide sense, thus inherently accounting for the occurrence of “hidden” stresses. Indeed, this has implications when considering genotype by environment interactions. Thus, such integrative traits, when evaluated under real (i.e. field) conditions, account for the crop’s performance under scenarios where the interaction between environmental growing conditions is the norm. Three categories of traits may comprise the ideotypic characteristics when phenotyping wheat for Mediterranean environments: phenology, water status and plant growth. While these characteristics are not fully independent of each other, they should represent the crop’s performance reasonably well over a wide range of Mediterranean scenarios. It is in such a context that this review examines the case of wheat and other small grain cereals growing under Mediterranean conditions and illustrates how a few phenotyping traits, related to crop growth, water status and phenology, may define ideotypes well adapted to a wide range of environmental conditions, where non-crossover interactions exist. This is despite the fact that across such a range of environmental conditions, multistressors are present and are variable in nature, intensity and timing. Thus, for a wide range of Mediterranean conditions, the genotypes chosen correspond to ideotypes that exhibit more effective use of water and stronger growth. It is not only the characteristics of the ideotypes, but also the appropriate phenotypic traits characterising these ideotypes that are integrative in nature, meaning that they inform about crop performance over time (e.g. stable carbon isotope composition) and/or at the highest organisational level (e.g. canopy assessed via remote sensing). At the functional level, these traits guide improvements in the capture of resources such as water or radiation, rather than how efficiently these resources are being used.
Why it matches plant phenotyping methods圃場フェノタイピングの方法論と、リモートセンシングを含む統合的な植物形質の評価を中心に扱うレビューであり、単なる生物学的実験ではない。
abstractThe review addresses the methodology of field phenotyping in environments with multiple stresses
Plant diseases pose a significant threat to global food security, leading to substantial yield losses and economic impacts. Early detection and effective monitoring are crucial for managing plant diseases, yet traditional diagnostic methods such as visual inspections, serological tests, and molecular assays face limitations in sensitivity, specificity, and scalability. In recent years, advancements in diagnostic and surveillance technologies have revolutionized plant health management. Next-Generation Sequencing (NGS) enables comprehensive pathogen profiling, while CRISPR-based diagnostics offer rapid and highly specific detection. Similarly, biosensors and portable devices provide on-site diagnostics, and machine learning and AI applications enhance the analysis of complex datasets, supporting automated disease identification and predictive modeling. Concurrently, advances in disease surveillance through remote sensing technologies, including satellites and Unmanned Aerial Vehicles (UAVs), enable large-scale, real-time monitoring of crop health, detecting disease outbreaks and facilitating targeted interventions. Integrating these diverse technologies into multi-platform systems offers a holistic approach to plant disease management, combining molecular diagnostics, environmental monitoring, and digital platforms to support data-driven decision-making. Several challenges remain, including high costs, technical complexities, and the need for standardized data integration. Addressing these barriers is essential to ensure that these technologies are accessible and effective across various agricultural systems, particularly in resource-limited settings. Future research should focus on enhancing the robustness, affordability, and scalability of these tools while promoting interdisciplinary collaborations.
Why it matches plant phenotyping methods植物病害の診断・監視技術を扱うレビューであり、画像・リモートセンシング・AIによる植物健康状態（病害）の推定を中心的に整理している。ただし分子診断も含むため、植物表現型に直接関係する部分に限定して採録する。
abstractmachine learning and AI applications enhance the analysis of complex datasets, supporting automated disease identification
Recent advances in deep neural networks in terms of convolutional neural networks (CNNs) have enabled researchers to significantly improve the accuracy and speed of object recognition systems and their application to plant disease and pest detection and diagnosis. This paper presents the first comprehensive review and analysis of deep learning approaches for disease and pest detection in tomato plants, using self-collected field-based and benchmarking datasets extracted from real agricultural scenarios. The review shows that only a few studies available in the literature used data from real agricultural fields such as the PlantDoc dataset. The paper also reveals overoptimistic results of the huge number of studies in the literature that used the PlantVillage dataset collected under (controlled) laboratory conditions. This finding is consistent with the characteristics of the dataset, which consists of leaf images with a uniform background. The uniformity of the background images facilitates object detection and classification, resulting in higher performance-metric values for the models. However, such models are not very useful in agricultural practice, and it remains desirable to establish large datasets of plant diseases under real conditions. With some of the self-generated datasets from real agricultural fields reviewed in this paper, high performance values above 90% can be achieved by applying different (improved) CNN architectures such as Faster R-CNN and YOLO.
Why it matches plant phenotyping methodsトマトの病害・害虫を実圃場画像から検出・診断する深層学習手法を体系的にレビューしており、植物の病徴・状態を画像から推定する方法論が中心である。
abstractThis paper presents the first comprehensive review and analysis of deep learning approaches for disease and pest detection in tomato plants, using self-collected field-based and benchmarking datasets extracted from real agricultural scenarios.
Greenhouse vegetables have become increasingly important in global crop production due to their ability to be cultivated out of season and ensure a year-round supply of vegetables. With the rapid advancement of “phenomics”, accurately measuring the phenotypic information of greenhouse vegetables is crucial for enhancing both their yield and quality. Over the past two decades, various technologies have been developed for phenotypic detection of fruits, vegetables, and other crops, based on the interaction between electromagnetic waves and matter. While some articles have investigated these applications, there is a lack of a systematic review specifically focused on the phenotypic detection of greenhouse vegetables. In this review, RGB imaging, Multispectral/Hyperspectral imaging, Chlorophyll fluorescence imaging, Thermal imaging, Raman imaging, X-ray imaging, Magnetic resonance imaging, and Terahertz imaging are collectively referred to as spectrum imaging technologies. We provide a comprehensive review of the origins, research progress over the past twenty years, and current challenges of spectrum imaging in the field of greenhouse vegetable research. It focuses on identifying the most suitable spectrum imaging technologies for detecting four categories of phenotypic traits: biochemical, physiological, morphological, and yield-related traits. Additionally, we highlight the issues that need optimization in the practical application of these technologies and the bottlenecks faced in different trait studies. Finally, based on existing research, we propose several potential solutions and future research directions to maximize the utility of spectrum imaging technologies in the phenotypic detection of greenhouse vegetables.
Why it matches plant phenotyping methods温室野菜の表現型検出に用いる各種スペクトル画像技術を体系的にレビューし、形態・生理・生化学・収量形質への適用と技術的課題を中心に扱っているため。
titleSpectrum imaging for phenotypic detection of greenhouse vegetables: A review
Plant diseases affect the quality and quantity of agricultural products and have an impact on food safety. These effects result in a loss of income in the production sectors which are particularly critical for developing countries. Visual inspection by subject matter experts is time-consuming, expensive and not scalable for large farms. As such, the automation of plant disease detection is a feasible solution to prevent losses in yield. Nowadays, one of the most popular approaches for this automation is to use drones. Though there are several articles published on the use of drones for plant disease detection, a systematic overview of these studies is lacking. To address this problem, a systematic literature review (SLR) on the use of drones for plant disease detection was undertaken and 38 primary studies were selected to answer research questions related to disease types, drone categories, stakeholders, machine learning tasks, data, techniques to support decision-making, agricultural product types and challenges. It was shown that the most common disease is blight; fungus is the most important pathogen and grape and watermelon are the most studied crops. The most used drone type is the quadcopter and the most applied machine learning task is classification. Color-infrared (CIR) images are the most preferred data used and field images are the main focus. The machine learning algorithm applied most is convolutional neural network (CNN). In addition, the challenges to pave the way for further research were provided.
Why it matches plant phenotyping methods植物病害をドローン画像から検出する手法を対象とした系統的レビューであり、植物の病害状態を推定するフェノタイピング手法のレビューが中心である。
abstracta systematic overview of these studies is lacking. To address this problem, a systematic literature review (SLR) on the use of drones for plant disease detection was undertaken
Abstract Crop disease detection is important due to its significant impact on agricultural productivity and global food security. Traditional disease detection methods often rely on labour‐intensive field surveys and manual inspection, which are time‐consuming and prone to human error. In recent years, the advent of imaging technologies coupled with machine learning (ML) algorithms has offered a promising solution to this problem, enabling rapid and accurate identification of crop diseases. Previous studies have demonstrated the potential of image‐based techniques in detecting various crop diseases, showcasing their ability to capture subtle visual cues indicative of pathogen infection or physiological stress. However, the field is rapidly evolving, with advancements in sensor technology, data analytics and artificial intelligence (AI) algorithms continually expanding the capabilities of these systems. This review paper consolidates the existing literature on image‐based crop disease detection using ML, providing a comprehensive overview of cutting‐edge techniques and methodologies. Synthesizing findings from diverse studies offers insights into the effectiveness of different imaging platforms, contextual data integration and the applicability of ML algorithms across various crop types and environmental conditions. The importance of this review lies in its ability to bridge the gap between research and practice, offering valuable guidance to researchers and agricultural practitioners.
Why it matches plant phenotyping methods植物病害を画像から検出する機械学習手法のレビューであり、感染植物の病徴・病害状態を観測画像から推定する方法が中心です。
titleImage‐based crop disease detection using machine learning
Modern agriculture is characterized by the use of smart technology and precision agriculture to monitor crops in real time. The technologies enhance total yields by identifying requirements based on environmental conditions. Plant phenotyping is used in solving problems of basic science and allows scientists to characterize crops and select the best genotypes for breeding, hence eliminating manual and laborious methods. Additionally, plant phenotyping is useful in solving problems such as identifying subtle differences or complex quantitative trait locus (QTL) mapping which are impossible to solve using conventional methods. This review article examines the latest developments in image analysis for plant phenotyping using AI, 2D, and 3D image reconstruction techniques by limiting literature from 2020. The article collects data from 84 current studies and showcases novel applications of plant phenotyping in image analysis using various technologies. AI algorithms are showcased in predicting issues expected during the growth cycles of lettuce plants, predicting yields of soybeans in different climates and growth conditions, and identifying high-yielding genotypes to improve yields. The use of high throughput analysis techniques also facilitates monitoring crop canopies for different genotypes, root phenotyping, and late-time harvesting of crops and weeds. The high throughput image analysis methods are also combined with AI to guide phenotyping applications, leading to higher accuracy than cases that consider either method. Finally, 3D reconstruction and a combination with AI are showcased to undertake different operations in applications involving automated robotic harvesting. Future research directions are showcased where the uptake of smartphone-based AI phenotyping and the use of time series and ML methods are recommended.
Why it matches plant phenotyping methods植物フェノタイピングにおける画像解析、AI、2D/3D再構成技術を中心に扱うレビューであり、方法論レビューとして適格です。
abstractThis review article examines the latest developments in image analysis for plant phenotyping using AI, 2D, and 3D image reconstruction techniques
Societal Impact Statement Genebanks contribute to global food security, directly influencing societal well‐being, by providing access to seed or genetic material that is more resilient to pests, diseases, and environmental stresses. The ability to develop crops adapted to changing environmental conditions and with high nutritional value means societies can better cope with the adverse effects of climate change, safeguarding food production and rural livelihoods. Enhancing sustainable crop traits, such as improved water‐use efficiency and nutrient utilization, supports the transition toward more sustainable agricultural practices. New technologically advanced phenotyping tools are required to optimally realize the potential of these genetic resources, to identify plant genetic resources that will best enable us to address current global challenges. Summary Plant genebanks have a crucial role as specialized repositories, preserving diverse plant genetic resources and providing essential access to researchers, breeders, and farmers for developing resilient crops. With over 7 million global accessions, these genebanks significantly contribute to global food security, climate change mitigation, and sustainable agriculture. This opinion paper, inspired by an international workshop in 2022 in Wageningen (Netherlands), focuses on the transformative impact of phenomics technology within genebanks, offering a view on its potential applications and implications for preserving and utilizing plant genetic resources. Discussions from the 2022 workshop organized by the International Plant Phenotyping Network are integrated, emphasizing the potential of phenomics for global collaboration without requiring major policy developments. The workshop prioritized traits like disease resistance and drought tolerance, highlighting roots as a critical organ for phenotyping. Participants expressed a keen interest in global collaboration, emphasizing the need for partnerships between genebanks and advanced phenotyping facilities. The workshop's outcomes underscore the transformative impact of phenomics on genebanks, promoting innovation, collaboration, and sustainable agricultural practices. These results will guide future pilot studies, marking a significant step toward integrating phenomics into genebank practices and ensuring the continued prosperity of plant genetic heritage.
Why it matches plant phenotyping methods植物遺伝資源・ジーンバンクにおけるフェノミクス技術の活用と今後の応用を中心に論じる意見・レビュー論文であり、植物フェノタイピングが主題である。
titleRealizing the potential of plant genetic resources: the use of phenomics for genebanks
Detecting plant pathogens and diagnosing diseases are critical components of successful pest management. These key areas have undergone significant advancements driven by breakthroughs in molecular biology and remote sensing technologies within the realm of precision agriculture. Notably, nucleic acid amplification techniques, with recent emphasis on sequencing procedures, particularly next-generation sequencing, have enabled improved DNA or RNA amplification detection protocols that now enable previously unthinkable strategies aimed at dissecting plant microbiota, including the disease-causing components. Simultaneously, the domain of remote sensing has seen the emergence of cutting-edge imaging sensor technologies and the integration of powerful computational tools, such as machine learning. These innovations enable spectral analysis of foliar symptoms and specific pathogen-induced alterations, making imaging spectroscopy and thermal imaging fundamental tools for large-scale disease surveillance and monitoring. These technologies contribute significantly to understanding the temporal and spatial dynamics of plant diseases.
Why it matches plant phenotyping methods植物病害の症状や病原体誘導変化をリモートセンシングで測定・解析する方法を扱うレビューであり、病害状態のフェノタイピング手法が中心的に含まれる。ただし分子診断の内容も併記される。
abstractThese innovations enable spectral analysis of foliar symptoms and specific pathogen-induced alterations, making imaging spectroscopy and thermal imaging fundamental tools for large-scale disease surveillance and monitoring.
Fungal plant diseases are a major threat to plants and vegetation worldwide. Recent technological advancements in biotechnological tools and techniques have made it possible to identify and manage fungal plant diseases at an early stage. These techniques include direct methods, such as ELISA, immunofluorescence, PCR, flow cytometry, and in-situ hybridization, as well as indirect methods, such as fluorescence imaging, hyperspectral techniques, thermography, biosensors, nanotechnology, and nano-enthused biosensors. Early detection of fungal plant diseases can help to prevent major losses to plantations. This is because early detection allows for the implementation of control measures, such as the use of fungicides or resistant varieties. Early detection can also help to minimize the spread of the disease to other plants. The techniques discussed in this review provide a valuable resource for researchers and farmers who are working to prevent and manage fungal plant diseases. These techniques can help to ensure food security and protect our valuable plant resources.
Why it matches plant phenotyping methods植物病害の早期検出技術を体系的に扱うレビューで、蛍光画像、ハイパースペクトル、サーモグラフィーなど、植物の病害状態を観測する手法が中心的に論じられている。分子診断も含むが、植物病害フェノタイピング手法のレビューとして採用可能。
abstractThe techniques discussed in this review provide a valuable resource for researchers and farmers who are working to prevent and manage fungal plant diseases.
One of the key roles of plant breeders is to improve crop productivity through development of varieties with desirable traits to feed the growing population. The merger of genomics and phenomics - where genomics refer to the study of an organism’s entire DNA sequence and phenomonics is the full explanation of observable characteristics has given a new face to breeding strategies. This paper provides information about this technique from beginning up to now, which implicates high-throughput phenotyping, genomic selection, artificial intelligence platform for crop improvement. It seems that coronal genomics and phenotypical imaging results in transgenic or super climate-resilient plants, therefore improving yield under sustainable conditions. Despite its bright future there are certain issues like data standardization, ethical concerns, and resource restraints that need considering. Development later on gets people thinking about technical fields such as inter-disciplinary researches as well as policy supports that have ability to bring these powerful technologies into assurance of food security together with sustainable agriculture initially collaboration doesn’t need manufacturing centres of technology including genome and phenome data can help breeders achieve wrists precisions in crops development which will result in having robust agricultural systems able to overcome environmental stressors.
Why it matches plant phenotyping methods植物フェノミクスと高スループット表現型計測、AIプラットフォームを扱うレビューであり、フェノタイピングが主要テーマとして明示されている。
titleIntegrating Genomics and Phenomics in Agricultural Breeding: A Comprehensive Review
During the last decade, the sorghum aphid (Melanaphis sorghi), previously identified as sugarcane aphid (Melanaphis sacchari), became a serious pest of sorghum, spreading to all sorghum-producing regions in the United States, Mexico, and South America, where crop losses of 50%-100% have been reported. Developing sorghum cultivars with resistance to this insect is the most sustainable strategy for long-term pest management. To design cultivars with aphid resistance, comprehensively understanding the mechanisms underlying aphid survival, host plant resistance, and aphid-sorghum interactions is critical. In this review, we summarize the comprehensive efforts to characterize the aphid populations as well as their interaction with sorghum plants via hormonal pathways that trigger various genes including leucine rich repeats, WRKY transcription factors, lipoxygenases, calmodulins, and others. We discuss efforts made during the last decade to identify specific genomic regions and candidate genes that confer aphid resistance, as well as describe recent successes and potential challenges in breeding for aphid resistance. Furthermore, we discuss the use of disruptive technologies like high-throughput phenotyping, artificial intelligence, or machine learning for developing aphid resistant sorghum cultivars. Integration of these new technologies has the potential to accelerate the development and design of novel traits that confer durable aphid resistance in new sorghum cultivars to defend sorghum against new aphid genotype development.
Why it matches plant phenotyping methodsソルガムのアブラムシ抵抗性研究を総説し、高スループット表現型解析・AI・機械学習の利用を明示的に扱うため、植物フェノタイピング手法のレビューとして中心性があります。
abstractFurthermore, we discuss the use of disruptive technologies like high-throughput phenotyping, artificial intelligence, or machine learning for developing aphid resistant sorghum cultivars.
Societal Impact Statement Pulse crops, including dry pea, lentil, and chickpea, are rich sources of protein, low digestible carbohydrates, and micronutrients. With the increasing demand for plant‐based protein with gluten‐free and allergen‐free foods, pulse crops have become of global importance for meeting the nutritional demand of growing populations. Breeding for nutritional quality is becoming a bottleneck for most breeding programs globally due to the cost of these available tools. Therefore, low‐cost, high‐throughput phenotyping tools will be a focus of interest for the selection of elite germplasm for cultivar development and gene identification for pulse cultivar development. This publication explains the emerging and future trends of phenotyping tools that are feasible for pulse breeding and improving nutritional quality. Summary Precision agriculture tools based on spectroscopic and imaging techniques now contribute to high‐throughput phenotyping (HTP) pipelines for nutritional and agronomic traits to speed breeding and selection for cultivar development. Fourier transform mid‐infrared (FT‐MIR) spectroscopy has been a reliable HTP tool for macro nutritional traits in pulse crops. Hyperspectral, multispectral, and RGB (red‐green‐blue) imaging with unmanned aerial systems (UAVs) have been developed to measure agronomic traits for cereals, but these techniques have yet to be developed and validated for pulse crops. This review summarizes different phenotyping techniques applied to nutritional and agronomic traits for crop breeding and reviews applications of machine learning tools for optimizing HTP.
Why it matches plant phenotyping methodsパルス作物の栄養・農業形質を測定する高スループット表現型解析技術を中心に、分光・画像・機械学習手法をレビューしているため。
abstractThis publication explains the emerging and future trends of phenotyping tools that are feasible for pulse breeding and improving nutritional quality.
Field / plotLiDAR / point cloudWhole plant / canopy / plot / fieldObject detectionStress / disease detectionYield / biomass estimationGrowth / development / phenologyYield / yield components
Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR’s potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods.
Why it matches plant phenotyping methodsLiDARによる作物の成長・収量推定、病害検出、植物健全性評価などの植物形質測定を中心に扱う包括的レビューであり、フェノタイピング手法レビューに該当する。
abstractThis extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests.
The advent of high-throughput phenotyping (HTP) technologies has revolutionized crop improvement by enabling rapid, non-destructive measurement of multiple plant traits. These advanced methods facilitate the efficient collection of phenotypic data, bridging the gap between traditional phenotyping and modern genomics. These technologies allow for the comprehensive analysis of complex traits, such as growth, yield and stress adaptations, under diverse environmental conditions. By integrating imaging techniques like near infrared, far infrared, thermal and hyper spectral imaging techniques with machine learning algorithms, high throughput phenotyping enhances the accuracy and efficiency of plant characters measurements. This dynamic approach enables the discovery of novel traits and accelerates breeding programs by providing deeper insights into genotype-phenotype relationships. Additionally, these technologies supports the continuous monitoring of plant development, stress responses and adaptive mechanisms, offering a more general perception of plant-environment interactions. The incorporation of robotics and automation in this technology not only increases precision but also allows for repeated, non-invasive measurements, fostering more informed breeding decisions. As these new technologies continue to advance, they hold the capacity to significantly accelerate the development of improved crop varieties, addressing the challenges of modern agriculture.
Why it matches plant phenotyping methods作物改良におけるハイスループット植物表現型解析技術を中心に、画像・センサー・機械学習・ロボティクスによる形質測定を総説しているため。
titleMulti-scale advanced approaches to high-throughput phenotyping in crop improvement
In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificial intelligence, microsensor networks, and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highly collaborative and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy to forge comprehensive solutions. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management, recognizing the intersection of technology's potential with its current practical limitations.
Why it matches plant phenotyping methods植物病害の検出・監視を対象に、光学センサー、AI、マイクロセンサーネットワーク等のデジタル手法をレビューする展望論文であり、病害状態という植物表現型の取得・推定が中心です。
abstractThis perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management.
In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificial intelligence, microsensor networks, and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highly collaborative and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy to forge comprehensive solutions. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management, recognizing the intersection of technology's potential with its current practical limitations.
Why it matches plant phenotyping methods植物病害の検出・監視に用いる光学センサー、AI、ロボット等のデジタル技術を主題とする展望論文であり、病害状態の表現型取得・推定に関する方法論的レビューに該当する。
titleFrom Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 14 Sept 2026
A major bottleneck in the crop improvement pipeline is our ability to phenotype crops quickly and efficiently. Image-based, high-throughput phenotyping has a number of advantages because it is nondestructive and reduces human labor, but a new challenge arises in extracting meaningful information from large quantities of image data. Deep learning, a type of artificial intelligence, is an approach used to analyze image data and make predictions on unseen images that ultimately reduces the need for human input in computation. Here, we review the basics of deep learning, assessments of deep learning success, examples of applications of deep learning in plant phenomics, best practices, and open challenges.
Why it matches plant phenotyping methods植物画像ベース表現型解析における深層学習の基礎、評価、応用、ベストプラクティスを扱う方法論レビューであり、表現型取得・抽出手法が中心である。
abstractHere, we review the basics of deep learning, assessments of deep learning success, examples of applications of deep learning in plant phenomics, best practices, and open challenges.
The study conducted with the aim of providing an in-depth understanding of the state-of-the-art technologies, their strengths, limitations, and potential areas for improvement proposes a comprehensive exploration of methodologies for the early identification of tomato plant leaf diseases, emphasizing the integration of advanced image processing techniques, convolutional neural networks (CNNs) and open-source algorithms. The culmination of this survey contributes to the development of a dependable, secure, and precise framework tailored to the specificities of tomato plant diseases. The insights derived are poised to inform and guide future research endeavours, offering a holistic perspective on the advancements in early disease detection and predictive mechanisms within the realm of agricultural practices.
Why it matches plant phenotyping methodsトマト葉の病徴を画像処理とCNNで早期検出する手法を対象としたレビューであり、植物病害状態の画像ベース表現型評価が中心です。
abstractproposes a comprehensive exploration of methodologies for the early identification of tomato plant leaf diseases, emphasizing the integration of advanced image processing techniques, convolutional neural networks (CNNs) and open-source algorithms
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Phenotyping is a systematic process of quantifying and assessing a wide range of structural and physiological traits to understand the intricate interplay between an organism’s genetic makeup, its surrounding environment, and management practices, often referred to as genome-to-environment (GxE) interaction. In the context of plants, these traits can include aspects such as plant height, stem diameter, leaf size, angle, and shape, chlorophyll content, biomass, leaf area, etc. 3D plant phenotyping plays a crucial role in advancing our understanding of plant biology, improving crop breeding, and agricultural practices. 3D imaging has become a powerful phenotyping tool, offering in-depth insights into plant structures and traits. In contrast to 2D imaging, 3D imaging enables precise measurement of plant traits that cannot be sufficiently evaluated in two dimensions by overcoming challenges such as partial occlusion through the utilization of depth perception and multiple viewpoints. However, even with significant recent progress, various challenges persist, including the need for well-designed experimental setups for standardized data collection, the automation of processing pipelines, and the robust analysis techniques of 3D representations, which still impede the widespread adoption of 3D plant phenotyping. To propel the progress of 3D imaging-based phenotyping, an all-encompassing assessment of existing strategies is imperative, yet there is currently a lack of specialized reviews that scrutinize and emphasize distinct facets for future enhancement. To bridge this gap, we perform a systematic survey of 81 research studies that employ 3D imaging for various trait assessments of plants. Our review thoroughly investigates the stages of data acquisition, encompassing sensing technologies, representations, preprocessing approaches, analysis methodologies, and techniques for estimating phenotypic traits. We believe that this comprehensive review will serve as a valuable guide for researchers and professionals engaged in high throughput plant phenotyping, equipping them to formulate effective experimental setups and utilize appropriate processing and analysis methods, thereby fostering its continued advancement.
Why it matches plant phenotyping methods植物表現型解析における3D画像取得・前処理・解析・形質推定を体系的にレビューしており、方法論が中心です。
titleUnlocking plant secrets: A systematic review of 3D imaging in plant phenotyping techniques
The present review focuses on recent reports on the contribution of the Raman method in the development of digital agriculture, according to the premise of maximizing crops with a minimal impact of agriculture on the environment. The Raman method is an optically based spectrum technique that allows for the species-independent study of plant physiology as well as the real-time determination of key compounds in a non-destructive manner. The review focuses on scientific reports related to the possibility of using the Raman spectrometer to monitor the physiological state of plants and, in particular, to effectively diagnose biotic and abiotic stresses. This review primarily aims to draw attention to and raise awareness of the potential of Raman spectroscopy as a digital tool capable of bridging the gap between scientists’ detailed knowledge of plants grown under laboratory conditions and farmers’ work. The Raman spectrometer allows plant breeders to take appropriate measures in a well-defined area, which will reduce the territory occupied by biotic and abiotic stresses, thus increasing yields and improving their quality. Raman technology applied to modern agriculture can positively affect the accuracy and speed of crop quality assessments, contributing to food safety, productivity and economic profitability. Further research and analysis on cooperation between farmers and scientists is indispensable to increase the viability and availability of Raman spectrometers for as many farmers and investors as possible.
Why it matches plant phenotyping methods植物の生理状態と生物・非生物ストレスをRaman分光で評価する方法に焦点を当てたレビューであり、植物フェノタイピング手法のレビューとして中心的です。
abstractThe present review focuses on recent reports on the contribution of the Raman method in the development of digital agriculture
This review paper addresses the critical need for advanced rice disease detection methods by integrating artificial intelligence, specifically convolutional neural networks (CNNs). Rice, being a staple food for a large part of the global population, is susceptible to various diseases that threaten food security and agricultural sustainability. This research is significant as it leverages technological advancements to tackle these challenges effectively. Drawing upon diverse datasets collected across regions including India, Bangladesh, Türkiye, China, and Pakistan, this paper offers a comprehensive analysis of global research efforts in rice disease detection using CNNs. While some rice diseases are universally prevalent, many vary significantly by growing region due to differences in climate, soil conditions, and agricultural practices. The primary objective is to explore the application of AI, particularly CNNs, for precise and early identification of rice diseases. The literature review includes a detailed examination of data sources, datasets, and preprocessing strategies, shedding light on the geographic distribution of data collection and the profiles of contributing researchers. Additionally, the review synthesizes information on various algorithms and models employed in rice disease detection, highlighting their effectiveness in addressing diverse data complexities. The paper thoroughly evaluates hyperparameter optimization techniques and their impact on model performance, emphasizing the importance of fine-tuning for optimal results. Performance metrics such as accuracy, precision, recall, and F1 score are rigorously analyzed to assess model effectiveness. Furthermore, the discussion section critically examines challenges associated with current methodologies, identifies opportunities for improvement, and outlines future research directions at the intersection of machine learning and rice disease detection. This comprehensive review, analyzing a total of 121 papers, underscores the significance of ongoing interdisciplinary research to meet evolving agricultural technology needs and enhance global food security.
Why it matches plant phenotyping methodsイネ病害を画像等から推定するCNN手法を体系的にレビューし、データセット、前処理、モデル、性能評価を分析しているため、植物表現型計測手法のレビューとして中心的である。
titleAdvancements in rice disease detection through convolutional neural networks: A comprehensive review.
Abstract Drought stress poses a significant threat to global agricultural productivity and food security. Understanding how plants adapt to drought conditions is crucial for developing drought-resistant crop varieties. Plants have been gifted with adaptation capacity to cope with situations arising from water deficit. Their capacity to acclimate is featured by adaptive changes in plants. The capacity to capture changes in shoot architecture has now been enhanced by the advent of non-invasive phenotyping techniques involving various imaging systems in plant phenomics platforms. These platforms thrive on the assumption that the plant responses reflected in terms of changes in the structure of the plant that can offer ample scope to employ machine vision for differentiating the responses of plants to soil-moisture deficit. Further, it is assumed that the detectable genetic variation in morphological traits responding to soil moisture deficit can provide hints about a plant’s tolerance to stress and can be exploited to improve crop productivity in drought-prone areas. Genomic interventions utilizing high throughput phenotyping, make the selection of drought-tolerant genotypes easier. In recent years, machine vision has emerged as a powerful tool to study and quantify plant responses to drought stress. This article reviews the current state of knowledge on drought-adaptive responses in plants and explores the potential of genomic-assisted breeding tools coupled with high-throughput phenotyping platforms and machine vision to accelerate the elucidation of genotypic differences in adaptive traits. We also highlighted its role in deciphering the complex interplay of genotypic variations in drought-adaptive traits and harnessing artificial intelligence (AI) for machine vision data processing for the transformative potential in enhancing our understanding of plant responses to drought and expediting the development of climate-resilient crop varieties.
Why it matches plant phenotyping methods植物の干ばつ応答形質を対象に、機械ビジョン、高スループット表現型解析、画像処理をレビューする方法論的論文であり、植物表現型計測が中心です。
abstractThis article reviews the current state of knowledge on drought-adaptive responses in plants and explores the potential of genomic-assisted breeding tools coupled with high-throughput phenotyping platforms and machine vision to accelerate the elucidation of genotypic differences in adaptive traits.
Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture. Recent advancements in high-throughput phenotyping technologies and artificial intelligence (AI) have revolutionized the field, enabling rapid and accurate assessment of crop traits on a large scale. The integration of AI and machine learning algorithms with high-throughput phenotyping data has unlocked new opportunities for crop improvement. AI algorithms can analyze and interpret large datasets, extracting meaningful patterns and correlations between phenotypic traits and genetic factors. These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection, reducing the time and cost required for variety development. However, further research and collaborations are needed to overcome the challenges and fully unlock the power of high-throughput phenotyping and AI in crop improvement. By leveraging AI algorithms, researchers can efficiently analyze phenotypic data, uncover complex patterns, and establish predictive models that enable precise trait selection and crop breeding. The aim of this review is to explore the transformative potential of integrating high-throughput phenotyping and AI in crop improvement. The review will encompass an in-depth analysis of recent advancements and applications, highlighting the numerous benefits and challenges associated with high-throughput phenotyping and intelligence.
Why it matches plant phenotyping methodsAIとハイスループット植物フェノタイピングの統合を主題とするレビューであり、フェノタイピング手法・応用・課題を中心に扱うため含める。
abstractThe aim of this review is to explore the transformative potential of integrating high-throughput phenotyping and AI in crop improvement.
We revisit the foundations of the Horsfall-Barratt (HB) scale, a widely cited and applied plant disease visual assessment tool introduced in 1945, a full 37 years prior to T. T. Hebert's 1982 critique that raised concerns regarding the scale's rationale, particularly its reliance on the Weber-Fechner law and visual perception assumptions. Although use of the HB scale and similar ordinal scales persists, comprehensive studies have revealed that direct visual estimation using percentage scales often proves more accurate and reliable. Challenges remain, such as biases due to estimator subjectivity and the potential for misclassification. The logarithmic assumptions of the HB scale have been debunked, and the importance of choosing appropriate interval sizes and numbers of classes in developing ordinal scales is emphasized. Analyzing ordinal scale data appropriately is crucial, and recent advances offer promising methods that reduce type II error rates. The closely related disease severity index is noted to have its shortcomings and potential for misuse. The letter underscores the need for continuous refinement and critical evaluation of disease assessment methodologies.
Why it matches plant phenotyping methods植物病害の重症度を評価する視覚スケールについて、その原理・精度・信頼性・バイアス・解析方法を批判的に検討しており、植物状態の測定法が中心です。
titleThe Nuances of Plant Disease Severity Estimation Using Quantitative Ordinal Scales—Lessons Learned over Four Decades
The last decades have witnessed a rapid development of noninvasive plant phenotyping, capable of detecting plant stress scale levels from the subcellular to the whole population scale. However, even with such a broad range, most phenotyping objects are often just concerned with leaves. This review offers a unique perspective of noninvasive plant stress phenotyping from a multi-organ view. First, plant sensing and responding to abiotic stress from the diverse vegetative organs (leaves, stems, and roots) and the interplays between these vital components are analyzed. Then, the corresponding noninvasive optical phenotyping techniques are also provided, which can prompt the practical implementation of appropriate noninvasive phenotyping techniques for each organ. Furthermore, we explore methods for analyzing compound stress situations, as field conditions frequently encompass multiple abiotic stressors. Thus, our work goes beyond the conventional approach of focusing solely on individual plant organs. The novel insights of the multi-organ, noninvasive phenotyping study provide a reference for testing hypotheses concerning the intricate dynamics of plant stress responses, as well as the potential interactive effects among various stressors.
Why it matches plant phenotyping methods非侵襲的な植物ストレス表現型計測、とくに多器官の光学フェノタイピング技術を中心に扱うレビューであり、方法論が主題である。
abstractThis review offers a unique perspective of noninvasive plant stress phenotyping from a multi-organ view.
Climate change poses significant challenges to agricultural productivity, making the efficient management of water resources essential for sustainable crop production. The assessment of plant water status is crucial for understanding plant physiological responses to water stress and optimizing water management practices in agriculture. Proximal and remote sensing techniques have emerged as powerful tools for the non-destructive, efficient, and spatially extensive monitoring of plant water status. This review aims to examine the recent advancements in proximal and remote sensing methodologies utilized for assessing the water status, consumption, and irrigation needs of fruit tree crops. Several proximal sensing tools have proved useful in the continuous estimation of tree water status but have strong limitations in terms of spatial variability. On the contrary, remote sensing technologies, although less precise in terms of water status estimates, can easily cover from medium to large areas with drone or satellite images. The integration of proximal and remote sensing would definitely improve plant water status assessment, resulting in higher accuracy by integrating temporal and spatial scales. This paper consists of three parts: the first part covers current plant-based proximal sensing tools, the second part covers remote sensing techniques, and the third part includes an update on the on the combined use of the two methodologies.
Why it matches plant phenotyping methods果樹の水分状態という植物生理形質を対象に、近接・リモートセンシング手法を体系的にレビューしており、フェノタイピング手法が中心です。
abstractThis review aims to examine the recent advancements in proximal and remote sensing methodologies utilized for assessing the water status, consumption, and irrigation needs of fruit tree crops.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Published9 May 2024Journal of genetics and genomics/Journal of Genetics and GenomicsCited by 21 · OpenAlex ↗
Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.
Why it matches plant phenotyping methods作物フェノミクスのセンサーによる多様な植物形質取得、データ処理、標準化の課題を扱う方法論的レビューであり、表現型計測が中心である。
abstractCrop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales
In order to rapidly breed high-quality varieties, an increasing number of plant researchers have identified the functions of a large number of genes, but there is a serious lack of research on plants’ phenotypic traits. This severely hampers the breeding process and exacerbates the dual challenges of scarce resources and resource development and utilization. Currently, research on crop phenotyping has gradually transitioned from traditional methods to HTP technologies, highlighting the high regard scientists have for these technologies. It is well known that different crops’ phenotypic traits exhibit certain differences. Therefore, in rapidly acquiring phenotypic data and efficiently extracting key information from massive datasets is precisely where HTP technologies play a crucial role in agricultural development. The core content of this article, starting from the perspective of crop phenomics, summarizes the current research status of HTP technology, both domestically and internationally; the application of HTP technology in above-ground and underground parts of crops; and its integration with precision agriculture implementation and multi-omics research. Finally, the bottleneck and countermeasures of HTP technology in the current agricultural context are proposed in order to provide a new method for phenotype research. HTP technologies dynamically monitor plant growth conditions with multi-scale, comprehensive, and automated assessments. This enables a more effective exploration of the intrinsic “genotype-phenotype-environment” relationships, unveiling the mechanisms behind specific biological traits. In doing so, these technologies support the improvement and evolution of superior varieties.
Why it matches plant phenotyping methods作物高通量表型技术的综述，系统总结其在表型数据获取、信息提取及地上地下部应用中的方法与平台，表型方法是核心内容。
abstractThe core content of this article, starting from the perspective of crop phenomics, summarizes the current research status of HTP technology
Plant diseases have the potential to damage the livelihoods of farmers and impede their capacity to generate an adequate quantity of food. Diagnosis and early detection of plant diseases are critical for their effective management and control. Leaf analysis shows great potential as a method for forecasting disease stages due to its ability to detect subtle alterations in leaf physiology and appearance that may occur prior to the onset of conspicuous symptoms. By utilizing the algorithms of machine learning and deep learning, it is possible to classify characteristics extracted from photographs of leaves into distinct disease stages. Recent studies have demonstrated the potential of these algorithms, as they have achieved remarkable accuracy in disease stage prediction despite having limited training data. The integration of machine learning and deep learning techniques with foliage analysis holds promise for revolutionizing plant disease management through the provision of timely identification, accurate diagnosis, and customized treatment. In order to formulate efficacious disease management strategies, precise determination of the developmental stage of plant diseases is imperative. Scholars are presently devising novel approaches to identify the stages of plant diseases by employing diverse methodologies, including spectroscopy, machine learning, and image processing. This can benefit producers in substantial economic and environmental ways, as well as contribute to the improvement of food security. The primary investigation comprises an assortment of articles spanning the years 2014 to 2023. After evaluating various search strategies, a total of 117 research publications were identified, of which 43 were pertinent. The article examines numerous developments in deep learning research. In addition, it will facilitate the assessment of the present and prospective state of plant disease research by employing deep learning methodologies.
Why it matches plant phenotyping methods植物の葉画像・分光・画像処理・機械学習によって病害段階や症状を推定する方法を体系的に扱うレビューであり、植物状態の取得・推定手法が中心です。
titleAdvancements in Crop Disease Detection: Analytical Methods for Recognizing Disease Stages through Leaf Analysis
This comprehensive review explores the most current developments in the use of image processing and machine learning methods for the diagnosis of plant diseases. After a thorough review of the literature, we critically assess the different frameworks and methods used to classify disorders affecting plants. Our study highlights the advantages and disadvantages of each approach while focusing on how accurate it is in diagnosing a wide variety of illnesses. In addition, we investigate new developments and trends in this quickly developing industry. We conclude by talking about the ongoing difficulties and suggesting some directions for further study to improve the effectiveness of plant disease detection systems. Keywords— CNN, VGG16, image processing, classification, neural networks, and machine learning
Why it matches plant phenotyping methods植物病害を画像処理・機械学習で診断する手法を中心に比較・批評するレビューであり、植物の病徴・病害状態を画像から推定するフェノタイピング手法レビューに該当する。
abstractThis comprehensive review explores the most current developments in the use of image processing and machine learning methods for the diagnosis of plant diseases.
Deep learning constitutes a fundamental component of artificial intelligence.Recently, it has gained wide attention from educational and industrial sectors due to its ability for automatic learning and feature extraction.Additionally, it has become a key area of research in agricultural plant protection, particularly in recognizing plant diseases and assessing pest populations.Using deep learning for disease recognition helps overcome the limitations of manually selecting diseaserelated features, making the extraction of plant disease characteristics more objective.This enhances analysis efficiency and accelerates technology advancements.It delves into the current trends and obstacles encountered in the realm of detecting plant leaf diseases through the utilization of deep learning methodologies and sophisticated imaging technologies.Our aim is for this study to offer a beneficial point of reference for researchers engaged in the exploration of plant disease detection and pest control.Furthermore, we highlight certain challenges and concerns that necessitate resolution within this domain.
Why it matches plant phenotyping methods植物葉の病徴・病害状態を画像と深層学習で検出する方法論レビューであり、植物表現型の取得・推定が中心です。
abstractIt delves into the current trends and obstacles encountered in the realm of detecting plant leaf diseases through the utilization of deep learning methodologies and sophisticated imaging technologies.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.
Why it matches plant phenotyping methods植物表現型計測を含むMASワークフローのレビューであり、UAVリモートセンシングと機械学習によるハイスループット表現型解析を中心的に扱っている。
abstractthis review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow.
The importance of identifying plant diseases has risen recently due to the adverse effect they have on agricultutal production. Plant diseases have been a big concern in agriculture, as they affect crop production, and constitute a major threat to global food security. In the domain of modern agriculture, effective plant disease management is vital to ensure healthy crop yields and sustainable practices. Traditional means of identifying plant disease are faced with lots of challenges and the need for better and efficient detection methods cannot be overemphazised. The emergence of advanced technologies, particularly deep learning and content-based filtering techniques, if integrated together can changed the way plant diseases are identified and treated. Such as speedy and correct identification of plant diseases and efficient treatment recommendations which are keys for sustainable food production. In this work, We try to investigate the current state of research, identified gaps and limitations in knowledge, and suggests future directions for researchers, experts and farmers that could help to provide better ways of mitigating plant disease problems.
Why it matches plant phenotyping methods植物病害の画像・深層学習による識別手法を中心に扱うレビューであり、感染植物の病害状態を観測から推定するフェノタイピング手法のレビューに該当する。
titleDeep learning and content-based filtering techniques for improving plant disease identification and treatment recommendations: A comprehensive review.
Plant leaf disease identification is a crucial aspect of modern agriculture to enable early disease detection and prevention. Deep learning approaches have demonstrated amazing results in automating this procedure. This paper presents a comparative analysis of various deep learning methods for plant leaf disease identification, with a focus on convolutional neural networks. The performance of these techniques in terms of accuracy, precision, recall, and F1-score, using diverse datasets containing images of diseased leaves from various plant species was examined. This study highlights the strengths and weaknesses of different deep learning approaches, shedding light on their suitability for different plant disease identification scenarios. Additionally, the impact of transfer learning, data augmentation, and sensor data integration in enhancing disease detection accuracy is discussed. The objective of this analysis is to provide valuable insights for researchers and practitioners seeking to harness the potential of deep learning in the agricultural sector, ultimately contributing to more effective and sustainable crop management practices.
Why it matches plant phenotyping methods植物葉の病徴を画像から識別する深層学習手法を比較・評価したレビューであり、植物病害状態の表現型推定手法が中心です。
abstractThis paper presents a comparative analysis of various deep learning methods for plant leaf disease identification, with a focus on convolutional neural networks.
Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.
Why it matches plant phenotyping methods植物フェノタイピングにおける機械学習応用を体系的に概観するレビューであり、表現型推定・取得手法が中心です。
abstractTherefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers.
Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field.
Why it matches plant phenotyping methods作物収量という植物形質の予測手法を対象に、深層学習モデル、入力データ、課題を体系的に整理したレビューであり、単なる生物学的実験の収量測定ではなく計算的な形質推定手法が中心。
abstractIn our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting
Precision agriculture has emerged as a transformative paradigm in modern farming, leveraging advanced technologies to optimize crop management. This paper presents a comprehensive survey of deep learning approaches for crop disease analysis in precision agriculture. The investigation focuses on four key aspects: leaf disease detection through deep learning techniques, leaf shape-based disease analysis, crop weed detection utilizing deep learning methods, and crop damage detection using aerial images. The survey encompasses a review of recent advancements, methodologies, challenges, and future prospects in each of these domains. By exploring the intersection of deep learning and precision agriculture, this paper aims to provide a holistic understanding of the current state-of-the-art and inspire further research initiatives to enhance crop health monitoring and management.
Why it matches plant phenotyping methods作物病害を画像から検出・解析する深層学習手法を体系的にレビューしており、植物の病害状態を推定するフェノタイピング手法のレビューが中心である。
titleA Survey on Deep Learning Approaches for Crop Disease Analysis in Precision Agriculture
The global population is rapidly increasing and is expected to exceed 9 billion by 2050, resulting in signi cant challenges for agriculture due to factors such as industrialization, reduced farmland, and biotic and abiotic stresses. To address these challenges and ensure future sustainability, the agriculture system needs to become more productive, e cient, and resilient. Arti cial intelligence (AI) and machine learning (ML) have emerged as powerful tools to transform the agricultural sector. Agricultural productivity is greatly in uenced by biotic and abiotic stresses, and developing climate-smart crops through conventional breeding techniques is time-consuming and challenging. Plant phenotyping, which involves measuring speci c plant features related to function, is crucial in breeding for target traits. However, traditional phenotyping methods are laborious, error-prone, and less accurate, particularly under stress conditions. To overcome these limitations, researchers have focused on developing high-throughput phenotyping technologies. State-of-the-art imaging techniques, such as light detection and ranging (LIDAR), remote sensing, and RGB imaging, combined with autonomous carriers like unmanned aerial vehicles (UAVs) and ground robots, enable real-time and high-throughput phenotyping of morphological, physiological, and stress-related traits. ML tools can compartmentalize big data, identify related traits, classify them, quantify their expression, and predict their function within the plant system. AI and ML o er multidisciplinary approaches for analyzing big data accumulated over time, leading to the discovery of patterns and systematic data of interest, such as stress phenotypes. Using these technologies, researchers worldwide can expedite agricultural research and develop climate-smart crops. The future of AI and ML in agriculture is promising, as they can lead to new scienti c discoveries and help overcome the challenges of limited resources in food production.
Why it matches plant phenotyping methods植物ストレスのハイスループット表現型解析に用いる画像・センシング技術とAI/ML解析を中心に扱う方法論レビューであり、植物フェノタイプ手法が主題である。
abstractState-of-the-art imaging techniques, such as light detection and ranging (LIDAR), remote sensing, and RGB imaging, combined with autonomous carriers like unmanned aerial vehicles (UAVs) and ground robots, enable real-time and high-throughput phenotyping of morphological, physiological, and stress-related traits.
Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocates for the development of a unified framework that harnesses an ensemble of ML and DL algorithms to address the complexities of multiple plant diseases effectively.
Why it matches plant phenotyping methods植物画像から病害の同定・分類・重症度推定を行う計算手法とデータセットを中心に扱うレビューであり、植物病害状態のフェノタイピング手法レビューに該当する。
abstractThis paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases.
Plant health is intricately linked to crop quality, food security and agricultural productivity. Obtaining accurate plant health information is of paramount importance in the realm of precision agriculture. Wearable sensors offer an exceptional avenue for investigating plant health status and fundamental plant science, as they enable real-time and continuous in-situ monitoring of physiological biomarkers. However, a comprehensive overview that integrates and critically assesses wearable plant sensors across various facets, including their fundamental elements, classification, design, sensing mechanism, fabrication, characterization and application, remains elusive. In this study, we provide a meticulous description and systematic synthesis of recent research progress in wearable sensor properties, technology and their application in monitoring plant health information. This work endeavours to serve as a guiding resource for the utilization of wearable plant sensors, empowering the advancement of plant health within the precision agriculture paradigm.
Why it matches plant phenotyping methods植物ウェアラブルセンサーによる生理的バイオマーカーと健康状態の連続モニタリングを体系的に評価するレビューであり、植物フェノタイピング手法が中心です。
abstracta comprehensive overview that integrates and critically assesses wearable plant sensors across various facets, including their fundamental elements, classification, design, sensing mechanism, fabrication, characterization and application, remains elusive.
Abstract One of the essential components of human civilization is agriculture. It helps the economy in addition to supplying food. Plant leaves or crops are vulnerable to different diseases during agricultural cultivation. The diseases halt the growth of their respective species. Early and precise detection and classification of the diseases may reduce the chance of additional damage to the plants. The detection and classification of these diseases have become serious problems. Farmers’ typical way of predicting and classifying plant leaf diseases can be boring and erroneous. Problems may arise when attempting to predict the types of diseases manually. The inability to detect and classify plant diseases quickly may result in the destruction of crop plants, resulting in a significant decrease in products. Farmers that use computerized image processing methods in their fields can reduce losses and increase productivity. Numerous techniques have been adopted and applied in the detection and classification of plant diseases based on images of infected leaves or crops. Researchers have made significant progress in the detection and classification of diseases in the past by exploring various techniques. However, improvements are required as a result of reviews, new advancements, and discussions. The use of technology can significantly increase crop production all around the world. Previous research has determined the robustness of deep learning (DL) and machine learning (ML) techniques such as k-means clustering (KMC), naive Bayes (NB), feed-forward neural network (FFNN), support vector machine (SVM), k-nearest neighbor (KNN) classifier, fuzzy logic (FL), genetic algorithm (GA), artificial neural network (ANN), convolutional neural network (CNN), and so on. Here, from the DL and ML techniques that have been included in this particular study, CNNs are often the favored choice for image detection and classification due to their inherent capacity to autonomously acquire pertinent image features and grasp spatial hierarchies. Nevertheless, the selection between conventional ML and DL hinges upon the particular problem, the accessibility of data, and the computational capabilities accessible. Accordingly, in numerous advanced image detection and classification tasks, DL, mainly through CNNs, is preferred when ample data and computational resources are available and show good detection and classification effects on their datasets, but not on other datasets. Finally, in this paper, the author aims to keep future researchers up-to-date with the performances, evaluation metrics, and results of previously used techniques to detect and classify different forms of plant leaf or crop diseases using various image-processing techniques in the artificial intelligence (AI) field.
Why it matches plant phenotyping methods植物病害の画像による検出・分類手法を比較するレビューであり、感染植物の症状・病害状態を画像から推定する方法が中心です。
titlePlant disease detection and classification techniques: a comparative study of the performances
Global agriculture is facing major challenges to ensure global food security, such as the need to breed high-yielding crops adapted tofuture climates. Plant phenotyping provides a set of tools to help researchers better understand gene function and environmental processes. Reliable, autonomous, multipurpose, and high-throughput phenotypic technologies are becoming more significant tools in breeding programs for rapid progression of genetic gain. With the rapid advancement of high-throughput phenotyping technology, research has entered a new era known as phenomics. Here we present an overview of phenomics research, focused on the collecting of phenotypic data utilizing various sensors. Finally, we discuss the crop phenomics’ challenges and prospects in order to make recommendations for developing new methods for mining genes associated with important agronomic traits and proposing new solutions for precision agriculture.
Why it matches plant phenotyping methods植物フェノタイピング技術とセンサーによる表現型データ収集を主題とするレビューであり、方法論の概観が中心。
abstractPlant phenotyping provides a set of tools to help researchers better understand gene function and environmental processes.
Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.
Why it matches plant phenotyping methods植物ストレス検出のための画像センサーとAI手法を体系的にレビューしており、植物表現型取得・解析手法が中心である。
abstractOur review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping.
Reproduction assets foundThe paper's authors publicly released the programmable-bot Python code used to conduct the systematic literature review's database searches and data processing, with explicit availability language and a GitHub URL. The Zotero group library of 262 studies is public but has no URL in the allowed list; Kaggle/Zindi/SpectrCode · publicJ.J.W. and E.M.; data curation and visualisation: J.J.W. writing original draft: all authors; writing, review and editing: J.J.W. and S.N.; funding acquisition: S.N.
Competing interests: The authors declare no conflict of interest.
Data Availability
All code used to create and run the programmable bots is available on GitHub ( https://github.com/Walshj73/data-processing-bot.git ) and licensed under the MIT license. All 262 studies found during this SLR process are available in a publicly accessible Zotero group library (titled “Advancements in Imaging Sensors and AI for Plant Stress Detection”). The group library can be accessed on Zotero by using the “Search for groups” feature found under Open asset ↗Walshj73/data-processing-botlines:160-199Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Millets, comprising a diverse group of small‐seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance. The comprehension of complex traits in millets, influenced by multifaceted genetic determinants, presents a compelling challenge and opportunity in agricultural research. This review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures. On the phenomics front, high‐throughput platforms generate rich datasets on plant morphology, physiology, and performance in diverse environments. This data, coupled with field trials and controlled conditions, helps to interpret how the environment interacts with genetics. Genomics provides the underlying blueprint for these complex traits. Genome sequencing and genotyping technologies have illuminated the millet genome landscape, revealing diverse gene pools and evolutionary relationships. Additionally, different omics approaches unveil the intricate information of gene expression, protein function, and metabolite accumulation driving phenotypic expression. This multi‐omics approach is crucial for identifying candidate genes and unfolding the intricate pathways governing complex traits. The review highlights the synergy between phenomics and genomics. Genomically informed phenotyping targets specific traits, reducing the breeding size and cost. Conversely, phenomics identifies promising germplasm for genomic analysis, prioritizing variants with superior performance. This dynamic interplay accelerates breeding programs and facilitates the development of climate‐smart, nutrient‐rich millet varieties and hybrids. In conclusion, this review emphasizes the crucial roles of phenomics and genomics in unlocking the genetic enigma of millets.
Why it matches plant phenotyping methodsミレットのフェノミクスをゲノミクスと統合して複雑形質を解明するレビューであり、高スループット表現型取得とその応用が中心的に扱われている。
abstractThis review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures.
Agriculture is the foundation of civilization which faces several problems in the 21st century. Crop diseases threaten global food security by reducing yields. Visual inspection to detect diseases can be time-consuming, subjective, and error-prone. Recent advances in various machine learning (ML) and deep learning (DL) techniques have led to ease in the identification of crop diseases. ML and DL demonstrate their versatility in image recognition, segmentation, and anomaly detection. In this paper, some of the recent works based on crop-disease detection using various ML and DL techniques are reviewed. It includes early illness detection and appropriate interventions to reduce yield loss. The review emphasizes the relevance of crop disease detection for food security and sustainable agriculture. ML and DL techniques can help farmers monitor crop health and optimize resource allocation.
Why it matches plant phenotyping methods作物病害を植物画像から検出する機械学習・深層学習手法を主題とするレビューであり、植物の病害状態を推定するフェノタイピング手法のレビューに該当する。
abstractIn this paper, some of the recent works based on crop-disease detection using various ML and DL techniques are reviewed.
Abstract Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in‐depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation.
Why it matches plant phenotyping methods植物病害を対象としたコンピュータビジョンによる症状・病害の検出手法を中心に扱うレビューであり、植物フェノタイピング手法の方法論的整理と評価が主題である。
abstractHere, we review the application and development of computer vision and machine learning methods for the detection of plant disease.
Reproduction assets foundThe paper's data availability statement provides public OSF deposits (view-only links) containing image data, annotations, training data, and semi-supervised model weights for the cocoa disease-detection case studies, plus public GitHub repositories with the authors' custom training/analysis code (CocoaReader, CocoaNetDataset · publicThe image data, annotations, and link to the accompanying GitHub repository for Case Study 1 can be found at: https://osf.io/79kx3/?view_only=4a2c1dccee1a4baeb85de5002c702f10 .Open asset ↗osflines:411-466Dataset · publicFor Case Study 2, the data used to train the initial supervised model, the .csv search terms file for the below web scraper, and the final semi‐supervised model weights can be found at: https://osf.io/h5gj7/?view_only=dbf9f245e21a41e185f5b73e718b4cad .Open asset ↗osflines:411-466Code · publicThe custom code used to train both the initial model and the final semi‐supervised model can be found at: https://github.com/jrsykes/CocoaReader/blob/main/PlantNotPlant .Open asset ↗github · jrsykes/CocoaReaderlines:411-466Code · publicThe custom code to run the sweep in Case Study 4 can be found in the following GitHub repository: https://github.com/jrsykes/CocoaReader/tree/main/CocoaNet .Open asset ↗github · jrsykes/CocoaReaderlines:411-466Dataset · publicThe data used to generate these results and the full wandb report can be found at: https://osf.io/2fw6g/?view_only=adc66ba66f83465a9e7b111515a60bf2 .Open asset ↗osflines:411-466Code · publicThe “contaminated” data used to train the semi‐supervised model were generated using the code at: https://github.com/jrsykes/Google-Image-Scraper .Open asset ↗github · jrsykes/Google-Image-Scraperlines:411-466Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 7 Sept 2026
The increasing demand for food production due to the growing population is raising the need for more food-productive environments for plants. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to look after the individual plant component traits manually. Plant breeders need computer vision-based plant monitoring systems to analyze different plants' productivity and environmental suitability. It leads to performing feasible quantitative analysis, geometric analysis, and yield rate analysis of the plants. Many of the data collection methods have been used by plant breeders according to their needs. In the presented review, most of them are discussed with their corresponding challenges and limitations. Furthermore, the traditional approaches of segmentation and classification of plant phenotyping are also discussed. The data limitation problems and their currently adapted solutions in the computer vision aspect are highlighted, which somehow solve the problem but are not genuine. The available datasets and current issues are enlightened. The presented study covers the plants phenotyping problems, suggested solutions, and current challenges from data collection to classification steps.
Why it matches plant phenotyping methods植物フェノタイピングのコンピュータビジョン手法、データセット、課題を体系的に扱うレビューであり、方法論が中心です。
titleComputer vision-based plants phenotyping: A comprehensive survey
Nowadays, it is a challenge for farmers to produce healthier food for the world population and save land resources. Recently, the integration of computer vision technology in field and crop production ushered in a new era of innovation and efficiency. Computer vision, a subfield of artificial intelligence, leverages image and video analysis to extract meaningful information from visual data. In agriculture, this technology is being utilized for tasks ranging from disease detection and yield prediction to animal health monitoring and quality control. By employing various imaging techniques, such as drones, satellites, and specialized cameras, computer vision systems are able to assess the health and growth of crops and livestock with unprecedented accuracy. The review is divided into two parts: Livestock and Crop Production giving the overview of the application of computer vision applications within agriculture, highlighting its role in optimizing farming practices and enhancing agricultural productivity.
Why it matches plant phenotyping methods作物の健康状態・成長・病害・収量などを画像解析で評価するコンピュータビジョン応用をレビューしており、植物フェノタイピング手法のレビューが中心です。
abstractThe review is divided into two parts: Livestock and Crop Production giving the overview of the application of computer vision applications within agriculture
The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen-plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.
Why it matches plant phenotyping methodsイネ病害の画像処理・機械学習による検出と、病原体—植物相互作用の表現型を扱うレビューであり、植物の病徴・病害状態を推定する方法が中心です。
abstractIn this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Machine vision and machine learning for plant phenotyping and precision agriculturePlant phenotyping (PP) describes the physiological and biochemical properties of plants affected by both genotypes and environments.It is an emerging research field assisting the breeding and cultivation of new crop varieties to be more productive and resilient to challenging environments.Precision agriculture (PA) uses sensing technologies to observe crops and then manages them optimally to ensure that they grow in healthy conditions, have maximum productivity, and have minimal adverse effects on the environment.Traditionally, the observation of plant traits heavily relies on human experts, which is labour-intensive, time-consuming, and subjective.Although PP and PA are two different fields, they share similar sensing and data processing technologies in many respects.Recently, driven by computer and sensor technologies, machine vision (MV) and machine learning (ML) have contributed to accurate, high-throughput and nondestructive sensing and data processing technologies to PP and PA.However, these technologies are still in their infant stage, and many challenges and questions related to them still need to be addressed.This Research Topic aims to share the latest research results on applying MV and ML to PP and PA.It demonstrates cutting-edge technologies, bottle-necks and future research directions for MV and ML in crop breeding, crop cultivation, and disease or pest management.This Research Topic of Frontiers in Plant Sciences published a total of 28 peer-reviewed research articles, including one review paper for the phenotyping of Prunoideae fruits (Liu et al.).These articles reveal the latest research trends regarding different crop species, data types and algorithms.The summary of the published reports shows that cotton (Gossypium), canola or oilseed rape (Brassica napus), wheat (Triticum) and maize (Z.mays) are the most important crops for study in PP and PA (Figure 1A).Cotton stands out as the most frequently examined crop, with a total of five articles dedicated to it.Yan et al. developed a leaf segmentation method in the field environments.Tang et al. investigated early detection
Why it matches plant phenotyping methods植物フェノタイピングにおける機械視覚・機械学習技術を中心に扱う編集論文であり、方法論の動向と応用を概説しているため。
titleEditorial: Machine vision and machine learning for plant phenotyping and precision agriculture
High-throughput phenotype monitoring systems for field crops can not only accelerate the breeding process but also provide important data support for precision agricultural monitoring. Traditional phenotype monitoring methods for field crops relying on artificial sampling and measurement have some disadvantages including low efficiency, strong subjectivity, and single characteristics. To solve these problems, the rapid monitoring, acquisition, and analysis of phenotyping information of field crops have become the focus of current research. The research explores the systematic framing of phenotype monitoring systems for field crops. Focusing on four aspects, namely phenotyping sensors, mobile platforms, control systems, and phenotyping data preprocessing algorithms, the application of the sensor technology, structural design technology of mobile carriers, intelligent control technology, and data processing algorithms to phenotype monitoring systems was assessed. The research status of multi-scale phenotype monitoring products was summarized, and the merits and demerits of various phenotype monitoring systems for field crops in application were discussed. In the meantime, development trends related to phenotype monitoring systems for field crops in aspects including sensor integration, platform optimization, standard unification, and algorithm improvement were proposed.
Why it matches plant phenotyping methods圃場作物の表現型モニタリングシステムを対象に、センサー、移動プラットフォーム、制御系、データ前処理アルゴリズムを体系的にレビューしており、フェノタイピング手法が中心である。
abstractThe research explores the systematic framing of phenotype monitoring systems for field crops.
The rapid emergence and evolution of deep learning methodologies in the field of plant disease classification and detection has resulted in significant progress. Their application has revolutionized the way agriculture is done. This paper provides an overview of the advancements in utilizing deep learning models to address the crucial task of identifying and categorizing plant diseases. By harnessing the power of deep convolutional neural networks (CNNs) and transfer learning, researchers have achieved remarkable accuracy in disease classification, often surpassing traditional methods. This study also delves into the challenges that persist in this field, such as the scarcity of labeled data and potential biases in models. To address these concerns, the integration of visualization techniques is explored, allowing for better model interpretation and transparency. The collaborative efforts of agricultural experts and machine learning researchers are deemed crucial for overcoming these challenges and driving the future direction of research. Looking ahead, the interdisciplinary approach is anticipated to play a pivotal role in refining deep learning models for plant disease detection. A seamless collaboration between domain-specific professionals, machine learning experts, and agricultural practitioners is essential to foster innovation, enhance the reliability of models, and create a sustainable agricultural ecosystem. With the integration of cutting-edge architectures, emerging technologies like edge computing, and broader datasets, the field is poised to bring about transformative changes in agricultural practices, bolstering crop health and productivity.
Why it matches plant phenotyping methods植物病害を画像等から検出・分類する深層学習手法を主題としたレビューであり、植物の病害状態を推定するフェノタイピング手法のレビューに該当する。
titleA Detailed Review on Plant Leaf Disease Detection and Classification Methodologies using Deep Learning Techniques
Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.
Why it matches plant phenotyping methods画像ベース植物フェノタイピングの応用動向を対象とする文献レビューであり、フェノタイピング手法が中心です。
titleAn overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review
Abstract: Crop disease detection is the process of identifying and classifying plant diseases from images or other data. This can be done manually or using automated methods. Manual methods typically involve a human expert visually inspecting the plant and identifying the disease. Automated methods use computer vision algorithms to identify the disease from images or other data. Crop diseases pose a significant threat to global food security by causing substantial yield losses and reduced quality in agricultural production. Timely and accurate detection of crop diseases is crucial to mitigate these losses and ensure sustainable agricultural practices. In recent years, advancements in sensor technology, data analysis techniques, and machine learning algorithms have enabled the development of various methods for crop disease detection. This survey paper aims to provide a comprehensive overview of the state-of-the-art techniques, methodologies, and challenges in the field of crop disease detection. The paper begins by introducing the importance of crop disease detection in modern agriculture and its impact on both economic and environmental aspects. It then categorizes the existing detection methods into several key approaches, including visual inspection, spectroscopy, image analysis, and sensor-based techniques. For each approach, the paper discusses its underlying principles, advantages, limitations, and representative studies.
Why it matches plant phenotyping methods植物病害を画像・分光・センサー等から検出する方法を中心に整理した調査論文であり、植物の病害状態を推定するフェノタイピング手法レビューに該当する。
titleA Research Paper on Crop Disease Detection Using Deep Learning Model
Anthracnose diseases, caused by Colletotrichum spp., are considered to be among the most destructive diseases that have a significant impact on the global production of strawberries. These diseases alone can cause up to 70% yield loss in North America. Colletotrichum spp. causes several disease symptoms on strawberry plants, including root, fruit, and crown rot, lesions on petioles and runners, and irregular black spots on the leaf. In many cases, a lower level of infection on foliage remains non-symptomatic (quiescent), posing a challenge to growers as these plants can be a significant source of inoculum for the fruiting field. Reliable detection methods for quiescent infection should play an important role in preventing infected plants' entry into the production system or guiding growers to take appropriate preventative measures to control the disease. This review aims to examine both conventional and emerging approaches for detecting anthracnose disease in the early stages of the disease cycle, with a focus on newly emerging techniques such as remote sensing, especially using unmanned aerial vehicles (UAV) equipped with multispectral sensors. Further, we focused on the acutatum species complex, including the latest taxonomy, the complex life cycle, and the epidemiology of the disease. Additionally, we highlighted the extensive spectrum of management techniques against anthracnose diseases on strawberries and their challenges, with a special focus on new emerging sustainable management techniques that can be utilized in organic strawberry systems.
Why it matches plant phenotyping methodsイチゴ植物の病徴・感染状態を検出する手法のレビューであり、UAV搭載マルチスペクトルセンサーによるリモートセンシングを中心に扱うため、植物病害フェノタイピング手法のレビューに該当する。
abstractThis review aims to examine both conventional and emerging approaches for detecting anthracnose disease in the early stages of the disease cycle, with a focus on newly emerging techniques such as remote sensing, especially using unmanned aerial vehicles (UAV) equipped with multispectral sensors.
This review study aims to provide an overview of the current state of research on the use of machine learning techniques for the detection of tomato leaf diseases in the context of climate change in Zambia. Plant diseases pose significant challenges to small-scale farmers in developing countries, impacting crop yields and livelihoods. The emergence of Industry 4.0 technologies, coupled with the power of machine learning, offers promising opportunities to address these challenges and empower farmers with valuable knowledge and tools This review study aims to provide an overview of the use of machine learning in Industry 4.0 as an educational tool specifically tailored to tackle plant diseases for small-scale farmers in developing countries. The study offers insights into the potential of these techniques to enhance disease detection and contribute to sustainable agricultural practices in the face of climate change. Climate change has had significant impacts on agricultural practices worldwide, leading to the emergence and spread of various plant diseases. The study examines existing literature, research articles, and practical implementations to analyze the potential applications of machine learning in plant disease management. The review focuses on four key areas: disease identification, early detection and prediction, knowledge sharing and education, and decision support systems. With further advancements in machine learning techniques and the integration of cutting-edge technologies, the agriculture sector can benefit from improved disease detection and mitigation strategies to ensure food security in the face of climate change.
Why it matches plant phenotyping methods植物病害を葉の観察に基づいて検出する機械学習手法を中心に整理したレビューであり、植物の病害状態を推定するフェノタイピング手法のレビューに該当する。
abstractThis review study aims to provide an overview of the current state of research on the use of machine learning techniques for the detection of tomato leaf diseases
Plant diseases of an infectious nature are the reason for major economic losses in agriculture throughout the world. The early, rapid and non-invasive detection of diseases and pathogens is critical for effective control. Optical diagnostic methods have a high speed of analysis and non-invasiveness. The review provides a general description of such methods and also discusses in more detail methods based on the scattering and absorption of light in the UV, Vis, IR and terahertz ranges, Raman scattering and LiDAR technologies. The application of optical methods to all parts of plants, to a large number of groups of pathogens, under various data collection conditions is considered. The review reveals the diversity and achievements of modern optical methods in detecting infectious plant diseases, their development trends and their future potential.
Why it matches plant phenotyping methods植物の病徴・疾病状態を光学センシングで検出する方法を体系的に扱うレビューであり、植物フェノタイピング手法のレビューとして中心的です。
titleOptical Methods for the Detection of Plant Pathogens and Diseases (Review)
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.
Why it matches plant phenotyping methodsアフリカ作物を対象とするフィールド植物フェノタイピング技術の現状、課題、導入戦略を扱うレビューであり、フェノタイピング手法・インフラが中心テーマである。
titleField phenotyping for African crops: overview and perspectives
Common root rot (CRR) caused by the soilborne pathogen Bipolaris sorokiniana (teleomorph Cochliobolus sativus) is becoming increasingly prevalent worldwide. Identification of CRR is difficult and time‐consuming for human assessors due to the non‐distinctive above‐ground symptoms, with browning of subcrown internodes and roots the most distinguishing symptom of infection. CRR disease has been recognized as a significant disease for cereal crops in many countries. In 2009, CRR in Australia was estimated to cause $30 million average annual yield loss for wheat and $13 million for barley. Recent evidence indicates CRR may be more prevalent than expected in Australian wheat cropping areas due to lack of research on this disease. Low levels of B. sorokiniana survive in the soil for up to 10 years and attack plants at early stages of growth. Therefore, mitigating CRR in wheat and barley may not be practical at the late stages of infection due to lack of effective methods; however, early detection might be viable to alleviate the impact of this disease. A comprehensive overview of CRR caused by B. sorokiniana, including disease background, worldwide economic losses, management methods, potential CRR detection using multispectral and hyperspectral sensors and the research focus over the past 50 years is provided in this article. This review paper is expected to provide thorough supplemental information for current studies about CRR and proposes recommendations for whole‐of‐field disease scouting methods to farmers, enabling reduced time and cost for CRR management and increasing wheat and barley production worldwide.
Why it matches plant phenotyping methodsCRRの植物症状を対象に、マルチスペクトル・ハイパースペクトルセンサーによる早期病害検出を扱うレビューであり、植物表現型計測手法のレビューとして中心的です。
abstractA comprehensive overview of CRR caused by B. sorokiniana, including disease background, worldwide economic losses, management methods, potential CRR detection using multispectral and hyperspectral sensors and the research focus over the past 50 years is provided in this article.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.
Why it matches plant phenotyping methods植物フェノタイピングにおけるマルチオミクスとAI統合の技術的課題・解決策を中心に扱うレビューであり、方法論レビューとして適格。
abstractThis review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping.
Timely, accurate spatial information on the health of neglected and underutilised crop species (NUS) is critical for optimising their production and food and nutrition in developing countries. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have significantly advanced remote sensing, enabling the provision of near-real-time data for crop analysis at the plot level in small, fragmented croplands where NUS are often grown. The objective of this study was to systematically review the literature on the remote sensing (RS) of the spatial distribution and health of NUS, evaluating the progress, opportunities, challenges, and associated research gaps. This study systematically reviewed 171 peer-reviewed articles from Google Scholar, Scopus, and Web of Science using the PRISMA approach. The findings of this study showed that the United States (n = 18) and China (n = 17) were the primary study locations, with some contributions from the Global South, including southern Africa. The observed NUS crop attributes included crop yield, growth, leaf area index (LAI), above-ground biomass (AGB), and chlorophyll content. Only 29% of studies explored stomatal conductance and the spatial distribution of NUS. Twenty-one studies employed satellite-borne sensors, while only eighteen utilised UAV-borne sensors in conjunction with machine learning (ML), multivariate, and generic GIS classification techniques for mapping the spatial extent and health of NUS. The use of UAVs in mapping NUS is progressing slowly, particularly in the Global South, due to exorbitant purchasing and operational costs, as well as restrictive regulations. Subsequently, research efforts must be directed toward combining ML techniques and UAV-acquired data to monitor NUS’ spatial distribution and health to provide necessary information for optimising food production in smallholder croplands in the Global South.
Why it matches plant phenotyping methodsNUSの作物の健康状態やLAI、バイオマス、クロロフィル等をUAV・衛星リモートセンシングで推定する研究を体系的にレビューしており、植物表現型計測手法の評価が中心です。
abstractThe objective of this study was to systematically review the literature on the remote sensing (RS) of the spatial distribution and health of NUS, evaluating the progress, opportunities, challenges, and associated research gaps.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
The increasing human population and variable weather conditions, due to climate change, pose a threat to the world's food security. To improve global food security, we need to provide breeders with tools to develop crop cultivars that are more resilient to extreme weather conditions and provide growers with tools to more effectively manage biotic and abiotic stresses in their crops. Plant phenotyping, the measurement of a plant's structural and functional characteristics, has the potential to inform, improve and accelerate both breeders' selections and growers' management decisions. To improve the speed, reliability and scale of plant phenotyping procedures, many researchers have adopted deep learning methods to estimate phenotypic information from images of plants and crops. Despite the successful results of these image-based phenotyping studies, the representations learned by deep learning models remain difficult to interpret, understand, and explain. For this reason, deep learning models are still considered to be black boxes. Explainable AI (XAI) is a promising approach for opening the deep learning model's black box and providing plant scientists with image-based phenotypic information that is interpretable and trustworthy. Although various fields of study have adopted XAI to advance their understanding of deep learning models, it has yet to be well-studied in the context of plant phenotyping research. In this review article, we reviewed existing XAI studies in plant shoot phenotyping, as well as related domains, to help plant researchers understand the benefits of XAI and make it easier for them to integrate XAI into their future studies. An elucidation of the representations within a deep learning model can help researchers explain the model's decisions, relate the features detected by the model to the underlying plant physiology, and enhance the trustworthiness of image-based phenotypic information used in food production systems.
Why it matches plant phenotyping methods植物フェノタイピングにおける画像ベース深層学習と説明可能AIを中心に扱うレビューであり、方法論レビューとして適格。
abstractIn this review article, we reviewed existing XAI studies in plant shoot phenotyping, as well as related domains
WheatField / plotWhole plant / canopy / plot / fieldGrowth / development / phenologyStress response / toleranceWater status / transpiration
While crop breeding represents a key factor in terms of effectiveness and affordability in the adaptation of agriculture to stress conditions, phenotyping is perceived as a major bottleneck to achieving genetic advance. Crops in the field experience the simultaneous occurrence of multiple stresses, which vary depending on the location, year and management conditions. Even under the so-called “optimal agronomic conditions”, crops under field conditions may experience some degree of stress. The review addresses the methodology of field phenotyping in environments with multiple stresses where genotype by environment (and even by management) interactions are common, the ideotypes that may work, and the phenotypic traits that characterise such ideotypes that are the most useful for identifying better adapted genotypes. Mediterranean wheat is taken as a case study. Integrative phenotypic traits have intrinsic value in terms of information about crop adaptability to growing conditions in a wide sense, thus inherently accounting for the occurrence of “hidden” stresses. Indeed, this has implications when considering genotype by environment interactions. Thus, such integrative traits, when evaluated under real (i.e. field) conditions, account for the crop’s performance under scenarios where the interaction between environmental growing conditions is the norm. Three categories of traits may comprise the ideotypic characteristics when phenotyping wheat for Mediterranean environments: phenology, water status and plant growth. While these characteristics are not fully independent of each other, they should represent the crop’s performance reasonably well over a wide range of Mediterranean scenarios. It is in such a context that this review examines the case of wheat and other small grain cereals growing under Mediterranean conditions and illustrates how a few phenotyping traits, related to crop growth, water status and phenology, may define ideotypes well adapted to a wide range of environmental conditions, where non-crossover interactions exist. This is despite the fact that across such a range of environmental conditions, multistressors are present and are variable in nature, intensity and timing. Thus, for a wide range of Mediterranean conditions, the genotypes chosen correspond to ideotypes that exhibit more effective use of water and stronger growth. It is not only the characteristics of the ideotypes, but also the appropriate phenotypic traits characterising these ideotypes that are integrative in nature, meaning that they inform about crop performance over time (e.g. stable carbon isotope composition) and/or at the highest organisational level (e.g. canopy assessed via remote sensing). At the functional level, these traits guide improvements in the capture of resources such as water or radiation, rather than how efficiently these resources are being used.
Why it matches plant phenotyping methods作物の多重ストレス環境におけるフィールドフェノタイピング手法と、表現型形質の評価方法を主題とするレビューであり、方法論が中心です。
abstractThe review addresses the methodology of field phenotyping in environments with multiple stresses where genotype by environment (and even by management) interactions are common
The automatic detection, visualization, and classification of plant diseases through image datasets are key challenges for precision and smart farming. The technological solutions proposed so far highlight the supremacy of the Internet of Things in data collection, storage, and communication, and deep learning models in automatic feature extraction and feature selection. Therefore, the integration of these technologies is emerging as a key tool for the monitoring, data capturing, prediction, detection, visualization, and classification of plant diseases from crop images. This manuscript presents a rigorous review of the Internet of Things and deep learning models employed for plant disease monitoring and classification. The review encompasses the unique strengths and limitations of different architectures. It highlights the research gaps identified from the related works proposed in the literature. It also presents a comparison of the performance of different deep learning models on publicly available datasets. The comparison gives insights into the selection of the optimum deep learning models according to the size of the dataset, expected response time, and resources available for computation and storage. This review is important in terms of developing optimized and hybrid models for plant disease classification.
Why it matches plant phenotyping methods植物病害を画像から検出・分類する深層学習およびIoT手法を体系的にレビューし、モデル性能比較も行っているため、植物フェノタイピング手法レビューとして中心的です。
abstractThis manuscript presents a rigorous review of the Internet of Things and deep learning models employed for plant disease monitoring and classification.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
High-throughput phenotyping is now central to the progress of plant sciences, accelerated breeding, and precision farming. The power of phenotyping comes from the automated, rapid, non-invasive collection of large datasets describing plant objects. In this context, the goal of extracting relevant information from different kinds of images is of paramount importance. We review both the spectral and machine learning-based approaches to imaging of plants for the purpose of their phenotyping. The advantages and drawbacks of both approaches will be discussed with a focus on the monitoring of plants. We argue that an emerging approach combining the strengths of the spectral and the machine learning-based approaches will remain a promising direction in plant phenotyping in the nearest future.
Why it matches plant phenotyping methods植物フェノタイピングのためのスペクトル画像解析と機械学習手法を中心にレビューしており、方法論的レビューに該当する。
abstractWe review both the spectral and machine learning-based approaches to imaging of plants for the purpose of their phenotyping.
A long-term goal of breeders and researchers is to develop crop varieties that can resist environmental stressors and produce high yields. However, prioritising yield often compromises improvement of other key traits, including grain quality, which is tedious and time-consuming to measure because of the frequent involvement of destructive phenotyping methods. Recently, non-destructive methods such as hyperspectral imaging (HSI) have gained attention in the food industry for studying wheat grain quality. HSI can quantify variations in individual grains, helping to differentiate high-quality grains from those of low quality. In this review, we discuss the reduction of wheat genetic diversity underlying grain quality traits due to modern breeding, key traits for grain quality, traditional methods for studying grain quality and the application of HSI to study grain quality traits in wheat and its scope in breeding. Our critical review of literature on wheat domestication, grain quality traits and innovative technology introduces approaches that could help improve grain quality in wheat.
Why it matches plant phenotyping methods小麦粒の品質形質を非破壊的に測定するハイパースペクトル画像法を中心に扱うレビューであり、植物フェノタイピング手法のレビューとして適格。
abstractIn this review, we discuss the reduction of wheat genetic diversity underlying grain quality traits due to modern breeding, key traits for grain quality, traditional methods for studying grain quality and the application of HSI to study grain quality traits in wheat and its scope in breeding.
Remote sensing technology is vital for precision agriculture, aiding in early issue detection, resource management, and environmentally friendly practices. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Given the growing body of scholarly research centered on UAV-based disease detection, a comprehensive review and analysis of current studies becomes imperative to provide a panoramic view of evolving methodologies in plant disease monitoring and to strategically evaluate the potential and limitations of such strategies. This study undertakes a systematic quantitative literature review to summarize existing literature and discern current research trends in UAV-based applications for plant disease detection and monitoring. Results reveal a global disparity in research on the topic, with Asian countries being the top contributing countries (43 out of 103 papers). World regions such as Oceania and Africa exhibit comparatively lesser representation. To date, research has largely focused on diseases affecting wheat, sugar beet, potato, maize, and grapevine. Multispectral, reg-green-blue, and hyperspectral sensors were most often used to detect and identify disease symptoms, with current trends pointing to approaches integrating multiple sensors and the use of machine learning and deep learning techniques. Future research should prioritize (i) development of cost-effective and user-friendly UAVs, (ii) integration with emerging agricultural technologies, (iii) improved data acquisition and processing efficiency (iv) diverse testing scenarios, and (v) ethical considerations through proper regulations.
Why it matches plant phenotyping methodsUAVによる植物病害症状の検出・モニタリング手法を体系的にレビューしており、植物状態の観測・推定方法が中心である。
titleA Review on UAV-Based Applications for Plant Disease Detection and Monitoring
Background Abiotic stressors impair crop yields and growth potential. Despite recent developments, no comprehensive literature review on crop abiotic stress assessment employing deep learning exists. Unlike conventional approaches, deep learning-based computer vision techniques can be employed in farming to offer a non-evasive and practical alternative. Methods We conducted a systematic review using the revised Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to assemble the articles on the specified topic. We confined our scope to deep learning-related journal articles that focused on classifying crop abiotic stresses. To understand the current state, we evaluated articles published in the preceding ten years, beginning in 2012 and ending on December 18, 2022. Results After the screening, risk of bias, and certainty assessment using the PRISMA checklist, our systematic search yielded 14 publications. We presented the selected papers through in-depth discussion and analysis, highlighting current trends. Conclusion Even though research on the domain is scarce, we encountered 11 abiotic stressors across 7 crops. Pre-trained networks dominate the field, yet many architectures remain unexplored. We found several research gaps that future efforts may fill.
Why it matches plant phenotyping methods作物の非生物的ストレスをコンピュータビジョンで分類・評価する研究を体系的にレビューしており、植物状態の画像ベース推定が中心です。
titleApplication of computer vision in assessing crop abiotic stress: A systematic review
Accelerating crop improvement with enhanced adaptability to changing climatic conditions and meeting the ever-increasing global food demand requires urgent action. To achieve this, we must employ advanced molecular breeding techniques, such as marker-assisted selection, marker-assisted backcrossing, genomic selection, genome editing, and targeted mutation. However, these approaches demand the screening of large populations to identify potential genes and genotypes. Unfortunately, a significant bottleneck lies in the absence of high-throughput plant phenotyping methods that can rapidly and cost-effectively facilitate data-driven genotype selection in plant breeding. Traditional phenotyping methods, reliant on trained experts, are slow, expensive, labour-intensive, subjective, and often require destructive sampling. Proximal remote sensing technologies, including RGB imaging, thermal imaging, hyperspectral imaging, multispectral imaging, and fluorescence imaging, offer a non-destructive and rapid collection of detailed phenotypic data, providing valuable insights into various plant traits at different growth stages. High-throughput phenotyping platforms, such as Conveyor-Type Indoor, Benchtop-Type Indoor, Unmanned Aerial Platform (UAP), and Manned Aerial Platform (MAP), utilize a combination of the aforementioned remote sensing technologies. This review article aims to explore the integration of proximal remote sensing and molecular breeding approaches, showcasing how this synergistic approach can expedite crop improvement efforts. By emphasizing the benefits, challenges, and future prospects of this integrative approach, we hope to pave the way for sustainable and productive agriculture, ensuring food security in the face of changing environmental conditions.
Why it matches plant phenotyping methods植物フェノタイピング手法（近接リモートセンシングとハイスループット・プラットフォーム）の統合を中心に扱うレビューであり、対象範囲に明確に合致する。
abstractUnfortunately, a significant bottleneck lies in the absence of high-throughput plant phenotyping methods that can rapidly and cost-effectively facilitate data-driven genotype selection in plant breeding.
Most agricultural land, as a result of climate change, experiences severe stress that significantly reduces agricultural yields. Crop sensing by imaging techniques allows early-stage detection of biotic or abiotic stress to avoid damage and significant yield losses. Among the top certified imaging techniques for plant stress detection is chlorophyll a fluorescence imaging, which can evaluate spatiotemporal leaf changes, permitting the pre-symptomatic monitoring of plant physiological status long before any visible symptoms develop, allowing for high-throughput assessment. Here, we review different examples of how chlorophyll a fluorescence imaging analysis can be used to evaluate biotic and abiotic stress. Chlorophyll a is able to detect biotic stress as early as 15 min after Spodoptera exigua feeding, or 30 min after Botrytis cinerea application on tomato plants, or on the onset of water-deficit stress, and thus has potential for early stress detection. Chlorophyll fluorescence (ChlF) analysis is a rapid, non-invasive, easy to perform, low-cost, and highly sensitive method that can estimate photosynthetic performance and detect the influence of diverse stresses on plants. In terms of ChlF parameters, the fraction of open photosystem II (PSII) reaction centers (q p ) can be used for early stress detection, since it has been found in many recent studies to be the most accurate and appropriate indicator for ChlF-based screening of the impact of environmental stress on plants.
Why it matches plant phenotyping methods植物ストレス状態をクロロフィル蛍光画像で早期検出・評価する手法を中心に扱うレビューであり、植物フェノタイピング手法レビューに該当する。
abstractHere, we review different examples of how chlorophyll a fluorescence imaging analysis can be used to evaluate biotic and abiotic stress.
Crop stress poses a huge challenge to food security, necessitating innovative approaches for early detection, monitoring, and management of stress. In recent years, the integration of spectroscopy, imaging, and machine learning techniques has emerged as a promising avenue for detecting various types of crop stress. This editorial work introduces recent publications within the field included in the research topic "Spectroscopy, Imaging, and Machine Learning for Crop Stress." By exploring these cutting-edge research findings, we can gain valuable insights into the application of these technologies to enhance agricultural resilience and productivity. The combination of spectroscopy, imaging, and machine learning has a high potential for improving crop stress analysis and management. By utilizing these technologies, we can enhance our understanding of crop stress dynamics, develop precise and targeted stress detection methods, and improve decision-making processes for farmers.Ongoing research, technological advancements, and collaborative efforts are necessary to unlock the full potential of spectroscopy, imaging, and machine learning in mitigating crop stress and ensuring global food security.
Why it matches plant phenotyping methods作物ストレスの検出・モニタリングに用いる分光法、イメージング、機械学習を扱う研究テーマのエディトリアルであり、植物ストレス状態の表現型取得・解析手法を中心に概説している。
abstractThe combination of spectroscopy, imaging, and machine learning has a high potential for improving crop stress analysis and management.
One of the main factors affecting crop output reduction globally is plant disease and crop losses must be avoided through early detection of these diseases.Automating the identification of plant diseases has been demonstrated to be highly promising when using image processing and machine learning approaches.In the present work, fusion techniques were utilised to combine data from several sources to increase the reliability and accuracy of identifying plant leaf diseases.Fusion approaches combine information from several sources, such as various pictures or feature kinds, to produce a more complete view of the plant leaf and its illness.Constructing a trustworthy and accurate system that can automatically recognise the symptoms of diseases in tomato leaves utilising several sources of data including pictures, spectral reflectance, and environmental parameters is the major objective of tomato leaf disease detection using machine learning.This approach can assist farmers and agricultural professionals in identifying the disease early, stopping it from spreading and acting quickly to reduce crop losses.
Why it matches plant phenotyping methods植物葉の病徴を画像処理・機械学習で識別する手法のレビューであり、植物病害状態の画像ベース表現型計測が中心です。
titleA Review of Image Processing and Machine Learning for Plant Leaf Disease Identification
The amount of images that can be extracted from crops such as soybean, corn, sorghum, etc., has increased exponentially due to the proliferation of remote sensing technologies such as Unmanned Aerial Vehicles (UAV). When processed and analyzed, images can provide valuable information and knowledge about High Throughput Phenotyping (HTP). Advances in HTP technology are essential to ensure that crop genetic improvement meets future global demands for food and fuel. In addition to UAVs, Digital Image Processing (DIP) and Machine Learning (ML) methods have shown to be promising tools in HTP to minimize the time and cost of analyzing entire crops. However, the performance and quality of the results obtained in HTP depend on the techniques used throughout the process. With this limitation in mind, the objective of this article is to present a Systematic Literature Review (SLR) on image capture techniques, DIP and ML applied to HTP. This review focuses on four sources of scientific searches, which initially returned 161 articles to be analyzed, of which 46 were excluded due to the Exclusion Criteria (EC), and 43 were duplicates, leaving only 72 for full reading. Of the 72 articles read, 27 were excluded due to the Exclusion and Quality Criteria (QC). Finally, 45 studies remained, resulting in a useful base on the cameras/sensors used in capturing the images, the most analyzed agronomic traits in the crops, in addition to a survey on the main DIP, ML techniques used in HTP.
Why it matches plant phenotyping methods植物のハイスループット表現型解析における画像取得、画像処理、機械学習手法を体系的にレビューしており、方法論が中心である。
abstractFinally, 45 studies remained, resulting in a useful base on the cameras/sensors used in capturing the images, the most analyzed agronomic traits in the crops, in addition to a survey on the main DIP, ML techniques used in HTP.
The most important cereal crop in the world is rice (Oryza sativa). Over half of the world's population uses it as a staple food and energy source. Abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, and viruses, among others, impact the yield production and quality of rice grain. Farmers spend a lot of time and money managing diseases, and they do so using a bankrupt "eye" method that leads to unsanitary farming practices. The development of agricultural technology is greatly conducive to the automatic detection of pathogenic organisms in the leaves of rice plants. Several deep learning algorithms are discussed, and processors for computer vision problems such as image classification, object segmentation, and image analysis are discussed. The paper showed many methods for detecting, characterizing, estimating, and using diseases in a range of crops. The methods of increasing the number of images in the data set were shown. Two methods were presented, the first is traditional reinforcement methods, and the second is generative adversarial networks. And many of the advantages have been demonstrated in the research paper for the work that has been done in the field of deep learning.
Why it matches plant phenotyping methodsイネ葉の病害を画像分類・セグメンテーション等で検出・評価する手法をレビューしており、植物病態の画像ベース表現型解析が中心である。
titleREVIEW ON DETECTION OF RICE PLANT LEAVES DISEASES USING DATA AUGMENTATION AND TRANSFER LEARNING TECHNIQUES
Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated.
Why it matches plant phenotyping methods植物葉の画像から病害の発生・重症度を推定する画像処理・機械学習手法を中心に扱うレビューであり、植物表現型計測手法のレビューに該当する。
titleTechniques of deep learning and image processing in plant leaf disease detection: a review
A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.
Why it matches plant phenotyping methods植物病害の画像ベース検出・分類手法を体系的にレビューしており、植物の病徴・状態を推定するフェノタイピング手法が中心です。
abstractThe present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant
The globe and more particularly the economically developed regions of the world are currently in the era of the Fourth Industrial Revolution (4IR). Conversely, the economically developing regions in the world (and more particularly the African continent) have not yet even fully passed through the Third Industrial Revolution (3IR) wave, and Africa’s economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth in the global human population, which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet the steep food demand and stabilize the economies of the African continent and countries such as India that are dependent on the agricultural sector to some extent. Technological advances in precision agriculture are already improving farm yields, although several opportunities for further improvement still exist. This study evaluated plant disease detection models (in particular, those over the past two decades) while aiming to gauge the status of the research in this area and identify the opportunities for further research. This study realized that little literature has discussed the real-time monitoring of the onset signs of diseases before they spread throughout the whole plant. There was also substantially less focus on real-time mitigation measures such as actuation operations, spraying pesticides, spraying fertilizers, etc., once a disease was identified. Very little research has focused on the combination of monitoring and phenotyping functions into one model capable of multiple tasks. Hence, this study highlighted a few opportunities for further focus.
Why it matches plant phenotyping methods植物病害検出モデルをレビューし、植物の症状・病害状態を観測する検出手法を扱う方法論レビューであるため、病害フェノタイピング手法の方法レビューとして中心的です。
abstractThis study evaluated plant disease detection models (in particular, those over the past two decades) while aiming to gauge the status of the research in this area and identify the opportunities for further research.
Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers and stakeholders for estimating traits such as crop yield and diseases. Early detection of crop disease is essential to prevent possible losses on crop yield and ultimately increasing the benefits. However, accurate estimation of crop disease requires modern data analysis techniques such as machine learning and deep learning. This work aims to review the actual progress in crop disease detection, with an emphasis on machine learning and deep learning techniques using UAV-based remote sensing. First, we present the importance of different sensors and image-processing techniques for improving crop disease estimation with UAV imagery. Second, we propose a taxonomy to accumulate and categorize the existing works on crop disease detection with UAV imagery. Third, we analyze and summarize the performance of various machine learning and deep learning methods for crop disease detection. Finally, we underscore the challenges, opportunities and research directions of UAV-based remote sensing for crop disease detection.
Why it matches plant phenotyping methodsUAV画像と機械学習・深層学習による作物病害（植物状態）の検出手法を中心に整理・評価するレビューであり、植物フェノタイピング手法レビューに該当する。
abstractThis work aims to review the actual progress in crop disease detection, with an emphasis on machine learning and deep learning techniques using UAV-based remote sensing.
Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping.
Why it matches plant phenotyping methods植物ストレス表現型計測の多次元データ取得技術と解析パイプラインを体系的に扱う方法論レビューであり、フェノタイピング手法が中心です。
abstractIn this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines
Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense . An infected tree shows few symptoms during early stage of infection, while potentially suffers an 80% lifetime yield loss and the tree may be dead within 2 years. Early detection of basal stem rot is crucial since disease control efforts can be done. Laboratory BSR detection methods are effective, but the methods have accuracy, biosafety, and cost concerns. This review article consists of scientific articles related to the oil palm tree disease, basal stem rot, Ganoderma Boninense , remote sensors and deep learning that are listed in the Web of Science since year 2012. About 110 scientific articles were found that is related to the index terms mentioned and 60 research articles were found to be related to the objective of this research thus included in this review article. From the review, it was found that the potential use of deep learning methods were rarely explored. Some research showed unsatisfactory results due to limitations on dataset. However, based on studies related to other plant diseases, deep learning in combination with data augmentation techniques showed great potentials, showing remarkable detection accuracy. Therefore, the feasibility of analyzing oil palm remote sensor data using deep learning models together with data augmentation techniques should be studied. On a commercial scale, deep learning used together with remote sensors and unmanned aerial vehicle technologies showed great potential in the detection of basal stem rot disease.
Why it matches plant phenotyping methods油ヤシの病害状態をリモートセンシングと深層学習で検出する手法群を対象としたレビューであり、植物病害フェノタイピング手法のレビューが中心である。
titleClassification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods.
A country's economy depends heavily on agriculture, and there are many varieties of crops that can be cultivated by farmers. The issue or problems arise when the farmers are unaware of a plant disease that is affecting their crops at the appropriate moment, thus harvests become afflicted. Most of the time farmers are not aware about the disease and its type when it is discovered.Given these challenges, research in the field of automatic leaf disease detection in agriculture is of significant importance, since it could offer benefits in the detection of bigger fields of crops and help to detect diseases since they appear on plants leaf. The examination to various pattern present above the plant leaves is necessary for study of plant disease.
Why it matches plant phenotyping methods植物葉の画像解析による病害検出手法を包括的に扱うレビューであり、罹病状態の画像ベース表現型計測が中心です。
titleA Comprehensive Survey on Plant Leaf Disease Detection Using Image Analytics
However, the improvement of the genetic structure of plants increased the complex trait like yield [6].To maximize agricultural productivity, crop management practices must
Why it matches plant phenotyping methods野菜作物のハイスループット・フェノタイピングを主題とするレビューであり、フェノタイピング手法の方法論的整理が中心と判断できる。
titleA Review on High Throughput Phenotyping for Vegetable Crops
The field of computer science known as machine learning is used to create algorithms that have the ability to self-learn or learn on their own. This is how the phrase "Machine Learning" came to be. Artificial intelligence in-cludes a subfield called machine learning. These days, machine learning and deep learning techniques are frequently used to classify and recognize leaf diseases. Recognizing leaf disease at an early stage is crucial in agricultural fields for all crops. Accurate disease detection at an early stage helps farmers boost production and their economy. The suggested study is a survey of more than 40 research papers that classify and identify plant leaf diseases using various machine learning and deep learning algorithms. It also discuss-es machine learning, its application to agriculture, as well as its benefits and drawbacks. Develop an automatic disease detection system for leaf disease classification and detection using web-based or mobile-based applications for future work. Using this survey to build a more accurate model for leaf disease classification and detection using machine learning with a wide range of datasets. This will be very beneficial for farmers to boost productivity and build their economies.
Why it matches plant phenotyping methods植物葉の病害を画像から分類・識別する機械学習手法のサーベイであり、植物の病害状態を推定するフェノタイピング手法のレビューが中心です。
abstractThe suggested study is a survey of more than 40 research papers that classify and identify plant leaf diseases using various machine learning and deep learning algorithms.
Plant phenotyping studies the complex characteristics of plants, with the aim of evaluating and assessing their condition and finding better exemplars. Recently, a new branch emerged in the phenotyping field, namely, high-throughput phenotyping (HTP). Specifically, HTP exploits modern data sampling techniques to gather a high amount of data that can be used to improve the effectiveness of phenotyping. Hence, HTP combines the knowledge derived from the phenotyping domain with computer science, engineering, and data analysis techniques. In this scenario, machine learning (ML) and deep learning (DL) algorithms have been successfully integrated with noninvasive imaging techniques, playing a key role in automation, standardization, and quantitative data analysis. This study aims to systematically review two main areas of interest for HTP: hardware and software. For each of these areas, two influential factors were identified: for hardware, platforms and sensing equipment were analyzed; for software, the focus was on algorithms and new trends. The study was conducted following the PRISMA protocol, which allowed the refinement of the research on a wide selection of papers by extracting a meaningful dataset of 32 articles of interest. The analysis highlighted the diffusion of ground platforms, which were used in about 47% of reviewed methods, and RGB sensors, mainly due to their competitive costs, high compatibility, and versatility. Furthermore, DL-based algorithms accounted for the larger share (about 69%) of reviewed approaches, mainly due to their effectiveness and the focus posed by the scientific community over the last few years. Future research will focus on improving DL models to better handle hardware-generated data. The final aim is to create integrated, user-friendly, and scalable tools that can be directly deployed and used on the field to improve the overall crop yield.
Why it matches plant phenotyping methods植物フェノタイピングのハードウェアおよびソフトウェア要因を体系的にレビューしており、方法論レビューが中心です。
abstractThis study aims to systematically review two main areas of interest for HTP: hardware and software.
Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.
Why it matches plant phenotyping methods植物病害を画像と機械学習・深層学習で検出する手法を中心にレビューしており、感染植物の状態・病害を観察から推定するフェノタイピング手法レビューに該当する。
titleAn advanced deep learning models-based plant disease detection: A review of recent research
LiDAR / point cloudYield / biomass estimationBiomass / plant weightGrowth / development / phenologyLeaf traits
Light detection and ranging (LiDAR) sensors have accrued an ever-increasing presence in the agricultural sector due to their non-destructive mode of capturing data. LiDAR sensors emit pulsed light waves that return to the sensor upon bouncing off surrounding objects. The distances that the pulses travel are calculated by measuring the time for all pulses to return to the source. There are many reported applications of the data obtained from LiDAR in agricultural sectors. LiDAR sensors are widely used to measure agricultural landscaping and topography and the structural characteristics of trees such as leaf area index and canopy volume; they are also used for crop biomass estimation, phenotype characterisation, crop growth, etc. A LiDAR-based system and LiDAR data can also be used to measure spray drift and detect soil properties. It has also been proposed in the literature that crop damage detection and yield prediction can also be obtained with LiDAR data. This review focuses on different LiDAR-based system applications and data obtained from LiDAR in agricultural sectors. Comparisons of aspects of LiDAR data in different agricultural applications are also provided. Furthermore, future research directions based on this emerging technology are also presented in this review.
Why it matches plant phenotyping methodsLiDARによる作物・樹木の構造特性、バイオマス、表現型、成長などの取得方法を農業応用として比較・総説しており、植物フェノタイピング手法のレビューが中心である。
abstractThis review focuses on different LiDAR-based system applications and data obtained from LiDAR in agricultural sectors.
Globally, more than 19,000 fungi are reported to infect agricultural crops with diseases. As the supplier of human energy, crops are seen as being significant. Plant diseases can harm leaves at any point during planting and harvest, greatly reducing crop productivity and the general market’s financial worth. Consequently, the early diagnosis of leaf disease is crucial in farmlands. Agriculture profitability is a key factor in economic growth. This is among the causes why plant disease identification is crucial in the farming sector, as the presence of illness in plants is extremely common. If necessary precautions aren’t followed in these regions, plants suffer major consequences, which impact the grade, volume, or production of the corresponding products. For example, the United States has pine trees that are susceptible to a dangerous illness called small-leaf disease and the backbone of the Indian economy is crop plants. It is advantageous to diagnose plant diseases (Black Spot, other leaf spots, powdery mildew, downy mildew, blight, and canker) using an automated method since it lessens the amount of manpower required to maintain megafarms of crops and does so at an incredibly preliminary phase(when they appear on plant leaves). The computerized identification and classification of plant leaf diseases using an imagery segmented system is presented in this work. It also includes an overview of various disease categorization methods that can be applied to the identification of plant leaf diseases. In order to detect disorders in diverse plant leaves, this study provides a review of diverse plant diseases and several classifying algorithms in deep machine learning.
Why it matches plant phenotyping methods植物葉の画像から病害状態を検出・分類する手法群を中心に扱うレビューであり、植物フェノタイピング手法レビューに該当する。
abstractThe computerized identification and classification of plant leaf diseases using an imagery segmented system is presented in this work.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Field / plotRGB / grayscaleMultispectral / hyperspectralThermalStress response / tolerance
Salinity is a key factor limiting agricultural production worldwide. Recent advances in field phenotyping have enabled the recording of the environmental history and dynamic response of plants by considering both genotype × environment (G×E) interactions and envirotyping. However, only a few studies have focused on plant salt tolerance phenotyping. Therefore, we analyzed the potential opportunities and major challenges in improving plant salt tolerance using advanced field phenotyping technologies. RGB imaging and spectral and thermal sensors are the most useful and important sensing techniques for assessing key morphological and physiological traits of plant salt tolerance. However, field phenotyping faces challenges owing to its practical applications and high costs, limiting its use in early generation breeding and in developing countries.
Why it matches plant phenotyping methods植物の塩耐性フェノタイピングにおけるフィールドフェノタイピング技術、RGB・スペクトル・熱センサーの活用機会と課題を中心に論じるレビューであり、方法論レビューに該当する。
titleOpportunity and challenges of phenotyping plant salt tolerance.
Plant phenotyping involves the measurement of observable traits of plants. Plant traits like leaf area, leaf count, leaf surface temperature, chlorophyll content, plant growth rate, emergence time of leaves, and reproductive organs depend on the interaction of its genotype with the environment. Plant phenotyping serves to analyze biotic and abiotic stresses on plants, select crop varieties resilient to the surrounding environment, and improve crop yield. Recent advancements in imaging technologies help expedite the growth of automatic, non-invasive, and efficient plant phenotyping systems. These plant phenotyping systems involve using different imaging techniques like visible imaging, hyperspectral imaging, chlorophyll fluorescence imaging (CFIM), thermal imaging to record, monitor, and analyze plant phenotypes using images. In the last few years, researchers have been working on developing image processing, computer vision, machine learning, and deep learning approaches for the accurate and precise analysis of plant images. Therefore, this paper reviews and presents insights about the research reported through patents in the area of automatic plant phenotyping. This review report uses patent databases like Espacenet, Lens, and Google Patents to search, review and analyze patent documents. The paper presents a patentometric analysis of all 67 patent documents available till date focusing on automatic image-based plant phenotyping. The review provides a summary and analysis of outstanding patents in terms of qualitative and quantitative patent indices. This article provides a comprehensive global patent study to aid researchers and scientists develop more efficient plant phenotyping algorithms, devices, and systems.
Why it matches plant phenotyping methods自動画像ベース植物フェノタイピングに関する特許を体系的に分析するレビューであり、フェノタイピング手法・装置・アルゴリズムが中心です。
abstractTherefore, this paper reviews and presents insights about the research reported through patents in the area of automatic plant phenotyping.
This review paper comprehensively explores the potential of high throughput phenotyping (HTP) in assessing and combating salinity stress in cotton production.Salinity stress presents a significant challenge for global cotton crop, impacting the plant's physiological and molecular functions.HTP emerges as a potentially transformative tool for understanding and addressing this issue, employing advanced imaging techniques, spectral reflectance, and molecular technologies to rapidly and accurately measure plant traits.While HTP holds considerable promise, its implementation is not without challenges.The review dissects these barriers, ranging from technical hurdles associated with data acquisition, storage, and analysis to economic considerations involving equipment and maintenance costs.Notably, advancements in machine learning and artificial intelligence (AI) offer promising solutions to many of these challenges, with proven applications in image analysis, trait prediction and data integration.The review showcases the successful application of HTP across various global case studies, demonstrating its potential to revolutionize plant research and breeding.Further, the integration of AI and machine learning is poised to significantly enhance the capabilities of HTP, ushering in a new era of data-driven, efficient plant phenotyping.The paper concludes with a set of research and policy recommendations to optimize the use of HTP for salinity stress in cotton.These include promoting the integration of genomics and phenomics, improving image analysis algorithms, developing predictive models, and standardizing phenotypic data.At the policy level, the authors call for investments in HTP infrastructure, increased collaboration and data sharing, capacity building, and the incorporation of HTP into breeding programs.This review illustrates the immense potential of HTP to revolutionize our approach to salinity stress in cotton, ultimately contributing to more sustainable and resilient cotton production.
Why it matches plant phenotyping methods植物の塩ストレス形質を対象とするハイスループットフェノタイピングの方法、課題、応用、AI解析を中心に扱うレビューであり、方法論的役割が明確。
abstractThis review paper comprehensively explores the potential of high throughput phenotyping (HTP) in assessing and combating salinity stress in cotton production.
The global population is rapidly increasing and is expected to exceed 9 billion by 2050, resulting in significant challenges for agriculture due to factors such as industrialization, reduced farmland, and biotic and abiotic stresses. To address these challenges and ensure future sustainability, the agriculture system needs to become more productive, efficient, and resilient. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools to transform the agricultural sector. Agricultural productivity is greatly influenced by biotic and abiotic stresses, and developing climate-smart crops through conventional breeding techniques is time-consuming and challenging. Plant phenotyping, which involves measuring specific plant features related to function, is crucial in breeding for target traits. However, traditional phenotyping methods are laborious, error-prone, and less accurate, particularly under stress conditions. To overcome these limitations, researchers have focused on developing high-throughput phenotyping technologies. State-of-the-art imaging techniques, such as light detection and ranging (LIDAR), remote sensing, and RGB imaging, combined with autonomous carriers like unmanned aerial vehicles (UAVs) and ground robots, enable real-time and high-throughput phenotyping of morphological, physiological, and stress-related traits. ML tools can compartmentalize big data, identify related traits, classify them, quantify their expression, and predict their function within the plant system. AI and ML offer multidisciplinary approaches for analyzing big data accumulated over time, leading to the discovery of patterns and systematic data of interest, such as stress phenotypes. Using these technologies, researchers worldwide can expedite agricultural research and develop climate-smart crops. The future of AI and ML in agriculture is promising, as they can lead to new scientific discoveries and help overcome the challenges of limited resources in food production.
Why it matches plant phenotyping methods植物ストレス表現型解析におけるAI・機械学習、高スループット画像・センシング技術を中心に扱う方法論レビューであり、植物形質の取得・解析手法が主題である。
abstractPlant phenotyping, which involves measuring specific plant features related to function, is crucial in breeding for target traits.
The occurrence of disease in plants might affect the crop production at a large scale, resulting into decline of the economic growth rate of the country. The disease in plants can be detected and treated at an early stage. Machine learning (ML), deep learning (DL), and computer vision-based techniques could play a pivotal role in detecting and classifying the diseases at an early stage. These approaches have even surpassed the human performance, as well as image processing based traditional approaches in the analysis and classification of plant diseases. Over the years, numerous authors have applied various image processing ML and DL techniques for the diagnosis of different ailments in plants that gives great hope to the farmers and landlords to cure the disease at an early stage. In this study, the authors addressed and evaluated the various currently existing state of art methods and techniques based on machine and deep learning. Besides, the authors have also focused on various limitations and challenges of these approaches that can explore greater possibly of these methods about their usability for disease detection in plants.
Why it matches plant phenotyping methods植物病害を画像処理・機械学習で検出・分類する手法を体系的に評価したレビューであり、植物の病徴・病害状態を対象とするフェノタイピング手法レビューとして中心的です。
titleA Systematic Review on the Detection and Classification of Plant Diseases Using Machine Learning
Phenomics research into plants allows for the non-invasive tracking of development, efficiency, and composition.The missing link between the phenome and the genome is connecting phenotypes to their underlying genetic causes.Plant phenotypic plasticity is affected by the complex interactions between the DNA, the environment, and the management of the plant.The relationship between plants and pathogens, as well as the susceptibility of plants to illness, can be investigated by using phonemics.New opportunities exist thanks to sensing technologies for detecting specific phenotypic reactions during plant-pathogen interaction, allowing for faster selection of genetic material resistant to specific pathogens or strains and greater insight into the physiological mechanisms linking pathogen infection and host disease symptoms.Changes in plant diseases that have not yet shown apparent symptoms can also be detected using phonemics.In plant diseases, digital imaging, chlorophyll fluorescence imaging, spectral imaging, and thermal imaging are all used.Magnetic resonance, soft x-ray imaging, ultrasound, and volatile chemical detection are briefly described as examples of less common techniques.It is challenging to generate representative and reliably labelled training data at this size due to the observation of only mixed spectra of plant and fungal components.For this purpose, clear spectra are required.Contaminants on a surface can be detected at an early stage using infrared light.The temperature sensitivity and real-time detection capabilities of thermal imaging make it useful.There have been significant advances in high-throughput, low-cost analysis of genetic data and in non invasive phenotyping.We hope that our work may hasten the introduction of automated, non-destructive methods for high-throughput phenotyping of plant-pathogen interactions.
Why it matches plant phenotyping methods植物病害に対するセンシング・画像計測・非破壊ハイスループット表現型解析を主題とする方法論的レビューであり、フェノタイピング手法が中心です。
titlePhenomics Approach for Identification and Management of Plant Disease
Agriculture is the backbone of Indian economy. Crop yield is decreased due to various disease-causing organism on plants. Plant disease identification has major impact on the agriculture production with respect to crop quality and quantity. Disease infected plants show symptoms on many parts of the plant like leaf, stem, bud, flower, fruit and root. Early identification of the disease will prevent further crop loss. In this paper, four different sections are covered. The first section focusses on different types of disease and its symptoms. Second section discuss about different traditional methods of disease identification. And the third section cover methodologies that can be used for disease identification in plants through image processing, deep learning and convolution neural network techniques. Fourth section highlights challenges and future trends in disease identification. Finally, this paper reveals that, particularly in rural areas and underdeveloped nations, relying solely on the expertise of professionals to identify and categorize diseases can be very much time-consuming and expensive.
Why it matches plant phenotyping methods植物病害の画像処理・深層学習による識別手法を扱うレビューであり、植物の病徴・病害状態を観測から推定する方法が中心。
titleContemporary Research Trends in Plant Leaf Disease Detection
Recent attempts, advances and challenges, as well as future perspectives regarding the application of proximal hyperspectral sensing (where sensors are placed within 10 m above plants, either on land-based platforms or in controlled environments) to assess plant abiotic stresses have been critically reviewed. Abiotic stresses, caused by either physical or chemical reasons such as nutrient deficiency, drought, salinity, heavy metals, herbicides, extreme temperatures, and so on, may be more damaging than biotic stresses (affected by infectious agents such as bacteria, fungi, insects, etc.) on crop yields. The proximal hyperspectral sensing provides images at a sub-millimeter spatial resolution for doing an in-depth study of plant physiology and thus offers a global view of the plant's status and allows for monitoring spatio-temporal variations from large geographical areas reliably and economically. The literature update has been based on 362 research papers in this field, published from 2010, most of which are from four years ago and, in our knowledge, it is the first paper that provides a comprehensive review of the applications of the technique for the detection of various types of abiotic stresses in plants.
Why it matches plant phenotyping methods植物の非生物ストレス評価に用いる近接ハイパースペクトルセンシングを中心に、応用・課題・展望を包括的にレビューしており、植物状態の取得手法が主題です。
abstractThe proximal hyperspectral sensing provides images at a sub-millimeter spatial resolution for doing an in-depth study of plant physiology and thus offers a global view of the plant's status
Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture. Therefore, this paper provides a comprehensive review of CNNs in computer vision for grain crop phenotyping. It is meaningful to provide a review as a roadmap for future research in such a thriving research area. The CNN models (e.g., VGG, YOLO, and Faster R-CNN) used CV tasks including image classification, object detection, semantic segmentation, and instance segmentation, and the main results of recent studies on crop phenotype detection are discussed and summarized. Additionally, the challenges and future trends of the phenotyping techniques in grain crops are presented.
Why it matches plant phenotyping methods穀類作物フェノタイピングにおけるCNN・コンピュータビジョン手法を中心に整理するレビューであり、方法論的役割が明確。
titleConvolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review
The five Nordic countries span the most northern region for field cultivation in the world. This presents challenges per se, with short growing seasons, long days, and a need for frost tolerance. Climate change has additionally increased risks for micro-droughts and water logging, as well as pathogens and pests expanding northwards. Thus, Nordic agriculture demands crops that are adapted to the specific Nordic growth conditions and future climate scenarios. A focus on crop varieties and traits important to Nordic agriculture, including the unique resource of nutritious wild crops, can meet these needs. In fact, with a future longer growing season due to climate change, the region could contribute proportionally more to global agricultural production. This also applies to other northern regions, including the Arctic. To address current growth conditions, mitigate impacts of climate change, and meet market demands, the adaptive capacity of crops that both perform well in northern latitudes and are more climate resilient has to be increased, and better crop management systems need to be built. This requires functional phenomics approaches that integrate versatile high-throughput phenotyping, physiology, and bioinformatics. This review stresses key target traits, the opportunities of latitudinal studies, and infrastructure needs for phenotyping to support Nordic agriculture.
Why it matches plant phenotyping methods作物の機能的フェノミクス、高スループット表現型解析、標的形質、フェノタイピング基盤を中心に論じるレビューであり、方法論的レビューとして適格。
abstractThis review stresses key target traits, the opportunities of latitudinal studies, and infrastructure needs for phenotyping to support Nordic agriculture.
The Pannonian Plain, as the most productive region of Southeast Europe, has a long tradition of agronomic production as well as agronomic research and plant breeding. Many research institutions from the agri-food sector of this region have a significant impact on agriculture. Their well-developed and fruitful breeding programmes resulted in productive crop varieties highly adapted to the specific regional environmental conditions. Rapid climatic changes that occurred during the last decades led to even more investigations of complex interactions between plants and their environments and the creation of climate-smart and resilient crops. Plant phenotyping is an essential part of botanical, biological, agronomic, physiological, biochemical, genetic, and other omics approaches. Phenotyping tools and applied methods differ among these disciplines, but all of them are used to evaluate and measure complex traits related to growth, yield, quality, and adaptation to different environmental stresses (biotic and abiotic). During almost a century-long period of plant breeding in the Pannonian region, plant phenotyping methods have changed, from simple measurements in the field to modern plant phenotyping and high-throughput non-invasive and digital technologies. In this review, we present a short historical background and the most recent developments in the field of plant phenotyping, as well as the results accomplished so far in Croatia, Hungary, and Serbia. Current status and perspectives for further simultaneous regional development and modernization of plant phenotyping are also discussed.
Why it matches plant phenotyping methods植物フェノタイピングの歴史、最新技術、非侵襲・デジタル手法、地域での開発状況と展望を扱うレビューであり、方法論が中心です。
abstractIn this review, we present a short historical background and the most recent developments in the field of plant phenotyping
The agriculture sector is subjected to constant challenge of yield deficit due to rising population, improper resource management and shrinking agricultural land. Advance yield estimates help in systematic planning to reduce such losses. However, prediction of accurate estimates is still an open challenge due to geographical diversity, crop diversity and crop area. Recently non-destructive approach has gained attention due to its robustness and provides easy availability of data from heterogeneous resources compared to its counterpart; destructive approach which is computational, resource intensive and hence less utilized. This paper conducts a detailed study on utilization of non-destructive approach to estimate yield taking into account, input feature, and methodology. We consider five major observations namely, data acquisition, pre-processing techniques, features, methodology, and result. Moreover, we summarize analysis of each observation, extract most prominent technique, the adopted methods, and finally recommends integration of different models that can be explored to improve accuracy.
Why it matches plant phenotyping methods作物収量という植物形質の非破壊推定について、データ取得・前処理・特徴量・手法・結果を体系的にレビューしており、フェノタイピング手法レビューが中心である。
titleReview of Crop Yield Estimation using Machine Learning and Deep Learning Techniques
Early detection and identification of plant diseases from leaf images using machine learning is an important and challenging research area in the field of agriculture. There is a need for such kinds of research studies in India because agriculture is one of the main sources of income which contributes seventeen percent of the total gross domestic product (GDP). Effective and improved crop products can increase the farmer’s profit as well as the economy of the country. In this paper, a comprehensive review of the different research works carried out in the field of plant disease detection using both state-of-art, handcrafted-features- and deep-learning-based techniques are presented. We address the challenges faced in the identification of plant diseases using handcrafted-features-based approaches. The application of deep-learning-based approaches overcomes the challenges faced in handcrafted-features-based approaches. This survey provides the research improvement in the identification of plant diseases from handcrafted-features-based to deep-learning-based models. We report that deep-learning-based approaches achieve significant accuracy rates on a particular dataset, but the performance of the model may be decreased significantly when the system is tested on field image condition or on different datasets. Among the deep learning models, deep learning with an inception layer such as GoogleNet and InceptionV3 have better ability to extract the features and produce higher performance results. We also address some of the challenges that are needed to be solved to identify the plant diseases effectively.
Why it matches plant phenotyping methods植物葉画像から病害を検出・同定する機械学習手法を体系的にレビューしており、植物の病害状態を画像から推定する方法が中心である。
titleA Survey on Different Plant Diseases Detection Using Machine Learning Techniques
Field / plotX-ray / CTRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Natural ecosystems and agricultural production have been threatened by multifaceted global environmental changes. Soil degradation, extreme drought and flooding events, shifting climatic patterns and other challenges have prompted many disciplines within plant science to pivot to find solutions. Accordingly, root research has expanded from fundamental studies on roots, as providers of physical support, water and essential nutrients uptake, towards identification of beneficial traits for stress adaptation and control of key biological soil processes. Advances in trait identification, data acquisition, management and modelling are enabling root researchers to develop predictive models to support ecosystems in these changing environments. Through technical presentations, posters, industry exhibits and a root phenotyping workshop, the international, jointly presented, completely virtual International Society of Root Research (ISRR)11/Rooting2021 meeting provided a unique platform for researchers across disciplines to share recent advances in root biology, from molecular to ecosystem-level scales, in agricultural and natural ecosystems, addressing critical questions in response to climate change and its impact on crop productivity and ecosystem services. In this report, the 2021 ISRR Ambassador cohort provides an overview of the current root research landscape and reflection on the importance of frontier research for a more sustainable future. In response to global travel restrictions imposed by the COVID-19 pandemic, the 11th Symposium of the International Society of Root Research (ISRR11, https://www.rootresearch.org/) and the 9th International Symposium on Root Development (Rooting2021) merged into a single online event co-organised by the Interdisciplinary Plant Group at the University of Missouri (Columbia, MO, USA) and the University of Nottingham (UK). Over 700 participants representing academia, government and industry from more than 53 countries (Supporting Information Fig. S1) joined the virtual event held 24–28 May 2021. The schedule ran almost uninterrupted across international time zones, featuring 74 talks (10 plenaries, 16 keynotes, and 48 invited) and c. 300 posters, spanning a broad range of disciplines. In addition, the 2021 ISRR Lifetime Achievement Award was presented to Wendy Silk, Emeritus Professor at the University of California-Davis (USA). ISRR11/Rooting2021 hosted the 3rd ISRR Ambassador Program, a unique platform for early-career root researchers. The virtual ISRR11/Rooting2021 Ambassador Program provided networking activities, experience with conference organisation, interaction with professionals in diverse career areas, and opportunities to discuss advances in the field with a broadly multidisciplinary cohort (Notes S1). Ambassador tasks at the ISRR11/Rooting 2021 meeting included session note-taking, the production of this Meeting report, and a set of recommendations for diversity and inclusion in future scientific events (Notes S2). The ISRR11/Rooting2021 meeting concluded with a root phenotyping workshop with virtual tours of major root phenotyping facilities and demonstrations of methods. Organised by Larry York (Oak Ridge National Laboratory, TN, USA) and Darren Wells (University of Nottingham, UK), in collaboration with other experts and the ISRR Ambassadors, the workshop with a Q&A format was used to discuss the latest advances in root phenotyping techniques. The potential complementarity of image analysis software tools emerged as a key topic as depicted in Fig. S2. The availability of standardised protocols for root collection and trait measurement was also highlighted by the participants of the online survey, organised by the Ambassadors in addition to the Symposium (Delory et al., 2022) and the workshop as an important issue for future research. The Root Ecology Handbook recently published in New Phytologist provides a comprehensive guide on root sampling, processing and measuring for a wide variety of traits in a standardised manner (Freschet et al., 2021). Root phenotyping for traits related to crop performance or ecosystem services has been a main focus in the field of root biology since the 1970s (Hurd, 1974). However, quantitative analysis of plant phenotypes and their linkages to plant functions remains a major bottleneck. ISRR11/Rooting2021 highlighted the current emphasis on phenotyping root traits that will provide resilience to changing environmental conditions (Fig. 1), including traits related to root–microbial interactions (Kawasaki et al., 2021). Rhizosphere processes related to root stress responses are key for sustainable food production systems, as they impact soil functioning and resource use efficiency. The impact of drought and limited nutrient supply on plants under global climate change, and the mechanisms of root response from molecular to field scales, have prompted focussed advances on well established areas in the field of root research. Therefore, the role of auxin and cytokinin in molecular crosstalk has prompted the rise of the ‘hormonics’ to explore their functions in root development under drought stress (Rodriguez-Alonso et al., 2018), and to identify signalling pathways that link nutrient availability to root developmental parameters (Shahzad & Amtmann, 2017). Hormonal signalling also underlies ‘nutritropism’, an extension of ‘chemotropism’ (Newcombe & Rhodes, 1904), which can now be explored with advanced imaging and microscopy technology (T. Fujiwara, University of Tokyo, Japan). Root-related strategies to mitigate drought stress related to root hydraulic architecture and water transport were also discussed (Maurel & Nacry, 2020). Root-system-level traits linked with water and nutrient use efficiency such as wheat root axial conductance (Hendel et al., 2021), architectural traits in rice (Ruangsiri et al., 2021) and maize (Kistler et al., 2018) have been identified with a combination of shovelomics, phenotyping, functional genomics and modelling. The long-standing challenges of grafting for the introduction of root traits related to stress tolerance have been partially overcome by recent progress on our understanding of graft compatibility and cell-to-cell adhesion (Notaguchi et al., 2020). Advancing our understanding of grafting mechanisms will certainly provide new avenues to understand the effects of specific root genotypes and/or traits on other parts of the plant body (J. Cantillo, Donald Danforth Plant Science Center, MO, USA). Current trends in root research seek to integrate stress responses inside the root system with a better understanding of these root–soil–microbe interactions. ISRR11/Rooting2021 highlighted the role of the rhizosphere microbiome in nutrient homeostasis, for example, in root diffusion (Salas-González et al., 2021), or during nitrogen acquisition (Arsova et al., 2012). Root exudates were introduced as potential targets for rhizosphere engineering to promote beneficial microbiome functionalities (Kawasaki et al., 2021) or to control harmful species. Novel studies looking into root–microbiome interactions have become possible due to precision genome editing, production of knocked-down lines and reconstruction of biosynthetic metabolic pathways (Huang et al., 2019), and advanced imaging techniques such as positron emission tomography (Schmidt et al., 2020). Recent advances in imaging techniques and image analysis (Fig. 1) can support high-throughput root phenotyping of relevant structural features within the root architecture (Fig. 2). Detailed image-based root phenotyping techniques such as X-ray computed tomography (CT) scanning can improve our interpretation of in-field studies (C. Topp, Donald Danforth Plant Science Center, MO, USA). Current advances allow high-resolution and/or high-throughput phenotyping studies, even in mature crops and under field conditions (Gore et al., 2020; Rich et al., 2020), although methodological challenges remain (Delory et al., 2022). For example, root phenotyping of rooting depth and its significance for deep water or nitrate uptake is being addressed with large-scale field experiments using minirhizotrons or soil coring on maize (A. Leakey, University of Illinois, USA), wheat (J. Christopher, University of Queensland, Australia) and potatoes (O. Popovic, Copenhagen University, Denmark). Automated, high-resolution minirhizotrons are also used for visualising the dynamics of roots and fungi interaction in experimentally warmed peatlands (C. Iversen, Oak Ridge National Laboratory, TN, USA; Defrenne et al., 2020). These imaging advances are complemented by the development of free, open source and high-performance image analysis software (Fig. S2). Pairing 3D imaging techniques (e.g. X-ray CT) with mathematical modelling is a powerful way to study plant–soil interactions on different scales, from soil pores to growing root systems (Roose et al., 2016). This hybrid approach has resulted in key milestones by allowing the elucidation of how root architecture and exudation jointly affect P mobilisation and uptake (McKay Fletcher et al., 2020), and to quantify the extent to which the dissolution of N fertiliser granules affects soil microbial activity (Ruiz et al., 2020). Imaging techniques can also be used for traits related to root–microbe interactions (Fig. 1), complementing other multidisciplinary approaches that seek to better understand the complex dialogue between roots, the associated microbiome and soil processes. Mathematical modelling complements phenotyping advances by overcoming the challenges of experimental approaches and benefits from the emerging field of functional phenomics (York, 2019). Highlights from the diversity of modelling approaches presented at ISRR/Rooting2021, in both spatial and temporal scales, include: a micro-hydrological model that describes a new symplastic water pumping mechanism (Couvreur et al., 2021); the dynamics and regulation of a fast brassinosteroid response pathway in Arabidopsis root tips (Großeholz et al., 2021); functional–structural plant (FSP) models to identify optimal root phenotypes for nutrient capture in contrasting environments (Rangarajan, 2021); and field-scale simulations of plant populations and communities (Postma et al., 2017; Schnepf et al., 2018; Faverjon et al., 2019). Future mathematical models will draw on larger, more complex, datasets incorporating novel imaging technology, high-throughput phenotyping and availability of relevant environmental data. The positive feedback cycles between these models and continued advances in phenotyping are what will surely advance the field of root science. To meet the challenges imposed by the global COVID-19 pandemic, online communication has provided new opportunities for international multidisciplinary cooperation. The ISRR11/Rooting2021 online event brought the root research community together to share knowledge on the latest developments in root and rhizosphere research, present new technological advances and identify pressing research questions that still require answers. The adoption of a holistic approach to root research, that is, one that takes into account all categories of root traits, from anatomy to root morphology, physiology and architecture, as well as interactions with the rhizosphere microbiota, was emphasised as a crucial step in facing the challenges posed by global change. We encourage root researchers to actively take advantage of the plethora of online resources currently available for plant phenotyping (https://quantitative-plant.org/), and to join the ISRR (https://www.rootresearch.org/). Collaborations to share knowledge, along with new technological advances, will help us further understand roots and rhizosphere processes. The authors thank the New Phytologist Foundation for supporting the Ambassador Program, and John Kirkegaard and Hallie Thompson for initiating the ISRR Ambassador Program in 2015. LAG acknowledges support from the Plant Genome Research Program, National Science Foundation (IOS-1444448). We thank Michelle Watt, Bob Sharp, Malcolm Bennett and the organisers of the ISRR11/Rooting2021 Symposium from the Interdisciplinary Plant Group at the University of Missouri (Columbia, USA) and the University of Nottingham (UK). In particular, the authors would like to thank Victoria Bryan as well as Jennifer Hartwick and her team for their exceptional support. We thank Larry York and Darren Wells for organising an excellent virtual root phenotyping workshop during the ISRR11/Rooting2021 conference with generous financial support from the International Plant Phenotyping Network. The ISRR Ambassadors are also very grateful to Charlie Messina, Michelle Watt, Genevieve Croft and Ronald Vargas for sharing their professional experience and for taking the time to discuss career opportunities for root scientists. The authors thank Christopher Topp, Larry York and Abraham Smith for their contributions to the preparation of the figures. Finally, thanks to all ISRR11/Rooting2021 participants for making this conference a success! See you at the next ISRR (organised in 2024 in Leipzig, Germany) and/or Rooting (organised in 2023 in Ghent, Belgium) conference. None declared. AJM, CNT, LAG and AT coordinated the ‘ISRR11 Ambassador Program’ and provided valuable feedback on the manuscript. The ISRR11 Ambassadors group (CNC, GC, KKD, BMD, AD, YD, APG, QH, P-WH, MCH-S, ML, JLPN, LM, JM-M, AER, JS, TSW, PW, XW, LX, CZ) compiled and collated minutes of the sessions throughout the meeting and wrote the initial draft and the revised versions. Ambassador JS prepared Notes S2 addressing diversity and inclusion at ISRR11/Rooting2021. Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. Fig. S1 Map depicting the distribution and number of attendees to the joined Symposium ISRR11-Rooting2021. Fig. S2 Example of root image analysis pairing RootPainter and RhizoVision explorer. Notes S1 The ISRR11 3rd Graduate Student and Postdoc Ambassador Program. Notes S2 Diversity and inclusion at ISRR11/Rooting2021. Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Why it matches plant phenotyping methods根系フェノタイピングのワークショップ、画像解析ソフトウェア、X線CTやミニリゾトロンなどの技術進展を実質的に概説しており、方法レビューとして中心的です。
abstractRecent advances in imaging techniques and image analysis (Fig. 1) can support high-throughput root phenotyping of relevant structural features within the root architecture (Fig. 2).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
Marker-assisted selection (MAS) has been widely used in the last few decades in plant breeding programs for the mapping and introgression of genes for economically important traits, which has enabled the development of a number of superior cultivars in different crops. In sugarcane, which is the most important source for sugar and bioethanol, marker development work was initiated long ago; however, marker-assisted breeding in sugarcane has been lagging, mainly due to its large complex genome, high levels of polyploidy and heterozygosity, varied number of chromosomes, and use of low/medium-density markers. Genomic selection (GS) is a proven technology in animal breeding and has recently been incorporated in plant breeding programs. GS is a potential tool for the rapid selection of superior genotypes and accelerating breeding cycle. However, its full potential could be realized by an integrated approach combining high-throughput phenotyping, genotyping, machine learning, and speed breeding with genomic selection. For better understanding of GS integration, we comprehensively discuss the concept of genetic gain through the breeder's equation, GS methodology, prediction models, current status of GS in sugarcane, challenges of prediction accuracy, challenges of GS in sugarcane, integrated GS, high-throughput phenotyping (HTP), high-throughput genotyping (HTG), machine learning, and speed breeding followed by its prospective applications in sugarcane improvement.
Why it matches plant phenotyping methodsサトウキビ育種におけるゲノム選抜と統合される高スループット表現型解析を、方法論・応用の一部として包括的に論じるレビューであり、植物フェノタイピングが明示的かつ実質的な主題です。
abstractwe comprehensively discuss the concept of genetic gain through the breeder's equation, GS methodology, prediction models, current status of GS in sugarcane, challenges of prediction accuracy, challenges of GS in sugarcane, integrated GS, high-throughput phenotyping (HTP), high-throughput genotyping (HTG), machine learning, and speed breeding followed by its prospective applications in sugarcane improvement.
Abstract In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the whole production process of crops, it is necessary to clarify not only the types of diseases, but also the severity of diseases. In recent years, the use of Deep Learning (DL) for plant disease species identification has been widely applied. In particular, the application of Convolutional Neural Networks (CNNs) for plant disease images has made breakthrough progress. However, little research has been done on disease severity assessment. This group first traces the mainstream views of existing disease research scholars and accordingly gives various scales for visual assessment of plant disease severity grading. According to the difference of network architecture, then, this study outlines the 16 researches on CNN-based plant disease severity assessment from three aspects of classical CNN framework, improved CNN architecture and CNN-based semantic segmentation network, and the advantages and disadvantages of each research method are compared and analyzed in detail. The common methods of datasets acquisition and the performance evaluation index of CNN models are examined in depth. Finally, this study discusses the major challenges in the practical application of CNN-based plant disease severity assessment methods, and provides feasible research ideas and possible solutions to address these challenges.
Why it matches plant phenotyping methods植物病害の重症度を画像から評価するCNN手法を体系的に比較・評価するレビューであり、植物状態の表現型取得法が中心である。
abstractthis study outlines the 16 researches on CNN-based plant disease severity assessment
In the past ten years, ground and aerial platforms with several sensors have been rapidly adopted for phenotyping diverse biotic, abiotic stressors and other characters during the crop plant's growth stages. An increase in yield achieved from traditional breeding programs worldwide are no longer sufficient to meet the projected demand for the three major cereals like Rice, Wheat and Maize. Thus the phenomics application which includes high throughput phenotyping (HTP) i.e artificial intelligence based techniques should be bought over traditional phenotyping in order to meet the needy demands of traditional ones. Also Artificial intelligence (AI)-based data analysis techniques can increase the reliability of diagnoses and, as a result, be included into instruments for effective treatment. These methods find pertinent data for use in plant breeding and pathology activities by using feature extraction, identification, classification, and prediction criteria as well as for precision breeding. This approach has various applications in various agriculture disciplines. The main steps under such techniques includes: Image acquisition, Preprocessing of images, segmentation of images, Image Representation and Description and Image recognition. The use of these helps to speed up genetic progress and lessen the phenotyping drawbacks.
Why it matches plant phenotyping methods植物フェノミクスおよび高スループット表現型解析のセンサー、画像取得・処理、特徴抽出、AI分析を農業分野横断で概説するレビューであり、方法論が中心です。
abstractground and aerial platforms with several sensors have been rapidly adopted for phenotyping diverse biotic, abiotic stressors and other characters during the crop plant's growth stages.
Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under shortterm, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.
Why it matches plant phenotyping methods作物ストレスに関する植物形質のリモートセンシング技術と検索・推定手法を体系的にレビューしており、植物フェノタイピング手法が中心である。
abstractthis study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture
The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers' naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.
Why it matches plant phenotyping methodsUAV画像と深層学習による作物病害の画像ベース同定を主題とするレビューであり、植物の病害状態を推定するフェノタイピング手法の総説として中心的です。
titleA survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images
Advances in high-throughput omics technologies are leading plant biology research into the era of big data. Machine learning (ML) performs an important role in plant systems biology because of its excellent performance and wide application in the analysis of big data. However, to achieve ideal performance, supervised ML algorithms require large numbers of labeled samples as training data. In some cases, it is impossible or prohibitively expensive to obtain enough labeled training data; here, the paradigms of unsupervised learning (UL) and semi-supervised learning (SSL) play an indispensable role. In this review, we first introduce the basic concepts of ML techniques, as well as some representative UL and SSL algorithms, including clustering, dimensionality reduction, self-supervised learning (self-SL), positive-unlabeled (PU) learning and transfer learning. We then review recent advances and applications of UL and SSL paradigms in both plant systems biology and plant phenotyping research. Finally, we discuss the limitations and highlight the significance and challenges of UL and SSL strategies in plant systems biology.
Why it matches plant phenotyping methods植物フェノタイピングにおける教師なし・半教師あり学習を扱うレビューであり、計算による表現型抽出手法が中心的テーマ。
abstractWe then review recent advances and applications of UL and SSL paradigms in both plant systems biology and plant phenotyping research.
Benefits of independent learning and extraction of features have received a lot of attention in recent years from both academic and professional circles. A subcategory of artificial intelligence is deep learning. The use of deep learning towards plant disease recognition can prevent the drawbacks associated with crop disease and production losses. In order to identify and characterize the signs of plant diseases, numerous established machine learning and deep learning architectures are used in conjunction with a number of visualization tools. The detection of leaf disease using image processing has been covered in this survey. Leaf disease diagnosis is enhanced when image segmentation is used in combination with deep learning or machine learning models. A big data collection can be segmented with the use of image segmentation, and the output is then fed to the AI algorithms on disease detection. Additionally, this survey covers the performance metrics of prior studies, which offered guidance for future advancements in plant disease detection and prevention methods.
Why it matches plant phenotyping methods植物葉の病徴を画像処理・機械学習で検出する方法を中心に整理したレビューであり、植物の病害状態を観測・推定するフェノタイピング手法レビューに該当する。
titleState of Art Survey on Plant Leaf Disease Detection
Field / plotRootMorphology / geometry measurementRoot system architecture
Roots assist plants in absorbing water and nutrients from soil. Thus, they are vital to the survival of nearly all land plants, considering that plants cannot move to seek optimal environmental conditions. Crop species with optimal root system are essential for future food security and key to improving agricultural productivity and sustainability. Root systems can be improved and bred to acquire soil resources efficiently and effectively. This can also reduce adverse environmental impacts by decreasing the need for fertilization and fresh water. Therefore, there is a need to improve and breed crop cultivars with favorable root system. However, the lack of high-throughput root phenotyping tools for characterizing root traits in situ is a barrier to breeding for root system improvement. In recent years, many breakthroughs in the measurement and analysis of roots in a root system have been made. Here, we describe the major advances in root image acquisition and analysis technologies and summarize the advantages and disadvantages of each method. Furthermore, we look forward to the future development direction and trend of root phenotyping methods. This review aims to aid researchers in choosing a more appropriate method for improving the root system.
Why it matches plant phenotyping methods根系表型方法の画像取得・解析技術を体系的にレビューしており、植物フェノタイピング手法が中心である。
titleRecent advances in methods for in situ root phenotyping
Background and Objectives: To a large extent, low production of maize can be attributed to diseases and pests. Accurate, fast, and early detection of maize plant disease is critical for efficient maize production. Early detection of a disease enables growers, breeders and researchers to effectively apply the appropriate controlled measures to mitigate the disease’s effects. Unfortunately, the lack of expertise in this area and the cost involved often result in an incorrect diagnosis of maize plant diseases which can cause significant economic loss. Over the years, there have been many techniques that have been developed for the detection of plant diseases. In recent years, computer-aided methods, especially Machine learning (ML) techniques combined with crop images (image-based phenotyping), have become dominant for plant disease detection. Deep learning techniques (DL) have demonstrated high accuracies of performing complex cognitive tasks like humans among machine learning approaches. This paper aims at presenting a comprehensive review of state-of-the-art DL techniques used for detecting disease in the leaves of maize.Methods: In achieving the aims of this paper, we divided the methodology into two main sections; Article Selection and Detailed review of selected articles. An algorithm was used in selecting the state-of-the-art DL techniques for maize disease detection spanning from 2016 to 2021. Each selected article is then reviewed in detail taking into considerations the DL technique, dataset used, strengths and limitations of each technique. Results: DL techniques have demonstrated high accuracies in maize disease detection. It was revealed that transfer learning reduces training time and improves the accuracies of models. Models trained with images taking from a controlled environment (single leaves) perform poorly when deployed in the field where there are several leaves. Two-stage object detection models show superior performance when deployed in the field. Conclusion: From the results, lack of experts to annotate accurately, Model architecture, hyperparameter tuning, and training resources are some of the challenges facing maize leaf disease detection. DL techniques based on two-stage object detection algorithms are best suited for several plant leaves and complex backgrounds images.
Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から検出する深層学習手法を体系的にレビューしており、植物フェノタイピング手法のレビューが中心である。
abstractThis paper aims at presenting a comprehensive review of state-of-the-art DL techniques used for detecting disease in the leaves of maize.
Field / plotWhole plant / canopy / plot / fieldCountingSegmentation
Plant stand count helps in estimating the yield and evaluating the planter’s efficiency and seed quality. Traditional methods of counting the plants by manual measurement are time-consuming, laborious, and error-prone. In contrast, the ground-based sensing methods are limited to smaller spaces. High spatial resolution images obtained from unmanned aerial vehicles (UAV) can be used in conjunction with computer vision algorithms to evaluate plant stand count, as it directly influences the yield. In spite of the importance of high-throughput plant stand count in row crop agriculture, no synthesized information in this specific subject matter is available. Therefore, the objective of this paper was to perform a systematic literature review of the current studies that focus on evaluating plant stand count using UAV imagery to provide well-synthesized information, identify research gaps, and provide suitable recommendations. In this study, a comprehensive literature search was performed on three academic databases (Agricola, Web of Science, and Scopus), and a total of 29 articles were found based on search terms and selection criteria for review. From the systematic review, it can be concluded that: an appropriate stage after plant emergence without canopy overlap is necessary for image acquisition; optimal flying height should be selected to balance the field coverage and accuracy; L*a*b* color space can provide better segmentation; hyperspectral camera imagery can provide good discrimination; deep learning with data augmentation and transfer learning models can be used to reduce the computational time and resources; the stand count methodology that is successful with corn and cotton could be extended to other row crops and horticultural crops; and application of direct image processing and use of open-source platforms is required for stakeholder participation. The review will be helpful to the farmers, producers, and researchers in selecting and employing the UAV-based algorithms for evaluating plant stand count.
Why it matches plant phenotyping methodsUAV画像とコンピュータビジョンによる植物個体数（plant stand count）評価手法を体系的にレビューしており、植物表現型取得法が中心である。
titleA review of unmanned aerial vehicle-based methods for plant stand count evaluation in row crops
Abstract: Artificial intelligence includes deep learning as a subset. Due to its advantages, autonomous learning and feature extraction have been hotly debated in academic and industrial circles in recent years. Image and video processing, voice processing, and natural language processing have all benefited from it. Simultaneously, it has grown into a centre for agricultural plant protection research, which includes, among other things, plant disease recognition and insect range evaluation. Deep learning can assist avoid the drawbacks of artificially selecting disease spot features, improve the objectivity of plant disease feature extraction, and accelerate research and technological change. In this review, we look at how deep learning technology has progressed in the field of agricultural leaf disease detection in recent years. The current trends and challenges in using deep learning and sophisticated imaging techniques to detect plant leaf disease are discussed in this paper. Our findings are expected to be valuable to researchers interested in detecting plant diseases and insect pests. We also discussed some of the current problems and issues that need to be addressed. Keywords: Analysis, Deep Learning, Prediction, Plant Leaf, Resnet 50
Why it matches plant phenotyping methods植物葉の病害を画像から検出する深層学習・画像処理手法の進展、動向、課題を扱う方法論レビューであり、植物病害状態の表現型取得が中心です。
abstractIn this review, we look at how deep learning technology has progressed in the field of agricultural leaf disease detection in recent years.
As we know that Plant disease detection is an interesting field. Plants are the way to live. In our daily life we are completely dependent on plants. There by plants should be taken care. In most of the studies it is been shown that quality of agricultural products shall be reduced due to various components. The plant diseases are such as bacteria, viruses and fungi. The disease in plant leaf restricts the growth of the plant and also destroys its yield. Every time there is the need of expert to identify plant diseases but manual identification is expensive and also time consuming. So, automatic methods are necessary for detection of disease. Through this paper, we have presented a survey on the different methods of plant leaf disease detection.
Why it matches plant phenotyping methods植物葉の病害を自動検出する手法を対象としたレビューであり、病害状態という植物表現型の取得・推定方法が中心である。
abstractThrough this paper, we have presented a survey on the different methods of plant leaf disease detection.
Societal Impact Statement Strawberry breeders are faced with increasing demands by propagators, growers, retailers and consumers for particular agronomic traits. This and the volume of plants requiring assessment during selection constrain breeders to rapid and qualitative rating methods. High‐throughput systems for assessing these traits automatically could indicate which families, or individual genotypes, should be singled out for further, more thorough evaluation, thus significantly increasing the selection intensity and accuracy. This review assesses the current status of and future potential for automated phenotyping in strawberry crops, highlighting key advances and the gaps which need to be addressed to facilitate the development of such technology. Summary Automated image‐based phenotyping has become widely accepted in crop phenotyping, particularly in cereal crops, yet few traits used by breeders in the strawberry industry have been automated. Early phenotypic assessment remains largely qualitative in this area since the manual phenotyping process is laborious and domain experts are constrained by time. Precision agriculture, facilitated by robotic technologies, is increasing in the strawberry industry, and the development of quantitative automated phenotyping methods is essential to ensure that breeding programs remain economically competitive. In this review, we investigate the external morphological traits relevant to the breeding of strawberries that have been automated and assess the potential for automation of traits that are still evaluated manually, highlighting challenges and limitations of the approaches used, particularly when applying high‐throughput strawberry phenotyping in real‐world environmental conditions.
Why it matches plant phenotyping methodsイチゴ育種における自動・画像ベース高スループット表現型計測を中心に、既存手法、課題、将来展望をレビューしているため。
abstractThis review assesses the current status of and future potential for automated phenotyping in strawberry crops
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
Why it matches plant phenotyping methods植物フェノミクスにおける近接・リモートセンシング技術を体系的に分析するレビューであり、フェノタイピング手法が中心である。
abstractProximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.
Why it matches plant phenotyping methods作物の非生物的ストレス応答を定量化する画像ベースの表現型解析技術を中心に、センサー、形質、指標、機械学習を体系的にレビューしているため。
abstractThis paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency
Abstract Remote sensing of vegetation phenology has long been used to characterize ecosystem functions and responses to climate at spatial and temporal scales unfeasible to field surveys. However, the potential of remote sensing to elucidate mechanistic drivers of phenology and the underlying plant community processes at such scales remains under‐discussed. This review synthesizes possibilities to advance this knowledge using multi‐temporal remote sensing and discusses remaining challenges and progress in instruments and analytical tools. Recent evidence indicates that, besides documenting vegetation seasonality and responses to climate, remote sensing of phenology can help meet emerging needs for indicators of plant diversity, vegetation structure and ecosystem change. Responses of phenological metrics to stressors over large, heterogeneous regions may provide clues on ecological resilience manifested in asynchronies, recovery of vegetation cycles and stable microrefugia. At the same time, important barriers persist in relation to choosing among phenological estimation methods and paradigms, characterizing phenological events beyond changes in photosynthetically active biomass, and mechanistic interpretation of phenological patterns. Synthesis . Increasing temporal frequency of products, opportunities for multi‐sensor data fusion, and advances in historically less available hyperspectral, active microwave and lidar instruments promise to help navigate these barriers and enable more comprehensive assessments of seasonality. Progress in customizable local platforms such as unoccupied aerial vehicles and phenocams may further enrich ground‐level understanding of phenology and validate satellite‐based assessments. However, remote sensing analyses alone are insufficient for mechanistic interpretation of phenology, which can be challenged by artefacts in remote sensing data and sensitivity of estimated metrics to landscape structure and spatial resolution of the inputs. Robust and informative phenological assessments call for rigorous collaborations with field ecological studies, strategic selection of ancillary environmental and geographic data, and wider adoption of causal inference approaches to address these needs and support novel explorations in plant ecology.
Why it matches plant phenotyping methods植物のフェノロジーをリモートセンシングで推定する方法、指標、センサー、解析ツールを体系的に検討するレビューであり、植物状態の取得・推定手法が中心である。
abstractThis review synthesizes possibilities to advance this knowledge using multi‐temporal remote sensing and discusses remaining challenges and progress in instruments and analytical tools.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
The research data life cycle from project planning to data publishing is an integral part of current research. Until the last decade, researchers were responsible for all associated phases in addition to the actual research and were assisted only at certain points by IT or bioinformaticians. Starting with advances in sequencing, the automation of analytical methods in all life science fields, including in plant phenotyping, has led to ever-increasing amounts of ever more complex data. The tasks associated with these challenges now often exceed the expertise of and infrastructure available to scientists, leading to an increased risk of data loss over time. The IPK Gatersleben has one of the world's largest germplasm collections and two decades of experience in crop plant research data management. In this article we show how challenges in modern, data-driven research can be addressed by data stewards. Based on concrete use cases, data management processes and best practices from plant phenotyping, we describe which expertise and skills are required and how data stewards as an integral actor can enhance the quality of a necessary digital transformation in progressive research.
Why it matches plant phenotyping methods植物フェノタイピング自体の測定法開発ではないが、植物フェノタイピングにおけるデータ管理プロセスとベストプラクティスを具体的ユースケースに基づいてレビューしており、方法論的レビューとして中心的である。
abstractBased on concrete use cases, data management processes and best practices from plant phenotyping, we describe which expertise and skills are required and how data stewards as an integral actor can enhance the quality of a necessary digital transformation in progressive research.
Abstract Key message The lack of efficient phenotyping capacities has been recognized as a bottleneck in forestry phenotyping and breeding. Modern phenotyping technologies use systems equipped with various imaging sensors to automatically collect high volume phenotypic data that can be used to assess trees' various attributes. Context Efficient phenotyping has the potential to spark a new Green Revolution, and it would provide an opportunity to acquire growth parameters and dissect the genetic bases of quantitative traits. Phenotyping platforms aim to link information from several sources to derive knowledge about trees' attributes. Aims Various tree phenotyping techniques were reviewed and analyzed along with their different applications. Methods This article presents the definition and characteristics of forest tree phenotyping and reviews newly developed imaging-based practices in forest tree phenotyping. Results This review addressed a wide range of forest trees phenotyping applications, including a survey of actual inter- and intra-specific variability, evaluating genotypes and species response to biotic and abiotic stresses, and phenological measurements. Conclusion With the support of advanced phenotyping platforms, the efficiency of traits phenotyping in forest tree breeding programs is accelerated.
Why it matches plant phenotyping methods森林樹木の画像ベース表現型技術とフェノタイピングプラットフォームを体系的にレビューしており、方法論が中心である。
abstractThis article presents the definition and characteristics of forest tree phenotyping and reviews newly developed imaging-based practices in forest tree phenotyping.
The detection of chemical species and understanding their respective localisations in tissues have important implications in plant science. The conventional methods for imaging spatial localisation of chemical species are often restricted by the number of species that can be identified and is mostly done in a targeted manner. Mass spectrometry imaging combines the ability of traditional mass spectrometry to detect numerous chemical species in a sample with their spatial localisation information by analysing the specimen in a 2D manner. This article details the popular mass spectrometry imaging methodologies which are widely pursued along with their respective sample preparation and the data analysis methods that are commonly used. We also review the advancements through the years in the usage of the technique for the spatial profiling of endogenous metabolites, detection of xenobiotic agrochemicals and disease detection in plants. As an actively pursued area of research, we also address the hurdles in the analysis of plant tissues, the future scopes and an integrated approach to analyse samples combining different mass spectrometry imaging methods to obtain the most information from a sample of interest.
Why it matches plant phenotyping methods植物組織の化学成分の空間分布を測定する質量分析イメージング手法を中心に、試料調製・データ解析・植物での応用と課題をレビューしており、植物フェノタイピング手法の方法論的レビューに該当する。
abstractThis article details the popular mass spectrometry imaging methodologies which are widely pursued along with their respective sample preparation and the data analysis methods that are commonly used.
Abstract: Deep learning is a branch of artificial intelligence. In recent years, with the benefits of automatic learning and feature extraction, it's been wide involved by educational and industrial circles. It has been wide utilized in image and video processing, voice processing, and natural language processing. At a similar time, it's conjointly become an enquiry hotspot within the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. the application of deep learning in disease recognition will avoid the disadvantages caused by artificial choice of illness spot options, make plant disease feature extraction additional objective, and improve the analysis potency and technology transformation speed. This paper provides the analysis progress of deep learning technology within the field of crop plant disease identification in recent years. during this paper, we tend to present this trends and challenges for the detection of plant leaf disease with deep learning and advanced imaging techniques. we tend to hope that this work are going to be a valuable resource for researchers UN agency study the detection of plant diseases and bug pests. At a similar time, we tend to conjointly mentioned some of the challenges and issues that require to be resolved. Keywords: Deep Learning, Disease Detection, Classification, Artificial Intelligence, Leaf Disease Detection;
Why it matches plant phenotyping methods植物葉の病害を画像・深層学習で検出・分類する方法の進展、課題、画像技術を扱うレビューであり、植物状態の表現型取得法が中心です。
abstractThis paper provides the analysis progress of deep learning technology within the field of crop plant disease identification in recent years.
Abstract: Early diagnosis of plant diseases is critical since they have a substantial impact on the growth of their unique species. Many Machine Learning (ML) models have been used to detect and categorize plant diseases, but recent breakthroughs in a subset of ML called Deep Learning (DL) look to hold a lot of promise in terms of improved accuracy. A variety of developed/modified DL architectures, as well as several visualization techniques, are utilized to recognize and identify the symptoms of plant ailments. In addition, a number of performance measurements are used to evaluate various architectures/techniques. This article explains how to use DL models to display a variety of plant diseases. Furthermore, several research gaps are identified, allowing for improved efficiency in detecting plant illnesses even before issues emerge. Keywords: Plant disease; deep learning; convolutional neural networks (CNN), Google Net Architecture, Tensorflow, and PyTorch are some of the tools that can be used;
Why it matches plant phenotyping methods植物病害症状の画像認識を深層学習で行う方法を扱い、複数モデルの性能評価も含むため、植物の病害状態を推定するフェノタイピング手法のレビューとして中心的です。
abstractA variety of developed/modified DL architectures, as well as several visualization techniques, are utilized to recognize and identify the symptoms of plant ailments.
To maintain the ecosystem of the environment major role is played by the plants. India largely depends on agriculture which is one of the major sectors in Indian economy. Majority of people are dependent on it to sustain themselves. Due to lack of technical knowledge and resources farmers recognize the plant disease by observing certain unusual patterns, colors, physical appearance etc. Sometimes expertise is required to detect plant infection and abnormalities which is a tedious process. Due to certain disease when the leaves of the plants are infected, production is reduced and also the quality of the food is also affected. The aim is to study different types of plants diseases and how these diseases can be detected by using modern advanced machines and techniques. Automation can help in early-stage disease detection stage which in turn helps to avert any further damage to the plant. Over the past few years, many promising technology and techniques have been launched that provides state-of-art solutions to modernize the agriculture sector and improve the quality and yield of the crops. Modern concept of smart farming provides agriculture sector with more efficient and effective planting system with the help of high precision algorithm. Efficient techniques lead to greater yields which keeps the population happy and healthy.
Why it matches plant phenotyping methods植物葉の病徴・異常を画像等から検出する手法を対象としたレビューであり、病害状態という植物表現型の取得・推定が中心です。
titlePlant Leaf Disease Detection using Deep Learning: A Review
A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system.
Why it matches plant phenotyping methods植物葉の病徴を画像処理・機械学習で検出・分類する手法を中心に扱うレビューであり、植物の病害状態を推定するフェノタイピング方法のレビューに該当します。
titleCrop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review
Field / plotWhole plant / canopy / plot / fieldGrowth / development / phenology
Climate change represents one of the most critical threats to biodiversity with far-reaching consequences for species interactions, the functioning of ecosystems, or the assembly of biotic communities. Plant phenology research has gained increasing attention as the timing of periodic events in plants is strongly affected by seasonal and interannual climate variation. Recent technological development allowed us to gather invaluable data at a variety of spatial and ecological scales. The feasibility of phenological monitoring today and in the future depends heavily on developing tools capable of efficiently analyzing these enormous amounts of data. Deep Neural Networks learn representations from data with impressive accuracy and lead to significant breakthroughs in, e.g., image processing. This article is the first systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research. In a multi-stage process, we selected 24 peer-reviewed studies published in the last five years (2016–2021). After carefully analyzing these studies, we describe the applied methods categorized according to the studied phenological stages, vegetation type, spatial scale, data acquisition- and deep learning methods. Furthermore, we identify and discuss research trends and highlight promising future directions. We present a systematic overview of previously applied methods on different tasks that can guide this emerging complex research field.
Why it matches plant phenotyping methods植物のフェノロジーを対象とする深層学習手法を体系的にレビューし、データ取得法と解析法を分類しており、方法論レビューが中心です。
abstractThis article is the first systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.
Why it matches plant phenotyping methods植物フェノタイピング基盤へのメタボロミクス統合を中心に、現状・課題・技術的将来展望をレビューしており、方法論レビューとして中心的です。
abstractIn this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.
Field / plotGrowth chamberObject detectionVisualization / data management
In the upcoming years, global changes in agricultural and environmental systems will require innovative approaches in crop research to ensure more efficient use of natural resources and food security. Cutting-edge technologies for precision agriculture are fundamental to improve in a non-invasive manner, the efficiency of detection of environmental parameters, and to assess complex traits in plants with high accuracy. The application of sensing devices and the implementation of strategies of artificial intelligence for the acquisition and management of high-dimensional data will play a key role to address the needs of next-generation agriculture and boosting breeding in crops. To that end, closing the gap with the knowledge from the other ‘omics’ sciences is the primary objective to relieve the bottleneck that still hinders the potential of thousands of accessions existing for each crop. Although it is an emerging discipline, phenomics does not rely only on technological advances but embraces several other scientific fields including biology, statistics and bioinformatics. Therefore, establishing synergies among research groups and transnational efforts able to facilitate access to new computational methodologies and related information to the community, are needed. In this review, we illustrate the main concepts of plant phenotyping along with sensing devices and mechanisms underpinning imaging analysis in both controlled environments and open fields. We then describe the role of artificial intelligence and machine learning for data analysis and their implication for next-generation breeding, highlighting the ongoing efforts toward big-data management.
Why it matches plant phenotyping methods植物フェノタイピングの概念、センシング機器、画像解析、AI・機械学習を体系的に扱うレビューであり、方法論が中心です。
abstractIn this review, we illustrate the main concepts of plant phenotyping along with sensing devices and mechanisms underpinning imaging analysis in both controlled environments and open fields.
Field / plotRootWhole plant / canopy / plot / fieldRoot system architecture
Root phenotyping describes methods for measuring root properties, or traits. While root phenotyping can be challenging, it is advancing quickly. In order for the field to move forward, it is essential to understand the current state and challenges of root phenotyping, as well as the pressing needs of the root biology community. In this letter, we present and discuss the results of a survey that was created and disseminated by members of the Graduate Student and Postdoc Ambassador Program at the 11th symposium of the International Society of Root Research. This survey aimed to (1) provide an overview of the objectives, biological models and methodological approaches used in root phenotyping studies, and (2) identify the main limitations currently faced by plant scientists with regard to root phenotyping. Our survey highlighted that (1) monocotyledonous crops dominate the root phenotyping landscape, (2) root phenotyping is mainly used to quantify morphological and architectural root traits, (3) 2D root scanning/imaging is the most widely used root phenotyping technique, (4) time-consuming tasks are an important barrier to root phenotyping, (5) there is a need for standardised, high-throughput methods to sample and phenotype roots, particularly under field conditions, and to improve our understanding of trait-function relationships.
Why it matches plant phenotyping methods根系フェノタイピングの手法、利用状況、限界、標準化ニーズを調査・整理したレビュー的研究であり、フェノタイピング方法論が中心です。
abstractRoot phenotyping describes methods for measuring root properties, or traits.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the survey raw data and R analysis code on Zenodo (DOI 10.5281/zenodo.5901959), a public, paper-specific, actionable asset. The Nottingham Hidden Half maize image URL is only a credited Figure 1 image source, not a paper-specific dataset, and is not listed asaCode · publicRaw data and R code are available on Zenodo at https://doi.org/10.5281/zenodo.5901959.Open asset ↗Zenodo · 10.5281/zenodo.5901959pdf-page:10 lines:1-39Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 14 Sept 2026
Raman / spectroscopyTissuePhysiological trait estimationGrowth / development / phenology
As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.
Why it matches plant phenotyping methods植物の非侵襲的な光学分光によるモニタリング・生理状態評価と、植物フェノタイピングへの応用を扱う方法論的展望であり、フェノタイピング手法のレビューとして中心的です。
abstractThe emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 13 Sept 2026
In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is much more difficult to conduct due to unstable environmental conditions compared to HTP in controlled environments, recent advances in HTP technology have allowed these difficulties to be overcome, allowing for rapid, efficient, non-destructive, non-invasive, quantitative, repeatable, and objective phenotyping. Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image sensors, laser sensors, environmental sensors, and drones, along with high-speed computational resources. This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance, in addition to crop organ detection and counting in the fields. Current topics in field HTP are also presented, followed by a discussion on the low rates of adoption of HTP in practical breeding programs.
Why it matches plant phenotyping methods圃場ハイスループット植物フェノタイピングの技術、センサー、画像解析、3D再構成、ロボット技術を中心に概説するレビューであり、植物形質取得手法が主題である。
abstractThis article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops
Recently, it has been proposed to switch molecular markers to near-infrared (NIR) spectra for inferring relationships between individuals and further performing phenomic selection (PS), analogous to genomic selection (GS). The PS concept is similar to genomic-like omics-based (GLOB) selection, in which molecular markers are replaced by endophenotypes, such as metabolites or transcript levels, except that the phenomic information obtained for instance by near-infrared spectroscopy (NIRS ) has usually a much lower cost than other omics. Though NIRS has been routinely used in breeding for several decades, especially to deal with end-product quality traits, its use to predict other traits of interest and further make selections is new. Since the seminal paper on PS , several publications have advocated the use of spectral acquisition (including NIRS and hyperspectral imaging) in plant breeding towards PS , potentially providing a scope of what is possible. In the present chapter, we first come back to the concept of PS as originally proposed and provide a classification of selected papers related to the use of phenomics in breeding. We further provide a review of the selected literature concerning the type of technology used, the preprocessing of the spectra, and the statistical modeling to make predictions. We discuss the factors that likely affect the efficiency of PS and compare it to GS in terms of predictive ability. Finally, we propose several prospects for future work and application of PS in the context of plant breeding.
Why it matches plant phenotyping methods植物育種におけるNIR・ハイパースペクトル取得と統計モデルによる表現型予測を中心に扱う方法論レビューであり、フェノミック選抜の技術・前処理・予測モデルを体系的に評価している。
abstractIn the present chapter, we first come back to the concept of PS as originally proposed and provide a classification of selected papers related to the use of phenomics in breeding.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
RootMorphology / geometry measurementRoot system architecture
Root system architecture (RSA) determines unevenly distributed water and nutrient availability in soil. Genetic improvement of RSA, therefore, is related to crop production. However, RSA phenotyping has been carried out less frequently than above-ground phenotyping because measuring roots in the soil is difficult and labor intensive. Recent advancements have led to the digitalization of plant measurements; this digital phenotyping has been widely used for measurements of both above-ground and RSA traits. Digital phenotyping for RSA is slower and more difficult than for above-ground traits because the roots are hidden underground. In this review, we summarized recent trends in digital phenotyping for RSA traits. We classified the sample types into three categories: soil block containing roots, section of soil block, and root sample. Examples of the use of digital phenotyping are presented for each category. We also discussed room for improvement in digital phenotyping in each category.
Why it matches plant phenotyping methods根系形態（RSA）のデジタル化・自動化による表現型計測を中心に、手法の動向、分類、改善点を論じるレビューであり、方法論が主題です。
titleImproving the efficiency of plant root system phenotyping through digitization and automation.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
The morphological traits of plants contribute to many important functional features such as radiation interception, lodging tolerance, gas exchange efficiency, spatial competition between individuals and/or species, and disease resistance. Although the importance of plant phenotyping techniques is increasing with advances in molecular breeding strategies, there are barriers to its advancement, including the gap between measured data and phenotypic values, low quantitativity, and low throughput caused by the lack of models for representing morphological traits. In this review, we introduce morphological descriptors that can be used for phenotyping plant morphological traits. Geometric morphometric approaches pave the way to a general-purpose method applicable to single units. Hierarchical structures composed of an indefinite number of multiple elements, which is often observed in plants, can be quantified in terms of their multi-scale topological characteristics using topological data analysis. Theoretical morphological models capture specific anatomical structures, if recognized. These morphological descriptors provide us with the advantages of model-based plant phenotyping, including robust quantification of limited datasets. Moreover, we discuss the future possibilities that a system of model-based measurement and model refinement would solve the lack of morphological models and the difficulties in scaling out the phenotyping processes.
Why it matches plant phenotyping methods植物形態形質のフェノタイピングに用いる形態記述子、幾何学的形態計測、トポロジカルデータ解析、モデルベース測定を中心に論じる方法論レビューである。
abstractIn this review, we introduce morphological descriptors that can be used for phenotyping plant morphological traits.
The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases and protect crops. However, manual disease identification is both time-consuming and error prone, and requires a thorough knowledge of plant pathogens. Instead, automated methods save both time and effort. This paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis. Additionally, a comparative study of several techniques applied to crop disease detection was carried out. Furthermore, this paper discusses the different challenges to be overcome and possible solutions. Then, several suggestions to address these challenges are provided. Finally, this research provides a future perspective that promises to be a highly useful and valuable resource for researchers working in the field of crop disease detection.
Why it matches plant phenotyping methods植物病害の画像・ハイパースペクトル・機械学習による識別手法を中心に扱うレビューであり、感染植物の病徴・病害状態を観測するフェノタイピング手法レビューに該当する。
abstractThis paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
High-throughput crop phenotyping, particularly under field conditions, is nowadays perceived as a key factor limiting crop genetic advance. Phenotyping not only facilitates conventional breeding, but it is necessary to fully exploit the capabilities of molecular breeding, and it can be exploited to predict breeding targets for the years ahead at the regional level through more advanced simulation models and decision support systems. In terms of phenotyping, it is necessary to determined which selection traits are relevant in each situation, and which phenotyping tools/methods are available to assess such traits. Remote sensing methodologies are currently the most popular approaches, even when lab-based analyses are still relevant in many circumstances. On top of that, data processing and automation, together with machine learning/deep learning are contributing to the wide range of applications for phenotyping. This review addresses spectral and red-green-blue sensing as the most popular remote sensing approaches, alongside stable isotope composition as an example of a lab-based tool, and root phenotyping, which represents one of the frontiers for field phenotyping. Further, we consider the two most promising forms of aerial platforms (unmanned aerial vehicle and satellites) and some of the emerging data-processing techniques. The review includes three Boxes that examine specific case studies.
Why it matches plant phenotyping methods植物フェノタイピングの測定対象と手法（リモートセンシング、RGB・スペクトルセンシング、根のフェノタイピング、データ処理等）を中心に扱う方法レビューである。
titleCrop phenotyping in a context of global change: What to measure and how to do it
Plant disease threatens the environmental and financial sustainability of crop production, causing $220 billion in annual losses. The dire threat disease poses to modern agriculture demands tools for better detection and monitoring to prevent crop loss and input waste. The nascent discipline of plant disease sensing, or the science of using proximal and/or remote sensing to detect and diagnose disease, offers great promise to extend monitoring to previously unachievable resolutions, a basis to construct multiscale surveillance networks for early warning, alert, and response at low latency, an opportunity to mitigate loss while optimizing protection, and a dynamic new dimension to agricultural systems biology. Despite its revolutionary potential, plant disease sensing remains an underdeveloped discipline, with challenges facing both fundamental study and field application. This article offers a perspective on the current state and future of plant disease sensing, highlights remaining gaps to be filled, and presents a bold vision for the future of global agriculture.
Why it matches plant phenotyping methods植物病害を近接・リモートセンシングで検出・診断する方法分野の現状、課題、将来展望を扱うレビューであり、病害状態という植物表現型の取得方法が中心です。
abstractThe nascent discipline of plant disease sensing, or the science of using proximal and/or remote sensing to detect and diagnose disease
Field / plotLaboratory / benchtopRootClassificationMorphology / geometry measurementCalibration / preprocessingRoot system architecture
In the context of a recent massive increase in research on plant root functions and their impact on the environment, root ecologists currently face many important challenges to keep on generating cutting-edge, meaningful and integrated knowledge. Consideration of the below-ground components in plant and ecosystem studies has been consistently called for in recent decades, but methodology is disparate and sometimes inappropriate. This handbook, based on the collective effort of a large team of experts, will improve trait comparisons across studies and integration of information across databases by providing standardised methods and controlled vocabularies. It is meant to be used not only as starting point by students and scientists who desire working on below-ground ecosystems, but also by experts for consolidating and broadening their views on multiple aspects of root ecology. Beyond the classical compilation of measurement protocols, we have synthesised recommendations from the literature to provide key background knowledge useful for: (1) defining below-ground plant entities and giving keys for their meaningful dissection, classification and naming beyond the classical fine-root vs coarse-root approach; (2) considering the specificity of root research to produce sound laboratory and field data; (3) describing typical, but overlooked steps for studying roots (e.g. root handling, cleaning and storage); and (4) gathering metadata necessary for the interpretation of results and their reuse. Most importantly, all root traits have been introduced with some degree of ecological context that will be a foundation for understanding their ecological meaning, their typical use and uncertainties, and some methodological and conceptual perspectives for future research. Considering all of this, we urge readers not to solely extract protocol recommendations for trait measurements from this work, but to take a moment to read and reflect on the extensive information contained in this broader guide to root ecology, including sections I-VII and the many introductions to each section and root trait description. Finally, it is critical to understand that a major aim of this guide is to help break down barriers between the many subdisciplines of root ecology and ecophysiology, broaden researchers' views on the multiple aspects of root study and create favourable conditions for the inception of comprehensive experiments on the role of roots in plant and ecosystem functioning.
Why it matches plant phenotyping methods植物の根形質測定プロトコル、標準化、分類、データ再利用を包括的に扱う方法論的ガイドであり、根の表現型取得・測定が中心です。
abstractproviding standardised methods and controlled vocabularies
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Author: Machwitz, M. et al.; Genre: Journal Article; Finally published : 2021-10-22; Title: Bridging the gap between remote sensing and plant phenotyping—Challenges and opportunities for the next generation of sustainable agriculture
Why it matches plant phenotyping methods植物フェノタイピングとリモートセンシングの課題・機会を主題とするレビューであり、方法論的スコープが明確。
titleBridging the Gap Between Remote Sensing and Plant Phenotyping—Challenges and Opportunities for the Next Generation of Sustainable Agriculture