Plant phenotyping methods.

High-throughput phenotyping platform for facility crops based on optical sensing technology: A review ↗

High-throughput acquisition of crop phenotypic information is one of the key technologies for achieving intelligent facility agriculture and precision breeding. Traditional phenotypic data collection methods suffer from low efficiency and strong subjectivity, making it difficult to achieve multi-scale continuous monitoring and meet the demands of modern research and production. This paper systematically reviews the technological framework and development trajectory of optical sensing technology-driven phenotypic platforms for facility crops. First, starting from optical sensing technologies, a comparative analysis highlights the advantages and limitations of RGB, multi-/hyperspectral, thermal infrared, and LiDAR sensors in phenotypic perception. Second, the characteristics and applicable scenarios of stationary, rail-mounted, mobile robot, and unmanned aerial vehicle (UAV) platform architectures are summarized. Furthermore, the evolution of phenotypic data processing methods is examined, focusing on the shift from traditional feature engineering to deep learning-driven approaches. Finally, key challenges such as multimodal data fusion, system cost, and real-time performance are discussed, along with the future direction of phenotypic platforms toward intelligent closed-loop decision-making systems. This article systematically reviews the facility agriculture phenotyping platforms driven by optical sensing technology, and also incorporates representative research progress in field phenotyping studies. These advances provide transferable sensing technologies, methodological frameworks, and platform design concepts that can facilitate the development of phenotyping platforms for controlled-environment agriculture.

Why it matches plant phenotyping methods施設作物の光学センシング型ハイスループット表現型解析プラットフォームを体系的にレビューしており、センサー、プラットフォーム構成、データ処理を中心に扱うため、方法論レビューとして明確に適格です。

abstractThis paper systematically reviews the technological framework and development trajectory of optical sensing technology-driven phenotypic platforms for facility crops.

A C-repeat binding factor-salicylic acid (CBF-SA) module links wound-induced evaporative cooling to tissue repair in plants. ↗

Repairing damaged tissues is essential for the survival of all organisms. In plants, tissue injury rapidly triggers defense and repair programs. However, the molecular mechanisms linking early injury cues to the later stage of wound repair remain unclear. Here, we show that wounding of Arabidopsis leaves induces localized low temperature at the injury site, likely caused by evaporative cooling, which is accompanied by an activation of cold-responsive genes. Using thermal imaging combined with computer vision and deep learning, we developed a workflow to monitor the dynamics of wound healing in a quantitative, non-invasive, and real-time manner. Mechanistically, we show that C-repeat Binding Factor (CBF) transcription factors are required for the activation of the injury-associated cold response and downstream salicylic acid (SA) signaling. Our findings suggest that the CBF-SA pathway acts coordinately to promote lignin and callose deposition, thereby facilitating wound repair. Together, these findings reveal a link between a wound-induced biophysical cue and the tissue repair program.

Why it matches plant phenotyping methods熱画像とコンピュータビジョン・深層学習を組み合わせ、植物の創傷治癒を定量的・非侵襲的・リアルタイムに測定するワークフローを開発しており、表現型取得法が研究の中心である。

abstractUsing thermal imaging combined with computer vision and deep learning, we developed a workflow to monitor the dynamics of wound healing in a quantitative, non-invasive, and real-time manner.

Wheat biomass estimation across crop development using UAV LiDAR structure–intensity fusion alongside multispectral and thermal data ↗

This study systematically evaluated the contribution of UAV LiDAR structural features such as crop height (CH) and multi-layer gap fraction (GF) and the amplitude of the returning signal represented by normalized intensity (INT), together with multispectral (MS) and thermal infrared (TIR) observations for aboveground biomass (AGB) estimation in winter wheat using a common artificial neural network (ANN) framework. Among the evaluated single sensor approaches, LiDAR features consistently provided the strongest performance, demonstrating the complementary value of crop height, vertically distributed canopy density, and normalized LiDAR intensity for characterizing canopy structure and within-canopy variability. Multi-layer GF improved AGB estimation relative to conventional ground-based GF approaches, highlighting the importance of incorporating the vertical distribution of canopy density. Multi-sensor fusion produced only modest additional improvements, indicating limited benefits relative to the increased acquisition and processing requirements. Temporal analysis showed that structural LiDAR features were most informative during early crop development, whereas normalized intensity, spectral reflectance, and thermal observations became increasingly valuable during canopy maturation and senescence. Comparisons with destructively measured plant area index (PAI), leaf area index (LAI), green leaf area index (GLAI), and green fraction of LAI further demonstrated that normalized LiDAR intensity (903 nm) was more closely associated with green canopy components than purely structural LiDAR metrics. Overall, the results demonstrate that fully exploiting both the structural and spectral information contained within LiDAR observations can substantially improve UAV-based biomass estimation, while multispectral and thermal observations provide complementary information whose contribution varies with crop development and monitoring objectives.

Why it matches plant phenotyping methodsUAV LiDAR・マルチスペクトル・熱画像とANNを用いた小麦バイオマス推定手法を系統的に比較・評価しており、植物形質推定の取得・解析方法が研究の中心である。

abstractThis study systematically evaluated the contribution of UAV LiDAR structural features such as crop height (CH) and multi-layer gap fraction (GF) and the amplitude of the returning signal represented by normalized intensity (INT), together with multispectral (MS) and thermal infrared (TIR) observations for aboveground biomass (AGB) estimation in winter wheat using a common artificial neural network (ANN) framework.

UAV multisensor data and GAMLSS improve forage biomass estimation in Cerrado integrated crop–livestock pastures ↗

Introduction Accurate estimation of aboveground biomass (AGB) is essential for monitoring pasture productivity and supporting sustainable management of integrated crop–livestock (ICL) systems. We hypothesized that integrating multispectral, thermal, and canopy-structural information derived from unmanned aerial vehicles (UAVs) would improve AGB prediction relative to spectral information alone, and that Generalized Additive Models for Location, Scale and Shape (GAMLSS) would accommodate seasonal heteroscedasticity while maintaining predictive performance comparable to Random Forest (RF) and Support Vector Machine (SVM) models. Methods We collected 280 destructive biomass samples from two ICL paddocks and one continuously grazed pasture in the Brazilian Cerrado between 2022 and 2024. Twenty-four UAV-derived predictors, including spectral bands, vegetation indices, canopy surface temperature, and canopy height, were evaluated using repeated five-fold cross-validation. Model transferability was assessed by withholding one management paddock at a time. Results and discussion Under repeated five-fold cross-validation, GAMLSS achieved the lowest prediction error (R² = 0.69 ± 0.01; RMSE = 2.15 ± 0.04 Mg ha⁻¹), followed closely by SVM (R² = 0.68 ± 0.01; RMSE = 2.19 ± 0.03 Mg ha -1 ); RF showed lower accuracy (R 2 = 0.53 ± 0.01; RMSE = 2.63 ± 0.02 Mg ha -1 ). In the paddock-transferability assessment, GAMLSS also showed the lowest error (R 2 = 0.63 ± 0.04; RMSE = 2.34 ± 0.26 Mg ha -1 ). For GAMLSS, the complete multisensor configuration reduced RMSE by 6.2% compared with the spectral-only configuration. The selected model was used to generate spatially explicit maps of AGB and standing aboveground biomass carbon, estimated from the mean measured carbon concentration of forage biomass. Integrating multispectral, thermal, and structural UAV data with distributional regression improves AGB estimation and enables spatial monitoring of tropical pastures under contrasting management conditions.

Why it matches plant phenotyping methodsUAVのマルチセンサーデータと統計モデルを用いて牧草の地上部バイオマスを推定し、交差検証と圃場間移 transferability 評価を行っており、植物形質の取得・推定手法が研究の中心である。

titleUAV multisensor data and GAMLSS improve forage biomass estimation in Cerrado integrated crop–livestock pastures

Comprehensive Review of Plant Disease Detection: Advancements in Imaging Sensors, AI Techniques, and Future Directions in Smart Agriculture ↗

Food safety globally is threatened by crop disease, which creates a major obstacle to yield losses, so there is an urgent need for rapid, precise, and large-scale diagnostic methods for all the global risks crops are exposed to from disease. While imaging sensors, as well as Artificial Intelligence (AI), have made great strides in recognising plant disease, most literature does not have a comprehensive analysis that combines methods, technology, and implementation. Therefore, a systematic literature review follows PRISMA methods; we review 61 excellent studies published within the last five years that outline the advancement of imaging modalities (Red, Green, Blue (RGB), multispectral/ hyperspectral, thermal), deep learning architectures, augmentation of data, explanation methods and IoT (Internet of Things)-edge-cloud for managing intelligent agriculture. These modern AI-based systems (AI systems) have consistently produced accurate results above 98%. However, there are problems with the generalisability (across hybrid plant species), robustness (when exposed to environmental stresses), and interpretability of the results presented to consumers. This review represents the first compilation of using imaging sensors, artificial intelligence models, Internet of Things architecture (IoT-edge), and robotics into one comprehensive framework for the detection of plant disease in the next generation. In addition, this review suggests future research directions, including lightweight edge-deployable models, multimodal sensor fusion, interpretable AI, larger validated datasets, and autonomous robotic systems for scalable and sustainable smart agriculture.

Why it matches plant phenotyping methods植物病害の画像・センサーによる検出手法を体系的にレビューしており、病害状態のフェノタイピング手法が中心です。

titleComprehensive Review of Plant Disease Detection: Advancements in Imaging Sensors, AI Techniques, and Future Directions in Smart Agriculture

Monitoring phases of plant stress in juvenile commercial forest cuttings using contemporary nursery sensor technologies ↗

Abstract Visual assessments of growing forest nursery plants are time-consuming and often result in a lack of information at a physiological level. There exists a need for health screening in nurseries, that is fast and efficient, to improve overall health monitoring and nursery productivity. Rapid handheld sensors such as rapid thermal devices, leaf porometers and moisture meters, can provide regular information at a physiological level, that can improve the understanding of the impact of stress on young plant cuttings and their decline in health over time. This paper evaluates the utility and reliability of contemporary sensor technologies, to operationally monitor stress phases in juvenile forest plant cuttings during progressive moisture (dry-down) conditions. Furthermore, to assess whether thermal sensors could be used as an indicator, in conjunction with other variables such as soil water content or stomatal conductance, is needed operationally for fast screening during limited planting windows. Near Infra-Red Analysis (NiRA) data was collected to understand detailed plant functions at a finer reflectance level. A relationship was found where the increase in thermal signals reflects a depletion of water content, resulting in an eventual decline in stomatal conductance and, ultimately, plant mortality. Several algorithms were used in a preliminary test, using RapidMiner software, to discriminate between the four phases of plant health decline using physiological variables and NiRA data. Both Gradient Boosting Trees (GBT) and Deep Learning (DL) showed the best performances, achieving favourable accuracies of 96.8% and 91.2% without NiRA data, 84.6% and 88.2% with NiRA data, with shorter training times. Using thermal technology weighted amongst the highest of the best performing variables using GBT, the utility and accuracy showed good discrimination between the stages of plant decline and is encouraged for future research in this field.

Why it matches plant phenotyping methods植物のストレス段階を熱センサー、ポロメータ、含水率計、NiRAおよび機械学習で測定・識別する方法の有用性と信頼性を評価しており、表現型取得・判定手法が中心である。

abstractThis paper evaluates the utility and reliability of contemporary sensor technologies, to operationally monitor stress phases in juvenile forest plant cuttings during progressive moisture (dry-down) conditions.

A Novel Drought-Resistance Index Balancing Foxtail Millet Yield and Quality and Its Prediction Based on UAV Multimodal Data ↗

Drought stress severely limits foxtail millet yield and quality, yet current drought-resistance indices are exclusively yield-oriented and ignore grain-filling quality. Our two-year (2024–2025) experiments with 24–48 varieties revealed that yield and blighted grain rate (BGR) are partially decoupled (e.g., Zhangzagu 18: yield 2307 kg/ha, BGR 0.444; Zhonggu 19: yield 1622 kg/ha, BGR 0.280). We therefore constructed the Yield–Quality Synergy Index (YQSI = DYI − BGR), which penalizes varieties with poor grain filling. The YQSI tied for first place with DYI in comprehensive screening performance and achieved the highest inter-annual stability (Spearman ρ = 0.823, Jaccard = 0.438, composite score = 1.261). Sensitivity analysis confirmed robustness of the equal-weight formula across a 4-fold range of quality-penalty weights. Six strongly drought-resistant germplasms with balanced yield and quality were identified. Using UAV multimodal data (RGB, multispectral, and thermal infrared) acquired during grain filling, a Random Forest model predicted a YQSI with overall R2 = 0.819 and an F1 score of 0.933 for variety screening. Feature-importance analysis highlighted NDVI, WDRVI, and red-edge texture as key predictors. This study provides a quality-constrained drought-resistance evaluation framework and demonstrates the potential of UAV-based high-throughput phenotyping for foxtail millet breeding.

Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル・熱赤外データから干ばつ耐性指標を予測する高スループット表現型解析手法が研究の中心であり、モデル性能も評価している。

abstractUsing UAV multimodal data (RGB, multispectral, and thermal infrared) acquired during grain filling, a Random Forest model predicted a YQSI with overall R2 = 0.819 and an F1 score of 0.933 for variety screening.

Phenomics and High-Throughput Phenotyping of Photosynthetic Traits for Improving Abiotic Stress Resilience in Wheat and Rice ↗

Photosynthesis is the fundamental biological process underlying plant growth, crop productivity, and global food security. However, its efficiency is highly vulnerable to abiotic stresses, which disrupt chlorophyll biosynthesis, electron transport, carbon assimilation, stomatal regulation, and photoprotective mechanisms, ultimately reducing crop yield. Improving photosynthetic resilience under adverse environments has therefore become a major objective of modern crop improvement. Recent advances in phenomics and high-throughput phenotyping (HTP) have transformed the evaluation of photosynthesis-related traits by enabling rapid, non-destructive, and large-scale assessment across diverse environments, while facilitating quantitative characterization of structural, physiological, biochemical, and thermal responses to abiotic stress. Technologies including chlorophyll fluorescence, gas-exchange analysis, thermal imaging, hyperspectral imaging, LiDAR, and UAV-based sensing provide comprehensive insights into plant physiological responses and stress adaptation. Integration of these phenomic approaches with genomic information and artificial intelligence (AI)-driven analytical frameworks has strengthened genomic and phenomic prediction, enabling more accurate identification of candidate genes, selection of superior genotypes, and accelerated genetic gain. This review critically synthesizes recent advances in photosynthesis-related traits, phenomics, HTP technologies, and their integration with genomics and AI-assisted breeding, highlighting current challenges, knowledge gaps, and future opportunities for developing climate-resilient wheat and rice cultivars and promoting sustainable crop production.

Why it matches plant phenotyping methods植物の光合成形質を対象に、HTP技術やセンサー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 review critically synthesizes recent advances in photosynthesis-related traits, phenomics, HTP technologies, and their integration with genomics and AI-assisted breeding

Rice evapotranspiration estimation and irrigation optimization based on coupling UAV multispectral and thermal infrared imagery with the FAO-56 model ↗

Abstract China's rice production and environmental sustainability are largely dependent on the cold black soil region in Northeast China, where precise water and nitrogen management is challenged by water scarcity and high carbon emissions. To overcome the limitations of conventional empirical management and improve the accuracy of evapotranspiration (ET) estimation in controlled-irrigation paddy fields, this study proposes a novel framework integrating unmanned aerial vehicle (UAV) multispectral and thermal infrared observations, the FAO-56 dual crop coefficient approach, and the NSGA-II multi-objective optimization model. To parameterize and validate this methodology, field data comprising four lower limit thresholds for controlled irrigation and four nitrogen fertilizer application rates were acquired from the Rice Research Site of Farm 856, Heilongjiang Province, China. This integrated approach was used to systematically evaluate rice growth, water consumption, resource use efficiency, and greenhouse gas emissions under different water-nitrogen treatments. Based on these evaluations, an irrigation optimization scheme was developed using daily crop evapotranspiration (ETc). The results indicated that water, nitrogen, and their interaction significantly affected rice yield, irrigation water use efficiency (IWUE), partial factor productivity of nitrogen (PFPN), and global warming potential (GWP). Treatments W3N2 (80%+155 kg/ha N) and W3N3 (80%+200 kg/ha N) achieved the highest yields, 11,883.51 and 11,436.82 kg/ha, respectively, whereas W2N1 (70%+110 kg/ha N) exhibited the best comprehensive performance, with a TCQ value of 0.65. Among the tested vegetation indices, the normalized difference vegetation index (NDVI) showed the strongest correlation with the basal crop coefficient, with an R²of 0.85. The NDVI -crop water stress index ( CWSI ) coupled model achieved the highest ET c estimation accuracy (R 2 = 0.89, RMSE = 0.39 mm/day), reducing the RMSE by 10.3% compared to the traditional, Multi-objective optimization revealed obvious trade-offs among high yield, water saving, high nitrogen efficiency, and low emissions. Scenario S5 was identified as the optimal solution, with an irrigation amount of 669.94 mm, a nitrogen rate of 117.48 kg/ha, a yield of 11,473.43 kg/ha, and the highest coordination degree of 0.86. These results demonstrate that coupling UAV multispectral and thermal infrared imagery with the FAO-56 model can effectively improve ETc estimation and provide reliable data support for water-nitrogen multi-objective optimization in cold-region rice production.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外画像とFAO-56を結合し、イネの蒸発散量を推定する手法を開発・検証しており、ETc推定精度も定量評価しているため、単なる灌漑試験ではなく植物状態の計測手法が中心です。

abstractthis study proposes a novel framework integrating unmanned aerial vehicle (UAV) multispectral and thermal infrared observations, the FAO-56 dual crop coefficient approach, and the NSGA-II multi-objective optimization model.

Advances in Multi-Scale Remote Sensing and Machine Learning for Canopy-to-Root Phenotyping of Drought Adaptation in Sorghum: A Systematic Review ↗

Sorghum (Sorghum bicolor L. Moench) is a major cereal in water-limited environments. Its C4 carbon-concentrating pathway suppresses photorespiration and supports comparatively high photosynthetic and water-use efficiency at high temperature, although yield remains sensitive to the timing and intensity of drought. This systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Eligible primary studies examined sorghum drought physiology, sensing-based phenotyping, trait retrieval, root-associated water capture, or breeding applications. Following duplicate removal and title, abstract and full-text screening, 45 sorghum-specific studies were included. Owing to substantial heterogeneity in experimental design, drought treatment, sensing platform, target trait, and validation metric, evidence was synthesised narratively rather than by meta-analysis. We compare sorghum studies across Light Detection and Ranging (LiDAR), multi-spectral, hyperspectral, thermal, structural, and fluorescence sensing, with emphasis on reported accuracy, transferability and physiological interpretation. We then examine how PROSAIL (PROSPECT coupled with Scattering by Arbitrarily Inclined Leaves) and SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) can be constrained for sorghum canopies and combined with machine learning. The central contribution is a sorghum-specific framework that distinguishes directly observed or model-retrieved canopy traits from indirect root-function predictions requiring ground validation. The synthesis identifies practical routes for measuring functional stay-green, high-vapour-pressure-deficit responses and post-anthesis water capture, while defining priorities for cross-environment validation and breeding deployment.

Why it matches plant phenotyping methodsソルガムの干ばつ適応に関するセンシング型フェノタイピング手法を体系的にレビューし、形質推定の精度・移植性・検証、およびモデルと機械学習の統合を扱うため、方法論が中心である。

abstractThis systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement.

UAV-Based Thermal Inversion for Canopy Temperature Retrieval and Precision Irrigation. ↗

Accurate assessment of crop water status is critical for precision irrigation and sustainable water management in agriculture. This study develops a UAV-based thermal infrared inversion framework for high-resolution canopy temperature retrieval and irrigation decision support in tea plantations. The proposed approach integrates multi-frame image mosaicking, threshold-based canopy extraction, and a gray-temperature calibration model to generate spatially continuous canopy temperature maps. Crop water stress was quantified using the Crop Water Stress Index (CWSI), and its reliability was further evaluated by analyzing its relationship with stomatal conductance. The framework further estimates soil moisture status and irrigation requirements based on a threshold-based irrigation strategy. The results show that the linear gray-temperature calibration model achieved a maximum absolute error of less than 0.3 °C and that the calculated CWSI and estimated irrigation requirement were strongly correlated with measured stomatal conductance, with R 2 up to 0.91. The proposed method provides a practical technical workflow from UAV thermal imagery acquisition to canopy temperature retrieval and quantitative irrigation decision-making, demonstrating its potential for precision irrigation management in tea plantations.

Why it matches plant phenotyping methodsUAV熱画像から茶園の樹冠温度と水ストレスを推定する取得・抽出・較正手法を開発し、気孔コンダクタンスとの関係で検証しており、植物状態の計測が中心である。

abstractThis study develops a UAV-based thermal infrared inversion framework for high-resolution canopy temperature retrieval and irrigation decision support in tea plantations.

Utilizing Vegetation Indices Derived from VNIR-SWIR Hyperspectral Data to Characterize Growth, Maturation, and Senescence in Wheat and Barley ↗

Cereal crops, including wheat and barley, are essential for global food security, but their productivity is strongly affected by nitrogen availability and water limitation. This study investigated the phenotypic responses of two commercially significant spring wheat cultivars, Videodur (DU) and Sensas (SW), and two spring barley cultivars, Tiroler Imperial (SG1) and Amidala (SG2), exposed to two nitrogen regimes, low nitrogen at 25 kg N/ha (N25) and high nitrogen at 130 kg N/ha (N130), under drought and well-watered conditions. Plants were monitored from the late vegetative stage through maturity under controlled multivariable climatic conditions similar to field settings. A high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence. The results revealed cultivar-specific responses to combined nitrogen and drought stress. Under drought conditions, the high nitrogen treatment (N130) increased plant temperature (Tplant) for barley (cv. SG1) and wheat (cv. SW) compared to N25, thereby accelerating early maturation. However, the decline in chlorophyll was not uniformly faster across all cultivars tested. The DU cultivar exhibited superior chlorophyll absorption and reflectance, indicating better drought adaptation compared to other tested species. The high nitrogen treatment (N130) reduced water use efficiency (WUE) in the SW and SG2 cultivars compared to N25, implying that these cultivars used more water. Enhanced nitrogen did not consistently improve water use efficiency but did accelerate the growth cycle. SG2 was particularly sensitive to drought, showing declines in vegetation indices, except for the Water Content Index, highlighting the need for precise water and nitrogen management. Overall, the integration of hyperspectral, thermal, RGB, and water use measurements enabled the identification of trait signatures linked to drought adaptation, nitrogen response, maturation, and senescence. These findings provide practical insights for optimizing nitrogen and irrigation management and for supporting breeding strategies aimed at improving cereal crop resilience under climate-change-associated stress conditions.

Why it matches plant phenotyping methodsRGB画像、赤外線サーモグラフィー、VNIR–SWIRハイパースペクトルを統合した高スループット表現型解析ワークフローが中心的に記述され、複数の植物形質・状態を定量化している。

abstractA high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence.

Remote Sensing and Machine Learning for Monitoring Soil Nitrogen Dynamics and Crop Nitrogen Status in Field Conditions ↗

Efficient nitrogen (N) management is essential for sustaining crop productivity while minimizing environmental impacts associated with nitrogen losses. However, the high spatial and temporal variability of soil nitrogen dynamics and crop nitrogen status makes field-scale monitoring challenging, while conventional soil and plant sampling methods are labor-intensive, destructive, and provide limited spatial coverage. Recent advances in remote sensing technologies and machine learning (ML) offer promising alternatives for high-throughput, non-destructive monitoring of crop nitrogen status and related nitrogen dynamics in agroecosystems. This review synthesizes current progress in the use of proximal and remote sensing platforms, including unmanned aerial vehicles (UAVs), satellites, and ground-based sensors for assessing crop nitrogen status and inferring soil nitrogen availability. We examine spectral, thermal, and structural indicators, together with emerging sensor-fusion and time-series approaches. We also evaluate ML algorithms, including emerging foundation model approaches, for estimating crop nitrogen status and inferring soil nitrogen indicators, highlighting their performance, limitations, and transferability across environments. Particular emphasis is placed on field-scale applications in heterogeneous and water-limited systems, where nitrogen-water interactions critically influence crop responses. Finally, we discuss current challenges, including data scarcity, model generalization, and operational constraints, and outline future directions toward integrated, real-time decision support systems for precision nitrogen management. Overall, this review provides a comprehensive framework for leveraging remote sensing and data-driven approaches to improve nitrogen monitoring and enhance nitrogen use efficiency in diverse cropping systems.

Why it matches plant phenotyping methods作物の窒素状態という植物形質を対象に、リモートセンシングと機械学習による推定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 review synthesizes current progress in the use of proximal and remote sensing platforms, including unmanned aerial vehicles (UAVs), satellites, and ground-based sensors for assessing crop nitrogen status and inferring soil nitrogen availability.

Early identification of glufosinate-resistant soybeans using plant image analysis ↗

The use of glufosinate-resistant GM soybean has expanded, raising concerns about resistant weed development and unintended transgene flow. To support monitoring for timely management, we propose an early, non-destructive identification method using spectral images acquired from whole soybean plants after glufosinate treatment. We evaluated the potential of spectral imaging, using RGB, infrared (IR) thermal, and chlorophyll fluorescence (CF) sensors, for early detection of glufosinate resistance in soybean. In the dose-response test, the key spectral indices including NDI, temperature difference, F v /F m , and NPQ distinguished between resistant and susceptible soybeans within 4 to 24 hours after treatment (HAT). IR thermal and CF imaging showed higher sensitivity in identifying resistance than RGB imaging by detecting spectral responses associated with physiological changes before visual symptoms appeared. Validation test with a single dose treatment of glufosinate reconfirmed that image analysis by both the naked eye and machine learning (ML) can discriminate between resistant and susceptible soybeans in a single day after glufosinate treatment. ML-based classification using IR thermal index achieved 100% accuracy as early as 6 HAT and the classification by the naked eye using IR thermal images showed 96.6% accuracy at 24 HAT. These results suggest that plant imaging enables early and non-destructive identification of herbicide-resistant individuals by detecting early spectral changes to herbicide treatment. These findings support its use as a potential alternative to conventional diagnostic methods for detecting individuals containing transgenes in herbicide-resistant GM soybean cultivation for future applications in herbicide-resistant weed monitoring.

Why it matches plant phenotyping methodsスペクトル画像（RGB、熱赤外、クロロフィル蛍光）と機械学習を用いて、薬剤処理後の植物の生理応答から耐性を早期識別する方法を開発・検証しており、植物表現型の取得が中心である。

abstractwe propose an early, non-destructive identification method using spectral images acquired from whole soybean plants after glufosinate treatment.

Nanosensors and Geospatial Technologies for Early Crop-stress Detection in Precision Agriculture: A Critical Multiscale Synthesis ↗

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.

Development of web-based YCPM-UAV interface for early yield prediction of canola crop using UAV multi-sensor data ↗

• Development of yellow color index (YCI) for yield estimation at flowering stage • Development of web-based interface (YCPM-UAV) for canola yield prediction using UAVs • Global application capability for UAVs datasets to predict canola yield using YCPM-UAV • Multi-sensor and multi-spectrum data fusion to find most suited indices for canola • Multiple and stepwise regression analysis for selection of most influencing VIs Canola ( Brassica napus L.) is a globally significant oilseed crop, yet accurate yield estimation remains challenging due to the complex and unique nature of the crop, especially at the flowering stage. Traditional field-based yield estimation methods are labor-intensive, time-consuming, and destructive, necessitating innovative approaches for early and non-destructive yield prediction. The main objective of the study is to develop a novel web-based platform, YCPM-UAV (Yellow Color Prediction Model using Unmanned Aerial Vehicles), for early and accurate canola yield estimation using high-resolution multi-sensor datasets acquired through low-altitude UAVs (LA-UAVs). To achieve this objective, a comprehensive two-year field study (2022-2024) was conducted across ten farmers’ fields in different geographical locations. Multisensor data (RGB, multispectral, and thermal) were acquired using UAVs at seven growth stages. Several vegetation indices (VIs), yellow color-based indices, and a thermal index were calculated. Linear, multiple, and stepwise regression analyses were performed to evaluate relationships of remote sensing indices with ground-truth yield data collected from 1200 sampling points. Multiple and stepwise regression analyses indicated that the newly developed Yellow Color Index (YCI) exhibited the strongest correlation with actual canola yield at the flowering stage across both years (Year 1: R 2 = 0.84, RMSE = 39.30 g m⁻²; Year 2: R² = 0.88, RMSE = 31.57 g m⁻²). Based on proposed predictive modeling, the YCPM-UAV web interface was developed, featuring automated data processing and spatial analysis with a testing accuracy of 88%. The YCPM-UAV platform provides farmers, researchers, and policymakers with a timely, user-friendly, and actionable decision-support tool for canola yield estimation at the field scale, contributing to improved crop management and food security. Future studies should incorporate additional canola varieties, irrigated and non-irrigated fields, and deep learning algorithms to further improve model robustness.

Why it matches plant phenotyping methodsUAVマルチセンサー画像からカノーラ収量を推定する指標・回帰モデル・Webプラットフォームを開発し、複数年データで検証しており、植物形質取得・推定が中心である。

titleDevelopment of web-based YCPM-UAV interface for early yield prediction of canola crop using UAV multi-sensor data

Estimation of cotton plant moisture content using UAV multimodal data and machine learning ↗

Accurate monitoring of cotton plant moisture content (PMC) is crucial for guiding irrigation practices. To address the limited capacity of single-source remote sensing data to characterize the water status of cotton plants, as well as the lack of quantitative reference values for suitable PMC levels at different growth stages, this study constructed a cotton PMC estimation model based on multimodal UAV remote sensing data. Furthermore, the suitable reference levels of PMC at different growth stages were investigated according to the response relationship between PMC and yield at each growth stage. Five soil moisture gradients were established, and at each growth stage, fresh and dry weights of cotton shoots were measured to calculate the PMC. A UAV platform equipped with multiple sensors was used to collect visible-light (RGB), multispectral (MS), and thermal infrared (TIR) images of the cotton canopy. Three feature selection methods were employed to identify moisture-sensitive parameters: Pearson correlation analysis, principal component analysis (PCA) for dimensionality reduction, and recursive feature elimination (RFE). Using the selected parameters, four machine learning algorithms, AdaBoost, random forest (RF), CatBoost, and k-nearest neighbors (KNN), were applied to construct and validate PMC estimation models. The suitable PMC levels at different growth stages were identified based on the response relationship between measured PMC and yield under different water gradients. The results showed that the RFE feature selection method identified eight water-sensitive parameters, and the CatBoost model integrating multimodal data performed best, with R² and RMSE reaching 0.807 and 0.033%, respectively, on the test set, providing a reliable method for high-resolution spatial mapping of field-scale PMC. On this basis, the response of yield to PMC was analyzed, revealing that when PMC was maintained at 83.8%, 85.9%, 79.3%, 78.0%, and 67.7% at the bud, initial flowering, peak flowering, peak boll-setting, and boll opening stages, respectively, the theoretical maximum yield of 6579–6667 kg/hm² could be achieved. This study realized high-precision remote sensing monitoring of PMC and further explored the appropriate moisture content thresholds for different growth stages, providing a quantitative reference for precision water regulation in cotton fields.

Why it matches plant phenotyping methodsUAVのマルチモーダル画像と機械学習により、綿植物の水分含量を推定・検証する手法が研究の中心であり、植物状態の高解像度マッピングにも応用している。

abstractthis study constructed a cotton PMC estimation model based on multimodal UAV remote sensing data

Diurnal cross-temporal features from UAV multispectral and thermal imagery enhance foxtail millet yield prediction accuracy under different irrigation regimes ↗

Accurate prediction of foxtail millet yield is essential for effective field management and high-throughput breeding. Despite advances in UAV-based yield prediction for major crops, existing studies predominantly rely on single-temporal features (SFs) extracted at noon, overlooking significant diurnal dynamic signals that characterize crop responses to water stress. To address this research gap, we propose a novel approach utilizing diurnal cross-temporal features (CFs) derived from UAV-based multispectral and thermal imagery to enhance yield prediction accuracy under different irrigation regimes. During the flowering and grain-filling stages, UAV images were acquired across eight time slots (T1–T8) within a single day to capture the complete diurnal trajectory of canopy physiological responses. SFs were extracted at each time slot, and CFs were derived through summation, averaging, and range operations across multiple slots. A systematic four-step workflow was developed to determine the optimal UAV flight frequency and timing by balancing prediction accuracy with operational costs. Three ensemble learning algorithms (Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost)) were evaluated using multiple feature sets incorporating SFs, CFs, and their integration. Results demonstrated that CFs more comprehensively captured dynamic crop responses to water stress than SFs. Canopy features from afternoon combinations generally exhibited stronger yield correlations than morning combinations. The [T5, T8] combination was identified as optimal, providing a practical balance between prediction accuracy and operational cost. Model comparison revealed that RF exhibited greater robustness across different water treatments, whereas AdaBoost achieved higher accuracy on the test set. Feature importance analysis confirmed the dominance of CFs, with ∑VSWI ranking first across both models and growth stages. This study provides a systematic framework for utilizing diurnal dynamic signals in crop yield prediction, offering new methodological insights for precision agriculture and high-throughput phenotyping of foxtail millet and other dryland crops.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像から作物特徴量を抽出し、収量という植物形質を推定する手法と、撮影頻度・時刻を最適化するワークフローが研究の中心であるため。

abstractwe propose a novel approach utilizing diurnal cross-temporal features (CFs) derived from UAV-based multispectral and thermal imagery to enhance yield prediction accuracy

Linking plant water status dynamics to yield and fruit cracking in citrus orchards using UAV multi-sensor data and machine learning ↗

Citrus fruit cracking causes substantial yield and economic losses, yet its relationship with plant water status (PWS) and irrigation management remains insufficiently characterized. Unlike previous UAV-based irrigation studies that focused on water-stress detection or yield estimation, this study introduces a dynamic, physiology-based framework that links temporal PWS trajectories during key phenological stages to fruit-cracking risk at the individual-tree scale. UAV-based multispectral, thermal, and LiDAR data, combined with field physiological measurements and machine-learning models, were evaluated in an irrigation management experiment in an ‘Ori’ mandarin orchard (Israel) across three contrasting growing seasons (2023–2025). Several irrigation treatments with different irrigation timings and water inputs were applied during the growing season to evaluate their effects on temporal PWS dynamics and fruit cracking. Trunk growth (TG), stem water potential (SWP), stomatal conductance (SC), and plant area index (PAI) were measured throughout the two seasons and estimated using Random Forest models (R 2 > 0.783). These indicators were subsequently used to predict yield and fruit cracking with high accuracy (yield: R² = 0.896; cracking: R² = 0.845). Cracking was lowest in 2023 (∼3%), with ∼25% lower irrigation, suggesting reduced irrigation may reduce cracking risk. Higher cracking in 2024 (∼14%, vs ∼8% in 2025) coincided with intense heat events. Mid-season SWP and SC were strongly associated with yield formation and cracking patterns. These findings demonstrate that monitoring temporal PWS dynamics can support precision irrigation management by identifying high-risk zones and enabling irrigation strategies that stabilize PWS, reduce the incidence of cracking, and improve yield under variable climatic conditions.

Why it matches plant phenotyping methodsUAVマルチセンサーと機械学習により、樹体水分状態などの植物形質を推定し、収量・果実裂果を予測する技術的枠組みが研究の中心である。

abstractthis study introduces a dynamic, physiology-based framework that links temporal PWS trajectories during key phenological stages to fruit-cracking risk at the individual-tree scale.

Identification of Unsound Soybean Seeds Based on Hyperspectral Imaging and a Dual‐Channel Residual‐Squeeze‐and‐Excitation Network With Gramian Angular Field Fusion ↗

The precise identification of unsound soybean seeds is a critical step in deep soybean processing and seed selection. The accuracy of this identification directly influences the quality of subsequent processed products, as well as the germination rate and yield of soybean crops. This study proposes a nondestructive identification method for unsound soybean seeds based on hyperspectral imaging (HSI), Gramian Angular Field (GAF), and a Dual-Channel Residual-Squeeze-and-Excitation Network with GAF Fusion (DC-RSEN-GF). According to common damage types, soybeans were categorized into six classes: sound seeds, thermal-damaged seeds, insect-damaged seeds, broken seeds, spotted seeds, and moldy seeds. Spectral data from these six soybean categories were acquired using a hyperspectral camera and transformed into two-dimensional GAF images. The DC-RSEN-GF network integrates one-dimensional spectral data with two-dimensional GAF images. After preprocessing with Savitzky-Golay (SG) smoothing, high-precision classification was achieved through residual blocks, an attention mechanism (using SENet), and feature fusion. Compared to five benchmark models-Extremely Randomized Trees (ERT), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), VGG19, and ResNet18-the DC-RSEN-GF model achieved superior performance, with accuracy, precision, specificity, and F1-scores of 96.36%, 96.43%, 97.92%, and 96.36%, respectively. The accuracy, precision, and F1-scores are all superior to traditional machine learning and existing deep learning models, demonstrating better classification capabilities. In addition, t-distributed Stochastic Neighbor Embedding (t-SNE) was employed for visual analysis of soybean spectra, further validating the reliability of the DC-RSEN-GF model. The proposed detection method, based on HSI and DC-RSEN-GF, enables accurate and nondestructive identification of unsound soybean seeds and holds significant potential for practical application.

Why it matches plant phenotyping methodsハイパースペクトル画像と深層学習を用いて、種子の損傷・病変状態を非破壊的に分類する取得・解析手法が研究の中心であり、植物状態の表現型測定に該当する。

abstractThis study proposes a nondestructive identification method for unsound soybean seeds based on hyperspectral imaging (HSI), Gramian Angular Field (GAF), and a Dual-Channel Residual-Squeeze-and-Excitation Network with GAF Fusion (DC-RSEN-GF).

Genomic Selection Integrated with High-Throughput Phenotyping and Speed Breeding for Smart and Greener Rice ( Oryza sativa ) Improvement. ↗

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.

High-resolution LiDAR and thermal UAV data for 3D analysis of urban vegetation structure and its cooling effect in San Nicolás, Mexico ↗

Abstract. Urban vegetation is essential for mitigating the Urban Heat Island effect, yet its cooling performance depends on its three-dimensional structure. This study combines high-resolution Unmanned Aerial Vehicle - based LiDAR (Zenmuse L2) and thermal imaging (Zenmuse H20) to analyze vegetation structure and surface temperature across 4 urban parks in San Nicolás de los Garza, Mexico. LiDAR data were processed to generate Digital Terrain Model, Digital Surface Model and Canopy Height Model models, enabling the segmentation of individual trees and extraction of structural metrics such as canopy height, crown area and point density. Thermal orthomosaics were co-registered with LiDAR models to quantify temperature contrasts between vegetated and impervious areas. Results reveal consistent cooling effects in all parks, with vegetated zones showing 8–15 °C lower surface temperatures depending on canopy density and maturity. Larger parks with continuous canopies displayed the strongest thermal regulation. This integrated LiDAR–thermal approach provides a precise and scalable framework for assessing microclimatic benefits of urban vegetation, supporting climate-resilient planning in rapidly urbanizing regions.

Why it matches plant phenotyping methodsUAV LiDAR・熱画像を用いて個体樹木の樹冠高や樹冠面積などの植物構造形質を抽出する手法と統合ワークフローが中心であり、単なる環境測定ではない。

abstractLiDAR data were processed to generate Digital Terrain Model, Digital Surface Model and Canopy Height Model models, enabling the segmentation of individual trees and extraction of structural metrics such as canopy height, crown area and point density.

A multimodal geospatial foundation model anticipates crop stress and yield failure across climates and species ↗

Introduction Climate extremes increasingly threaten agricultural production, yet many artificial intelligence systems in agriculture remain local, reactive and narrowly trained for one crop, region or sensing modality. Methods We present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions. AgriFM was pretrained using self-supervised objectives on 2.4 million field-season sequences and evaluated on a curated benchmark spanning maize, wheat, soybean, rice and sorghum across five agroclimatic regions. Results In held-out geography and time-split evaluations, AgriFM improved early stress detection and yield-failure prediction over statistical, crop-model and deep-learning baselines. The largest gains occurred during compound drought and heat events, for which AgriFM produced alerts 18 to 24 days earlier than the satellite-only baseline while maintaining improved calibration. Phenology-conditioned fusion improved transfer across planting calendars, and uncertainty calibration reduced false alerts at fixed recall. Discussion Because the study is based on retrospective datasets, these findings establish cross-region retrospective performance rather than prospective field efficacy. The results support further field-based evaluation of multimodal foundation models for climate-resilient crop monitoring.

Why it matches plant phenotyping methods作物ストレス状態と収量失敗リスクを衛星・レーダー・熱画像等から推定する基盤モデルを開発し、複数作物・地域のベンチマークで評価しており、植物状態の取得・推定手法が中心である。

abstractWe present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions.

In-Situ Gaussian Splatting-generated 3D Thermal Mesh Visualization for Urban Trees in Augmented Reality ↗

Abstract. Urban trees provide critical ecosystem services in dense city environments, yet current workflows for monitoring their thermal behaviour remain confined to 2D desktop-based analysis with no three-dimensional spatial context or field-deployable visualization capability. This paper presents a complete pipeline for in-situ 3D thermal mesh visualization of urban trees in Augmented Reality (AR), combining Thermal InfraRed (TIR) image acquisition, Gaussian Splatting-based mesh reconstruction, quantitative validation, and mobile AR deployment. TIR images of a Tilia tomentosa acquired with a FLIR T560 camera are preprocessed with a standardized false-colour palette and fed into the MILo (Mesh-In-the-Loop Gaussian Splatting) framework to reconstruct a thermally attributed 3D mesh. Geometric evaluation against a Z+F IMAGER 5016 TLS reference using the M3C2 algorithm demonstrates that MILo recovers 13.5 times more canopy geometry than traditional multi-view stereo under thermal imagery, with a standard deviation of 4.0 cm. A colourmap inversion procedure recovers per-vertex temperature estimates from the GS-derived mesh colours, yielding a mean absolute difference of 0.7°C against direct T-Cam measurements (thermal camera mounted on the laser scanner), within the combined instrument accuracy of both sensors. The resulting thermal Gaussian Splat was deployed in a custom Android AR application supporting hybrid marker-based and GPS-based spatial anchoring for in-situ visualization. These results demonstrate the technical feasibility of GS-based thermal reconstruction and mobile AR as a medium for communicating three-dimensional canopy thermal information to educators and urban forestry practitioners.

Why it matches plant phenotyping methods都市樹木の葉冠温度と3D形状を取得・可視化する熱画像ベースの再構成パイプラインを開発し、TLSおよび熱カメラとの定量検証まで行っており、植物フェノタイピング手法が研究の中心である。

abstractThis paper presents a complete pipeline for in-situ 3D thermal mesh visualization of urban trees in Augmented Reality (AR), combining Thermal InfraRed (TIR) image acquisition, Gaussian Splatting-based mesh reconstruction, quantitative validation, and mobile AR deployment.

INTEGRATING UAV-BASED THERMAL IMAGING, DREB EXPRESSION, AND AGRONOMIC INDICES TO EVALUATE DROUGHT TOLERANCE IN DIVERSE TRITICUM SPECIES ↗

Selecting ideal drought-tolerant wheat varieties requires a holistic synthesis of digital phenotypes, molecular markers, and agronomic indices. This study evaluated 16 wheat genotypes for drought tolerance by integrating digital phenotyping (UAV-based thermal imaging), molecular data (DREB gene expression profiles), and 12 agronomic indices. While vegetative DREB accumulation remained mostly homogeneous, the generative stage triggered pronounced transcriptional shifts, and late-stage thermal screening revealed highly significant genotypic differences during grain filling. A multivariate PCA biplot identified early canopy temperature differences during tillering (ΔCT_TL) as the most informative non-destructive selection indicator. ΔCT_TL showed a strong positive association with terminal yield stability metrics (YSI and RSI) and a marked negative relationship with the drought sensitivity index (SDI). This early canopy temperature regulation contributed to the maintenance of yield stability in the modern hexaploid variety MFTBY-T and advanced tetraploid lines OR2-T and OR4-S. In contrast, poorly adapted ancient varieties (P5-S and S3-S) exhibited high drought sensitivity accompanied by pronounced late-stage induction of DREB1 and DREB2, suggesting a delayed stress-response mechanism activated under severe tissue dehydration. Conversely, the modern tetraploid variety KZLTN-T and hexaploid landraces appeared to rely on an early vegetative molecular priming strategy. These findings suggest that breeding programs should prioritize the incorporation of vegetative transcriptional traits associated with effective canopy temperature homeostasis into elite genetic backgrounds.

Why it matches plant phenotyping methodsUAV熱画像によるキャノピー温度の非破壊測定をデジタル表現型として用い、乾燥耐性選抜指標として評価しており、表現型取得・解析が研究の主要な構成要素である。

titleINTEGRATING UAV-BASED THERMAL IMAGING, DREB EXPRESSION, AND AGRONOMIC INDICES TO EVALUATE DROUGHT TOLERANCE IN DIVERSE TRITICUM SPECIES

Phenotyping of genotypes and diagnosis of water status in cowpea using thermographic images and machine learning ↗

Abstract Purpose The variability in tolerance to water stress among cowpea genotypes requires fast and accurate phenotyping methods. The integration of infrared thermography with artificial intelligence is emerging as a robust solution for large-scale, non-invasive monitoring. Thus, the objective was to train models to identify genotypes and diagnose water stress in cowpea using artificial intelligence algorithms to process infrared thermographic images. Methods Ten genotypes (five varieties: Corujinha – G1, Paulistinha – G2, Sempre Verde – G3, Pintado – G4, and Rabo de Tatu – G5) and the cultivars BRS Novaera – G6, BRS Pajeú – G7, IPA 206 – G8, BRS Tapaihum – G9, and BRS Miranda – G10) were subjected to four water regimes (25%, 50%, 75%, and 100% of ETc). Thermographic images were collected at the V3 and R2 stages and processed using Deep Learning architectures (InceptionV3, SqueezeNet, VGG16, and VGG19) to extract features (vectorization). The k-NN, Decision Tree, Random Forest, SVM, Neural Network, and AdaBoost algorithms were trained to classify stress levels and genotypes. Results The vegetative stage (V3) proved more effective for diagnosis than the reproductive stage (R2), exhibiting more stable thermal signatures. The SVM algorithm, combined with the VGG16 vectorizer, achieved the best performance, achieving an accuracy greater than 0.910 in classifying water regimes. The landrace varieties exhibited thermal patterns distinct from those of the improved cultivars, enabling high-precision genotypic identification. Conclusions The proposed approach demonstrates that infrared thermography, combined with machine learning models, is an effective tool for high-throughput digital phenotyping, optimizing the selection of drought-tolerant materials and irrigation management in precision agriculture.

Why it matches plant phenotyping methods赤外線サーモグラフィ画像から水ストレス状態と遺伝型を抽出する機械学習手法を開発・評価しており、植物フェノタイピングが研究の中心である。

abstractThus, the objective was to train models to identify genotypes and diagnose water stress in cowpea using artificial intelligence algorithms to process infrared thermographic images.

Toward Autonomous Crop Sensing: High-Frequency UAV-Based RGB and Thermal Imaging of Maize and Soybean ↗

Abstract Precision field management and high-throughput plant phenotyping increasingly rely on remote sensing to capture spatial and temporal variability in crop performance. Unmanned aerial vehicle (UAV) – based sensing offers unique advantages for field-scale data collection, including high spatial resolution, flexible deployment, and scalable throughput. However, the full potential of UAV platforms remains constrained by labor-intensive operations across flight execution, data transfer, and processing workflows. This study presents a systematic evaluation of an automatic UAV-based crop sensing platform through a season-long, multi-crop field experiment. Data acquisition was conducted over a maize irrigation trial and a soybean breeding experiment, resulting in 176 completed flights over 28 days during the growing season. High-frequency flights on selected days captured diurnal dynamics in key canopy traits, including maize leaf rolling under drought stress and genotype-dependent plot temperature variation in soybean. In the maize irrigation experiment, significant differences in diurnal canopy cover ratio (CCR) were observed among irrigation treatments. The predictive relationship between CCR and final grain yield strengthened throughout the day, with the coefficient of determination (R 2 ) increasing from 0.05 in the early morning (RMSE = 3.05 Mg ha − 1 ) to 0.65 at midday (RMSE = 1.87 Mg ha − 1 ), highlighting the importance of temporal optimization in UAV-based sensing. Temperature measurements from the onboard thermal infrared camera showed a strong overall linear correlation with ground truth measurements (R 2 = 0.85). In the soybean trial, the highest plot temperature was observed on the fast-wilting genotype. Additionally, regression models were developed to estimate key crop traits, including canopy height (CH) and leaf area index (LAI), demonstrating the platform’s quantitative sensing capability. Overall, this study demonstrates that automatic UAV systems enable high-temporal-resolution crop monitoring while substantially reducing operational cost. The results highlight their potential for precise crop management and scalable field phenotyping. Future work will focus on integrating automated data processing pipelines to support near-real-time analytics and decision-making.

Why it matches plant phenotyping methods自動UAVのRGB・熱画像センシング platform を圃場で系統的に評価し、温度・キャノピー被覆率・高さ・LAIなどの植物形質を定量化しているため、フェノタイピング手法が研究の中心である。

abstractThis study presents a systematic evaluation of an automatic UAV-based crop sensing platform through a season-long, multi-crop field experiment.

Estimating maize canopy water content using UAV-based multispectral-thermal infrared imagery and canopy signal distributional features. ↗

Introduction Canopy water content (CWC) is an important indicator of crop water status **and** supports precision irrigation decision-making. Plot-level CWC estimation using UAV imagery often relies on canopy mean features, whereas the role of within-plot canopy-signal distributional information remains insufficiently examined. Methods In this study, spring maize at the Shiyanghe site was monitored using UAV-based multispectral and thermal infrared imagery. Mean, percentile, and dispersion features were extracted from effective canopy pixels within each plot. RFECV feature selection, 50 repeated random train-test splits, paired statistical tests, simulated spatial aggregation, and four regression models were used to evaluate the stage- and scale-dependent contribution of these features. Results and discussion Water stress affected both overall spectral-thermal responses and within-plot signal distributions. Before tasseling, percentile and dispersion features were frequently selected and provided complementary information, especially for tree-based models and finer aggregation scales. After tasseling, mean features generally showed more stable performance, although some distributional features still contained CWC-related information. The supplementary Xinxiang site-internal analysis suggested that, under weak water-gradient and small-sample conditions, distributional features may be frequently selected but may not consistently improve prediction accuracy. Overall, the contribution of distributional features was growth-stage-, scale-, and model-dependent.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外画像からトウモロコシ群落の水分含量を推定する特徴抽出・選択・回帰手法を中心に、反復分割や統計検定で技術的に評価しているため。

abstractPlot-level CWC estimation using UAV imagery often relies on canopy mean features, whereas the role of within-plot canopy-signal distributional information remains insufficiently examined.

Thermal imaging as a tool for studying circadian rhythms in roots. ↗

Plants rely on the circadian clock to anticipate daily environmental fluctuations and to coordinate key physiological, metabolic, and developmental processes. Most if not all plant cells have semi-autonomous circadian oscillators. Roots possess a modified yet robust circadian oscillator that is entrained by external cues such as light and temperature to synchronize nutrient uptake, water transport, and metabolic activity. It has been shown that the root and shoot oscillators can communicate through long-distance signals including mobile proteins and carbon assimilates such as sucrose. Moreover, recent studies indicate root-microbe interactions; root-associated microbial communities exhibit diurnal oscillations structured by the host circadian system, while microbes can in turn modulate the circadian period and rhythmic outputs of the plant. However, in general, while the shoot circadian oscillator has been extensively characterized, much less is known about the root circadian system. Progress has been hampered by a lack of high-throughput, non-invasive methods to study root rhythmicity. Existing methods including luciferase reporters, quantitative RT-PCR, and microscopy remain limited by cost, destructive sampling, or require transgenic lines with reporter genes. We have developed a thermal infrared imaging platform that enables non-invasive, high resolution of circadian rhythms in roots across plant species and growth conditions. We show that our system can be used to analyse metabolite and microbial effects on root circadian regulation. This platform provides new opportunities to investigate below-ground circadian regulation and the possibilities of harnessing the root clock to enhance plant performance and resilience.

Why it matches plant phenotyping methods根の概日リズムという植物状態を、非破壊・高解像度の熱赤外画像で測定するプラットフォームを開発しており、フェノタイピング手法が研究の中心である。

abstractProgress has been hampered by a lack of high-throughput, non-invasive methods to study root rhythmicity.

Regional-Scale Estimation of Maize Plant Moisture Content in Arid Regions Integrating Multi-Source Remote Sensing and Machine Learning ↗

Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R2 of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R2 increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments.

Why it matches plant phenotyping methodsマルチソースリモートセンシングと機械学習により、トウモロコシの植物含水量という明示的な植物状態を地域スケールで推定・検証する手法開発が中心である。

abstractthis work develops a regional PMC estimation approach by combining multi-source remote sensing data.

A physics-informed neural network for continuous rice canopy thermal monitoring and forecasting from sparse UAV observations ↗

Continuous monitoring of canopy temperature (Tc), a key indicator of crop water-heat stress and physiological dynamics, using unmanned aerial vehicle (UAV) imagery is inherently limited by temporal discontinuity and the limited physical realism of purely data-driven models. This study develops a physics-informed neural network (PINN) framework to transform temporally sparse UAV thermal observations into continuous hourly rice Tc reconstruction and 48 h forecasting products. The model leverages sparse UAV thermal measurements as supervisory signals while integrating them with continuous meteorological forcing and daily UAV-derived crop phenotypic features. Validated through a comprehensive season-long rice field experiment using walk-forward cross-validation, the proposed PINN framework demonstrated superior performance. It achieved R 2 values of 0.92 for reconstruction and 0.90 for forecasting, with RMSE of 0.71 °C and 0.82 °C, respectively. Ablation analysis further showed that crop phenotypic variables contributed more strongly than temporal descriptors, reducing predictive uncertainty by approximately 4.8–14.3 %, while the integration of SEB physical constraints and uncertainty modeling improved R 2 by 8.4–9.5 % and reduced Total STD by 28.4–37.7 %. The model successfully captures diurnal dynamics, spatial variability, and canopy thermal hysteresis while maintaining physical consistency through improved energy closure. This framework bridges sparse aerial observations with continuous physiological monitoring and highlights its potential to support precision irrigation, early stress detection, and high-throughput phenotyping in smart agriculture.

Why it matches plant phenotyping methodsUAV熱画像による疎な観測からイネ群落温度を連続再構成・予測するPINNを開発し、交差検証とアブレーション分析で性能評価しており、表現型取得・推定手法が研究の中心である。

abstractThis study develops a physics-informed neural network (PINN) framework to transform temporally sparse UAV thermal observations into continuous hourly rice Tc reconstruction and 48 h forecasting products.

Spatiotemporal Canopy Temperature Forecasting for Precision Irrigation Management ↗

Abstract Canopy temperature (Tc) is a critical physiological indicator of water and heat stress in cotton. Although weather-driven Tc forecasting is used by 60% of Australian cotton growers for irrigation scheduling, current methods typically rely on a single in situ sensor to represent an entire management area. This uniform assumption overlooks substantial spatial variability in Tc and can lead to suboptimal water application. We propose UAV-linear, a novel spatio-temporal forecasting model that integrates high-accuracy in-situ sensors with weekly Unmanned Aerial Vehicle (UAV) thermal imagery to generate high-resolution hourly spatial Tc forecasts. Experimental results show that UAV-linear forecast stress conditions at unmeasured locations as effectively as models trained on exhaustive historical data, achieving a 25-35% improvement over the standard uniform-forecast assumption. Furthermore, in a large-scale validation across 50,000 hectares of commercially active farms (practical dataset), UAV-linear improved stress-hour prediction by 22% relative to the uniform assumption while maintaining accuracy comparable to historical benchmarks. These findings show that the proposed spatio-temporal framework provides the spatial detail needed for differentiated precision management, with potential to improve crop yield and water-use efficiency.

Why it matches plant phenotyping methods綿花のキャノピー温度という植物の生理状態を、UAV熱画像とセンサーから推定・予測する手法を開発し、大規模に検証しているため、灌漑管理への応用でもフェノタイピング手法が中心です。

abstractWe propose UAV-linear, a novel spatio-temporal forecasting model that integrates high-accuracy in-situ sensors with weekly Unmanned Aerial Vehicle (UAV) thermal imagery to generate high-resolution hourly spatial Tc forecasts.

Assessing plant water status: Part 2 – Non‐destructive and remote sensing approaches ↗

Precise, real time and non-destructive assessment of plant water status is important for advancing plant physiological understanding, optimizing water usage, improving crop resilience and supporting precision agriculture in the face of increasingly variable climatic conditions. Classical methods for measuring plant water status reviewed in Part 1 of this two-part review have significant limitations for field level applications, providing only discrete, single-point measurements and potentially altering plant physiology through destructive sampling. This second of a two-part review synthesizes recent advances in non-destructive approaches for measuring plant water status, evaluating their principles, applications and limitations. We review techniques such as ZIM-probe, terahertz spectroscopic techniques, microwave remote sensing, infrared transmission sensor, microtensiometers, dendrometers and leaf thickness sensors, light detection and ranging (i.e. LiDAR), imaging spectroscopy, NMR relaxation, spectroscopy based on equivalent water thickness, spectral indices, derivative spectra, post-continuum removal indicators, visible and near-infrared spectroscopy, and infrared thermography. These emerging techniques facilitate high-resolution, real-time monitoring of water status across leaf, canopy and ecosystem scales. This comprehensive comparison provides guidance for selecting most appropriate technique based on experimental objectives, guiding applications ranging from single leaf to canopy scale ecosystem assessment. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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.

Dual-signal MOF nanozyme microneedle patch for on-site monitoring of hydrogen peroxide in postharvest lettuce. ↗

Real-time monitoring of H 2 O 2 in plant tissues is useful for evaluating oxidative changes during postharvest storage, but direct on-site detection in vegetables remains difficult because most assays still require tissue disruption and laboratory instruments. In this study, a dual-signal microneedle biosensor was developed by integrating polydopamine-coated Fe/Zr-MOF nanozyme (PDA@Fe/Zr-MOF) into a gelatin/sodium alginate microneedle patch for H 2 O 2 detection in lettuce. The polydopamine coating improved the peroxidase-like response of Fe/Zr-MOF through •OH generation and also contributed to photothermal conversion under 808 nm near-infrared (NIR) irradiation. After contact with lettuce leaves, the microneedles extracted interstitial fluid and allowed H 2 O 2 -triggered TMB oxidation to be read by both colorimetric imaging and thermal imaging. The two outputs were not independent recognition mechanisms, but they provided mutually supportive information and helped reduce the influence of sample color and environmental fluctuations. The sensor achieved detection limits of 0.42 μM for the colorimetric mode and 0.34 μM for the photothermal mode. During 15 days of storage at 4°C, the sensor tracked H 2 O 2 accumulation in lettuce and showed a clear relationship with spoilage progression. These results indicate that PDA@Fe/Zr-MOF-based microneedle sensing is a feasible approach for monitoring oxidative freshness changes in postharvest vegetables.

Why it matches plant phenotyping methodsレタス組織内H2O2という植物の生理状態を、マイクロニードルとカラー・熱画像で現場測定するセンサーを開発しており、取得手法が研究の中心である。

abstracta dual-signal microneedle biosensor was developed by integrating polydopamine-coated Fe/Zr-MOF nanozyme (PDA@Fe/Zr-MOF) into a gelatin/sodium alginate microneedle patch for H 2 O 2 detection in lettuce.

Recent trends in crop water stress monitoring using remote sensing technologies: A review ↗

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

Integrated UAV-Based Multispectral Scouting and Variable-Rate Aerial Spraying for Enhanced Crop Yield and Input Efficiency: A Field-Validated Study ↗

Precision agriculture demands integrated systems that couple accurate crop stress detection with targeted intervention to mitigate climate volatility and input overuse. Traditional manual scouting and uniform chemical application are spatially imprecise, labour-intensive, and environmentally burdensome. This study field-validates a closed-loop unmanned aerial vehicle (UAV) framework integrating AI-driven multispectral scouting with prescription-mapped variable-rate aerial spraying (VRS). A randomized complete block design with four replications was implemented in maize (Zea mays L.) across a 2.4 ha field in Davangere Karnataka, India. Scouting flights at 25 m altitude (1.8 cm ground sampling distance) utilized a MicaSense RedEdge-P and FLIR thermal sensor, with imagery processed through a radiometrically calibrated YOLOv8-Seg pipeline to detect early-stage disease, nutrient deficiency, and water stress. Prescription maps derived from NDRE and CWSI thresholds directly controlled a DJI Agras T40 centrifugal sprayer calibrated to ASABE S572.1 standards. The integrated system achieved an AI detection F1-score of 0.91, reduced agrochemical volume by 34.2%, and improved spray deposition uniformity (coefficient of variation = 18.4%) relative to conventional blanket spraying. Grain yield increased significantly by 11.7% (p

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像とAI解析により、作物の病害、栄養欠乏、水ストレスを検出する方法を開発・現地検証しており、植物状態の取得が統合システムの中心的要素である。

abstractThis study field-validates a closed-loop unmanned aerial vehicle (UAV) framework integrating AI-driven multispectral scouting with prescription-mapped variable-rate aerial spraying (VRS).

Review and perspective of key generic technologies in crop phenomics ↗

传感技术、人工智能算法及高性能计算能力的飞速发展正推动作物表型检测从单一性状描述向多尺度、智能化感知体系的转变。在此背景下，作物表型智能检测应运而生，其核心原理在于解析电磁波谱与植物组织相互作用的物理机制，即不同波段的光子与植物组织发生电子跃迁、分子振动及热辐射等能量交换，从而携带形态结构、生理生化及环境互作等多维信息。基于此，本文系统阐述了可见光成像、三维成像、光谱成像、叶绿素荧光成像、热红外成像等传感器的技术特性与适用场景，并梳理了从细胞/组织到器官、单株及群体水平的表型检测平台，比较了不同平台的应用场景及其优势与局限。针对当前多源数据异构性高、标准化体系缺失及模型泛化能力不足等核心瓶颈，本文提出发展作物表型智能检测的关键路径：建立面向多平台的数据标准化采集与校准体系，从源头保证数据质量；发展鲁棒性强、可迁移的智能解析算法，推动表型解析从传统特征工程向视觉基础模型及多模态大模型的演进；构建符合FAIR原则的共享数据库，打破“数据孤岛”现象；明确模型在不同物种、环境及生育阶段的适用边界，提升其在真实农业场景下的可靠性与泛化能力。利用智能检测技术实现作物表型的高通量、精准化解析，是未来作物表型组学发展的必然趋势，也为智慧育种与精准农业提供核心技术与理论支撑。

Why it matches plant phenotyping methods作物表型检测技术综述，系统评述多类成像传感器、表型检测平台及智能解析算法，方法学内容是核心。

abstract本文系统阐述了可见光成像、三维成像、光谱成像、叶绿素荧光成像、热红外成像等传感器的技术特性与适用场景，并梳理了从细胞/组织到器官、单株及群体水平的表型检测平台

Development of a Multimodal Deep Learning Framework for Crop Disease Detection ↗

Agricultural crop diseases can greatly reduce production quality and overall farm output, making early identification important for sustainable farming. This study introduces a smart agricultural rover that applies a multimodal deep learning approach for real-time crop disease monitoring in field environments. The proposed system gathers RGB images, thermal information, and environmental measurements such as temperature, humidity, and soil moisture through integrated sensors connected to a Raspberry Pi 4. For on-device analysis, a lightweight TensorFlow Lite (TFLite) model is utilized to classify crop diseases efficiently at the edge. To improve detection performance under different illumination conditions, the system evaluates both original and CLAHE-enhanced images using a dualinference mechanism supported by entropy and confidence-based decision metrics. The rover is implemented on a mobile robotic platform equipped with motor control and battery support to enable autonomous movement in agricultural fields. By combining sensor fusion, edge intelligence, and robotic mobility, the developed system supports accurate identification of diseases such as Powdery Mildew and Rust, helping farmers take preventive action and improve crop management practices

Why it matches plant phenotyping methodsRGB・熱画像とセンサ融合、エッジ推論、画像強調による作物病害検出システムを開発しており、植物の病害状態を推定する方法が中心である。

abstractThis study introduces a smart agricultural rover that applies a multimodal deep learning approach for real-time crop disease monitoring in field environments.

Using shortwave infrared spectral indices to monitor short-term water stress dynamics in peach orchards ↗

Abstract Purpose Capturing rapid changes in water status is key to optimizing deficit irrigation in Mediterranean orchards, but thermal remote sensing is constrained by the availability of high-spatial-resolution data. This study assesses the ability of visible, near and shortwave infrared (VNIR/SWIR) indices to detect short-term water stress in peach orchards. Methods An experiment was conducted in two commercial orchards in south-eastern Spain, where mild water stress was induced by withholding irrigation for four days. High-resolution hyperspectral and thermal imagery were acquired concurrently with stem water potential measurements (ψ stem ). Structural, pigment-related, and water-sensitive indices were evaluated at high (20–50 cm) and medium (30 m) spatial resolutions to analyze the effects of pixel size on stress detection. The Crop Water Stress Index (CWSI), derived from thermal imagery, served as a reference indicator. Results Those optical indices based on SWIR reflectance at 1240 nm, the Normalized Difference Water Index (NDWI₁₂₄₀) and the Simple Ratio Water Index (SRWI), showed the strongest sensitivity to ψ stem variability (R² = 0.63, p

Why it matches plant phenotyping methods桃樹の水ストレス状態を高解像度ハイパースペクトル・熱画像とスペクトル指標で推定し、茎水ポテンシャルを用いて検証しており、植物表現型取得手法が中心である。

abstractThis study assesses the ability of visible, near and shortwave infrared (VNIR/SWIR) indices to detect short-term water stress in peach orchards.

Advances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement. ↗

Abstract Climate change increasingly threatens global agriculture by intensifying abiotic stresses and destabilizing crop productivity, necessitating a deeper understanding of root-mediated traits governing resource acquisition and stress resilience. Here, we synthesize recent advances in root-centred plant phenomics, emphasizing how high-throughput phenotyping enables high-resolution, scalable characterization of complex root traits and robust comparative analysis across diverse genotypes and environments. Innovations in multimodal imaging, notably X-ray computed tomography, MRI, and machine learning-integrated rhizotrons, facilitate detailed reconstruction of root system architecture and its temporal dynamics under both controlled and semi-field conditions. Furthermore, root phenotyping is increasingly interpreted within an integrated whole-plant framework. The integration of organ-specific assessments with physiological phenomics leveraging spectral and thermal data enables the characterization of developmental plasticity and root-mediated processes, including water-use dynamics, nutrient acquisition, and canopy stress responses under heterogeneous field conditions. These approaches link root traits such as rooting depth and spatial distribution to canopy-level physiological responses under stress. Despite these advances, significant bottlenecks persist in data interoperability, analytical scalability, and protocol standardization. Future progress will require integration of root phenomics with genomics, predictive modelling, and digital twin frameworks to improve resource-use efficiency, yield stability, and climate resilience in global cropping systems.

Why it matches plant phenotyping methods根系フェノタイピングの高スループット画像化、計算ツール、機械学習統合、データ標準化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。

titleAdvances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement.

High-throughput phenotyping for climate-resilient forests: integrating multi-sensor fusion and root-shoot dynamics. ↗

Climate change is increasing the frequency of compound drought and heat events, threatening forest stability worldwide. While genomics has helped identify resilient genotypes, our ability to characterize adaptive traits - phenotyping - has not kept pace. This creates a bottleneck: we can sequence trees faster than we can understand how they physically respond to stress. Moving away from single-sensor monitoring, the field is now embracing multi-sensor data fusion, in which thermal imaging, Solar-Induced Fluorescence (SIF), hyperspectral remote sensing, and LiDAR are combined on platforms ranging from Unmanned Aerial Vehicles (UAVs) to ground-based robotic systems. These integrated approaches are proving effective for detecting physiological stress - such as changes in stomatal conductance - before visible damage appears. Deep learning models, meanwhile, are beginning to outperform traditional vegetation indices for specific tasks such as tree-crown segmentation and stress classification, although their performance remains constrained by overfitting, limited transferability, and domain shift across forest types in analyzing complex forest canopies. A major limitation remains, however: most high-throughput phenotyping (HTP) focuses on the canopy, largely ignoring the root system and the soil-plant-atmosphere continuum (SPAC), which are critical for drought resilience. In this review, we argue that developing climate-resilient forests requires looking below the canopy. We propose a constraint-based framework that couples aerial sensor data with eco-hydrological approaches and process-based modeling to narrow the range of plausible root functional strategies-rather than to directly identify root phenotypes, while critically evaluating the assumptions and validation challenges inherent in this approach. Future research should focus on standardized protocols, open benchmark datasets, and Explainable AI (XAI) to strengthen the link between above-ground signals and below-ground traits.

Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、マルチセンサー融合、深層学習、検証課題、標準化・ベンチマークをレビューしているため。

abstractIn this review, we argue that developing climate-resilient forests requires looking below the canopy.

UAV-based field phenotyping to assess yield-related traits in potato genotypes ↗

Abstract Unmanned aerial vehicle (UAV)-based phenotyping has been applied to assess potato traits, however, its use to identify canopy traits associated with tuber yield across diverse genotypes remains limited. The objective of this study was to evaluate the use of UAV-based field phenotyping, integrating RGB and thermal imaging, to identify key canopy traits associated with tuber yield and it´s agronomic components in a set of eight potato genotypes grown across two environments and two growing seasons. Despite higher seasonal rainfall in Chiloé, tuber yields were consistently greater in Osorno, underscoring that total precipitation alone is less important than its temporal distribution and effective crop water availability; this makes it necessary to supplement with irrigation during the period of highest demand. RGB-derived vegetation indices and canopy temperature successfully differentiated genotypes, although their discriminatory power varied according to developmental stage and environmental conditions, with intermediate to late growth stages generally providing the strongest genotype separation. Canopy temperature supplied complementary physiological information related to canopy water status, whereas RGB traits captured broader variation in canopy structure and greenness. These findings highlight the importance of integrating phenological stage and environmental context when interpreting remote sensing data, and demonstrate the strong potential of UAV-based HTP to support breeding and agronomic strategies aimed at improving drought resilience, yield stability, and selection efficiency in potato.

Why it matches plant phenotyping methodsUAVによるRGB・熱画像を用いた圃場フェノタイピングが中心で、ジャガイモのキャノピー形質を抽出・評価し、遺伝子型間比較や収量関連性を検討している。

abstractevaluate the use of UAV-based field phenotyping, integrating RGB and thermal imaging, to identify key canopy traits associated with tuber yield

Monitoring plant moisture content and optimizing irrigation prescriptions based on UAV multimodal data ↗

Introduction With the continuous advancement of smart agriculture, multi-modal remote sensing based on unmanned aerial vehicles (UAVs) offers new technical approaches for monitoring and managing crop moisture in fields. However, significant challenges remain in developing high-precision field-scale crop Plant Moisture Content (PMC) prediction models and translating them into actionable irrigation strategies. Methods This study focuses on winter wheat, employing field experiments with PMC and water use efficiency (WUE) as indicators of crop water status. Vegetation indices (VIs) derived from UAV data were used to construct a leaf area index (LAI) inversion model. Crop Height was extracted from oblique photogrammetry point cloud data. By combining the Penman-Monteith equation with dual crop coefficients, an improved evapotranspiration (ET) model was developed, utilizing multispectral data from UAVs, thermal infrared data, point cloud-derived plant height, and LAI inversion results. Further utilizing VIs, temperature indices (TIs), and machine learning algorithms (Random Forest Regression (RFR), Back Propagation Neural Network (BPNN), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), we established PMC prediction models for winter wheat at different growth stages. These models, integrated with WUE, form the basis for an irrigation scheduling optimization framework at the field scale. Results Results indicate that VIs, the difference between canopy temperature and air temperature (ΔT), Crop Water Stress Index (CWSI), and ET exhibit varying correlations with PMC during three critical growth stages of winter wheat, with ET showing the highest correlation during the jointing and heading stages (absolute correlation coefficient |r| ≥ 0.639). Compared to PMC prediction models constructed with different combinations of VIs, ET, VIs+ET, and VIs+TIs, the model employing the RFR algorithm with multimodal inputs (VIS+TIs+ET) demonstrated the best performance. The model’s predictive accuracy gradually improved across all growth stages, peaking during the grain-filling stage, with the coefficient of determination(R 2 ) of 0.900 and a normalized root mean square error (nRMSE) of 2.688%. Optimal WUE varied across growth stages under different irrigation treatments. The highest values were achieved at the jointing stage under treatment W3 (PMC = 81.8%), and at the heading and grain-filling stages under treatment W1 (PMC = 76.8% and 64.0%, respectively). Discussion The study suggests that stage-specific irrigation scheduling based on PMC thresholds can improve overall water use efficiency. This study shows that integrating multi-modal UAV data with machine learning and an improved ET model enables high-precision PMC monitoring, supporting data-driven irrigation scheduling in precision agriculture.

Why it matches plant phenotyping methodsUAVマルチモーダルデータと機械学習により、作物水分状態（PMC）、LAI、草高、蒸発散量を推定する手法を開発・評価しており、フェノタイピング手法が研究の中心である。

abstractCrop Height was extracted from oblique photogrammetry point cloud data.

A Multi-Sensor UAV Platform: Design, Testing, and Application for High-Throughput Plant Phenotyping ↗

Unmanned aerial vehicles (UAVs) are broadly used for high-throughput plant phenotyping, yet their long-term use in public-sector research is increasingly challenged by regulatory restrictions and reliance on proprietary platforms. This study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver flexible, high-quality phenotyping data without dependence on restricted ecosystems. A dual-mount, open-architecture payload integrated RGB, multispectral, and thermal sensors, enabling simultaneous acquisition of structural, spectral, and thermal information within a unified workflow. Field validation in a lantana (Lantana camara) breeding trial demonstrated high-precision multi-sensor data fusion and reliable trait extraction. Spatial co-registration achieved centimeter-level accuracy, with alignment errors of 0.88 cm (multispectral) and 3.23 cm (thermal) relative to the RGB reference. UAV-derived canopy height closely matched ground measurements (R2 up to 0.98; RMSE as low as 1.57 cm), while canopy coverage estimates showed consistency across sensing modalities (R2 = 0.99; RMSE = 0.02 m2). Calibrated thermal orthomosaics provided robust canopy temperature estimation (RMSE = 3.13 °C), supporting a quantitative assessment of plant physiological status. Together, these results demonstrate that a regulation-compliant, open-architecture UAV platform can achieve high accuracy in multi-modal phenotyping while maintaining flexibility and cost efficiency. This work demonstrates a scalable and sustainable framework for UAV-based phenotyping, enabling researchers to adapt to evolving regulations while advancing data-driven crop improvement.

Why it matches plant phenotyping methods植物フェノタイピング用のマルチセンサーUAVプラットフォームを設計・検証し、植物形質の抽出精度を評価しているため、方法が中心的である。

abstractThis study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver flexible, high-quality phenotyping data without dependence on restricted ecosystems.

A Survey on Sugarcane Plant Disease Detection Using Deep Learning With Fusion Method ↗

ABSTRACT - Sugarcane is one of the most important commercial crops worldwide but its productivity is greatly affected by diseases such as red rot, rust, mosaic, smut and yellow leaf disease. Conventional disease detection techniques are based on manual inspection which is a time-consuming, labor-intensive and error prone process. This paper gives a detailed review of the deep learning methods for the automated detection of sugarcane diseases with a special focus on the fusion methods of stem and leaf features. Different deep learning architectures such as CNN, VGG, ResNet, EfficientNet, DenseNet, MobileNet, and YOLO are analyzed and compared in terms of accuracy, efficiency, and deployment capability. The study also explores multimodal approaches, such as hyperspectral imaging, thermal imaging and environmental data integration, to enhance prediction performance. Reported results show that advanced models like EfficientNet-B7 and DenseNet201 achieve accuracies above 99%, while lightweight models like MobileNet allow for real-time mobile deployment. The review highlights significant research gaps such as small datasets, lack of stem-leaf fusion studies, no severity classification, and real-world deployment issues. Future research directions are related to explainable AI, multimodal fusion, lightweight edge computing models, and precision agriculture applications for sustainable sugarcane cultivation. Key Words: Sugarcane disease detection, Deep learning, CNN, Stem-leaf fusion, Computer vision, Precision agriculture.

Why it matches plant phenotyping methodsサトウキビ病害の画像・深層学習による検出手法を中心にレビューしており、植物の病態を観測・推定するフェノタイピング手法レビューに該当する。

abstractThis paper gives a detailed review of the deep learning methods for the automated detection of sugarcane diseases with a special focus on the fusion methods of stem and leaf features.

AI Camera Sensor-Based Detection of Crop Water Stress and Pesticide Requirement ↗

Artificial intelligence (AI)-enabled camera sensor systems are increasingly transforming precision agriculture by providing non-destructive, rapid, and scalable methods for monitoring crop health. Two of the most critical applications are the detection of crop water stress and the assessment of pesticide requirement through pest, disease, and symptom recognition. This literature review synthesizes published work on RGB, thermal, multispectral, and hyperspectral imaging integrated with machine learning and deep learning methods for agricultural decision support. The reviewed studies show that thermal and hyperspectral imaging are particularly effective for water stress detection, whereas RGB and multispectral systems are highly practical for identifying disease symptoms, pest infestation, and spray targets. The literature further indicates a shift from simple classification toward real-time decision support, multimodal fusion, explainable AI, and precision input application. This review discusses core sensing technologies, major algorithmic approaches, research findings from key studies, present limitations, and future research directions. Overall, AI camera sensor systems offer substantial potential for reducing water wastage, minimizing excessive pesticide use, and improving sustainable agricultural productivity.

Why it matches plant phenotyping methods作物の水ストレスや病害症状を画像・センサーから推定する手法を中心に整理したレビューであり、植物状態の取得・推定方法が中核です。

abstractThis literature review synthesizes published work on RGB, thermal, multispectral, and hyperspectral imaging integrated with machine learning and deep learning methods for agricultural decision support.

Diversity of photosynthesis-related and high-throughput phenotyping traits in indica rice ↗

Manual phenotyping of photosynthesis-related traits in rice is labor-intensive and limits the scale and temporal resolution of genetic analysis under field conditions. Here, we integrated unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) with genome-wide association studies (GWAS) to dissect the diversity and genetic architecture of photosynthesis-related traits in a large indica rice diversity panel (>300 accessions) evaluated across three dry seasons. A total of 45 traits, including UAV-derived NDVI, canopy height, and canopy temperature, together with leaf gas-exchange, stomatal, anatomical, and agronomic traits, were quantified. UAV-derived traits captured temporal growth and senescence dynamics and showed strong and consistent correlations with leaf photosynthetic rate, stomatal conductance, flowering time, biomass, and grain yield. GWAS identified multiple QTLs for photosynthetic and HTP traits, including a cross-year stable transpiration-rate QTL (qTRMMOL-2-2) and a photosynthetic-rate QTL (qPHOTO-1-2). Haplotype analyses revealed that the wall-associated receptor-like kinase gene OsWAK6 and the potassium transporter gene OsHAK1 were strongly associated with variation in photosynthetic rate and transpiration, respectively. Several elite accessions with consistently high photosynthetic performance carried superior haplotypes at multiple qPHOTO loci, suggesting their potential value for breeding. Together, our results demonstrate that UAV-based HTP provides reliable field-scale proxies for physiological performance, and that integrating HTP with GWAS can enable the identification of genetic targets for improving photosynthesis, water use, and yield potential in rice. • Forty-five traits, including HTP, photosynthesis, and leaf morphology, were measured across three dry seasons in diverse Indica rice. • GWAS identified genes linked to photosynthesis and stomatal density, aiding in breeding resilient, high-yield rice. • UAV-based HTP data effectively tracked plant growth and senescence, correlating with photosynthetic rate. • GWAS co-localization revealed shared QTLs, suggesting multi-trait regulation by common genes.

Why it matches plant phenotyping methodsUAVベースのHTPによる植物形質取得と生理性能の推定が研究の中心であり、45形質を大規模・反復的に測定し、信頼性や他の生理形質との相関も評価している。

abstractwe integrated unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) with genome-wide association studies (GWAS)

Integrating Satellite Remote Sensing and Artificial Intelligence for the Presymptomatic Detection of Crop Diseases and Nutrient Deficiencies: A Comprehensive Narrative Review ↗

ABSTRACT Global food security is threatened by crop diseases and nutrient deficiencies. Traditional detection methods—visual scouting, molecular diagnostics and soil testing—are reactive and only identify problems once visible symptoms appear, which often misses intervention windows. This narrative review synthesizes 173 peer‐reviewed articles (from 2012 to 2025) to critically evaluate the synergistic potential of artificial intelligence (AI) and multisensor satellite remote sensing (RS) for presymptomatic detection. We propose a four‐principal framework: (1) sensor choice must align with pathogen infection strategy; (2) detection becomes actionable when spectral deviation exceeds twice baseline noise; (3) spectral time series can estimate epidemiological parameters (e.g., latent period, Area Under the Disease Progress Curve); and (4) explainable AI (XAI) converts black‐box predictions into interpretable diagnostics. Key findings uncovered were that multispectral sensors detect biotrophic pathogens 5–10 days pre‐symptomatically via red‐edge sensitivity; hyperspectral platforms offer 7–14 days warning and that thermal sensors detect vascular wilts 1–7 days earlier. Key challenges remain, including trade‐offs between resolution and revisit frequency, atmospheric interference causing 60%–80% optical data loss in tropical regions, spectral confusion between biotic and abiotic stresses, and limited scalability for smallholder farms (

Why it matches plant phenotyping methods衛星リモートセンシングとAIによる作物病害・栄養欠乏の早期検出手法を体系的に評価するレビューであり、植物の病害状態を推定するセンシング手法が中心です。

abstractThis narrative review synthesizes 173 peer‐reviewed articles (from 2012 to 2025) to critically evaluate the synergistic potential of artificial intelligence (AI) and multisensor satellite remote sensing (RS) for presymptomatic detection.

Multimodal AI for plant stress classification across multiple plant species ↗

Plant stress monitoring is invaluable in realizing sustainable agriculture because it enables the people practicing it to take early measures to counteract losses in yield caused by environmental stressors like drought and nutrient deficiencies, as well as caused by pathogen infections. The proposed study presents a new Multi-Modal Vision Transformer (MMViT) architecture that is designed to combine both thermal and RGB imagery to take detection to the next level. In order to support this methodology and further studies, we are now publicly releasing a new collection of synchronized thermo-RGB image pairs of stressed and healthy plants, collected both in controlled settings and in the field. The data is labeled to differentiate various stress phenotype and contains over 4286 of images, and hence forms a substantial platform to evaluate multimodal plant phenotyping methods. Empirical evaluations indicate that MMViT model achieves a general classification of 94.3% when using the two modalities, which is better than the single-modality ViT used on the thermal images (85.5%) and the RGB images (93.3%). These experimental results emphasize the performance of multimodal fusion whereby the other spectral cues are used to complement a stress classification. The described framework, together with the useful dataset, will contribute to the advancement of precision agriculture as it is an open and data-driven instrument to monitor plant health automatically.

Why it matches plant phenotyping methods熱画像とRGB画像を統合して植物ストレス表現型を分類するモデルを開発し、公開データセットと性能評価も提示しており、植物フェノタイピング手法が中心である。

abstractThe proposed study presents a new Multi-Modal Vision Transformer (MMViT) architecture that is designed to combine both thermal and RGB imagery to take detection to the next level.

Non‐Invasive Estimation of Short‐Term Changes of Transpiration Using a Combination of 3D Imaging and Energy Balance Modelling ↗

Conventional approaches to measuring stomatal conductance (gs) and transpiration often rely on instruments that interfere with plant physiology. Porometers, for example, restrict natural leaf movement, apply pressure, and introduce dry airflow that can alter stomatal behaviour, thereby reducing the relevance of such measurements. Prior studies report discrepancies among devices attributable to such interferences (Toro et al. 2019). To minimise artefacts, transpiration should be estimated remotely without physical contact, which theoretically can be achieved via a thermal leaf energy-balance approach that infers gs from leaf temperature, radiative load, and boundary-layer terms. In this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely. Approaches to estimate stomatal conductance based on the energy-balance equation were developed recently to aid phenotyping of plantss. Most methods either imposed rapid changes in air humidity to perturb transpiration and, consequently, leaf temperature (Driever et al. 2023), or relied on ‘dry’ and ‘wet’ reference surfaces (as in Leinonen et al. 2006) to compute stress indices (Vialet-Chabrand and Lawson 2020). These methods require reference materials to assess surface temperatures under maximum and zero transpiration, showing the effect of longwave radiation. However, reference-material methods were constrained by heterogeneity in light interception caused by variation in leaf angle and orientation, because reference surfaces could not reorient like real leaves (Zhang et al. 2025). In this study, we addressed this challenge by using thermal imaging and 3D photogrammetry to capture leaf temperature and geometry noninvasively, allowing parameter estimation for each leaf individually. Here, ρ is the density of air (kg m−3), cp is the specific heat capacity of air (J kg−1 K−1) and rHR is the parallel resistance to heat and radiative transfer on the leaf surface (s m−1), s is the slope of the curve relating saturating water vapour pressure to temperature (Pa °C−1). TL and TA are leaf and air temperatures (°C), respectively, δe is air vapour pressure deficit (Pa), γ is the psychrometric constant (Pa K−1) and rva is the boundary layer resistance to water vapour (s m−1) (Supporting Information S2: Equation S1). The net radiative energy Rn in the energy-balance term was obtained from the same 3D light interception model, which integrates measured direct and lateral scattered irradiance (W m−2) (Supplement Material and Methods, File S2). Stomatal conductance gs (m s−1) is the inverse of stomatal resistance rs (s m−1). To experimentally obtain a wide range of gs values, we grew eggplant (Solanum melongena L.) plants in hydroponic units in growth chambers under four sets of environmental conditions (Table 1). Thirty-day-old plants (4–5-leaf stage) were placed on balances (Supplementary Materials and Methods, File S2). Units were sealed with plastic film to minimise evaporation. Mass loss attributable to transpiration was logged automatically every 30 s. To induce short-term changes in stomatal conductance, we imposed an acute osmotic stress by delivering a saline NaCl solution with high electrical conductivity (60 mS cm−1) to the root zone, producing a steep drop in root osmotic potential. This created rapid physiological and morphological responses that altered incident irradiance at the leaves, leaf temperature, and consequently energy balance, stomatal conductance and transpiration. We chose this stressor for operational simplicity. Any perturbation that modifies transpiration dynamics and thus gas exchange could have served our purpose. The total transpiration of a leaf, Et (kg s−1), is the product of the total conductance to water vapour from the mesophyll to the atmosphere, gv (m s−1), calculated from the estimated stomatal resistance rs (s m−1) and the boundary layer conductance gva (m s−1), the difference between water vapour concentration inside the leaf Cvs (dimensionless), and in surrounding air Cva (dimensionless), the leaf area A (m2), and the density of water ρw (kg/m3) (Jones 1992). Estimated stomatal conductance was obtained from leaf energy balance calculation (Equation 1). Boundary-layer conductance was computed from measured wind speed and leaf dimensions (leaf area, length, width) extracted from structure-from-motion 3D reconstructions (Supporting Information S1: Equation S6; Grace et al. 1980). Transpiration was then calculated for each leaf at each thermal 3D imaging time point, and whole-plant transpiration for comparison with gravimetric logs was the sum of all per-leaf estimates. As a non-invasive approach, we evaluated the plausibility or our model derived stomatal conductance (Equation 2) indirectly by comparing calculated and measured whole plant transpiration. We emphasise that this is not a direct validation of gs. Rather, the close agreement between modelled and measured transpiration across the wide range of environmental treatments, both stressed and non-stressed, provides confidence that the inferred gs is realistic. RGB and thermal images acquired before, during, and after stress application enabled dynamic tracking of leaf position and temperature (Supplementary Material and Methods, File S2). As expected, osmotic stress application had immediate effects on morphology and physiology. While control leaves maintained an angle of around 110° throughout, osmotic shock induced immediate turgor loss and drooping in all environments except one (Figure 1A,B). Leaf angles recovered to pre-stress positions within 1 h, indicating adaptation to the osmotic shock and restoration of turgor. Only environment 4 (high light, low air temperature and low humidity) maintained turgor during stress. Angle shifts were most pronounced in older leaves, which drooped and reduced light interception; younger leaves better preserved structure and turgor (Supporting Information S1: Figure S2). These angle changes also altered incident irradiance at the leaf surface (Supporting Information S1: Figure S3). These morphological responses coincided with increases in leaf temperature, consistent with altered water fluxes and stomatal regulation after stress. Across environments, plants showed a uniform rise in leaf temperature following osmotic stress, regardless of initial temperature (Supporting Information S1: Figure S4). This response held across leaf ages, encompassing older (Figure 1C) and younger (Figure 1D) leaves. Stomatal conductance estimated with our method followed the same pattern, dropping rapidly after osmotic shock in both older (Figure 1E) and younger (Figure 1F) leaves (Supporting Information S1: Figure S5). We estimated no stomatal conductance recovery to pre-stress conditions over the time course of stress exposure. Model-estimated and gravimetrically measured transpiration showed identical time courses across all four environmental conditions (Figure 1G–J). Transpiration rates did not recover to the same extent as leaf turgor, indicating long-term effects of the osmotic shock. Across environments and time points, correlation between model estimated and measured whole-plant transpiration was high (Figure 1K). In this study, stomatal conductance (gs) is a model-derived quantity inferred from the same physically constrained framework and model (leaf temperature, boundary-layer conductance and vapour pressure deficit). Since we did not measure gs directly, we cannot validate gs directly. Instead, we used a non-invasive check via transpiration. Model predictions closely tracked measured transpiration across the four controlled environments. This agreement increases confidence that the inferred gs is realistic, while we acknowledge that transpiration agreement alone is not a rigorous validation and cannot fully rule out compensating errors. Our study demonstrated the potential of our approach to estimate transpiration accurately by combining 3D imaging and thermography with physiological modelling without the use of reference materials that imitate real leaves. This remote approach enables simultaneous assessment of morphological and physiological responses to stress, yielding a more integrated view on plant transpiration and gas exchange. In contrast to chamber and porometer measurements or IR methods requiring wet and dry references or calibration plates, our workflow is reference-free. Absorbed shortwave radiation is derived from measured irradiance and a 3D reconstruction of leaf geometry, with no external reference materials. Moreover, remote measurements avoid continuous pressure from clamp-on porometers, permitting long-term observation and capture of rapid stress responses without sustained damage or microclimate artifacts. Further, the approach is not limited by any clamp on sensors and as such enables multi-leaf tracking. Applied to crop canopies, this approach could improve understanding of canopy processes that influence productivity and enable remote estimation of canopy transpiration. Future research could further improve by replacing our strong saline solution stress by gradual soil drying to depict a more realistic and natural stress while testing the approach under long-term conditions. Recent studies indicate that, with rising atmospheric CO2 concentrations, breeding for reduced stomatal conductance could increases WUE without affecting photosynthetic capacity (Srivastava et al. 2024). As such, remote systems for high-throughput plant phenotyping (HTP) are required to scan vast quantities of plants. We see a potential use of our system for such purposes to quickly estimated whole plant and individual leaf transpiration, as initial image capturing is very fast. A large bottleneck in our work was 3D model generation speed and manual extraction of leaf parameters from these 3D models. Both could be streamlined with more automated software, possibly including neural network solutions. The authors have nothing to report. The authors declare no conflict of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request. 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 methods3D画像、熱画像、光遮断モデル、エネルギーバランスモデルを統合し、葉ごとの蒸散と気孔コンダクタンスを非侵襲的に推定する手法を開発・評価しており、植物表現型取得が研究の中心である。

abstractIn this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely.

Towards a standard approach to investigating the Thermal Load Sensitivity of photosystem II via chlorophyll fluorescence ↗

Evaluating the drivers of variation in plant thermal tolerance limits requires a clearer understanding of how methodological matters can lead to different tolerance estimates. Chlorophyll fluorometry – to measure the temperature-dependent change in F V / F M – is a well-established approach to derive tolerance thresholds of photosystem II (PSII) in plants, but one-off, time-specific thermal exposures do not consider the fundamental dose-dependent effect of heat. The resurgent thermal death time (TDT) approach integrates both the temperature intensity and the exposure duration to derive time-based critical temperature thresholds and sensitivity parameters. We build upon this foundation to develop a protocol for evaluating thermal load sensitivity (TLS; non-lethal heat stress) of PSII in plants. Through five experiments across four diverse species, we tested the moderating effects of light, leaf sectioning, time since collection, and the temporal dynamics of F V / F M recovery. There were dramatic changes in tolerance threshold estimates based on thermal load (i.e. dose-dependent) effects on F V / F M , and strong effects of light intensity during heat and the presence of light post-heat. We offer recommendations pertaining to method implementation and discuss future empirical avenues. Appraising cumulative heat stress will enhance the utility of thermal tolerance estimates – the TLS approach outlined here moves us toward a new standard.

Why it matches plant phenotyping methods植物のPSII熱耐性をクロロフィル蛍光で定量する方法を開発・検証し、実装上の条件を評価した研究であり、方法が中心的です。

abstractThrough five experiments across four diverse species, we tested the moderating effects of light, leaf sectioning, time since collection, and the temporal dynamics of F V / F M recovery.

Using Multispectral Imaging and Artificial Intelligence to Detect Crop Diseases and Pests Early ↗

Abstract In sustainable agriculture, detecting pests and diseases early is critical. Recent technological advances in deep learning (DL) and multimodal imaging like multispectral and thermal data crop health monitoring is promising. Despite the progress, obtaining high accuracy across various crops with real-time performance is still a challenge. The hybrid convolutional neural network (CNN)-attention model integrating multispectral and thermal data for pest and disease detection has been introduced. A total of 1760 samples were collected from six crops (maize, rice, wheat, tomato and cassava), across different growth stages, labelled fungal, bacterial, viral and pest infections. The data was divided into 70% training, 15% validation, and 15% test sets. 3,500 samples were used for training. 750 samples were used for validation and test set. The hybrid CNN-attention model was contrasted with certain baseline models (SVM, Random Forest, CNN-RGB, CNN-Multispectral) and certain fusion methods (early, late, and hybrid fusion) based on accuracy, precision, recall, F1-score, and early detection sensitivity. The highest accuracy of 91.0% for rice at the vegetative stage was achieved by the hybrid model. It beats baseline and fusion models. The F1-score of the classification was reasonably high. Rice's sensitivity is 88.1%, and maize is 87.3%. The model fared well for all classes, getting 92.0 % for the healthy plant and 88.2 % for pest infestation. Future work can enhance the dataset with more crops and diseases and environmental factors and optimize detection time and early sensitivity for real-time deployment in agricultural decision support systems.

Why it matches plant phenotyping methodsマルチスペクトル・熱画像から植物の病害および害虫状態を推定するCNNモデルを開発し、複数モデルとの比較検証を行っており、表現型取得・判定手法が中心である。

titleUsing Multispectral Imaging and Artificial Intelligence to Detect Crop Diseases and Pests Early

Photosynthetic traits and canopy-level thermal imaging to assess plant-water relations in two apple cultivars under waterlogging and recovery conditions ↗

Waterlogging is an increasingly important constraint in orchard systems under climate extremes. Understanding cultivar-specific physiological responses and identifying reliable, non-invasive indicators of plant water status are essential for improving orchard management under excess soil moisture. In this study, we evaluated the physiological, hydraulic, and canopy thermal responses of two commercially important apple cultivars, ‘Fuji’ and ‘Gamhong' grafted onto M.9, under controlled waterlogging and recovery conditions. We quantified photosynthetic traits and plant hydraulic parameters—including sap flow (SF), leaf water potential ( Ψ Leaf ), and whole-plant hydraulic conductivity ( K s )—together with canopy thermal indicators, canopy temperature ( T c ), and a modified crop water stress index ( mCWSI ) calculated using empirically derived, day-specific canopy temperature references. Waterlogging significantly reduced photosynthetic performance and hydraulic function in both cultivars, but responses differed in magnitude and recovery dynamics. ‘Fuji’ exhibited greater resilience, with smaller declines and faster recovery of gas exchange and water-relation traits, whereas ‘Gamhong’ showed earlier photosynthetic limitation and delayed recovery, indicating lower tolerance to saturated soil conditions. Leaf mass per area (LMA) increased under waterlogging, reflecting constraints on leaf expansion rather than enhanced photosynthetic activity. Among the thermal indicators, mCWSI showed the strongest correlations with Ψ Leaf , stomatal conductance ( g s ), and net photosynthetic rate ( P n ), outperforming T c as an indicator of plant water status. These findings demonstrate that canopy-based thermal metrics, particularly mCWSI when interpreted alongside physiological traits, provide a robust tool for detecting cultivar-specific responses to waterlogging stress. This multi-trait framework supports cultivar selection and precision water management in orchard systems exposed to episodic flooding.

Why it matches plant phenotyping methodsキャノピー熱画像から算出したmCWSIを生理・水分状態の指標として検証し、従来のキャノピー温度と比較しているため、表現型取得・評価法が研究の中心的要素である。

abstracta modified crop water stress index ( mCWSI ) calculated using empirically derived, day-specific canopy temperature references

Early Plant Stress Detection using Thermal Leaf Patterns with TAP-EfficientNet for Precision Agriculture ↗

The objective of this study is to employ the proposed TAP-EfficientNet model for the early detection of plant stress using thermal leaf patterns, aiming to improve diagnostic accuracy and computational efficiency in precision agriculture. Group 1 is the standard EfficientNet baseline model. Group 2 is the proposed TAP-EfficientNet model. A sample size of 500 thermal leaf images is used for each group, and data is collected across various time intervals and stress conditions (e.g., water deficit, disease). The models' classification accuracy, precision, recall, F1-score, and inference delay are all calculated. The output demonstrated that the TAP-EfficientNet model has better classification results than the standard EfficientNet model in terms of 5.4% higher accuracy, 4.8% higher precision, 6.2% higher F1-score, and [e.g., 12.5%] lower inference delay. The results of the experiment indicate that the suggested TAP-EfficientNet model can detect early plant stress more effectively than the standard EfficientNet model, making it highly suitable for real-time monitoring and deployment in precision agriculture.

Why it matches plant phenotyping methods熱画像から植物ストレス状態を推定する深層学習モデルを提案し、既存モデルと精度・推論遅延を比較検証しており、植物フェノタイピング手法が中心である。

abstractThe objective of this study is to employ the proposed TAP-EfficientNet model for the early detection of plant stress using thermal leaf patterns

Thermography Applied to the Assessment of Podosphaera xanthii Infection in Susceptible Melon Plants ↗

Powdery mildew, a disease caused by the biotrophic fungus Podosphaera xanthii, is one of the most destructive diseases affecting melon crops worldwide. This pathogen causes alterations in the physiology of the host plant even before visible symptoms appear, which in turn can be detected using non-invasive imaging techniques. In this piece of work, infrared thermography was used to evaluate the temperature dynamics of melon leaves infected with P. xanthii during the first 72 h after infection. Infected leaves showed a significant decrease in temperature compared to mock-controls from 18.5 hpi onwards, before the appearance of visible mycelium. This temperature difference between mock-control and P. xanthii-infected melon leaves remained significant throughout the experiment, suggesting a sustained disruption of water-balance regulation caused by the fungus. This imbalance could be linked to haustorium-mediated interference with stomatal function or epidermal osmotic homeostasis. Overall, these results highlight thermography as a powerful and sensitive tool for detecting early physiological responses during P. xanthii infection of melon leaves. Therefore, thermography could be used as a valuable complement to ‘omics’ and other image-based phenotyping methods, helping to provide a comprehensive view of the responses that different diseases trigger in host plants.

Why it matches plant phenotyping methodsメロン葉の感染に伴う温度変化を赤外線サーモグラフィーで非侵襲的に測定し、可視症状前の病態・生理状態を評価する方法の適用が中心である。

abstractwhich in turn can be detected using non-invasive imaging techniques

Evaluation of Sugar Content and Yield in Sugar Beet Based on UAV Multi-Sensor ↗

Sugar beet is a major sugar crop in temperate regions and rapid, high-throughput, and accurate estimation of field phenotypes is essential for variety selection and production optimization. In this paper, ten commercial sugar beet varieties adapted to high latitudes are investigated using unmanned aerial vehicle (UAV) based red-green-blue (RGB), multispectral, and thermal infrared imaging across multiple growth stages. Canopy structural, texture, spectral, and temperature features are extracted, and three machine learning algorithms, random forest (RF), partial least squares (PLS), and support vector machine (SVM), are used to predict sugar content, root fresh weight, and yield. The results show that all three methods estimate sugar content well, with relative root mean square error (rRMSE) values below 11.0%, while RF and PLS outperform SVM. Multispectral features provide higher accuracy than RGB features, and multi-sensor feature combinations generally improve sugar content prediction compared with single-sensor inputs. For root fresh weight, SVM slightly outperforms RF and PLS, and RGB features are more informative than multispectral features. The integration of thermal infrared features does not notably improve RF or PLS models, but the combination of multispectral and thermal infrared features achieves the best SVM performance ( R2=0.58, RMSE = 75.3 g, and rRMSE = 23.7%). For yield estimation, RF achieves the highest accuracy, with rRMSE values ranging from 15.4% to 18.8%. Yield prediction accuracy increases as the time of image acquisition approaches harvest, and combining multi-temporal data from periods close to harvest further improves model performance. Overall, multi-sensor UAV data can effectively estimate sugar content, root fresh weight, and yield in sugar beet, providing a useful approach for phenotypic analysis, precision management, and variety selection.

Why it matches plant phenotyping methodsUAVマルチセンサー画像から糖含量、根 fresh weight、収量という植物形質を抽出・推定し、センサー特徴量と機械学習モデルの性能を比較しているため、表現型取得・推定手法が中心である。

abstractrapid, high-throughput, and accurate estimation of field phenotypes is essential for variety selection and production optimization.

Comparative Evaluation of Ground-based, Manual, and Remote Sensing Approaches for Crop Stress Detection: A Review between Malaysia and China ↗

This study presents a comparative evaluation of manual inspection, ground-based sensors, and UAV-based remote sensing for detecting crop stress in paddy, maize, and coconut fields across Malaysia and China. Although sensing technologies have advanced considerably, cross-country comparisons between regions with differing levels of technological maturity remain limited. China, recognised for its advanced adoption of UAV and sensor-based agriculture, provides a benchmark against Malaysia’s developing digital agriculture landscape. Each method was assessed based on accuracy, responsiveness, scalability, and cost-effectiveness under field conditions. UAV-based remote sensing achieved the highest overall accuracy (mean 92%) and demonstrated superior scalability, enabling rapid large-area monitoring using vegetation indices such as NDVI and NDRE. Ground-based sensors, including soil moisture probes and chlorophyll meters, showed moderate accuracy (mean 81%) and were suitable for plot-level monitoring with real-time feedback. Manual inspection recorded the lowest accuracy (mean 68%) and limited scalability due to labour dependency and subjective assessment. UAV methods were particularly effective in early stress detection, with thermal imaging identifying canopy temperature anomalies 3–5 days before visible symptoms, especially in maize and coconut fields. Integrating UAV and ground-based sensing provided more comprehensive and timely assessments than individual approaches. These findings support the development of scalable precision agriculture frameworks tailored to tropical and subtropical systems.

Why it matches plant phenotyping methods作物ストレスという植物状態を対象に、手動観察・地上センサー・UAVリモートセンシングを精度、応答性、拡張性、費用で比較評価しており、センシング手法の技術評価が中心である。

abstractThis study presents a comparative evaluation of manual inspection, ground-based sensors, and UAV-based remote sensing for detecting crop stress in paddy, maize, and coconut fields across Malaysia and China.

Estimation of Crop Coefficients of a High-Density Hazelnut Orchard Using Traditional Methods vs. UAV-Derived Thermal and Spectral Indices ↗

Evapotranspiration and crop coefficients are key variables for designing efficient irrigation strategies in tree crops, yet standard tabulated coefficients derived for mature, fully covering orchards often fail to represent the water use of young, high-density hazelnut systems. In recent years, updated crop coefficients for temperate fruit trees, including hazelnut, and transpiration-based models have been proposed, while several studies have successfully linked Vegetation Indices and thermal metrics to single and basal crop coefficients in vineyards, orchards and field crops. However, no information is available on the use of UAV-derived spectral and thermal indices to estimate crop coefficients in high-density hazelnut orchards. This study compares crop coefficients obtained from traditional approaches (the FAO56 single crop coefficient, a transpiration-based coefficient, and ground cover reduction factors) with coefficients estimated from UAV-derived Normalized Difference Water Index (NDWI) and Crop Water Stress Index (CWSI) in a subsurface-drip-irrigated hazelnut orchard (cv. Tonda Francescana®) with two planting densities (625 and 1250 trees ha−1) in central Italy. Multispectral and thermal UAV surveys carried out between 2021 and 2024 were used to derive canopy geometrical traits, ground cover, NDWI, and CWSI, while a local weather station provided reference evapotranspiration. Empirical relationships were calibrated between crop coefficients and ground cover, NDWI, and CWSI, and mid-season coefficients were applied to estimate daily crop evapotranspiration, which was then compared with the irrigation volumes supplied during the 2024 season. The standard FAO56 crop coefficient (Kc = 0.9) overestimated evapotranspiration, especially at the lower planting density, whereas ground cover-based reduction factors recalibrated for hazelnut and the transpiration-based coefficient provided estimates more consistent with the applied irrigation. UAV-based NDWI- and CWSI-derived crop coefficients produced mid-season values close to those obtained with the transpiration-based method for both planting densities, confirming that spectral and thermal information can effectively capture the combined effects of canopy development and water status. These results indicate that combining traditional methods with UAV-derived indices offers a flexible framework to refine crop coefficients in high-density hazelnut orchards and support more accurate and spatially explicit irrigation scheduling.

Why it matches plant phenotyping methodsUAVのマルチスペクトル・熱画像からキャノピー形状、被覆率、NDWI、CWSIを抽出し、作物係数との関係を較正・比較している。植物の水分状態やキャノピー特性の測定・推定が研究の中心であり、単なる灌漑試験の routine measurement ではない。

abstractMultispectral and thermal UAV surveys carried out between 2021 and 2024 were used to derive canopy geometrical traits, ground cover, NDWI, and CWSI

Comparative Assessment of UAV-Based TSEB and Field-Calibrated AquaCrop for Evapotranspiration on the Arid Coast of Peru ↗

Precise estimation of evapotranspiration (ET) is essential for sustainable water management in arid agroecosystems, particularly for high-water-demand crops such as rice. This study integrated very-high-resolution UAV thermal–multispectral imagery with a Two-Source Energy Balance model (UAV–TSEB) and a field-calibrated AquaCrop model to quantify daily ET and its components under continuous flooding on the arid Peruvian coast during the 2024–2025 season. A network of 24 drainage lysimeters provided an independent observational benchmark (ETlys); to represent the treatment-level response, lysimeter observations were aggregated as the mean across the 24 units for each UAV campaign. Thirteen UAV surveys supplied radiometric surface temperature and biophysical inputs (e.g., NDVI and fractional cover) to derive spatially explicit ET, while AquaCrop provided continuous daily simulations between flight dates. Direct lysimeter-based validation indicated high agreement for AquaCrop (R2 = 0.85; RMSE = 0.26 mm d−1; MBE = 0.01 mm d−1) and moderate agreement for UAV–TSEB (R2 = 0.66; RMSE = 0.81 mm d−1; MBE = 1.01 mm d−1). Model intercomparison further showed consistent temporal dynamics of ET (R2 = 0.70; RMSE = 1.35 mm d−1) and robust partitioning of crop transpiration (R2 = 0.79; RMSE = 0.99 mm d−1) and soil evaporation (R2 = 0.76; RMSE = 1.03 mm d−1) while revealing a systematic divergence under near-complete canopy cover: AquaCrop tended to suppress evaporation, whereas UAV–TSEB detected residual evaporation from the flooded surface. Overall, the results highlight the complementarity of both approaches—UAV–TSEB as a spatial diagnostic tool and AquaCrop as a temporally continuous simulator—providing a robust framework for ET monitoring, flux partitioning, and water-use-efficiency assessment in water-scarce rice systems.

Why it matches plant phenotyping methodsUAV熱・マルチスペクトル画像とTSEB/AquaCropによる作物キャノピーの蒸発散・蒸散・蒸発推定を、ライシメータで独立検証・比較しており、植物の生理状態計測手法が中心である。

abstractThis study integrated very-high-resolution UAV thermal–multispectral imagery with a Two-Source Energy Balance model (UAV–TSEB) and a field-calibrated AquaCrop model to quantify daily ET and its components under continuous flooding on the arid Peruvian coast during the 2024–2025 season.

Early screening of salt-tolerant and high-yielding rice varieties via cost-effective UAV data. ↗

Breeding rice varieties that are both salt-tolerant and high-yielding is essential for utilizing saline-alkaline lands and ensuring food security. However, However, high-throughput and accurate phenotyping at early growth stages remains a major bottleneck in breeding programs. In this study, unmanned aerial vehicle (UAV) imaging was employed to screen salt-tolerant and high-yielding varieties among 60 rice varieties under saline-alkaline field conditions. Red-green-blue (RGB), multispectral, and thermal canopy images were acquired throughout the growing season by UAV, from which 41 phenotypic traits were extracted at each growth stage. These traits were categorized into early-stage (tillering and jointing), late-stage (booting, flowering, and maturity), and whole-growth-stage (from tillering to maturity) and subsequently used to screen salt-tolerant and high-yielding rice varieties. Results showed that: (1) An early high-throughput screening method for salt-tolerant rice varieties was developed based on the membership function and UAV phenotypes (MFuav), achieving high performance (Precision >0.8, OA > 0.7). MFuav demonstrated the highest accuracy at the early-stage, with Precision increasing by 0.29 and 0.43 compared to the late- and whole-stage models, respectively. (2) A machine learning based UAV phenotypes framework (MLuav) was developed to further improve salt-tolerance screening performance. Within this framework, the partial least squares regression (PLSR) was employed for early-stage salt-tolerance screening, which achieved a Precision of 0.97 and an OA of 0.78, outperforming the MFuav by 0.11 and 0.08, respectively. In addition, within the same MLuav framework, early-stage UAV phenotypes were further used for actual yield prediction using a Random Forest (RF) model. The model achieved a high Recall for high-yielding varieties (Recall = 1.00), ensuring that no potentially high-yielding germplasm was missed, although this was accompanied by a moderate Precision (0.51) and an overall accuracy of 0.70. (3) The MLuav consistently outperformed the MFuav in screening salt-tolerant and high-yielding varieties across all 60 rice varieties. Among the five referenced salt-tolerant and high-yielding rice varieties, the MLuav correctly screened four using early-stage phenotypes, whereas the MFuav only screened three. Overall, the proposed method enables early screening of salt-tolerant and high-yielding rice varieties, offering an efficient tool for the screening and utilization of elite stress-resilient germplasm.

Why it matches plant phenotyping methodsUAV画像から多数のイネ表現型形質を抽出し、塩耐性・収量性を早期スクリーニングする方法と機械学習フレームワークを開発・評価しており、表現型取得・解析手法が研究の中心である。

abstracthigh-throughput and accurate phenotyping at early growth stages remains a major bottleneck in breeding programs

High resolution thermal and multispectral remote sensing for detecting devitalized trees in Monarch Butterfly Biosphere Reserve ↗

Aim of study: To identify different levels of devitalization in individuals of Abies religiosa (Kunth) Schltdl. & Cham. (oyamel) in the core zone of the Monarch Butterfly Biosphere Reserve (MBBR), using multispectral and thermal information captured with drone. Area of study: The study area, comprising 26 ha, is located within the federal property of the MBBR. This area also belongs to the core zone of the Protected Natural Area. Material and methods: Overflights were conducted with drones equipped with multispectral and thermographic sensors in monitoring plots. Using high-resolution images captured by drone and photogrammetric processing, the normalized difference vegetation index (NDVI), surface temperature (ST), and temperature-vegetation dryness index (TVDI) were calculated. These values were combined with field data to determine three different levels of tree devitalization (i.e., healthy, devitalized, and dead trees). Main results: The results demonstrated that this technology can be used to statistically distinguish the different levels of devitalization in oyamel individuals. Research highlights: This is the first research in Mexico that use Thermal and Multiespectral high resolution information related with field data applied to Abies religiosa and provides a methodological precedent for identify forest decline symptoms.

Why it matches plant phenotyping methodsドローン搭載のマルチスペクトル・熱赤外センサーと画像解析で樹木の活力度を推定し、健全・衰弱・枯死を識別する手法が研究の中心である。

abstractUsing high-resolution images captured by drone and photogrammetric processing, the normalized difference vegetation index (NDVI), surface temperature (ST), and temperature-vegetation dryness index (TVDI) were calculated.

UAV-Based Remote Sensing and Artificial Intelligence for Climate-Smart Agriculture: A Systematic Review of Technologies, Analytics, and Applications in Smallholder Systems ↗

Unmanned aerial vehicle (UAV) remote sensing has evolved from experimental imaging into an operational diagnostic infrastructure supporting climate-smart agriculture through high-resolution, flexible, and timely crop observation. This review synthesizes advances in UAV platforms, multisensor payloads, artificial intelligence (AI) analytics, and multisource data fusion to evaluate their combined potential for monitoring heterogeneous smallholder systems. A PRISMA-guided analysis of 59 studies (2013–2024) classified sensing architectures, analytical approaches, and application domains across diverse agroecological contexts. Integrated UAV–AI frameworks improve detection of crop stress, yield variability, biomass distribution, and phenological dynamics compared with conventional monitoring, particularly when multimodal sensor data are fused with satellite and ground observations. Predictive performance and diagnostic reliability increase when spectral, thermal, and structural datasets are analyzed jointly using machine-learning or deep-learning models. However, scalability remains constrained by operational, infra-structural, and regulatory factors, especially in resource-limited systems. These findings demonstrate that integrated sensing–analytics systems form a critical foundation for scalable climate-smart agricultural transformation and data-driven decision support across farm, landscape, and institutional scales.

Why it matches plant phenotyping methodsUAVセンシングとAIによる作物ストレス、収量変動、バイオマス、フェノロジーの観測・推定技術を体系的にレビューしており、植物形質・状態の取得方法が中心である。

abstractThis review synthesizes advances in UAV platforms, multisensor payloads, artificial intelligence (AI) analytics, and multisource data fusion

TraitDiscover: An automated high-throughput platform for multimodal plant phenotyping with real-time trait analysis ↗

Plant phenotyping is essential for elucidating genotype–environment interactions, yet conventional methods remain labor-intensive and low-throughput. TraitDiscover transcends these constraints by uniting multimodal sensing with tightly coupled hardware-software orchestration in a single, end-to-end phenotyping platform. Aligned with the ”Plant Phenotyping Trinity” framework, the system comprises a millimetre-accurate triaxial automation unit, a modular sensor array–RGB imaging, three-dimension laser scanner or LiDAR (3D), infrad (IR) thermal imaging, hyperspectral imaging (HSI), and photosynthesis (PS) imaging–and the dedicated software TraitNavigator suite into one cohesive system. A unified spatiotemporal synchronization mechanism enables robust time-series analysis and fusion of multisource phenotypic data across the entire crop growth period, while the DepthCropSeg algorithm and a night-time imaging module enhance trait extraction under complex conditions, providing G × E × P-ready, multimodal phenotypic datasets. Validation across soybean, maize, and rice trials demonstrated high sensitivity—detecting drought stress four days before visible symptoms, identifying glyphosate injury 24 hours ahead of manual scoring, and quantifying local adaption patterns across ecological gradients. While challenges remain in scaling to complex open-field conditions, TraitDiscover offers a scalable, data-driven approach to accelerate stress phenotyping and breeding decisions and is readily poised for deeper integration with AI to advance sustainable agriculture.

Why it matches plant phenotyping methodsマルチモーダルセンシング、画像解析、同期機構、形質抽出アルゴリズムを統合した植物フェノタイピング基盤の開発と検証が中心であり、ストレス検出や形質定量も実証している。

abstractTraitDiscover transcends these constraints by uniting multimodal sensing with tightly coupled hardware-software orchestration in a single, end-to-end phenotyping platform.

Fusing UAV multiple data and phenology to predict crop biomass ↗

Robust quantification of crop status in real-time is essential for agile decision-making. While use of unmanned aerial vehicle data (UAV) appears promising in this vein, the contribution and transferability of various features (e.g. vegetation indices, plant height and texture features) in crop above-ground biomass (AGB) prediction remain poorly understood. Here, our objectives were to (1) evaluate the performance of various machine learning (ML) algorithms in the synthesis of multiple features, (2) elicit the contribution of various UAV features, (3) assess the transferability of features across growth stages and sites. Four field experiments, incorporating several water and nitrogen treatments across two sites, were assembled for use in AGB prognostics. We invoked four ML algorithms—Random forest (RF), Lasso regression (LR), K-nearest neighbors (KNN) and a stacked ensemble integrating the three methods (SML)—to predict wheat AGB using multiple UAV data and phenological information. Additionally, interpretable ML techniques were employed to elucidate the influence of UAV features on AGB prediction across growth stages. Our results showed that all algorithms exhibited robust performance in predicting wheat biomass, with RMSE values of 1.64, 1.71, 1.71, and 1.57 Mg ha −1 for RF, LR, KNN, and SML, respectively. RF predominantly relied on plant height features, LR leveraged vegetation indices, and KNN prioritized texture features, while SML synthesized the advantages of multiple ML algorithms. Fusion of multiple datasets amplified model prognostic capacity and scalability, with R 2 and rRMSE of 0.92 and 22 % when using data from external sites. Features pertaining to vegetation indices and plant height during vegetative growth and around flowering had seminal contributions of model predictions. Texture features significantly reduced the saturation effect during the reproductive stage but diminished the model’s transferability during the vegetative stage. Complementarity among data types enhanced effectiveness of ensemble machine learning, which leverages strengths of diverse data to improve the accuracy and robustness of AGB predictions. Future studies could combine multiple sources of remote sensing, such as LiDAR and thermal infrared alongside system modeling, to improve ML accuracy and generalization capability.

Why it matches plant phenotyping methodsUAV由来の植物高・植生指数・テクスチャ等から小麦バイオマスを推定する機械学習手法を比較・検証し、異なる生育段階や圃場への転移性も評価しており、表現型推定法が中心である。

abstractevaluate the performance of various machine learning (ML) algorithms in the synthesis of multiple features

Drone-Based Phenotyping and its Utilization in Crop Improvement: A Review ↗

Drone-based phenotyping using unmanned aerial vehicles (UAVs) has emerged as a revolutionary approach for high-throughput, precise, and scalable measurement of plant traits critical to crop improvement. This technology integrates advanced imaging sensors—including RGB, multispectral, hyperspectral, and thermal cameras—with sophisticated image processing and artificial intelligence algorithms to non-destructively capture key phenotypic data such as plant height, biomass, canopy temperature, maturity timing, and disease symptoms under natural field conditions. Compared with traditional manual phenotyping and satellite-based remote sensing, UAV phenotyping offers superior spatial and temporal resolution, enabling dynamic monitoring of complex traits such as drought tolerance and disease resistance. Applications span early stress detection, quantitative trait assessment, yield prediction, and accelerating breeding cycles by facilitating objective, rapid selection of superior genotypes across multiple crop species. Despite its transformative potential, challenges remain in standardizing protocols, managing large-scale complex datasets, integrating phenotypic with genomic and environmental data, and providing training resources for widespread adoption. Ongoing advancements in sensor technology, data analytics, open-source tools, and capacity building are poised to cement drone-based phenotyping as a cornerstone technology for sustainable, climate-resilient crop breeding and global food security.

Why it matches plant phenotyping methodsUAV画像・センサーによる植物形質計測を中心に扱う明示的なフェノタイピングレビューであり、手法の応用、技術、課題を総合的に論じている。

abstractDrone-based phenotyping using unmanned aerial vehicles (UAVs) has emerged as a revolutionary approach for high-throughput, precise, and scalable measurement of plant traits critical to crop improvement.

UAV-based spatial sampling bridges ground measurements and satellite data for multi-scale estimation of sugar beet aboveground biomass. ↗

Aboveground biomass (AGB) is a critical indicator for assessing crop growth status and productivity, yet accurately linking fine-scale ground measurements with coarse-resolution satellite imagery remains challenging. Here, we propose an integrated ground-UAV-satellite framework that combines high-resolution UAV observations with an optimized systematic sampling-Global Moran's I (SS-GMI) procedure and a simple allometric growth model. Multi-variety sugar beet cultivated across heterogeneous habitats was used as a case study. Results indicate that a power-law model effectively captures the allometric relationships between AGB, plant height, and the Dreg vegetation index in sugar beet, achieving high accuracy and strong transferability. Incorporating phenological information from Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) codes and a thermal index further enhanced model robustness across independent habitat trials, yielding coefficients of determination ( R 2 ) of 0.80 and 0.83. The SS-GMI sampling procedure integrates systematic sampling with Global Moran's I to reduce spatial autocorrelation while ensuring uniform spatial coverage, thereby enabling the acquisition of representative and spatially independent samples from UAV-derived AGB maps. These samples were used to develop satellite-based AGB estimation models for PlanetScope and Sentinel-2A imagery, achieving R 2 values of 0.83 and 0.73, respectively. This study provides a practical and scalable framework for field-to-satellite AGB upscaling, offering new insights for the scale conversion of multi-source data in agricultural remote sensing.

Why it matches plant phenotyping methodsUAV・衛星観測とSS-GMIサンプリング、モデル化を組み合わせ、サトウダイコンの地上部バイオマスという植物形質を推定する統合手法が研究の中心である。

abstractHere, we propose an integrated ground-UAV-satellite framework that combines high-resolution UAV observations with an optimized systematic sampling-Global Moran's I (SS-GMI) procedure and a simple allometric growth model.

SMART DETECTION OF CROP DISEASES: UAV APPLICATIONS IN THE ERA OF PRECISION PLANT PATHOLOGY ↗

Against the backdrop of global food security concerns and the impending threat of phytopathogens, precision plant pathology technology has emerged as a key tool in ensuring the optimisation of agricultural sustainability. The conventional methods adopted in crop disease diagnosis, based on visual examination and laboratory analysis, are found to be lacking in terms of providing timely, geographically precise and scalable solutions. With recent advances in Unmanned Aerial Vehicle (UAV), or drone, technology, there is a paradigm shift in meeting such challenges. This review discusses in depth the synergistic integration of UAVs with multispectral, hyperspectral, thermal, and RGB imaging modalities in conjunction with artificial intelligence (AI) and deep learning approaches for the detection, classification, and quantification of diseases in plants at an early stage. Machine learning algorithms and optical sensors on unmanned aerial vehicles (UAVs) enable real-time high-resolution monitoring of disease signs on large crop fields. Vegetation indices, thermal stress maps, and spectral signatures are used by these systems to detect subtle physiological changes in crops before any visible sign of the disease. Their uses include disease detection, irrigation optimization, nutrient mapping, aerial sowing, yield prediction and precision pesticide application. Deep learning models, particularly CNNs and U-Net architectures, show the high accuracy of disease diagnosis and the estimation of their severity in field scenarios. In addition, UAV-based systems are fully compatible with Geographic Information Systems (GIS), IoTs, and cloud platforms, which facilitate data-driven decisionmaking for crop management. Nevertheless, there are obstacles in the shape of high data acquisition costs, model generalizability, regulatory restrictions, and low dataset diversity. The current article is concerned with recent developments, field-scale case studies, and existing challenges and discusses future directions for drone-based plant disease monitoring. The integration of UAV technologies with AI is highly promising to change the face of plant pathology and render disease monitoring more accurate, proactive, and sustainable.

Why it matches plant phenotyping methodsUAV画像・分光/熱センシングとAIによる植物病徴の検出・重症度推定を中心に扱うレビューであり、植物状態の表現型取得手法が主題である。

abstractThis review discusses in depth the synergistic integration of UAVs with multispectral, hyperspectral, thermal, and RGB imaging modalities in conjunction with artificial intelligence (AI) and deep learning approaches for the detection, classification, and quantification of diseases in plants at an early stage.

Transferability and Robustness in Proximal and UAV Crop Imaging ↗

AI-driven imaging is becoming central to crop monitoring, with proximal and unmanned aerial vehicle (UAV) platforms now routinely used for disease and stress detection, yield estimation, canopy structure, and fruit counting. Yet, as these models move from plots to farms, the main bottleneck is no longer raw accuracy but robustness under distribution shift. Systems trained in one field, season, cultivar, or sensor often fail when the scene, sensor, protocol, or timing changes in realistic ways. This review synthesizes recent advances on robustness and transferability in proximal and UAV imaging, drawing on a corpus of 42 core studies across field crops, orchards, greenhouse environments, and multi-platform phenotyping. Shift types are organized into four axes, namely scene, sensor, protocol, and time. The article also maps the empirical evidence on when RGB imaging alone is sufficient and when multispectral, hyperspectral, or thermal modalities can potentially improve robustness. This serves as a basis to synthesize acquisition and evaluation practices that often matter more than architectural tweaks, which include phenology-aware flight planning, radiometric standardization, metadata logging, and leave-one-field/season-out splits. Adaptation options are consolidated into a practical symptom/remedy roadmap, ranging from lightweight normalization and small target-set fine-tuning to feature alignment, unsupervised domain adaptation, style translation, and test-time updates. Finally, a benchmark and dataset agenda are outlined with emphasis on object-oriented splits, cross-sensor and cross-scale collections, and longitudinal datasets where the same fields are followed across seasons under different management regimes. The goal is to outline practices and evaluation protocols that support progress toward deployable and auditable systems, noting that such claims require standardized out-of-distribution testing and transparent reporting as emphasized in the benchmark specification and experiment suite proposed here.

Why it matches plant phenotyping methods植物の近接・UAV画像による病害・ストレス・収量・キャノピー構造・果実数の推定について、頑健性、転移性、取得・評価プロトコル、ベンチマークを体系化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 synthesizes recent advances on robustness and transferability in proximal and UAV imaging, drawing on a corpus of 42 core studies across field crops, orchards, greenhouse environments, and multi-platform phenotyping.

3D crop reconstruction: A review of hyperspectral and multispectral approaches ↗

Hyperspectral imaging (HSI) has emerged as a powerful tool for precision agriculture, enabling the non-destructive monitoring of crop biochemical and physiological traits. However, HSI alone lacks structural context, which limits its ability to accurately capture complex canopy architectures and organ-level traits. Integrating HSI with depth-sensing modalities such as Light Detection and Ranging (LiDAR), Red, Green, Blue, and Depth (RGB-D) cameras, and computational reconstruction technique such as photogrammetry enables the generation of three-dimensional hyperspectral point clouds, combining spectral richness with geometric fidelity. This multi-modal fusion enhances crop trait estimation, including biomass, leaf chlorophyll content, canopy height, leaf area, and stress indicators, while improving the robustness of phenotyping under occlusions, shadows, and varying illumination. Dimensionality reduction, feature selection, and machine learning approaches, including deep learning and explainable AI, are useful for handling high-dimensional hyperspectral data and extracting actionable agronomic insights. Moreover, the integration of thermal, radar, and Global Navigation Satellite System (GNSS) data further expands the capabilities of multi-modal sensing, enabling continuous, all-weather crop monitoring and accurate spatial referencing. Despite these advances, most studies to date focus on controlled environments, highlighting the need for field-based validation to ensure the reliability and scalability of HSI-depth fusion techniques. This review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges, and outlines future directions for implementing high-throughput, real-time phenotyping and precision agriculture solutions.

Why it matches plant phenotyping methods植物形質推定のためのハイパースペクトル・深度センシング融合と3D再構成を中心に扱うレビューであり、フェノタイピング手法の方法論的整理が主題。

abstractThis review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges

RGB-to-synthetic-thermal image translation using generative AI to support crop water stress assessment ↗

Thermal imaging is becoming a valuable tool for monitoring plant canopy temperature, which can serve as an indicator of crop water stress. However, specialized thermal sensors are often cost-prohibitive. This study explored strategies for supplementing crop water stress monitoring by generating synthetic thermal images from standard Red-Green-Blue (RGB) imagery captured using an unmanned aerial vehicle system (UAVs) equipped with a Zenmuse XT2 sensor and leveraging deep learning models. UAV-based RGB and thermal images were collected from 32 experimental plots of sweet corn and green beans over three growing seasons from 2020 to 2023. Each crop was subjected to one full and three deficit irrigation treatments, replicated four times. A total of 3,400 UAV images were collected over three seasons. Image processing was done in Pix4D software, and orthomosaic RGB and thermal maps were spatially aligned using ground control points (GCPs). The UAV RGB and thermal map data were split into 80 % and 20 % for training and testing, respectively. Two image-to-image translation generative adversarial network (GAN) deep learning models, specifically Pix2PixGAN and CycleGAN, were used to generate synthetic thermal images from RGB inputs. Image quality evaluation metrics, i.e., correlation coefficients (r), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), were used to evaluate the models’ performance. Crop water stress index (CWSI) values were also computed from measured and generated thermal imageries to assess practical applicability. Generated thermal canopy temperature outputs from the Pix2PixGAN model showed a strong correlation with the measured data using a thermal camera (r >0.95). Moreover, Pix2PixGAN resulted in lower MSE (5.63) and higher PSNR (42.98) than CycleGAN (MSE = 7.09, PSNR = 40.56), whereas CycleGAN had a slightly higher SSIM (0.44) than Pix2PixGAN (0.31). CWSI values derived from the generated thermal images reflected the expected gradients of water stress across irrigation treatments, with the highest CSWI observed from deficit irrigation treatments compared to the full irrigation. These results demonstrate that RGB-to-synthetic-thermal image translation using GAN models could be used to support crop water stress assessment and irrigation scheduling.

Why it matches plant phenotyping methodsRGB画像から合成熱画像を生成し、作物キャノピー温度と水ストレス指標を推定するGAN手法の開発・比較・性能評価が中心であり、植物状態の表現型取得に直接関係する。

abstractTwo image-to-image translation generative adversarial network (GAN) deep learning models, specifically Pix2PixGAN and CycleGAN, were used to generate synthetic thermal images from RGB inputs.

A pixel-aligned co-registration and DSM-grid fusion framework for UAV multispectral and thermal imagery and point-cloud data: 3D Characterization of crop canopy water status ↗

Accurate estimation of crop water status is essential for monitoring plant senescence and enabling intelligent agricultural management. This study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT). To address the low spatial resolution of thermal imagery, a downsampling–upsampling simulation framework was developed to evaluate interpolation errors. This framework quantitatively compares three common interpolation methods—nearest neighbor, bilinear, and bicubic interpolation—using RMSE and PSNR metrics. Results show that bicubic interpolation performs best in preserving spatial details and minimizing errors, and is therefore adopted in the subsequent image fusion process. A 3D grid was constructed based on the digital surface model (DSM), enabling grid-cell (pixel-aligned) spectral and thermal features to be mapped onto point-cloud units. Vegetation and thermal indices extracted from the mapped features were used as input variables. Combined with recursive feature elimination (RFE) and random forest (RF) models, the prediction of LEWT and LWC achieved R² values of 0.792 and 0.752, and rRMSE values of 13.84% and 9.68%, respectively. These results significantly outperformed those of partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models. By integrating the predicted results with the point cloud data, a 3D representation of canopy water parameters was constructed, revealing a typical top-down gradient of water loss. The experiment also revealed that nitrogen treatment significantly influenced the vertical distribution of water content. High-nitrogen application delayed water loss in the middle and lower canopy layers, highlighting the coupled regulation between nitrogen and water. Parameter comparisons showed that LEWT exhibited higher sensitivity than LWC across both temporal and spatial scales, making it a more robust indicator for canopy water monitoring. Additionally, point clouds generated from Cross-circling oblique (CCO) photogrammetry outperformed UAV LiDAR systems in terms of point density, structural completeness, and image fusion potential. In summary, this study validated the feasibility and effectiveness of integrating point cloud, MS, and thermal imagery via the proposed pixel-aligned co-registration and DSM-grid fusion framework for 3D crop water monitoring. The proposed method provides a reliable technical foundation for drought detection, irrigation management, and yield prediction in precision agriculture.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像・点群を融合し、綿花の葉水分状態を3D推定・可視化する手法の開発と検証が研究の中心である。

abstractThis study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT).

Estimating wheat grain filling course by fusing digital and thermal infrared images ↗

Grain filling plays a vital role in determining both the yield and quality of wheat. Therefore, timely and accurate monitoring of the grain filling course (GFC) is essential for assessing the feasibility of harvest timing optimization. Traditional methods based on field sampling are time-consuming and destructive. This study presents a non-destructive method for estimating the wheat grain filling course (GFC) by integrating ground-based RGB and thermal infrared imagery. Wheat ears were first segmented using a temperature-threshold approach, after which colour and temperature features were extracted. Grain water content (GWC) was then estimated using a Normalised Relative Ear Temperature (NRET) index, while days after anthesis (DAA) were retrieved using a piecewise linear model derived from ear colour features. Finally, a grain filling index (Kf) was developed using DAA corresponding to 25 % moisture content (DAA25%) to quantify the GFC. Results showed that thermal images acquired at 17:00 showed the greatest separability between ears and background canopy and the highest sensitivity to irrigation differences. Both NRET and DAA based models provided accurate GWC estimates (R² = 0.86 and 0.91; RMSE = 3.13 % and 4.21 %; rRMSE = 0.07 and 0.09, respectively). The Kf index effectively captured differences in GFC under different irrigation treatments and detected early maturity under water stress (p < 0.05). This study demonstrates the potential of combining thermal and RGB imagery for high-resolution, non-destructive monitoring of wheat grain filling and for supporting timely harvest management.

Why it matches plant phenotyping methodsRGB画像と熱赤外画像を統合し、穂の分割・特徴抽出から穀粒水分含量と登熟進行を推定する非破壊フェノタイピング手法が研究の中心である。

abstractThis study presents a non-destructive method for estimating the wheat grain filling course (GFC) by integrating ground-based RGB and thermal infrared imagery.

A pixel-aligned co-registration and DSM-grid fusion framework for UAV multispectral and thermal imagery and point-cloud data: 3D Characterization of crop canopy water status ↗

Accurate estimation of crop water status is essential for monitoring plant senescence and enabling intelligent agricultural management. This study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT). To address the low spatial resolution of thermal imagery, a downsampling–upsampling simulation framework was developed to evaluate interpolation errors. This framework quantitatively compares three common interpolation methods—nearest neighbor, bilinear, and bicubic interpolation—using RMSE and PSNR metrics. Results show that bicubic interpolation performs best in preserving spatial details and minimizing errors, and is therefore adopted in the subsequent image fusion process. A 3D grid was constructed based on the digital surface model (DSM), enabling grid-cell (pixel-aligned) spectral and thermal features to be mapped onto point-cloud units. Vegetation and thermal indices extracted from the mapped features were used as input variables. Combined with recursive feature elimination (RFE) and random forest (RF) models, the prediction of LEWT and LWC achieved R² values of 0.792 and 0.752, and rRMSE values of 13.84% and 9.68%, respectively. These results significantly outperformed those of partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models. By integrating the predicted results with the point cloud data, a 3D representation of canopy water parameters was constructed, revealing a typical top-down gradient of water loss. The experiment also revealed that nitrogen treatment significantly influenced the vertical distribution of water content. High-nitrogen application delayed water loss in the middle and lower canopy layers, highlighting the coupled regulation between nitrogen and water. Parameter comparisons showed that LEWT exhibited higher sensitivity than LWC across both temporal and spatial scales, making it a more robust indicator for canopy water monitoring. Additionally, point clouds generated from Cross-circling oblique (CCO) photogrammetry outperformed UAV LiDAR systems in terms of point density, structural completeness, and image fusion potential. In summary, this study validated the feasibility and effectiveness of integrating point cloud, MS, and thermal imagery via the proposed pixel-aligned co-registration and DSM-grid fusion framework for 3D crop water monitoring. The proposed method provides a reliable technical foundation for drought detection, irrigation management, and yield prediction in precision agriculture.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像・点群を統合し、綿花キャノピーの水分形質を3D推定・可視化する手法の開発と検証が中心である。

abstractThis study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT).

MULTIMODAL CROP DISEASE DETECTION: A SYSTEMATIC REVIEW MULTIMODAL CROP DISEASE DETECTION: A SYSTEMATIC REVIEW ↗

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.

Spatiotemporal assessment of maize evapotranspiration and surface energy fluxes under varying irrigation regimes using UAV based METRIC. ↗

Evapotranspiration (ET) is a key component of the hydrological cycle and is critical for determining crop water requirements. Accurate ET estimation is essential for improving irrigation efficiency, particularly under increasing water scarcity and climate variability. Conventional approaches such as the soil water balance, empirical formulations, the FAO Penman-Monteith method, eddy covariance flux towers, lysimeters, and scintillometers each have limitations related to spatial representativeness, accuracy, or operational cost. Unmanned aerial vehicles (UAVs) equipped with multispectral and thermal sensors offer a high spatial resolution and cost-effective alternative for field-scale assessment of surface energy balance components and ET. In this study, a field experiment was conducted on maize during rabi season of 2022-23 under two irrigation regimes based on depletion of available soil moisture (20% DASM and 40% DASM). UAV-based multispectral (0.05 m) and thermal imagery (0.33 m) were acquired at five crop growth stages and processed using the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model to estimate actual evapotranspiration (ETa) and surface energy fluxes. Spatiotemporal analysis showed that the 20% DASM treatment (400 mm) resulted in a 1.7 °C lower land surface temperature, a 16.5% higher NDVI, and an 11% increase in daily ETa compared with the 40% DASM treatment (316 mm), which experienced water stress and a 20% reduction in seasonal ETa. The UAV-based METRIC estimates of daily ETa showed strong agreement with that of Penman-Monteith (PM) combination approach (R² = 0.84; RMSE = 0.22 mm day⁻¹; MAPE = 6.1%), with a slight underestimation of seasonal ETa (-7%). Agreement with the soil water balance method ranged from - 3% to + 3%, demonstrating the capability of the approach to capture irrigation-induced variability in ETa and surface energy fluxes. Overall, the results highlight the potential of UAV-based METRIC for spatiotemporal assessment of crop evapotranspiration and surface energy dynamics to support precision irrigation management.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像とMETRICモデルにより、トウモロコシの蒸発散量・表面エネルギーフラックスを取得し、複数手法との一致性を検証している。植物キャノピーの生理状態の定量が研究の中心であり、単なる灌漑試験のルーチン測定ではない。

abstractUAV-based multispectral (0.05 m) and thermal imagery (0.33 m) were acquired at five crop growth stages and processed using the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model to estimate actual evapotranspiration (ETa) and surface energy fluxes.

Monitoring Maize Phenology Using Multi-Source Data by Integrating Convolutional Neural Networks and Transformers ↗

Effective monitoring of maize phenology under stress conditions is crucial for optimizing agricultural management and mitigating yield losses. Crop prediction models constructed from Convolutional Neural Network (CNN) have been widely applied. However, CNNs often struggle to capture long-range temporal dependencies in phenological data, which are crucial for modeling seasonal and cyclic patterns. The Transformer model complements this by leveraging self-attention mechanisms to effectively handle global contexts and extended sequences in phenology-related tasks. The Transformer model has the global understanding ability that CNN does not have due to its multi-head attention. This study, proposes a synergistic framework, in combining CNN with Transformer model to realize global-local feature synergy using two models, proposes an innovative phenological monitoring model utilizing near-ground remote sensing technology. High-resolution imagery of maize fields was collected using unmanned aerial vehicles (UAVs) equipped with multispectral and thermal infrared cameras. By integrating this data with CNN and Transformer architectures, the proposed model enables accurate inversion and quantitative analysis of maize phenological traits. In the experiment, a network was constructed adopting multispectral and thermal infrared images from maize fields, and the model was validated using the collected experimental data. The results showed that the integration of multispectral imagery and accumulated temperature achieved an accuracy of 92.9%, while the inclusion of thermal infrared imagery further improved the accuracy to 97.5%. This study highlights the potential of UAV-based remote sensing, combined with CNN and Transformer as a transformative approach for precision agriculture.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外画像とCNN/Transformerを統合し、トウモロコシのフェノロジー形質を定量推定する手法を開発・検証しており、植物表現型取得が中心である。

abstractThis study, proposes a synergistic framework, in combining CNN with Transformer model to realize global-local feature synergy using two models, proposes an innovative phenological monitoring model utilizing near-ground remote sensing technology.

Leveraging sensor technologies for seed phenotyping by genebanks ↗

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種子形質の高スループット取得に用いるセンサー・イメージング技術を中心に整理したフェノタイピングレビューであり、方法論的役割が明確です。

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.

Leveraging sensor technologies for seed phenotyping by genebanks ↗

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画像・センサー型フェノタイピング技術を中心に扱うレビューであり、方法論的役割が明確。

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.

Assessing neglected and underutilised taro crop water status using physiological indicators and UAV multi-modal thermal-multispectral data ↗

Abstract Purpose Taro (Colocasia esculenta (L)) , a neglected and underutilized crop species (NUS), holds great potential as a future smart crop that can thrive under climate variability and change, hence sustaining food security. While taro exhibits tolerance to drought conditions, variations in physiological attributes such as leaf temperature that rises under water stress and the associated stomatal closure that is initiated to conserve water, compromise crop productivity and overall yield. Therefore, monitoring taro crop physiological indicators of water status allows for the implementation of timely interventions and targeted adaption strategies to mitigate the effects of water deficit on taro crop productivity. Methods Unmanned Aerial Vehicles (UAV), integrated with high-resolution thermal sensors, provide valuable platform for generating near-real-time spatially explicit information suitable for assessing taro crop water status physiological indicators at farm scale. Hence, this study sought to evaluate the utility of UAV multi-modal thermal remote sensing and deep neural network techniques to estimate the equivalent water thickness, fuel moisture content, stomatal conductance, canopy temperature, and the chlorophyll content of smallholder taro crops. Results Findings showed that the multi-modal variable method achieves higher estimation accuracies in comparison to a single-modal technique, achieving R 2 values greater than 0.91 and rRSME values less than 14.15% of equivalent water thickness, fuel moisture content, stomatal conductance, canopy temperature, and chlorophyll content. Additionally, the results illustrated that the thermal wavebands and derived thermal indices are the most influential variables in estimating stomatal conductance and leaf temperature, yielding R 2 of 0.96 and 0.95, respectively. Conclusion These research findings underscore the applicability of UAV-acquired thermal remote sensing in providing rapid and robust spatially explicit information on smallholder taro crop water status for ensuring crop productivity and developing early warning systems of water stress. These findings serve as a stepping stone towards advancing agricultural monitoring frameworks and integrating NUS, such as taro, into traditional farming.

Why it matches plant phenotyping methodsUAV熱・マルチスペクトルデータと深層学習により、タロイモの水分状態、生理形質、クロロフィルなどを推定し、精度も評価しているため、植物フェノタイピング手法の応用・技術評価が中心である。

abstractthis study sought to evaluate the utility of UAV multi-modal thermal remote sensing and deep neural network techniques to estimate the equivalent water thickness, fuel moisture content, stomatal conductance, canopy temperature, and the chlorophyll content of smallholder taro crops.

Thermo-Photochemical Hysteresis Principle: A Cross-Modal Loop-Area Assay for Dynamic Plant Stress Phenotyping ↗

Abstract We present a protocol-defined, scalar, cross-modal hysteresis phenotype for plant stress phenotyping that quantifies dynamic decoupling between a thermal channel (e.g., leaf tem-perature proxy ∆T or canopy temperature) and a photochemical channel (e.g., ΦPSII, NPQ, or fluorescence-derived yields). The core measurement is a signed loop-area in a phase planespanned by the two signals under a symmetric perturbation (light or VPD ramp; option-ally sinusoidal forcing). We formalize this as the Sakib Thermo-Photochemical Hys-teresis Index (Sakib-Index) and provide mathematically grounded normalizations: the Sakib Coupling Coefficient (SCC) and the Sakib Phase-Lag Constant (SPLC). We show how loop area connects to phase-lag for periodic forcing and propose minimalcomputational checks for robustness (closure, sampling invariance, and directionality). Tendata-based illustrations are generated from open-access plant datasets (tomato chlorophyllfluorescence/reflectance and cottonwood leaf-temperature microclimate records), plus sixconceptual diagrams clarifying the assay pipeline.

Why it matches plant phenotyping methods植物ストレスの熱・光化学シグナルから新たな定量表現型を抽出する測定プロトコルと計算指標を中心に提案しており、方法開発に該当する。

abstractWe present a protocol-defined, scalar, cross-modal hysteresis phenotype for plant stress phenotyping

Leveraging sensor technologies for seed phenotyping by genebanks. ↗

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.

A Uav-based multisensor framework for legal industrial Cannabis monitoring and open-access dataset development. ↗

Industrial hemp cultivation is expanding and requires reliable monitoring for legal compliance and agricultural management. This paper presents a standardized UAV-based multisensor framework designed for Cannabis sativa L. It integrates RGB, multispectral, and thermal imaging as core modules, with hyperspectral and LiDAR as optional extensions. The framework sets protocols for sensor integration, flight planning, field measurements, and annotation, ensuring datasets that meet EU altitude limits (≤120 m AGL). Multi-altitude and multi-time-of-day acquisitions are proposed to capture spatial and diurnal variability. These data improve model robustness for phenotyping, stress detection, and THC compliance verification. Potential applications include precision agriculture, breeding, regulatory monitoring, environmental assessment, and illicit crop detection. Open-access datasets generated through this framework will support reproducibility, machine learning development, and collaboration among researchers, farmers, and regulators.

Why it matches plant phenotyping methodsUAVマルチセンサーフレームワークの設計、取得プロトコル、アノテーション、オープンデータセット開発が中心で、植物表現型やストレスを測定する方法論的貢献が明確です。

abstractThis paper presents a standardized UAV-based multisensor framework designed for Cannabis sativa L.

Plant stress detection using multimodal imaging and machine learning: from leaf spectra to smartphone applications. ↗

Plant leaf spectrophotometry has been used successfully as a means to detect stress, and it has been complemented by fluorescence analysis. This identification can be achieved in the ultraviolet (UV), visible (red, green, blue; RGB), near-infrared (NIR), and infrared (IR) spectral regions. Hyperspectral (measuring continuous wavelength bands) and multispectral (measuring discrete wavelength bands) imaging modalities can provide detailed information concerning the physiological well-being of plants, often diagnosing them at an earlier stage than visual or other more traditional biochemical assays. Because hyperspectral methods are highly sensitive and accurate, they cost a lot and produce vast quantities of data, which demand sophisticated computing software, and compared to multimedia, multispectral, and RGB cameras, they are less expensive and easier to carry but have reduced spectral resolution. Such methods are justified by thermal and fluorescence images revealing variations in the temperature and efficiency of photosynthesis of the leaves in response to stress. New digital imaging, thermal imaging, and optical filter technologies, and advancements in smartphone cameras have rendered low-cost, field-deployable platforms to monitor plant stress in real time feasible. Machine learning also supports these techniques by automating feature extraction, classification, and prediction to reduce the use of expensive instrumentation and human skill. But also problems like sensor calibration in a changing field, low model generalization across species and environments, and large, annotated datasets are needed. Beyond highlighting the relative strengths of the conventional and contemporary sensing approaches, the paper also examines the possibility of applying machine learning to multimodal images, as well as the growing impact of smartphone- based solutions in supplying inexpensive agricultural diagnostics. It concludes by overviewing the current limitations and limits to future research into scalable, cost-effective, and generalizable plant stress models.

Why it matches plant phenotyping methods植物ストレスを対象としたマルチモーダル画像・分光・熱・蛍光センシングと機械学習による表現型抽出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範囲に明確に該当する。

titlePlant stress detection using multimodal imaging and machine learning: from leaf spectra to smartphone applications.

Novel indices and multi-source data fusion for monitoring plant moisture stress in winter wheat fields. ↗

Drought is a significant challenge to winter wheat production. Its impact can be mitigated by preventing plant moisture stress through precision agriculture. Remote sensing and machine learning have proven effective for managing moisture stress in winter wheat. This study highlights the potential of new indices that combine visible (VIS) and near-infrared (NIR) bands along with canopy temperature (Tc), to monitor plant moisture content (PMC) and leaf moisture content (LMC) in winter wheat under irrigation treatments: W0 (no irrigation), W1 (45-65%), W2 (55-75%), W3 (65-85%), W4 (75-95%) of field capacity, and Z (irrigation and rainfall). Our findings show that the ratio stress index (RSI), with band combinations such as RSI7 (650, 428) , RSI8 (663, 422) , and RSI9 (671, 450) , performs better in tracking PMC and LMC, demonstrating high correlation and improved average prediction metrics for vegetation index (VI) models with R 2 , RMSE, and MAE of 0.838, 2.791, and 2.093 respectively, for LMC and VI-Tc input models with 0.850, 2.731, and 2.105 for PMC. Incorporating Tc into RSI models enhances prediction accuracy, increasing R² by up to 13.82% in the RSI-Tc-SVM-PMC model and decreasing RMSE and MAE by 15.89% and 18.33%, respectively. Therefore, a combination of RSI-Tc-SVM-ANN is recommended to monitor winter wheat moisture stress.

Why it matches plant phenotyping methods冬小麦の植物・葉の含水量および水分ストレスを、VIS/NIRと冠層温度のデータ融合および機械学習で推定する手法が研究の中心であり、植物生理状態の定量的フェノタイピングに該当する。

abstractThis study highlights the potential of new indices that combine visible (VIS) and near-infrared (NIR) bands along with canopy temperature (Tc), to monitor plant moisture content (PMC) and leaf moisture content (LMC) in winter wheat

Multi-sensor information fusion to characterise 3D spatial distribution of water stress in strawberries ↗

Moisture plays a critical role in crop growth and development, making accurate, efficient, and non-destructive detection and monitoring of crop water stress essential for advancing crop science research and optimizing production management. Traditional non-destructive methods for monitoring water stress primarily rely on color imaging or partial 2D spectral analysis. However, these methods are limited to two-dimensional features and fail to capture the spatial variability of water stress within the three-dimensional canopy structure of crops. To address this limitation, this study integrates RGB-D cameras and thermal infrared cameras and introduces a method for calculating the 3D spatial distribution characteristics of crop water stress using RGB-D-T fusion analysis. This approach enables high-precision detection and analysis of water stress in strawberry plants. An RGB-D-T acquisition system was designed and implemented to collect RGB images, depth images, and thermal infrared images of strawberries subjected to different moisture gradient treatments. Using the YOLOv8-seg deep learning model, semantic segmentation of the crop canopy and the wet reference surface was performed. The segmentation results were fused with 3D point cloud data to generate a 3D dataset incorporating temperature, color, and semantic information. Subsequently, the three-dimensional distribution characteristics and dynamic changes in the canopy water stress index (CWSI) of strawberry plants were analyzed under varying moisture conditions. The results demonstrated that under low moisture gradients (15%–30%), the CWSI value increased significantly and exhibited a concentrated distribution, indicating severe water stress. Conversely, under high moisture gradients (75%–90%), the CWSI value approached zero, reflecting sufficient water supply and complete stress alleviation. Additionally, the study highlighted the variation in the temperature difference between strawberry leaves and the surrounding air, confirming the sensitivity of strawberries to water stress across different reproductive stages. The response to water deficit was most pronounced during the growth phase. By fusing multi-source data, this study achieves 3D visualization and precise quantification of water stress in strawberries, providing innovative insights and technical support for precision irrigation and crop phenotyping research.

Why it matches plant phenotyping methodsRGB-D・熱赤外センサーの融合、3D点群化、深層学習セグメンテーションにより、イチゴの水ストレスを3D定量化する取得・解析手法が研究の中心である。

abstractAn RGB-D-T acquisition system was designed and implemented to collect RGB images, depth images, and thermal infrared images of strawberries subjected to different moisture gradient treatments.

FoScenes: A high-fidelity, large-scale 3D forest plant area density product derived from open-access airborne lidar data ↗

The accurate three-dimensional (3D) distribution of plant area density (PAD) within forests is crucial for understanding canopy structure and provides essential scene inputs for 3D Radiative Transfer Models (RTMs) to facilitate remote sensing interpretation. However, current lidar-based voxelization methods that estimate detailed PAD distributions often cover limited areas, constraining their applications in conducting broad forest studies and interpreting Earth Observation Satellite (EOS) data of various scales and resolutions. To address this, we developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow capable of producing extensive high-resolution 3D voxelized forest scenes (up to 100 km² with ≤2 m voxel size) from worldwide open-access airborne lidar scanning (ALS) data. By applying LS-PVlad to the ALS data acquired during the extensive NASA Goddard's LiDAR, Hyperspectral & Thermal Imager (G-LiHT) campaigns, we developed the first release of FoScenes—a high-fidelity PAD product comprising 40 seamless scenes from 28 diverse forest sites, with individual area ranging from ∼50 to ∼11,000 ha. The leaf area estimates of LS-PVlad have been validated by two-year field-measured leaf area index (LAI) from litter collection (best RMSE = 0.35 m²/m²) and digital hemispherical photography (DHP) images (RMSE = 0.46 m²/m²) across multiple plots at a deciduous forest site. Additionally, a broad comparison between FoScenes and MODIS plant/leaf area index product demonstrates high consistency (R² = 0.70, RMSE = 0.86 m²/m²). By providing multi-dimensional forest characterizations, FoScenes enables temporal insights into structure dynamics. Its integration with the discrete anisotropic radiative transfer (DART) model underscores the potential of FoScenes for extensive 3D RTM applications at various scales.

Why it matches plant phenotyping methods森林の植物面積密度を推定する3D再構成ワークフローを開発し、実測LAI等で検証した大規模フェノタイピング製品・データセットであり、植物形質取得が中心である。

abstractwe developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow

Automated calibration of stomatal conductance models from thermal imagery by leveraging synthetic images generated from Helios 3D biophysical model simulations ↗

Stomatal conductance (gs) is indicative of plant carbon dioxide uptake via photosynthesis and water loss via transpiration, making it a crucial plant biophysical trait. Direct measurement of gs is labor-intensive and usually not scalable to large fields. Using manual measurements to estimate parameters of gs models is even more labor-intensive and prone to sampling errors. This study aimed to develop an automated pipeline for gs measurement and model calibration using thermal imagery data, which not only disentangles the impacts of genotype-specific stomatal traits and environmental conditions but also enables the prediction of gs in new environments. The methodology involved using simulated thermal imagery data generated from a 3D biophysical model to train a machine learning model that could be applied to real thermal images to predict stomatal model parameters and gs itself. The method was evaluated by comparing predictions against manual gs measurements, all of which were not part of the model training process, as the model was trained against only simulated images. When compared against manual gs measurements using a porometer, the prediction R2 was 0.7, which is likely comparable to the accuracy of the manual porometer-based gs measurements (relative to a leaf gas exchange system). The developed pipeline enables high-throughput gs model parameter calibration and gs estimation.

Why it matches plant phenotyping methods熱画像と機械学習を用いて植物の気孔コンダクタンスを推定・モデル較正するパイプラインを開発し、手動測定と比較検証しており、植物フェノタイプ取得法が研究の中心である。

abstractThis study aimed to develop an automated pipeline for gs measurement and model calibration using thermal imagery data

Upscaling instantaneous ET obtained using UAV multispectral and thermal data into daily ET with and without UAV flights ↗

Accurate estimation of daily actual evapotranspiration (ETₐ ₐcₜ) is important for many aspects of research and field management. ETₐ ₐcₜ can be calculated from the reference crop ET (ETₒ) and actual crop coefficient (Kc ₐcₜ) which are influenced by the actual crop growth conditions and soil water conditions. In this study, a new framework was developed to estimate the daily actual Kc ₐcₜ using multispectral and thermal data obtained from unmanned aerial vehicles (UAVs). With UAVs flights, the daily ETc ₐcₜ was calculated by the Surface Energy Balance Algorithm for Land model (SEBAL). Without UAVs flights, daily ETc ₐcₜ was corrected by crop coefficient under full water condition (Kc fᵤₗₗ wₐₜₑᵣ) and water stress coefficient (Kₛ), based on remote sensing data, SEBAL model, and soil water balance equation. This framework was tested on winter wheat grown under six irrigation treatments from no irrigation (I0) up to five irrigations (I5) for four seasons from 2019 to 2023. The six irrigation treatments created a wide range of soil moisture and crop growing conditions. The results showed that the best timing to estimate daily ETc ₐcₜ was using the remote sensing data obtained at 11:00 local time with an R² of 0.88 and an RMSE of 0.53 mm/day. The daily ET c ₐcₜ estimated by the new framework on days without UAV flights was consistent with the ET c ₐcₜ calculated using soil water balance equation, with R² value varying from 0.74 to 0.80 under the different irrigation treatments. The results from this study demonstrated that the new framework based on UAV remote sensing data could estimate the daily ET c ₐcₜ in real time and could be further used to estimate daily ET c ₐcₜ on days without UAV flights.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像とSEBAL等を統合し、作物の実蒸発散量・水ストレス状態を推定する枠組みを開発・検証しており、測定手法が中心である。

abstractIn this study, a new framework was developed to estimate the daily actual Kc ₐcₜ using multispectral and thermal data obtained from unmanned aerial vehicles (UAVs).

Advances in plant phenomics and digital breeding in Korea ↗

Global demographic expansion and accelerating climate change are heightening the need for sustainable enhancement of crop productivity and resilience to environmental stresses. As conventional breeding approaches based on empirical selection approach their limits, digital breeding is emerging as an integrated framework that combines high-throughput imaging, multi-source data, and artificial intelligence (AI). Although next-generation sequencing (NGS) and smart-farming systems have enabled large-scale accumulation of genomic and environmental datasets, the phenotypic dimension remains a critical bottleneck in predictive breeding. To overcome this limitation, Korea has established six large-scale national phenotyping platforms equipped with advanced RGB, hyperspectral, and thermal imaging systems for high-throughput, precision phenotypic data acquisition. These platforms support quantitative and non-destructive monitoring of plant morphology, stress responses, and developmental dynamics and employ AI-driven classification and predictive modeling to derive biologically relevant traits. The integration of genomic, phenomic, and environmental datasets through AI-based analytical pipelines is expected to accelerate digital breeding, facilitating the development of climate-resilient and consumer-oriented cultivars within a data-driven agricultural paradigm. Collectively, plant phenomics research is evolving beyond image-based observation toward a comprehensive, predictive framework that provides the technological basis for next-generation precision agriculture.

Why it matches plant phenotyping methods植物フェノミクスとデジタル育種に関するレビューであり、RGB・ハイパースペクトル・熱画像を用いた大規模表現型プラットフォーム、形態・ストレス応答・発達動態の定量化、AI解析を中心的に扱っている。

abstractKorea has established six large-scale national phenotyping platforms equipped with advanced RGB, hyperspectral, and thermal imaging systems for high-throughput, precision phenotypic data acquisition.

Recent advances in plant imaging technology: a concise review ↗

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

Advances in UAV and AI Applications for Crop Disease Monitoring ↗

Plant diseases are estimated to cause a reduction of 20–40% in worldwide crop yields leading to over USD 220 billion in lost productivity and food security each year. Detecting these diseases early is essential for sustainable management, however traditional methods like scouting and laboratory diagnostics are often slow and impractical for large-scale or pre-symptomatic monitoring. This review looks at recent developments in using Unmanned Aerial Vehicles (UAVs) combined with Artificial Intelligence (AI) for overseeing crop health. It compares different sensor types such as RGB, multispectral, hyperspectral, thermal and LiDAR and explains the process from data collection to AI-driven classification. A particular focus is on machine learning (ML) and deep learning (DL) including Convolutional Neural Network (CNN) architectures, which have achieved 90–98% accuracy in identifying diseases in crops like wheat, potatoes, citrus and grapevines. The review further explores exciting new directions like data fusion, edge computing and autonomous scouting, pointing towards a future of more proactive, scalable and precise disease management. Keywords: Climate change, hi-tech agriculture, remote sensing, pre-symptomatic, autonomous scouting, machine learning.

Why it matches plant phenotyping methodsUAVセンサーとAIによる作物病害状態の観測・分類手法を中心に比較・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に該当する。

abstractThis review looks at recent developments in using Unmanned Aerial Vehicles (UAVs) combined with Artificial Intelligence (AI) for overseeing crop health.

Integration of High-Throughput Water-Sensitive Phenotyping for Crop Water Demand Diagnosis: Technical Pathways, Research Progress, and Challenges ↗

Accurate diagnosis of crop water demand is a core challenge in alleviating agricultural water scarcity. Traditional diagnostic methods, which rely mainly on soil moisture sensor monitoring or empirical models based on meteorological data, suffer from limitations such as insufficient spatiotemporal representativeness and an inability to reflect crop physiological status in real time, leading to an annual water waste of 10–30%. Therefore, developing technologies that enable real-time, non-destructive, and precise monitoring of crop water status is crucial. In recent years, the rapid advancement of high-throughput phenotyping technology has provided revolutionary tools to address this challenge. By integrating multi-source sensors (e.g., thermal infrared and hyperspectral imaging), multi-dimensional response characteristics of crops under water stress can be rapidly acquired. This paper systematically reviews research progress in using high-throughput phenotyping to obtain water-sensitive phenotypic traits and construct crop water demand diagnosis models. It focuses on: (1) the connotation and acquisition techniques of key water-sensitive phenotypic indicators, such as canopy temperature, spectral indices, and chlorophyll fluorescence; (2) the advantages, limitations, and fusion strategies of multi-platform data acquisition systems, including unmanned aerial vehicles (UAVs), ground mobile platforms, and satellite remote sensing; and (3) the construction methods, performance evaluation, and practical application cases of diagnostic models based on machine learning (e.g., Random Forest, XGBoost), deep learning (e.g., CNN, LSTM), and mechanism-coupled models. The innovation of this review lies in its systematic integration of the entire technological chain—"phenotyping acquisition → model construction → decision-making"—while identifying current research challenges, including field environmental complexity, model generalization capability, data barriers, and interpretability. Future development pathways are proposed, focusing on low-cost sensing, explainable AI, multi-source data fusion, and cloud-edge collaborative decision systems. This review aims to provide a systematic theoretical and practical reference for water management in precision irrigation and smart agriculture.

Why it matches plant phenotyping methods作物の水状態に関する表現型形質の取得技術と診断モデル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 paper systematically reviews research progress in using high-throughput phenotyping to obtain water-sensitive phenotypic traits and construct crop water demand diagnosis models.

Estimation of Chlorophyll Content in Potato Leaves Based on UAV Multi-Spectral and Thermal Infrared Images ↗

Soil and plant analyser development (SPAD) is a key indicator of plant nutritional status and nitrogen stress, reflecting crop growth. During potato tuber formation, multispectral and thermal infrared sensors were used to monitor leaf chlorophyll content. Four data sources—texture indices (TIs), vegetation indices (VIs), thermal infrared vegetation indices (TVIs), and texture features (TFs)—were analysed for correlation with SPAD values. The correlation coefficient was calculated, and the feature variables were screened. Then, the selected features were randomly combined with the ground measured data to construct random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR) models. Results showed that among TIs, the ratio texture index (RTI) had the highest correlation with SPAD (R = 0.703). Among VIs, the visible light difference vegetation index (VDVI) correlated best (R = 0.576). Among TVIs, normalised canopy temperature (NRCT) showed the strongest correlation (R = 0.640). Nearly half of TFs reached significant levels (P < 0.01). VIs provided the highest accuracy (R² = 0.741) in chlorophyll monitoring, with TIs improving prediction accuracy by up to 17.81% compared to TFs. Multi-source fusion (VIs + TIs + TVIs) achieved the highest model accuracy (R² = 0.854), a 15.2% improvement over traditional VIs input, with mean square error (RMSE) reduced by 40.8% and mean relative error (MRE) by 77.7%. RF models outperformed others under identical input conditions. This study offers a robust methodology for UAV-based multi-source remote sensing to monitor potato leaf chlorophyll, supporting precision agriculture practices.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外画像からジャガイモ葉のクロロフィル含量を推定する画像・センサー解析手法が中心であり、特徴量選択と複数の予測モデルの比較・検証を含むため。

titleEstimation of Chlorophyll Content in Potato Leaves Based on UAV Multi-Spectral and Thermal Infrared Images

Remote and proximal sensing assessment of water status and its correlation with yield on almond orchards in Southeast Spain ↗

This study evaluates the potential of UAS-based and proximal sensing tools to assess water stress and how derived indices correlates with yield in almond orchards in the semiarid conditions of southeast Spain. Two commercial orchards with contrasting irrigation regimes were monitored in 2023 using multispectral and thermal UAS imaging, alongside ground-based physiological and agrometeorological measurements. The Crop Water Stress Index (CWSI), calculated empirically from thermal data, and multispectral vegetation indices (VIs) were validated against stomatal conductance, stem water potential, and gas exchange parameters. Spatial variability in water status was explored using growth variability maps derived from NDVI and cumulative transpiration estimates. Results revealed significant correlations between UAS-based CWSI and water-related traits, with R² values exceeding 0.85 for stem water potential and intrinsic water-use efficiency. VIs, particularly those related to pigment composition (e.g., CCCI, MTCI, and CRI2), also demonstrated predictive capacity for physiological traits while NIR-related indices showed notable correlations with yield. Yield correlations were most accurate when integrating CWSI with pigment-sensitive indices such as PSRIm and chlorophyll-related VIs. Findings in this work are promising; however, challenges including proper calibration of UAS data and the influence of post-harvest physiological changes were also noted. This study highlights the value of combining thermal and multispectral remote sensing to optimize water management, while presenting promising results that open new windows for future yield prediction in almond orchards, offering a scalable approach for precision agriculture.

Why it matches plant phenotyping methodsUASの熱・マルチスペクトルセンシングによりアーモンドの水分状態や生理形質を推定し、地上測定値との検証および校正課題を扱っており、表現型取得手法が研究の中心である。

abstractThis study evaluates the potential of UAS-based and proximal sensing tools to assess water stress and how derived indices correlates with yield in almond orchards in the semiarid conditions of southeast Spain.

Improvement of crop growth simulations under different drip irrigation modes by jointly assimilating UAV multimodal data into crop models ↗

Accurate crop monitoring is essential for agricultural planning and food security. This study developed a coupling framework of unmanned aerial vehicle (UAV) multimodal data and crop models based on a sequential data assimilation method, offering technical support for crop growth simulation and precision management under drip irrigation modes in the Hexi Corridor of Northwest China. Multispectral and thermal infrared image data of spring maize at different growth stages were acquired via UAVs. The UAV-derived leaf area index (LAI) and soil moisture (SM) were assimilated into the WOFOST model using the ensemble Kalman filter (EnKF). Three assimilation schemes including (a) LAI, (b) SM, and (c) LAI+SM were compared to explore the effects of different mulching treatments (mulched vs. non-mulched) and irrigation gradients on assimilation performance under drip irrigation modes. Our results showed that the fusion of UAV-based multispectral and thermal infrared multimodal data enabled accurate retrieval of LAI and SM, with a maximum R² of 0.85. The three assimilation schemes exhibited significant differences, and the joint assimilation of LAI and SM outperformed the others. This may be since LAI and SM, as key indicators of crop growth and development, undergo dynamic changes throughout the growth period, and their joint assimilation fully captures the temporal variability of crops and soil. In addition, the proposed framework demonstrated marked variations in simulation accuracy across different drip irrigation modes. Overall, the performance for shallow buried drip irrigation (SBDI) was superior to that for surface drip irrigation (SDI) and film-mulched drip irrigation (FDI). This may be attributed to the direct influence on soil evaporation and evapotranspiration under the latter two modes, which in turn modifies crop growth and development processes and ultimately affects the model's simulation accuracy.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外データからLAIを推定し、作物モデルへ同化する手法の開発と性能比較が研究の中心であり、植物形質取得の技術的評価を含む。

abstractThis study developed a coupling framework of unmanned aerial vehicle (UAV) multimodal data and crop models based on a sequential data assimilation method

Salinity stress and water availability inferred from satellite leaf area index assimilated into a water-energy-crop model ↗

PURPOSE: Climate change, increasing aridity, water scarcity and population growth, enhancing food demand and irrigated land expansion, are expected to increase the extent of salinity-affected areas. This study aims to combine the crop-energy-water balance model FEST-EWB-SAFY with Leaf Area Index (LAI) and Land Surface Temperature (LST) data from remote sensing to monitor maize development in a field with a shallow water table and highly affected by salinity. METHODS: The FEST-EWB-SAFY model couples the distributed energy-water balance FEST-EWB model, which computes time-continuous soil moisture and evapotranspiration, and the SAFY crop model for yield prediction. The model was employed in synergy with satellite observations of LST and LAI. LST was used for the calibration/validation of the water and energy balances, whereas LAI was used both for the calibration of crop parameters and a data assimilation scheme. RESULTS: The data assimilation scheme was able to reproduce the observed spatial heterogeneity in crop development, associated to the uneven water table depth and salinity distribution, as these effects were picked up from satellite. A good correspondence was also found between modelled yield and the distributed samplings from a combined harvester equipped with a yield monitor. CONCLUSION: The results, comparable to those obtained with the a posteriori calibration, show that data assimilation of remote sensing observations allow to improve the model as the agricultural season progresses, including information which is difficult to monitor continuously in-situ.

Why it matches plant phenotyping methods衛星リモートセンシングによるLAI・LST観測を作物モデルに同化し、圃場内のトウモロコシの発達と空間的不均一性を推定する技術的ワークフローが中心である。

abstractThe model was employed in synergy with satellite observations of LST and LAI.

Toward Resilience in Broadacre Agriculture: A Methodological Review of Remote Sensing in Crop Productivity, Phenology, and Environmental Stress Detection ↗

Large-scale rainfed cropping systems (broadacre agriculture) face intensifying climate and resource stresses that undermine yield stability and farm livelihoods. Remote sensing (RS) offers critical tools for improving resilience by monitoring crop performance—productivity, phenology, and environmental stress—across large areas and timeframes. This review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring and to identify key challenges and integration opportunities. Peer-reviewed studies across diverse crops and regions were systematically examined to evaluate the strengths, limitations, and emerging trends across the three RS application themes. The review finds that (1) RS enables spatially explicit yield estimation from regional to paddock scales, with vegetation indices (VIs) and phenology-adjusted metrics closely correlated with yield. (2) Time-series analyses of RS data effectively capture phenological transitions critical for forecasting, supported by advances in curve fitting, sensor fusion, and machine learning. (3) Thermal and multispectral indices support the early detection of abiotic (drought, heat, salinity) and biotic (pests, disease) stresses, though specificity remains limited. Across themes, methodological silos and sensor integration barriers hinder holistic application. Emerging approaches, such as multi-sensor/scale fusion, RS–crop model data assimilation, and operational and big data integration, provide promising pathways toward resilience-focused decision support. Future research should define quantifiable resilience metrics, cross-theme predictive integration, and accessible tools to guide climate adaptation.

Why it matches plant phenotyping methods作物の生産性、フェノロジー、環境ストレスをリモートセンシングで推定する方法論的レビューであり、植物状態の取得・推定手法が中心である。

abstractThis review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring

Advances in Growing Degree Days Models for Flowering to Harvest: Optimizing Crop Management with Methods of Precision Horticulture—A Review ↗

Temperature plays a vital role in plant metabolism, and effective crop temperature appears to be influenced by variables related to climate change. While extreme weather events are widely discussed, the effects of moderate temperature changes pose consistent yet underexplored challenges for farmers. The “growing degree days” (GDD) also termed “heat unit”, is the most widely used approach in agricultural and ecological studies to quantify the relationship between temperature and plant development. This review provides a comprehensive examination of GDD methodology as applied to horticultural crop production, specifically from initial fruit development to fruit maturity, and postharvest. It is the first integrated synthesis of the conceptual evolution, methodological refinement, and broad application of GDD, thereby highlighting the need to optimize GDD approaches in light of emerging technological tools. While the GDD model is valuable for predicting crop development based on heat accumulation, it has limitations in capturing the effects of other environmental factors. Additionally, air temperature may not provide precise data on each plant organ. Recent advances in remote sensing, such as the integration of thermal imaging, RGB cameras, and lidar have enabled the measurement of spatially resolved temperature distribution within crop canopies, including fruit surface temperature. Recent advances, highlighted in the literature, suggest that integrating sensor innovations with machine learning approaches holds high potential for improving the precision of modeling temperature-dependent growth responses and their interactions with other environmental variables. By addressing these challenges and expanding its applications, GDD can continue to serve as an essential tool in promoting sustainable horticultural practices and adapting to global warming.

Why it matches plant phenotyping methodsGDDを用いて温度から作物の発育段階・成熟を推定する方法論を中心にレビューしており、植物状態の計算的な表現型推定に該当する。

abstractThis review provides a comprehensive examination of GDD methodology as applied to horticultural crop production, specifically from initial fruit development to fruit maturity, and postharvest.

PlantCV v4: Image analysis software for high-throughput plant phenotyping ↗

ABSTRACT PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection. CORE IDEAS PlantCV is an open-source, open-development, Python-based software package that has a new release for improved functionality and usability to make image analysis flexible and easier for researchers without a coding background. PlantCV is now capable of handling new data types that are relevant to researchers, such as thermal and hyperspectral, and has built in functionality for extracting information from these image types. The software project aims to lower the barrier to entry into image analysis for researchers by providing numerous, versioned, interactive tutorials that cover most common use cases, particularly in plant science.

Why it matches plant phenotyping methodsPlantCVは画像から植物形質を自動抽出するソフトウェアであり、形態形質測定、蛍光・熱・ハイパースペクトル画像対応、手動測定との比較が中心的な方法論的貢献です。

abstractPlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions.

Almond yield prediction at orchard scale using satellite-derived biophysical traits and crop evapotranspiration combined with machine learning ↗

Accurate almond yield prediction is essential for supporting decision-making across multiple scales, from individual growers to international markets. This is crucial in the Mediterranean region, where diminishing water resources pose significant challenges to the almond industry. In this study, remote sensing-based evapotranspiration estimates were evaluated for predicting almond yield at the orchard scale using machine learning (ML) algorithms. The almond prediction models were calibrated and validated using data provided by commercial growers, along with meteorological reanalysis and remote sensing products. The remote sensing products included: i) spectral indices, ii) vegetation biophysical traits retrieved from Sentinel-2, and iii) actual evapotranspiration (ET a ) estimated using the Priestley-Taylor two-source energy balance (TSEB-PT) model driven by Copernicus-based data. Almond yield data were collected from commercial orchards located in Spain’s Ebro and Guadalquivir basins from 2017 to 2022. Data collected from growers enables the establishment of almond water production functions at the orchard scale, yielding results comparable to those reported in experimental study sites. Almond yield prediction models calibrated with remote sensing data demonstrated predictive accuracy comparable to that of models relying on ground-truth variables provided by farmers, such as irrigation, orchard age, tree density, and cultivar. Among them, the PM CRS model—which integrates the fraction of absorbed photosynthetically active radiation (fAPAR), the normalized difference moisture index (NDMI), canopy chlorophyll content (C ab ), ETa, and meteorological data—achieved a RMSE of 399.1 kg ha - ¹ in July. These findings highlight the potential of remote sensing-based models for accurately estimating almond yield. Furthermore, the PM CRS model proved scalable and effective when applied across four almond-producing regions in the Ebro basin. Future improvements may be realized through enhanced ET a retrieval using upcoming thermal satellite missions, integration of irrigation estimates, and the adoption of advanced machine learning and deep learning algorithms.

Why it matches plant phenotyping methods衛星リモートセンシング由来の植物生理・生物物理形質と蒸発散を用いて果樹園単位のアーモンド収量を推定し、機械学習モデルを較正・検証している。収量形質の取得・推定ワークフローが研究の中心である。

abstractIn this study, remote sensing-based evapotranspiration estimates were evaluated for predicting almond yield at the orchard scale using machine learning (ML) algorithms.

Integrating Time-Series Meteorological Data and sUAS Information into a Machine Learning Framework for California Vineyard Water Stress Monitoring ↗

Abstract Efficient irrigation management is fundamental to sustainable crop production, particularly under increasing temperatures and limited water availability. In vineyards, water stress significantly influences grapevine development and productivity. Controlled water stress is intentionally applied in deficit-irrigated systems to regulate yield and enhance fruit quality. Therefore, vineyards must be routinely monitored to prevent excessive stress that could cause detrimental effects. In this study, we developed a machine-learning framework based on the eXtreme Gradient Boosting (XGB) machine-learning model to estimate grapevine leaf water potential (Y leaf ) using meteorological data and high-resolution imagery from small unmanned aerial systems (sUAS) over commercial vineyards of different varieties and in different climatic zones in California. The framework incorporates key meteorological and image-derived features, including maximum air temperature in the 24 hours prior to the flight, air temperature at the time of flight, the difference between these two temperatures, as well as canopy temperature derived from sUAS thermal imagery. These features were included to indirectly capture plant-water-weather interaction during the 24-hour period preceding data collection, enhancing the model’s practical applicability. The XGB model demonstrated robust performance, achieving an RMSE of 0.16 MPa, a bias of -0.06 MPa, and a correlation coefficient of 0.83 while minimizing computational cost. Model generalizability was further validated in an independent vineyard, demonstrating its potential for commercial application in precision irrigation and vineyard water management. Our research highlights the potential for broader applicability, particularly in addressing flash drought and promoting adaptive water resource management.

Why it matches plant phenotyping methodssUAS熱画像と気象データからブドウ葉の水ポテンシャルを推定する機械学習手法を開発し、独立圃場で妥当性を検証しており、植物表現型取得が中心である。

abstractwe developed a machine-learning framework based on the eXtreme Gradient Boosting (XGB) machine-learning model to estimate grapevine leaf water potential (Y leaf ) using meteorological data and high-resolution imagery from small unmanned aerial systems (sUAS)

Characterizing and screening of wheat genotypes under salinity stress condition using thermography and multivariate techniques. ↗

Salinity is one of the major abiotic stresses affecting the growth and yield of wheat crops, particularly in arid and semi-arid regions, where irrigation water or soil with high salt content is often present. With increasing soil salinization and abrupt climate change at the global level, identifying salt-tolerant wheat genotypes has become crucial. The present study aimed to characterize and screen the salt tolerance of 25 wheat genotypes at 25, 52, 69, 90, and 118 Days After Sowing (DAS), under field conditions using thermography and bio-physiological parameters. Wheat genotypes were irrigated with saline irrigation water (with threshold EC of 4dSm/m) and performances of the genotypes were monitored using thermal image-based indices e.g., CWSI (Crop Water Stress Index), IG (index of Stomatal Conductance) and bio-physiological parameters i.e., Photosynthesis (Pn), Stomatal conductance (Ig), Transpiration rate, Leaf Area Index (LAI), Normalized difference vegetation index (NDVI), Relative water content (RWC), Total leaf chlorophyll, Membrane stability index (MSI), Osmotic pressure (OP) of leaf, Leaf Na and K. With these biophysical parameters, a new screening index named as Normalized Salinity Stress Tolerance Index (NSSTI) was developed using different multivariate analysis e.g., Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Discriminant Analysis (DA). Based on the criteria developed in this study, NSSTI could classify the 25 wheat genotypes for salinity stress into: 6 - tolerant, 16 - moderate, and 3 - sensitive genotypes. DA confirmed the classification by NSSTI with 92-100% accuracy based on canonical discriminant functions. Further, thermal image-derived CWSI and IG differentiated tolerant and sensitive genotypes across all DAS under salt stress conditions. Irrespective of different DAS, NSSTI showed significant (p < 0.01) correlation with CWSI (0.70-0.83) and IG (0.78-0.84). The study also identified transpiration rate, RWC, OP, NDVI, and Pn as important parameters to characterize and screen wheat genotypes under salinity stress conditions at different DAS. The newly developed index - NSSTI, exhibited significant (p < 0.01) correlations with wheat yield (0.76-0.84) and biomass (0.73-0.82), indicating the usefulness of NSSTI in evaluating and screening wheat genotypes for salt tolerance. The identified wheat genotypes and key bio-physiological traits can be used in breeding programs to develop advanced salt-tolerant wheat lines. In future, the newly developed salinity stress index NSSTI would play a potential role in the screening and selection of salt-tolerant wheat genotypes under field conditions.

Why it matches plant phenotyping methods熱画像からCWSI・気孔コンダクタンス指標を抽出し、多変量解析で新規の耐塩性スクリーニング指標NSSTIを開発・検証しており、表現型取得と解析手法が研究の中心である。

abstractusing thermography and bio-physiological parameters

Fusion of Robotics, AI, and Thermal Imaging Technologies for Intelligent Precision Agriculture Systems. ↗

The world population is expected to grow to over 10 billion by 2050 and therefore impose further stress on food production. Precision agriculture has become the main approach used to enhance productivity with sustainability in agricultural production. This paper conducts a technical review of how robotics, artificial intelligence (AI), and thermal imaging (TI) technologies transform precision agriculture operations, focusing on sensing, automation, and farm decision making. Agricultural robots promote labor solutions and efficiency by utilizing their sensing devices and kinematics in planting, spraying, and harvesting. Through accurate assessment of pests/diseases and quality assurance of the harvested crops, AI and TI bring efficiency to the crop monitoring sector. Different deep learning models are employed for plant disease diagnosis and resource management, namely the VGG16 model, InceptionV3, and MobileNet; the PlantVillage, PlantDoc, and FieldPlant datasets are used respectively. To reduce crop losses, AI-TI integration enables early recognition of fluctuations caused by pests or diseases, allowing control and mitigation in good time. While the issues of cost and environmental variability (illumination, canopy moisture, and microclimate instability) are taken into consideration, the advancement in artificial intelligence, robotics technology, and combined technologies will offer sustainable solutions to the existing gaps.

Why it matches plant phenotyping methodsロボティクス、AI、熱画像による植物センシングと病害診断を技術レビューとして扱っており、植物の病害状態を取得・推定する方法が主要な内容に含まれるため。

abstractThis paper conducts a technical review of how robotics, artificial intelligence (AI), and thermal imaging (TI) technologies transform precision agriculture operations, focusing on sensing, automation, and farm decision making.

Non-invasive imaging and biochemical profiling reveal genotypic variation in Tagetes erecta L. to cold stress ↗

Aim: This study aimed to establish a phenomic-based screening protocol for cold tolerance in African marigold (Tagetes erecta L.) by integrating non-invasive imaging with physiological and biochemical analyses, addressing the gap between field crop and ornamental breeding applications where cold stress significantly constrains cultivation by affecting growth, development, and productivity. Methodology: Ten marigold genotypes were evaluated under controlled polyhouse and natural cold stress conditions across two growing seasons. High-throughput plant phenotyping utilizing RGB, near-infrared, and thermal imaging quantified key traits including morphological characteristics (via RGB), tissue water content (via near-infrared), and thermal regulation (via thermal imaging), complemented by targeted physiological and biochemical analyses. Results: Significant genotypic variation was observed, as cold stress caused 70.7% reduction in plant area and 24.3% decrease in the photosynthetic rate. Genotype Af./W-4 exhibited superior cold tolerance through enhanced photosynthetic maintenance, minimal reductions in greenness (4.4%), membrane stability (11%), and photosynthetic rate (14.2%), followed by genotypes PB and Af./W-6. Multivariate analysis indicated that key determinants of cold stress performance include traits like plant area, caliper length, greenness, and photosynthetic rate. Interpretation: Integration of non-invasive imaging with biochemical analysis successfully differentiated cold-tolerant from the susceptible genotypes. This comprehensive approach provides an efficient screening methodology for identifying climate-resilient genotypes in ornamental crops, potentially accelerating cold-tolerant genotype development for sustainable floriculture production. Key words: African marigold, Cold tolerance, Genotypic-variation, High through put phenotyping, Tagetes erecta L.

Why it matches plant phenotyping methods非侵襲イメージングを用いた高スループット植物表現型解析とスクリーニングプロトコルの確立が中心であり、冷ストレス耐性の形質抽出・評価に実質的に関与している。

abstractThis study aimed to establish a phenomic-based screening protocol for cold tolerance in African marigold (Tagetes erecta L.) by integrating non-invasive imaging with physiological and biochemical analyses

Automated Assessment of Green Infrastructure Using E-nose, Integrated Visible-Thermal Cameras and Computer Vision Algorithms. ↗

The parameterization of vegetation indices (VIs) is crucial for sustainable irrigation and horticulture management, specifically for urban green infrastructure (GI) management. However, the constraints of roadside traffic, motor and industrially related pollution, and potential public vandalism compromise the efficacy of conventional in situ monitoring systems. The shortcomings of prevalent satellites, UAVs, and manual/automated sensor measurements and monitoring systems have already been reviewed. This research proposes a novel urban GI monitoring system based on an integration of gas exchange and various VIs obtained from computer vision algorithms applied to data acquired from three novel sources: (1) Integrated gas sensor data using nine different volatile organic compounds using an electronic nose (E-nose), designed on a PCB for stable performance under variable environmental conditions; (2) Plant growth parameters including effective leaf area index (LAIe), infrared index (Ig), canopy temperature depression (CTD) and tree water stress index (TWSI); (3) Meteorological data for all measurement campaigns based on wind velocity, air temperature, rainfall, air pressure, and air humidity conditions. To account for spatial and temporal data acquisition variability, the integrated cameras and the E-nose were mounted on a vehicle roof to acquire information from 172 Elm trees planted across the Royal Parade, Melbourne. Results showed strong correlations among air contaminants, ambient conditions, and plant growth status, which can be modelled and optimized for better smart irrigation and environmental monitoring based on real-time data.

Why it matches plant phenotyping methods植物のLAI、赤外線指数、樹冠温度差、水ストレス指数を、カメラ・E-nose・コンピュータビジョンで取得する統合的な植物モニタリング手法が研究の中心である。

abstractThis research proposes a novel urban GI monitoring system based on an integration of gas exchange and various VIs obtained from computer vision algorithms

Efficient Deep Learning Ensemble of Lightweight CNNs and Vision Transformers for Real-Time Plant Disease Diagnosis ↗

Timely identification of plant diseases plays a vital role in protecting crop yield and supporting effective decision-making in precision agriculture. Conventional computer vision models achieve high recognition accuracy but often require substantial computing power, making them impractical for low-cost edge hardware widely used in rural areas. In this work, a compact deep learning ensemble is presented, combining three lightweight convolutional neural networks—MobileNetV3-Small, EfficientNet-B0, and ShuffleNetV2—with a Vision Transformer (ViT-B/16). The models operate in parallel, and their outputs are merged using a weighted late-fusion approach, with fusion weights determined through systematic grid search to achieve the best trade-off between predictive performance and processing speed. The Plant Village dataset, consisting of 54,303 images from 38 healthy and diseased leaf categories, was used for evaluation. To improve robustness, the training data were augmented through geometric transformations, contrast adjustment, and controlled noise addition. When tested on a Raspberry Pi 4 device, the ensemble reached an accuracy of 97.85%, precision of 97.67%, recall of 97.92%, and F1-score of 97.79%, with an average inference time of 20.5 ms and a total size of 14.6 MB. These results surpassed those of all individual models and conventional machine-learning baselines. Statistical testing using McNemar’s method confirmed the significance of the improvement (p 0.05). Precision–Recall analysis indicated strong resistance to false positives, while accuracy–latency assessment confirmed suitability for real-time field operation. The proposed system offers a practical, resource-efficient framework for on-site plant disease diagnosis in areas with limited connectivity and computing resources. Further development will focus on adaptation to field-captured imagery, hardware-aware model compression, and the integration of additional sensing modalities such as hyperspectral and thermal imaging.

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開発・評価し、精度とエッジ実行性能を検証しているため、植物フェノタイピング手法が中心である。

abstractIn this work, a compact deep learning ensemble is presented, combining three lightweight convolutional neural networks—MobileNetV3-Small, EfficientNet-B0, and ShuffleNetV2—with a Vision Transformer (ViT-B/16).

End-to-end pipeline for simultaneous temperature estimation and super resolution of low-cost uncooled infrared camera frames for precision agriculture applications ↗

Radiometric infrared (IR) imaging is a valuable technique for remote-sensing applications in precision agriculture, such as irrigation monitoring, crop health assessment, and yield estimation. Low-cost uncooled non-radiometric IR cameras offer new implementations in agricultural monitoring. However, these cameras have inherent drawbacks that limit their usability, such as low spatial resolution, spatially variant nonuniformity, and lack of radiometric calibration. In this article, we present an end-to-end pipeline for temperature estimation and super resolution of frames captured by a low-cost uncooled IR camera. The pipeline consists of two main components: a deep-learning-based temperature-estimation module, and a deep-learning-based super-resolution module. The temperature-estimation module learns to map the raw gray level IR images to radiometric-grade temperature maps while also correcting for nonuniformity. The super-resolution module uses a deep-learning network to enhance the spatial resolution of the IR images by scale factors of ×2 and ×4. We evaluated the performance of the pipeline on both simulated and real-world agricultural datasets composing of roughly 20,000 frames of various crops. For the simulated data, the results were on par with the real-world data with sub-degree accuracy — 0.54∘C mean absolute error (MAE) for ×2 scale factor, and 0.84∘C MAE for ×4 scale factor. For the real data, the proposed pipeline was compared to a high-end radiometric thermal camera, and achieved sub-degree accuracy — 0.81∘C MAE for ×2 scale factor, and 0.81∘C MAE for ×4 scale factor. The results of the real data are on par with the simulated data. We show that our pipeline can compete with high-end thermal cameras in terms of quality and accuracy of the temperature and crop water stress index (CWSI) estimations using affordable hardware, with errors of 1.42% for ×2 and 1.86% for ×4 between the ground truth and the estimated CWSI. The runtime of the pipeline is less than 1sec per frame on a CPU, allowing it to run at video rates. The proposed pipeline can enable various applications in precision agriculture that require high quality thermal information from low-cost IR cameras.

Why it matches plant phenotyping methods低コスト赤外線カメラから植物温度と作物水ストレス指数を推定する深層学習パイプラインを開発し、実データ・シミュレーションおよび高性能熱画像カメラとの比較で精度を検証しており、植物フェノタイピング手法が中心である。

abstractIn this article, we present an end-to-end pipeline for temperature estimation and super resolution of frames captured by a low-cost uncooled IR camera.

Diagnostic study of defoliation and boll opening effects on machine-harvested cotton using multi-source UAV remote sensing data ↗

Accurate assessment of cotton defoliation (DF) and boll opening (BO) is essential for optimizing yield and fiber quality during mechanized harvesting, as improper timing can reduce yield and impair fiber quality. Unmanned aerial vehicle (UAV)-based remote sensing has become an effective tool for monitoring these indicators, but most current methods rely on single-sensor data, limiting diagnostic accuracy and generalizability. To address this limitation, we propose a multi-source data fusion framework integrating RGB, multi-spectral (MS), and thermal infrared (TIR) sensors for comprehensive canopy information. The fused dataset includes vegetation indices (VIs), color indices (CIs), texture features (Tex), and canopy temperature (TC). Feature selection was performed using pearson correlation coefficients (PCCs), recursive feature elimination with cross-validation (RFECV), and the Boruta algorithm to identify key variables. Three machine learning models—partial least-squares regression (PLSR), random forest regression (RFR), and extreme gradient boosting regression (XGBR)—were developed and compared. The RFECV-selected RGB+MS+TIR features in the XGBR model achieved the highest predictive accuracy, with R² values of 0.918 for defoliation rate and 0.867 for boll opening rate, improving by 1.9 % and 4.3 %, respectively, over single-sensor models. Root mean square error (RMSE) and relative RMSE (rRMSE) were reduced by 1.11 %-1.99 % and 1.66 %-2.28 %, respectively. These findings demonstrate that multi-source UAV data fusion, combined with advanced machine learning techniques, significantly enhances the accuracy and robustness of cotton defoliation and boll opening diagnosis. This approach offers a practical solution for precision agriculture to improve harvest scheduling and defoliant management.

Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル・熱赤外データを融合し、綿花の落葉率と綿花開絮率という植物状態を推定する手法を開発・比較しており、フェノタイピング手法が研究の中心である。

abstractwe propose a multi-source data fusion framework integrating RGB, multi-spectral (MS), and thermal infrared (TIR) sensors for comprehensive canopy information.

3D thermal volume mapping to assess the biological and physical characteristics of olive crops using remote sensing and photogrammetric methods ↗

Abstract Drones, as well as ground-based and satellite platforms, offer the possibility to carry sensors able to obtain timely and precise indications about vegetation health conditions. These systems can serve as tools for agricultural monitoring and the management of crops. Nowadays, Unmanned Aerial Vehicles (UAV) systems are equipped with sophisticated sensors, such as those operating in the Thermal InfraRed spectral range, which can provide indications about the water content of vegetation at very-high spatial resolution. This study explores the feasibility of exploiting drone-based thermal imagery and Structure-from-Motion (SfM) photogrammetry to derive 3-D representations in Precision Agriculture. The health condition of olive trees was evaluated using thermal observations collected by a UAV system over an olive orchard located in the Basilicata region (Southern Italy). Following the SfM pipeline, accurate 2-D/3-D thermal photogrammetric products have been created, and analyzed by means of the Normalized Relative Canopy Temperature (NRCT) index. The goal was to explore how 3D thermal volume analysis can enhance the detection and interpretation of early signs of water stress and related plant health descriptors. Although evident symptoms of stress were not yet visible during the survey, our preliminary results highlight the added value of 3D thermal information over traditional 2D approaches, particularly in capturing spatial variability within individual tree canopies. These findings demonstrate the potential of UAV-based 3D thermal analysis as a valuable tool for advanced monitoring in Precision Agriculture and Smart Farming practices.

Why it matches plant phenotyping methodsUAV熱画像とSfMによる3D熱画像から、樹冠温度・水ストレスなどのオリーブ樹の状態を抽出する手法が中心であり、2D手法との比較を含む実質的なフェノタイピング手法研究である。

abstractThis study explores the feasibility of exploiting drone-based thermal imagery and Structure-from-Motion (SfM) photogrammetry to derive 3-D representations in Precision Agriculture.

Plant Stress Classification on Thermal Images Using MobileNetV3 ↗

ABSTRACT Early detection of plant stress is crucial for minimizing crop loss and promoting sustainable food production. Traditional methods often fail to identify stress indicators before visible symptoms emerge. Normal images primarily capture visible signs of stress, which become apparent only after significant damage has occurred, limiting timely intervention and leading to lower accuracy in early detection due to their inability to capture hidden stress markers. In contrast, thermal images reveal temperature variations that indicate stress at an earlier stage, even before it becomes visible to the human eye, allowing for improved accuracy by identifying subtle physiological changes. The proposed system involves collecting and preprocessing thermal images of plants under varying stress conditions, enabling the detection of underlying stress through these temperature variations. A MobileNetV3 model, known for its lightweight architecture, speed, and efficiency, is trained on these thermal images to classify them into stress and non‐stress categories. The experimental results compare three deep learning models—MobileNetV3, DenseNet, and VGG16 for plant stress classification using thermal images. MobileNetV3 achieved the highest accuracy, with an average F1‐score of 0.67, significantly outperforming DenseNet and VGG16. MobileNetV3 strikes an optimal balance between accuracy and computational efficiency, outperforming more complex models while maintaining lower processing demands. This makes it particularly suitable for real‐time, on‐device applications. The proposed system harnesses these advantages to provide farmers and agronomists with an automated, non‐invasive solution for real‐time plant health monitoring.

Why it matches plant phenotyping methods熱画像から植物ストレス状態を推定し、複数の深層学習モデルを比較・評価する手法が研究の中心であるため、植物フェノタイピング手法として含める。

abstractThe proposed system involves collecting and preprocessing thermal images of plants under varying stress conditions, enabling the detection of underlying stress through these temperature variations.

High-Throughput Phenotyping of Cereal Crops Under Stress: Unveiling Evapotranspiration and Respiration Patterns ↗

Addressing crop responses to drought and nitrogen stress is crucial for improving resilience and ensuring sustainable agriculture under changing climatic conditions. This study investigates the physiological responses of wheat (Videodur [DU], Sensas [SW]) and barley (Tiroler Imperial [SG1], Amidala [SG2]) cultivars to drought and nitrogen stress during early reproductive to full maturity stages (BBCH 70 to 90) using infrared (IR) and visible near-infrared–shortwave infrared (VNIR-SWIR) hyperspectral imaging. Evapotranspiration (ET) and respiration were analyzed as functions of mean plant temperature (Tplant), light intensity, plant water status (indicated by the Normalized Difference Water Index, NDWI), and air humidity. Results revealed that drought stress significantly reduced NDWI and ET while increasing Tplant, with wheat cultivars showing greater sensitivity to water deficit. Barley, particularly SG2, exhibited superior water retention and thermal regulation, highlighting its potential for drought resilience with consistently higher NDWI values and lower Tplant. Temporal analysis identified the reproductive stage as the most vulnerable to stress, with a sharp decline in NDWI and rise in Tplant, emphasizing the need for stage-specific interventions. Regression models explained 74% of ET variance and 67% of respiration variance, underscoring the predictive power of NDWI and Tplant as proxies for plant water status and metabolic activity. Real-time evapotranspiration (ET) measurements using a balance during precision watering further validated the predictive capabilities of NDWI and Tplant. These findings provide valuable insights into growth stage-specific breeding programs and sustainable crop management strategies under environmental stress conditions.

Why it matches plant phenotyping methods赤外・ハイパースペクトル画像からNDWI、植物温度、蒸発散量、呼吸を推定し、回帰モデルと実測バランスで検証しており、植物ストレス形質の取得・推定ワークフローが中心的である。

abstractusing infrared (IR) and visible near-infrared–shortwave infrared (VNIR-SWIR) hyperspectral imaging

Towards an End-to-End Digital Framework for Precision Crop Disease Diagnosis and Management Based on Emerging Sensing and Computing Technologies: State over Past Decade and Prospects ↗

Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production.

Why it matches plant phenotyping methods植物病害の画像・センサ計測、機械学習による病害分類・重症度推定を中心に扱うレビューであり、植物状態の取得・評価手法が主題。

abstractThis review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies.

Estimating short-term changes of stomatal conductance using a combination of 3D imaging and energy balance modelling ↗

Estimating stomatal conductance poses significant challenges in plant stress research, since traditional measurement methods interact physically with leaves, thereby altering their position and microclimate. To overcome this problem, we developed a contactless approach that combines 3D modeling, thermal imaging, and a modified energy balance equation to estimate stomatal conductance remotely and accurately. We evaluated this method by comparing model estimated total plant transpiration with gravimetric measurements. The estimates provided by our approach corresponded favorably with measurements across different environmental conditions, including non-stressed and short-term salinity stress scenarios. This method effectively tracks stomatal responses to rapid osmotic stress, offering a reliable tool for remote assessment of plant physiological dynamics.

Why it matches plant phenotyping methods3D画像・熱画像・エネルギー収支モデルを組み合わせ、植物の気孔コンダクタンスを非接触推定する手法の開発と検証が中心である。

abstractwe developed a contactless approach that combines 3D modeling, thermal imaging, and a modified energy balance equation to estimate stomatal conductance remotely and accurately.

Application of multimodal data fusion and explainable AI for classifying water stress in sweet potatoes. ↗

Sweet potato (Ipomoea batatas L.) exhibits strong resilience in nutrient-poor soils and contains high levels of dietary fiber and antioxidant compounds. It also is highly tolerant to water stress, which has also contributed to its global distribution, particularly in regions prone to climatic variability. However, frequent abnormal climatic events have recently caused declines in both the quality and yield of sweet potatoes. To address this, machine learning (ML) and deep learning (DL) models based on a Vision Transformer-Convolutional Neural Network (ViT-CNN) were developed to classify water stress levels in sweet potato. RGB-thermal imagery captured from low-altitude platforms and various growth indicators were used to develop the classifier. The K-Nearest Neighbors (KNN) model outperformed other ML models in classifying water stress levels at all growth stages. The DL model simplified the original five-level water stress classification into three levels. This enhanced its sensitivity to extreme stress conditions, improve model performance, and increased its applicability to practical agricultural management strategies. To enhance practical applicability under open-field conditions, several environmental variables were newly defined to calculate the crop water stress index (CWSI). Furthermore, an integrated system was developed using gradient-weighted class activation mapping (Grad-CAM), explainable artificial intelligence (XAI), and a graphical user interface (GUI) to support intuitive interpretation and actionable decision-making. The system will be expanded into an online and fixed-camera platform to enhance its applicability to smart farming in diverse field crops.

Why it matches plant phenotyping methodsRGB・熱画像と生育指標を用いてサツマイモの水ストレス状態を分類するモデルを開発し、CWSI、XAI、GUIを統合したシステムを構築しており、植物状態の取得・推定手法が研究の中心である。

abstractmachine learning (ML) and deep learning (DL) models based on a Vision Transformer-Convolutional Neural Network (ViT-CNN) were developed to classify water stress levels in sweet potato.

Estimating water use efficiency in maize: a UAV-based approach integrating multisensory data with SEBAL evapotranspiration modeling ↗

Rapid, accurate, and non-destructive estimation of crop water use efficiency (WUE) at the field scale is crucial not only for evaluating water efficient cultivars and practices in scientific research but also for optimizing irrigation schedule in agricultural production. The current lack of efficient methods for high-throughput phenotyping WUE hinders development of sustainable agriculture under globally intensified water scarcity. This study aimed to utilize unmanned aerial vehicle (UAV) multisensory remote sensing data combined with a process model to achieve rapid WUE determination via accurate daily-scale evapotranspiration and aboveground biomass (AGB) estimates. First, vegetation indices, canopy temperature, and canopy structural parameters were extracted from multispectral (MS), thermal imaging (TIR), and radar data and combined with an automated machine learning (AutoML) for AGB estimation. The beta function was then employed to accurately estimate AGB accumulation at a daily step (AGBdₐᵢₗy) over the entire growth period. The daily evapotranspiration (ETdₐᵢₗy) was calculated by the surface energy balance algorithm for land (SEBAL) model driven by MS, TIR, and meteorological data. Finally, the WUE was determined by the ratio of AGBdₐᵢₗy to ETdₐᵢₗy. Multisensory data fusion and further integration with process-based model proved effective for simultaneously estimating AGBdₐᵢₗy, ETdₐᵢₗy, and WUE with R² values of 0.71, 0.93, and 0.79, respectively. Notably, the proposed WUE estimation method can capture different temporal pattern between cultivars with different levels of tolerance to drought. We applied this approach to screen water efficient cultivars and found that appropriate reduction of irrigation can improve WUE. In conclusion, this study shows promising perspective in the use of a UAV-based approach integrating multisensory data with SEBAL evapotranspiration modeling for monitoring and evaluating water consumption and utilization in maize.

Why it matches plant phenotyping methodsUAVマルチセンサーデータとモデルを統合し、トウモロコシのAGB、蒸発散、WUEという植物形質・状態を推定する方法を開発・評価しており、表現型取得が研究の中心である。

abstractThe current lack of efficient methods for high-throughput phenotyping WUE hinders development of sustainable agriculture under globally intensified water scarcity.

Development of multi-sensing technologies for high-throughput morphological, physiological, and biochemical phenotyping of drought-stressed watermelon plants. ↗

High-throughput plant phenotyping (HTPP) technologies are rapidly transforming plant science by enabling real-time, non-invasive, and large-scale monitoring of complex morphological, physiological, and biochemical traits. However, existing platforms often lack integration across sensing modalities and analytical depth necessary for early and comprehensive phenotypic trait analysis. In this study, we developed a fully automated, multimodal HTPP system combining RGB, shortwave infrared (SWIR) hyperspectral, multispectral fluorescence imaging (MSFI), and thermal imaging to characterize drought-stressed watermelon (Citrullus lanatus) plants. RGB imaging facilitated detailed morphological analysis by extracting color-based traits, quantifying plant height and canopy area, and accurately distinguishing growth stages. SWIR hyperspectral imaging (HSI) enabled non-invasive biochemical assessment by detecting drought-responsive compounds, such as flavonoids, phenolics, and antioxidant activities, while also supporting the classification of stress severity. This spectral profiling revealed key biochemical alterations triggered by water deficit. MSFI liquid crystal tunable filter (LCTF-based) measured chlorophyll a (Chl-a), chlorophyll b (Chl-b), and total chlorophyll (t-Chl) levels, providing critical insights into photosynthetic performance under drought stress. Thermal imaging further enhanced drought assessment by capturing canopy temperature variations, which were used to derive thermal indices for indirect estimation of soil volumetric water content (SVWC). By integrating complementary imaging modalities, the proposed system captured comprehensive phenotypic responses with high predictive accuracy for early detection of drought stress and assessment of plant health. Advanced machine learning (ML) and deep learning (DL) models further enhanced trait extraction and classification, enabling robust analysis of complex, high-dimensional data. This automated, multimodal platform offers scalable, non-invasive crop monitoring, providing precise insights to support drought resilience and precision agriculture.

Why it matches plant phenotyping methods複数の画像・センシングモダリティを統合した自動高スループット植物表現型解析システムを開発し、形態・生理・生化学的形質および乾燥ストレスを抽出することが研究の中心である。

abstractIn this study, we developed a fully automated, multimodal HTPP system combining RGB, shortwave infrared (SWIR) hyperspectral, multispectral fluorescence imaging (MSFI), and thermal imaging to characterize drought-stressed watermelon (Citrullus lanatus) plants.

Opportunities and Challenges in Combining Optical Sensing and Epidemiological Modeling. ↗

Plant diseases impair the yield and quality of crops and threaten the health of natural plant communities. Epidemiological models can predict disease and inform management. However, data are scarce, because traditional methods to measure plant diseases are resource intensive, which often limits model performance. Optical sensing offers a methodology to acquire detailed data on plant diseases across various spatial and temporal scales. Key technologies include multispectral, hyperspectral, and thermal imaging, as well as light detection and ranging; the associated sensors can be installed on ground-based platforms, uncrewed aerial vehicles, airplanes, and satellites. However, despite enormous potential for synergy, optical sensing and epidemiological modeling have rarely been integrated. To address this gap, we first review the state of the art to develop a common language accessible to both research communities. We then explore the opportunities and challenges in combining optical sensing with epidemiological modeling. We discuss how optical sensing can inform epidemiological modeling by improving model selection and parameterization and providing accurate maps of host plants. Epidemiological modeling can inform optical sensing by boosting measurement accuracy, improving data interpretation, and optimizing sensor deployment. We consider outstanding challenges in (A) identifying particular diseases; (B) data availability, quality, and resolution; (C) linking optical sensing and epidemiological modeling; and (D) emerging diseases. We conclude with recommendations to motivate and shape research and practice in both fields. Among other suggestions, we propose standardizing methods and protocols for optical sensing of plant health and developing open access databases including both optical sensing data and epidemiological models to foster cross-disciplinary work. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Why it matches plant phenotyping methods植物病害の光学センシングによる症状・健康状態の取得を疫学モデルと統合する方法論的レビューであり、光学計測手法の課題、標準化、データベース化を中心に扱う。

abstractWe then explore the opportunities and challenges in combining optical sensing with epidemiological modeling.

Combined effect of salt stress and high light in plants: from basic statistical approach to machine learning methods. ↗

Infrared thermal imaging offers a rapid and sensitive approach to assessing temperature changes in plants caused by salt stress, even in the early stages of exposure. Given the increasing prevalence of salt contamination in the environment, it is essential to accurately estimate salinity levels, as the effects strongly depend on salt concentration: moderate salinity elicits a reversible, osmotic driven rise in leaf temperature, whereas higher salinity induces a larger, sustained temperature increase indicative of ion toxicity related stress. We propose a method to evaluate the severity of salt stress in plants exposed to sodium chloride, using a series of thermograms captured through a non-invasive infrared imaging technique under illuminated conditions. Thermal measurements are then used to train machine learning models used to perform multi-class classification to distinguish between four different salt concentrations. To test the proposed method, we cultivated Arabidopsis thaliana plants under controlled conditions. Data collected from the prepared samples were used to assess the accuracy of various approaches and classifiers with lead-one-out cross-validation. This experimental evaluation shows that the optimal performance is achieved when the datasets used for training consist of longer sequences of thermal data provided to models using neural networks.

Why it matches plant phenotyping methods植物の熱画像から塩ストレスの重症度を推定・分類する画像計測と機械学習手法が研究の中心であり、植物状態の表現型取得・推定に該当する。

abstractWe propose a method to evaluate the severity of salt stress in plants exposed to sodium chloride, using a series of thermograms captured through a non-invasive infrared imaging technique under illuminated conditions.

Multi-Trait Phenotypic Extraction and Fresh Weight Estimation of Greenhouse Lettuce Based on Inspection Robot ↗

In situ detection of growth information in greenhouse crops is crucial for germplasm resource optimization and intelligent greenhouse management. To address the limitations of poor flexibility and low automation in traditional phenotyping platforms, this study developed a controlled environment inspection robot. By means of a SCARA robotic arm equipped with an information acquisition device consisting of an RGB camera, a depth camera, and an infrared thermal imager, high-throughput and in situ acquisition of lettuce phenotypic information can be achieved. Through semantic segmentation and point cloud reconstruction, 12 phenotypic parameters, such as lettuce plant height and crown width, were extracted from the acquired images as inputs for three machine learning models to predict fresh weight. By analyzing the training results, a Backpropagation Neural Network (BPNN) with an added feature dimension-increasing module (DE-BP) was proposed, achieving improved prediction accuracy. The R2 values for plant height, crown width, and fresh weight predictions were 0.85, 0.93, and 0.84, respectively, with RMSE values of 7 mm, 6 mm, and 8 g, respectively. This study achieved in situ, high-throughput acquisition of lettuce phenotypic information under controlled environmental conditions, providing a lightweight solution for crop phenotypic information analysis algorithms tailored for inspection tasks.

Why it matches plant phenotyping methods温室内ロボット、複数センサー、画像解析、形質抽出、重量推定を一体化した植物表現型取得手法の開発が中心である。

abstractthis study developed a controlled environment inspection robot

Application of non-destructive imaging techniques to evaluate cold tolerance in French marigold (Tagetes patula) genotypes ↗

Climate change-induced erratic weather patterns necessitate the development of cold-tolerant marigold cultivars for sustainable floricultural production. The present study was carried out during winter (rabi) season 2021–22 and 2022–23 at ICAR-Indian Agricultural Research Institute, New Delhi to evaluate the efficacy of high-throughput, non-destructive image-based phenotyping techniques, including Red-Green-Blue (RGB), Near-Infrared (NIR), and Infrared (IR) imaging, for quantitative assessment of essential plant traits such as plant area, greenness, water content, and temperature. Ten French marigold (Tagetes patula L.) genotypes (Pusa Deep, Pusa Arpita, Dainty Marietta, Valencia Yellow, Orange Winner, Hisar Beauty, Hisar Jafri, Gulzafri Orange, Fr./W-20, Fr./W-21) were evaluated. The experiment was laid out in a complete randomized design (CRD) with two factors (genotype and environment) and three replications, with 18 plants/environment and 6 plants/replication. Technologies were applied to assess cold tolerance during the early reproductive phase of French marigold genotypes, grown under contrasting environments: Controlled conditions (polyhouse, 30.1°-33.7°C/3.4°-3.7°C) and cold stress (open field, 26.4°-28°C/0.8°-1.2°C) during winter season. Comparative analysis revealed that cold stress significantly impacted morpho-physiological parameters: Plant area decreased by 1.38-fold, caliper length by 1.07-fold, and compactness by 2.10-fold compared to the polyhouse environment. Convex hull area and circumference were reduced by 1.22-fold and 1.05-fold, respectively. Additionally, greenness and plant temperature decreased by approximately 1.03-fold, roundness by 2.07-fold, and plant water content by 1.44-fold. Statistical analysis revealed that open field conditions significantly decreased all measured morpho-physiological parameters, with plant compactness showing the greatest reduction compared to controlled conditions. Notably, genotypes including ‘Hisar Beauty’ and ‘Hisar Jafri ’ exhibited superior cold tolerance, demonstrating the least reductions in measured parameters under cold stress, while maintaining higher water content (NIR reflectance, 140.98%) and lower plant surface temperatures (19.06°C) compared to other genotypes. These findings underscore the potential of non-destructive image-based phenotyping as an efficient tool in screening for cold tolerance in marigold breeding programmes, offering a viable and precise alternative to traditional screening methods for accelerated cultivar development.

Why it matches plant phenotyping methodsRGB・NIR・IRによる非破壊画像計測を用いて植物形態・水分・温度などの形質を定量化し、耐寒性スクリーニングへの有効性を評価しており、表現型取得法が研究の中心です。

abstractevaluate the efficacy of high-throughput, non-destructive image-based phenotyping techniques, including Red-Green-Blue (RGB), Near-Infrared (NIR), and Infrared (IR) imaging, for quantitative assessment of essential plant traits such as plant area, greenness, water content, and temperature

A comprehensive review of crop stress detection: destructive, non-destructive, and ML-based approaches ↗

Agriculture stands as a foundational element of life, closely linked to the progress and development of society. Both humans and animals depend on agriculture for a wide range of essential services, such as producing oxygen and food, along with vital raw materials for clothing, medicine, and other necessities. Given agriculture’s vital role in supporting individual well-being and driving global progress, protecting and ensuring the long-term sustainability of agriculture is essential. This is crucial for securing resources and maintaining environmental balance for future generations. In this context, in our review we have examined the various factors that can interfere with the normal physiological and developmental functions of plants and crops. These factors, referred to scientifically as stressors or stress conditions, include a wide range of both biotic and abiotic challenges. In this work we have systematically addressed all the major categories of stress that plants may encounter throughout their lifecycle. Additionally, because plants tend to exhibit recognizable physiological or biochemical responses to stress, we have cataloged the associated stress indicators. These indicators were identified through various assessment techniques, including both destructive and non-destructive approaches. A significant advancement highlighted in our review is the integration of Machine Learning (ML) algorithms with non-destructive methodologies, which has substantially enhanced the accuracy, scalability, and real-time capability of plant stress detection. These ML-enhanced systems leverage high-dimensional data acquired through remote sensing modalities, such as hyperspectral imaging, thermal imaging, and chlorophyll fluorescence. These ultimately help in enabling the early identification of biotic and abiotic stress signatures. Through advanced pattern recognition, feature extraction, and predictive modeling, ML facilitates proactive anomaly detection and stress forecasting, thereby mitigating yield losses and supporting data-driven precision agriculture. This convergence represents a significant step toward intelligent, automated crop monitoring systems. Finally, we conclude the article with a concise discussion of the potential positive roles that certain stress conditions may play in enhancing plant resilience and productivity.

Why it matches plant phenotyping methods植物ストレスの非破壊検出手法を、リモートセンシング、画像計測、MLとともに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。

titleA comprehensive review of crop stress detection: destructive, non-destructive, and ML-based approaches

Digital assessment of plant diseases: A critical review and analysis of optical sensing technologies for early plant disease diagnosis ↗

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.

TAM-Net: A deep network combining tabular diffusion algorithm, attention mechanism, and multi-task learning for monitoring crop water status from UAV multi-source images ↗

Rapid and accurate monitoring of crop water status is essential for ensuring sustainable agricultural development and food security. Crops exhibit a complex set of growth and physiological responses under water deficit. Existing studies primarily focused on the monitoring of phenotypic parameters, while the physiological indicators highly relevant to crop water status were ignored. In this context, we aimed to develop a novel model to comprehensively and accurately monitor maize water status using multi-source UAV data and multiple growth and physiological indicators. We first composed the original dataset, including feature variables based on multi-source UAV data (spectral indices, texture indices, thermal indices, and structural indices) and prediction variables based on field measurements (equivalent water thickness, stomatal conductance, transpiration rate, and actual photochemical efficiency) in 2023 and 2024. Next, the tabular denoising diffusion probabilistic model (TabDDPM) was employed for synthesizing new samples to adequately train the models. Then, a deep learning network named TAM-Net, with the hybrid attention mechanism and multi-task learning, was trained on the synthetic dataset. Finally, the fuzzy comprehensive water index (FCWI) considering uncertainty and variability was obtained in 2023–2024. The results indicated that multi-source data significantly improved the model performance, with the R² of 0.52–0.63, and NRMSE of 27.89 %–29.86 %. TabDDPM was able to synthesize new datasets with high similarity and effectiveness. TAM-Net achieved the highest monitoring accuracy for the four indicators of crop water status (R² of 0.76–0.90, NRMSE of 12.92 %–22.95 %). FCWI effectively assessed the water status across different treatments. Overall, TAM-Net was demonstrated with powerful performance for monitoring maize water status, which has potential in supporting precision irrigation practices.

Why it matches plant phenotyping methodsUAVマルチソース画像からトウモロコシの水分状態・生理形質を推定するTAM-Netを開発し、精度評価まで行っており、表現型取得・推定手法が研究の中心である。

abstractwe aimed to develop a novel model to comprehensively and accurately monitor maize water status using multi-source UAV data and multiple growth and physiological indicators.

Rapid diagnosis of herbicidal activity and mode of action using spectral image analysis and machine learning ↗

Herbicide screening requires a substantial amount of time, effort, and cost, making a new herbicide discovery expensive and time-consuming. Various diagnostic methods have been developed, but most of them are destructive and require significant time and effort to identify herbicide activity. Therefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs). RGB, chlorophyll fluorescence (CF), and infrared (IR) thermal images were acquired after treating herbicides with different MOAs to a model plant, oilseed rape (Brassica napus), and analyzed using MATLAB 2021b to quantify NDI, ExG, Fd/Fₘ, and plant leaf temperature. NDI, ExG and Fd/Fₘ decreased, while plant leaf temperature increased after herbicide treatment. Distinctive spectral responses were found depending on the herbicide MOAs. PSII and PPO inhibitors showed rapid responses in IR thermal and CF images within 1 day after herbicide treatment. HPPD inhibitor showed a continuous decrease in Fd/Fₘ, while EPSPS inhibitor showed gradual changes in all spectral indices. Machine learning by Subspace Discriminant algorithm of spectral indices acquired at 6 ​h enabled the diagnosis of herbicide MOAs with 89.6 ​% accuracy, which gradually increased by adding new spectral indices acquired later time points until 3 DAT, when validation accuracy scored 100 ​%. The indices acquired at 6 ​h, and Fd/Fₘ and leaf temperature data were shown to contribute to higher accuracies of identifying herbicide MOAs. Overall test accuracy scored 87.5 ​%, verifying the possibility of diagnosing herbicide MOAs based on spectral indices. Therefore, we could conclude that herbicide activity and MOAs can be diagnosed by analyzing spectral images combined with machine learning, suggesting the possibility of high-throughput screening of herbicide MOAs using plant image analysis.

Why it matches plant phenotyping methods植物のスペクトル画像から葉の生理状態・温度指標を抽出し、機械学習で除草剤作用機構を診断する手法の開発と精度検証が研究の中心であるため、植物フェノタイピング手法として採用。

abstractthis study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs).

Using sub-diurnal surface-air temperature difference anomaly derived from Himawari-8 geostationary satellite and meteorological grids for early detection of vegetation drought stress: Application to Australia's 2017–2019 Tinderbox Drought ↗

Satellite land surface temperature (Ts) provides valuable information on vegetation drought stress via its physical linkage to plant stomatal activity and transpiration. New-generation geostationary satellites offer opportunities to monitor sub-diurnal variations in Ts and thus track plant physiological stress response occurring at sub-daily timescales. Nevertheless, the potential of satellite Ts and its derived metrics for early detection of vegetation drought stress before visible canopy changes occur has not been widely assessed. Here, we developed a parsimonious Surface-Air Temperature Difference Anomaly (SATDA) method for tracking vegetation drought stress using the cumulative sub-diurnal difference from late-morning to early-afternoon between Ts from the Himawari-8 geostationary satellite and hourly air temperature (Ta) from meteorological grids. SATDA utilised Ts−Ta as the physical driving gradient for sensible heat flux (H) to capture anomalous sensible heating due to reduced plant transpiration. We used SATDA to monitor the spatio-temporal patterns of the 2017–2019 Tinderbox Drought in southeast Australia. We benchmarked the skill of SATDA in forecasting visible drought-induced vegetation greenness decline against both conventional water availability-based indices (i.e., precipitation and soil moisture anomalies) and existing satellite Ts indices (i.e., Temperature Condition Index and Temperature Rise Index) across diverse climates and land covers. SATDA effectively captured a rapidly intensifying flash drought event at multi-week timescales (Jul to Sep 2019) embedded within the multi-year Tinderbox Drought, which contributed to detrimental impacts on agricultural production and increased wildfire risk. SATDA showed the best vegetation greenness forecast skill in the transitional semi-arid and sub-humid climates, with forecast correlation >0.5 at 32-day lead time. The advantage over water availability-based indices was more evident in woody-dominated ecosystems than herbaceous-dominated ecosystems, likely due to the importance of physiological regulations by trees during droughts such as deeper roots and stronger stomatal control. SATDA, based on Ts−Ta, showed overall better vegetation greenness forecasts than two Ts-only indices, especially in woody vegetation. Finally, SATDA showed consistently greater advantage over water availability-based and Ts-only indices in forecasting visible vegetation decline as the drought intensity increased. The parsimonious process-based SATDA method suits global-scale operational implementation to complement vegetation drought monitoring and early warning systems.

Why it matches plant phenotyping methods衛星温度と気象データから植物の干ばつストレスを推定するSATDA法を開発し、既存指標と比較検証しており、植物状態の取得手法が研究の中心である。

abstractHere, we developed a parsimonious Surface-Air Temperature Difference Anomaly (SATDA) method for tracking vegetation drought stress

Development of a Machine vision system for apple bud thinning in precision crop load management ↗

Thinning is a critical practice in apple orchard management, directly influencing crop load and fruit quality. To assist automated crop load management, a machine vision system for apple bud detection was developed to be integrated with robotic platforms. The system employed a Kinect Azure sensor for real-time bud detection and branch diameter measurement, utilizing a YOLOv8-based object detection model trained and evaluated across multiple datasets. The evaluation identified the best-performing model by balancing precision, recall, and robustness in the complex and unstructured environments of apple orchards. Several training configurations were assessed, with the selected setup demonstrating a strong balance between precision (68 %), recall (55 %), F1-score (61 %), and mean average precision (mAP: 59 %) across diverse and unstructured orchard environments. This configuration, trained on a combination of FLIR and Kinect Azure data, was chosen for deployment due to its robustness and compatibility with the Kinect Azure sensor in real-world applications. Two proposed imaging methods for branch diameter measurement were validated against manual caliper-based measurements, with statistical analysis revealing no significant differences (p = 0.98). These findings confirm the semi-automated methods as reliable and labor-efficient alternatives for field applications. Additionally, the bud counting algorithm demonstrated accurate tracking and counting of apple buds, effectively avoiding omissions and duplications in real orchard settings. This study underscores the potential of vision systems to revolutionize apple bud thinning, providing a strong foundation for the development of fully automated solutions in precision orchard management.

Why it matches plant phenotyping methodsリンゴ芽の画像検出に加え、枝径という植物形質の画像計測法を開発・手動測定と検証しており、フェノタイピング手法が中心である。

abstracta machine vision system for apple bud detection was developed to be integrated with robotic platforms

Use of Drones In Crop Health Monitoring: A Comprehensive Review (2014–2025) ↗

Drones (unmanned aerial vehicles, UAVs) have rapidly transitioned from experimental tools to dependable, field-scale systems for crop health monitoring. By delivering on-demand, centimeter-level imagery and thermal/structural measurements, UAVs enable early diagnosis of nutrient limitations, water stress, pest and disease outbreaks, and stand establishment issues. This review synthesizes the state of the art in UAV platforms and sensors (RGB, multispectral, hyperspectral, thermal, LiDAR), radiometric and geometric workflows, vegetation indices and biophysical proxies, and analytics using machine learning and deep learning. Practical agronomic applications—nutrient management, irrigation scheduling, weed mapping, variable-rate prescriptions, lodging assessment, and yield forecasting—are evaluated alongside economics, environmental benefits, and operational constraints. We also discuss policy and capacity considerations for large-scale deployment, with emphasis on emerging markets. Finally, we outline future directions in multimodal sensor fusion, 3D/temporal retrievals, edge autonomy, and foundation AI models for robust, field-ready decision support.

Why it matches plant phenotyping methodsUAV画像・熱・構造計測、センサー、処理ワークフロー、植生指数、生物物理形質推定を中心にレビューしており、植物状態の取得・抽出手法が実質的な主題である。

abstractThis review synthesizes the state of the art in UAV platforms and sensors (RGB, multispectral, hyperspectral, thermal, LiDAR), radiometric and geometric workflows, vegetation indices and biophysical proxies, and analytics using machine learning and deep learning.

A promising method for diagnosing plant diseases and determining their phenotype ↗

Traditional methods of early diagnosis of diseases, such as pure culture method, microscopic, mycological, polymerase chain reaction, enzyme immunoassay are invasive and require highly qualified personnel, expensive equipment and are not suitable for their effective use in practice. Since plant health is a fundamental indicator in assessing the phenotype of a crop plant, modern non-invasive methods for early diagnosis and determination of plant phenotype are considered. The purpose of the research is to select a rational method for early diagnosis of plant diseases and determination of their phenotype directly in the field of cultivated crops. Advantages and disadvantages of the vision method based on the analysis of the changes in color parameters of RGB images of plant leaves; fluorescence analysis, in which the efficiency of photosynthesis is estimated; multispectral and hyperspectral imaging methods carried out by determining the limited or continuous spectrum reflected from the surface of plant leaves; thermal imaging method in which the distribution of infrared radiation emitted by the plant is recorded. The analysis of the methods showed that the determination of thermal energy dissipation is a promising potential indicator of health and the presence of disease. In addition, when exposed to most environmental factors, the thermal properties of plant organs, such as leaf, stem, root, and reproductive organs, change. The reason for the limited use of thermometry in the early diagnosis of plant diseases is explained: false rejection by researchers of the fact that it is a highly organized complex of terrestrial and underground organisms. The requirements for devices for obtaining and processing thermal images are formulated and justified. An experimental setup based on the TE-Q1 thermal imaging camera, capable of working with Android devices, has been developed. Its operation has been tested on garden strawberry samples.

Why it matches plant phenotyping methods植物病害の表現型を非侵襲的に取得する画像・熱画像手法を比較検討し、熱画像取得装置を開発・検証しており、フェノタイピング手法が中心である。

abstractmodern non-invasive methods for early diagnosis and determination of plant phenotype are considered

Very-High-Resolution, Multi-Season Monitoring of Crop Evapotranspiration and Water Stress with UAV Data and TSEB Integration ↗

Abstract. Field-scale estimation of evapotranspiration (ET) using high-resolution data supports water conservation and yield optimization by enabling localized water use monitoring and early detection of crop stress. This study applies the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model at 15 cm resolution using unmanned aerial vehicle (UAV) data over a 10-hectare field across three seasons: sugar beet (2021), potato (2022), and winter wheat (2023). Key inputs included thermal infrared (TIR) for land surface temperature (LST), multispectral (MS) and LiDAR data for canopy characterization, and a fusion of MS derived green area index (GAI) and LiDAR derived plant area index (PAI) to derive the fraction of green LAI (fg). Model outputs were validated against eddy covariance (EC) flux data using footprint modeling. Results showed high sensitivity to LST, emphasizing the importance of accurate thermal calibration. While both GAI and PAI provided comparable LAI inputs during peak growth, GAI better captured functional canopy decline during stress and senescence, especially in winter wheat, where dense structure led to cooling effects unrelated to transpiration. Dynamic fg improved ET accuracy across all crops, particularly under declining canopy function. Overall, TSEB-PT showed strong agreement with EC measurements (RMSE = 0.14 mm/h, R² = 0.49; R² = 0.81 excluding senescence). UAV TIR based ET maps also revealed early stress signals prior to changes in MS or LiDAR based metrics. This study demonstrates the value of integrating very-high-resolution UAV data with the TSEB-PT model for multi-crop and season-long ET monitoring and early stress detection.

Why it matches plant phenotyping methodsUAVの熱・マルチスペクトル・LiDARデータとTSEB-PTモデルにより、作物の蒸発散と水ストレスを推定する手法を中心に扱い、渦相関データで技術検証しているため、植物フェノタイピング手法研究に該当する。

abstractThis study applies the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model at 15 cm resolution using unmanned aerial vehicle (UAV) data over a 10-hectare field across three seasons: sugar beet (2021), potato (2022), and winter wheat (2023).

Influence of almond's canopy-induced shadows on actual evapotranspiration estimation by the TSEB model using sUAS multispectral and thermal imagery ↗

Abstract Over the past decade, the estimation of water requirements in almond orchards has improved through the application of remote sensing models like the Two-Source Energy Balance (TSEB) model using various remote sensing platforms. However, there is limited understanding of how canopy-induced shadows influence surface reflectance and thermal infrared (TIR) signals particularly from small Unmanned Aircraft System (sUAS) imagery in energy balance models, and the effect on Latent Heat Flux (LE) estimations. This study evaluates LE estimates from the Priestley-Taylor TSEB model (TSEB-PT) with and without shadow filtering using sUAS-based multispectral and TIR imagery. It establishes a baseline for the impact of shadow exclusion on model inputs and performance. Datasets were collected in 2021 and 2022, as part of the USDA led Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) in almonds orchards across California. LAI-2200C Plant Canopy Analyzer measurements facilitated the calibration of an empirical Leaf Area Index (LAI) model based on canopy fractional cover (FC) and NDVI (R 2 = 0.68). Shadow filtering caused land surface temperature (LST) differences up to 5°C in young to semi-mature orchards (FC 0.40–0.80). In contrast, mature orchards (FC > 0.80) showed minimal influence due to the limited shadow occurrence on the imagery. Shadows appeared to reduce surface albedo (α alb ), mainly in interrow areas, thereby affecting the absorption of radiation and the partitioning of energy balance components. Their presence in sUAS imagery also hindered canopy delineation, impacting the accuracy of key TSEB inputs derived from canopy physical characteristics. Thus, the influence of shadow on TSEB estimated LE was more significant in lower fractional tree covers. While LE estimated by TSEB-PT without shadow filtering showed better agreement with observations, combining instantaneous TIR imagery with solar-noon shortwave data is recommended for accurate ETa assessment using sUAS datasets. These baseline results can be improved with more advanced formulations, supporting continued research on E/T partitioning and water stress in almond orchards under varying environmental conditions, particularly when there is advection of hot dry air.

Why it matches plant phenotyping methodssUASのマルチスペクトル・熱画像を用いたTSEBモデルについて、影の除去がLAI、LST、蒸発散・潜熱フラックス推定に与える影響と精度を評価しており、植物キャノピーの生理状態・水利用の測定手法が中心である。

abstractThis study evaluates LE estimates from the Priestley-Taylor TSEB model (TSEB-PT) with and without shadow filtering using sUAS-based multispectral and TIR imagery.

Agronomic Information Extraction from UAV-Based Thermal Photogrammetry Using MATLAB ↗

Thermal cameras are becoming popular in several applications of precision agriculture, including crop and soil monitoring, for efficient irrigation scheduling, crop maturity, and yield mapping. Nowadays, these sensors can be integrated as payloads on unmanned aerial vehicles, providing high spatial and temporal resolution, to deeply understand the variability of crop and soil conditions. However, few commercial software programs, such as PIX4D Mapper, can process thermal images, and their functionalities are very limited. This paper reports on the implementation of a custom MATLAB® R2024a script to extract agronomic information from thermal orthomosaics obtained from images acquired by the DJI Mavic 3T drone. This approach enables us to evaluate the temperature at each point of an orthomosaic, create regions of interest, calculate basic statistics of spatial temperature distribution, and compute the Crop Water Stress Index. In the authors’ opinion, the reported approach can be easily replicated and can serve as a valuable tool for scientists who work with thermal images in the agricultural sector.

Why it matches plant phenotyping methodsUAV熱画像から温度分布とCrop Water Stress Indexを抽出するMATLAB手法を開発・提示しており、植物の水ストレス状態を定量化する方法が中心である。

abstractThis paper reports on the implementation of a custom MATLAB® R2024a script to extract agronomic information from thermal orthomosaics obtained from images acquired by the DJI Mavic 3T drone.

Development of a High-Speed Time-Synchronized Crop Phenotyping System Based on Precision Time Protocol ↗

Aiming to address the problems of asynchronous acquisition time of multiple sensors in the crop phenotype acquisition system and high cost of the acquisition equipment, this paper developed a low-cost crop phenotype synchronous acquisition system based on the PTP synchronization protocol, realizing the synchronous acquisition of three types of crop data: visible light images, thermal infrared images, and laser point clouds. The paper innovatively proposed the Difference Structural Similarity Index Measure (DSSIM) index, combined with statistical indicators (average point number difference, average coordinate error), distribution characteristic indicators (Charm distance), and Hausdorff distance to characterize the stability of the system. After 72 consecutive hours of synchronization testing on the timing boards, it was verified that the root mean square error of the synchronization time for each timing board reached the ns level. The synchronous trigger acquisition time for crop parameters under time synchronization was controlled at the microsecond level. Using pepper as the crop sample, 133 consecutive acquisitions were conducted. The acquisition success rate for the three phenotypic data types of pepper samples was 100%, with a DSSIM of approximately 0.96. The average point number difference and average coordinate error were both about 3%, while the Charm distance and Hausdorff distance were only 1.14 mm and 5 mm. This system can provide hardware support for multi-parameter acquisition and data registration in the fast mobile crop phenotype platform, laying a reliable data foundation for crop growth monitoring, intelligent yield analysis, and prediction.

Why it matches plant phenotyping methods作物表現型取得のための低コスト・高速・時刻同期システムを開発し、可視画像、熱画像、レーザ点群の取得性能と安定性を検証しており、表現型取得法が中心である。

abstractthis paper developed a low-cost crop phenotype synchronous acquisition system based on the PTP synchronization protocol

UAV-borne hyperspectral and thermal imagery integration empowers genetic dissection of wheat stomatal conductance ↗

Implementing advanced approaches such as marker-assisted selection into classic breeding programs is critical for increasing genetic gain and meeting the population’s ever-growing food demand. Genome-wide association studies (GWAS) is a well-known method for detecting genetic markers related to various morphological and physiological traits. However, the ability to collect phenotypic data in large panels often limits the feasibility of genetic studies. This study aimed to assess the potential of UAV-borne thermal and hyperspectral imaging for estimating key wheat traits and identifying their genetic architecture. A diversity panel (300 genotypes) was characterized under well-watered and terminal-drought conditions in a rainout shelter facility. Stomatal conductance, leaf area index, and total chlorophyll content were estimated across two growing seasons. A support vector machine model that integrates canopy spectral reflectance and temperature emittance from UAV-borne imagery reduced the root mean square error of stomatal conductance estimation by 28% compared to using canopy reflectance alone. The models were further used to estimate the traits in the entire panel and to detect genomic markers associated with them and their dynamics throughout the season. Altogether, 16 genetic markers associated with alleles conferring these traits were detected, and the most promising markers were validated during an additional growing season. In the validation experiment, both the spectral estimation models and the allelic effect of the markers were consistent with the previous season. This study introduces, for the first time, the use of stomatal conductance estimation based on combining UAV hyperspectral and thermal imagery for genomic mapping. Implementing this integrated approach could promote the development of new climate-resilience wheat varieties to ensure food security worldwide by screening for stomatal conductance, which is not practical with manual measurements.

Why it matches plant phenotyping methodsUAV熱・ハイパースペクトル画像を統合し、気孔コンダクタンス等の植物形質を推定するモデルを開発・検証しており、フェノタイピング手法が研究の中心である。

abstractThis study aimed to assess the potential of UAV-borne thermal and hyperspectral imaging for estimating key wheat traits and identifying their genetic architecture.

A new method to monitor the brown planthopper population size and its damage using canopy temperature of rice plants ↗

The brown planthoppers (BPHs) are serious pests of rice in Southeast Asia which often cause a heavy loss of rice. Monitoring BPH populations and prediction of their damages to rice are more important for the precious control of this pest. Nowadays, highly efficient monitoring and predicting methods for BPHs are still rare. Here, the canopy temperatures of rice damaged by different number of BPHs were examined using the thermal imaging technique, and relationships between canopy temperature and population size of BPHs or rice yield were analyzed. The result showed that there was a significant and stable correlation between rice canopy temperatures and BPH population sizes on these rice plants in four consecutive years of plot experiments. Further, canopy temperatures of rice measured at 8:00–10:00 am were negatively related to the population size of BPHs, but not the temperature measured at noon and in the afternoon. Canopy temperatures of rice at booting and heading growth periods were also strongly related to the rice yield and its loss rate damaged by BPHs. Based on the difference between air temperature and mean canopy temperature of rice, the BPH population size could be monitored using an exponential function model, and the rice yield could be predicted by a linear model. A promising framework of automatic monitoring BPH populations was developed based on canopy temperatures of rice.

Why it matches plant phenotyping methodsイネの熱画像によるキャノピー温度から害虫個体数と被害状態を推定する測定・予測手法を開発しており、表現型取得が研究の中心である。

abstractBased on the difference between air temperature and mean canopy temperature of rice, the BPH population size could be monitored using an exponential function model, and the rice yield could be predicted by a linear model.

Assessment of different remote sensing techniques to estimate the CWSI of almond trees using canopy temperature ↗

The radiometric temperature of the plants is known to be a good indicator of their level of water stress. The use of thermal cameras on board UAVs allows operational monitoring of the canopy temperature in orchard plantations at the single-tree level. The radiometric processing of the flight data becomes critical in this task to maintain the accuracy provided by field measurements using proximal thermal radiometers. This work focuses on evaluating the Crop Water Stress Index (CWSI) as a good indicator of the plant water status in almond orchards. This study compares the performance of CWSI by three different techniques: i) using proximal high-precision thermal radiometry (CWSI_CIMEL); ii) by UAV thermal flights for canopy temperature assessment (CWSI_UAV) and iii) combining multispectral and thermal data, also by UAV, to run a simplified two-source surface energy balance for the traditional formulation of the CWSI in terms of canopy transpiration (CWSI_STSEB). This study was conducted on two commercials almonds (Prunus dulcis (Mill.) D.A. Webb) orchards located in Albacete (SE Spain), one of them with 3 irrigation treatments (well-watered, moderate water stress, and severe water stress). Periodic measurements of stem water potential (SWP) were carried out around noon throughout 3 experimental campaigns from 2019 to 2021. Canopy temperature measurements were made with a high-precision thermal radiometer, the CIMEL CE312-C2. In addition, six flights were carried out using a DJI-M600 drone equipped with a FLIR Tau2 thermal sensor and a Micasense RedEdge camera. Maps of the CWSI were performed during these dates, showing temporal and spatial variability. The three different techniques showed similar CWSI trends across dates and treatments. When treatments were pooled within the same date, the assessment with SWP measurements showed correlations (R²) of 0.86, 0.68, and 0.70 for CWSI_CIMEL, CWSI_UAV, and CWSI_STSEB, respectively. These results reinforce the potential of accurate measurements of radiometric canopy temperatures using both proximal and remote sensing techniques to reproduce the crop water status in almond orchards. However, this study points to the necessity for accurate sensor calibrations and an appropriate methodology for the treatment of both canopy temperature and meteorological data. Monitoring CWSI serves as an operational tool for the early detection of water deficits in almond trees and meets farmerś needs to improve water use efficiency and optimize irrigation scheduling at the plot level.

Why it matches plant phenotyping methodsアーモンド樹の水ストレス状態を示すCWSIを、近接熱放射計およびUAV搭載熱・マルチスペクトルセンサーで取得・比較し、SWPとの相関で技術評価しているため、植物フェノタイピング手法が中心である。

abstractThis work focuses on evaluating the Crop Water Stress Index (CWSI) as a good indicator of the plant water status in almond orchards.

A framework of crop water productivity estimation from UAV observations: A case study of summer maize ↗

This investigation establishes Crop Water Productivity (CWP) - quantified as yield per unit water consumption (kg/m³) - as a pivotal metric for agricultural water resource optimization. However, current methodologies face limitations in estimation accuracy and operational efficiency due to the multidisciplinary complexity integrating agronomic and hydrological expertise. To address this challenge, our research develops an innovative UAV-based monitoring framework through systematic integration of long-term multispectral/thermal infrared observations with multi-model fusion: (1) Surface Energy Balance Algorithm for Land (SEBAL) and FAO-56 Penman-Monteith models for evapotranspiration (ET) estimation; (2) Random Forest algorithm incorporating four phenotypical growth indicators for yield estimation, ultimately enabling CWP quantification. Key scientific findings demonstrate: (1) SEBAL outperformed FAO-56 in daily ET estimation (R² = 0.76 vs. 0.71, RMSE = 1.15 vs. 1.31 mm/d). (2) The machine learning yield model exhibited robust predictive capability (R² = 0.77, RMSE = 0.98 t/ha), successfully capturing yield variability across treatments. (3) Error propagation analysis validated framework reliability (CWP RMSE = 0.67 kg/m³), effectively differentiating CWP performance among management practices. This breakthrough validates the operational efficacy of UAV remote sensing for precision agricultural water assessment, providing decision-support for field-scale irrigation scheduling optimization, drought-resilient cultivar selection through CWP benchmarking and sustainable intensification strategies. The methodology establishes novel methodological benchmarks for crop-water relationship studies through its innovative fusion of multi-source remote sensing data and multiple model combination.

Why it matches plant phenotyping methodsUAVのマルチスペクトル・熱赤外観測と機械学習を統合し、植物の生育指標から収量を推定する監視・解析フレームワークが研究の中心であり、単なる農業実験の routine 測定ではない。

abstractour research develops an innovative UAV-based monitoring framework through systematic integration of long-term multispectral/thermal infrared observations with multi-model fusion

Advancements in Plant Disease Detection: A Comprehensive Review of Traditional, Modern, and AI-Driven Approaches ↗

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.

Non-Invasive Estimation of Crop Water Stress Index and Irrigation Management with Upscaling from Field to Regional Level Using Remote Sensing and Agrometeorological Data ↗

Precision irrigation plays a crucial role in managing crop production in a sustainable and environmentally friendly manner. This study builds on the results of the GreenWaterDrone project, aiming to estimate, in real time, the actual water requirements of crop fields using the crop water stress index, integrating infrared canopy temperature, air temperature, relative humidity, and thermal and near-infrared imagery. To achieve this, a state-of-the-art aerial micrometeorological station (AMMS), equipped with an infrared thermal sensor, temperature–humidity sensor, and advanced multispectral and thermal cameras is mounted on an unmanned aerial system (UAS), thus minimizing crop field intervention and permanently installed equipment maintenance. Additionally, data from satellite systems and ground micrometeorological stations (GMMS) are integrated to enhance and upscale system results from the local field to the regional level. The research was conducted over two years of pilot testing in the municipality of Trifilia (Peloponnese, Greece) on pilot potato and watermelon crops, which are primary cultivations in the region. Results revealed that empirical irrigation applied to the rhizosphere significantly exceeded crop water needs, with over-irrigation exceeding by 390% the maximum requirement in the case of potato. Furthermore, correlations between high-resolution remote and proximal sensors were strong, while associations with coarser Landsat 8 satellite data, to upscale the local pilot field experimental results, were moderate. By applying a comprehensive model for upscaling pilot field results, to the overall Trifilia region, project findings proved adequate for supporting sustainable irrigation planning through simulation scenarios. The results of this study, in the context of the overall services introduced by the project, provide valuable insights for farmers, agricultural scientists, and local/regional authorities and stakeholders, facilitating improved regional water management and sustainable agricultural policies.

Why it matches plant phenotyping methods熱・マルチスペクトル画像と気象センサーを統合し、作物の水ストレス状態（crop water stress index）を推定する取得・推定システムが研究の中心であり、フィールドおよび衛星データとの相関検証も行っている。

abstractaiming to estimate, in real time, the actual water requirements of crop fields using the crop water stress index, integrating infrared canopy temperature, air temperature, relative humidity, and thermal and near-infrared imagery.

The Evaluation of Small-Scale Field Maize Transpiration Rate from UAV Thermal Infrared Images Using Improved Three-Temperature Model. ↗

Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (T C ) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of T C . Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (T r -3T C ), derived from T C data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with T r -3T C . The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H 2 O m -2 s -1 and 0.555 mmol H 2 O m -2 s -1 for the respective years, indicating strong interannual stability.

Why it matches plant phenotyping methodsUAV熱画像のセグメンテーションと改良3温度モデルにより、トウモロコシの蒸散速度という生理形質を推定する手法を開発・検証しており、フェノタイピング手法が中心である。

abstractThe Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of T C .

Nanosensors Convert H2O2 to Machine-Learnable Thermal Signatures in Plants ↗

Abiotic stresses, such as drought and salt stress, can significantly affect plant growth, posing substantial threats to agricultural productivity. As a central signaling mediator in plant adversity response mechanisms, real-time monitoring of the spatiotemporal dynamics of hydrogen peroxide (H 2 O 2 ) Current monitoring is essential for research in plant phenology. In this study, we developed an innovative detection method, which uses nanosensors to convert endogenous H 2 O 2 fluctuations at sub-micromolar concentrations into infrared thermal signals that can be learned by a machine, and processes these thermal imaging data through an advanced deep learning architecture to enable the monitoring of plant exposure to This enables non-invasive in situ monitoring of H 2 O 2 in plants under stress. Experimental validation shows that the average accuracy of multiple deep learning architectures reaches 98.8% and 99.6% under the challenges of drought and salinity stress test sets, respectively. In contrast to traditional methods, our focus is no longer on processing data and improving models in one go, but rather on the source of the data, which greatly improves the classification accuracy by acquiring thermal data with distinctive features. This integration of interdisciplinary techniques provides a non-destructive, rapid and accurate method for the early-stage stress monitoring of various plant stresses, and offers a new perspective for the study of plant stress characterization.

Why it matches plant phenotyping methods植物内H2O2をナノセンサーで熱信号に変換し、熱画像と深層学習でストレス状態を非破壊モニタリングする手法の開発・検証が中心であるため。

abstractIn this study, we developed an innovative detection method, which uses nanosensors to convert endogenous H 2 O 2 fluctuations at sub-micromolar concentrations into infrared thermal signals that can be learned by a machine

Thermography reveals the potential of traditional bean varieties against common bacterial blight ↗

Abstract The common bean ( Phaseolus vulgaris L.) is of great socioeconomic importance in Brazil, being widely cultivated by family farmers who preserve traditional varieties adapted to regional conditions. These varieties represent a strategic source of genetic variability for breeding programs. Among the main phytosanitary obstacles to cultivation, common bacterial blight (CBB), caused by Xanthomonas phaseoli pv. phaseoli stands out as it compromises bean productivity. This study aimed to evaluate 54 traditional genotypes for resistance to CBC, using visual severity scales and infrared thermography as a complementary tool. The experiment was carried out in a greenhouse, in a randomized block design with three replicates, in two seasons (May and October 2019). Inoculation was performed by two methods (cutting with scissors at 10⁷ CFU·mL -1 and infiltration with a syringe at 10⁶ CFU·mL -1 ) with the strain Xpp ‘139-y’. The variables analyzed included area under the disease progress curve (AUDPC), incubation period (IP), and final score (FS). Thermal images were obtained up to three days after inoculation, allowing the calculation of the mean temperature difference (MTD) between healthy and infected tissues. Thermographic analysis enabled early detection of infection, before the appearance of visual symptoms, distinguishing resistant genotypes such as BAC-6 and UENF 2599. The results highlight the potential of thermography as a fast, accurate, and non-destructive method for selecting resistant genotypes, contributing to the modernization and sustainability of bean breeding programs.

Why it matches plant phenotyping methods赤外線サーモグラフィーで感染植物組織の温度差を測定し、視覚症状前の病害状態を推定する方法を、抵抗性選抜へ実質的に適用しているため含める。

abstractusing visual severity scales and infrared thermography as a complementary tool

Exploring the Application of Drone Imagery in Early Identification of Crop Diseases and Pests ↗

Early and accurate identification of crop diseases and pests is critical to ensuring food security and sustainable agricultural development. The rapid advancement of high-resolution drone remote sensing technology provides innovative tools for early pest and disease detection. This paper explores the research progress, technical bottlenecks, and future directions of drone imagery technology in crop disease and pest monitoring. The study concludes that multi-spectral, thermal infrared and RGB sensors integrated on drone platforms can collaboratively capture centimeter-level high-resolution data. Through multi-source fusion of spectral, texture, and temporal data combined with lightweight model deployment, early spectral and morphological characteristics of crop stress caused by diseases and pests can be accurately identified, significantly improving detection accuracy compared to traditional satellite remote sensing and single machine learning methods. Case studies demonstrate that drone technology achieves 85%–95% recognition accuracy in monitoring typical diseases such as wheat rust and rice blast while reducing field inspection costs by over 60%. This paper provides a theoretical framework and technical roadmap for precision agriculture, offering practical significance for promoting agricultural digital transformation.

Why it matches plant phenotyping methodsドローン画像とRGB・マルチスペクトル・熱赤外センサーによる作物病害ストレスの検出手法を中心に、技術進展、データ融合、精度、技術課題をレビューしており、植物の病害状態を推定するフェノタイピング手法が中核である。

abstractThis paper explores the research progress, technical bottlenecks, and future directions of drone imagery technology in crop disease and pest monitoring.

Advances in Precision Farming: a contribute for estimating crop health and water stress by comparing UAV Multispectral and Thermal Imagery ↗

Abstract. Current climate change is largely due to the continuing increase in the anthropogenic greenhouse effect, with major environmental repercussions, especially in agriculture. The increase of global warming, salinity of water resources and frequency of extreme weather events has devastating consequences on the primary sector, in particular on the photosynthetic activity of crops and, therefore, their agricultural yield. The current climate crisis, in fact, leads to an increase in water requirements, the proliferation of weeds, and the depletion of nutrients in the soil, necessitating the massive use of fertilisers, herbicides and pesticides, which, in turn, trigger substantial alterations in ecosystem balances. In response to these critical issues, precision agriculture (PA) constitutes a data-driven approach based on the interpretation of multispectral and thermal datasets obtained by different remote sensing techniques and the use of latest-generation sensors to recognise the state of health of crops and, therefore, optimise agricultural production with a more rational and sustainable management of resources.This paper presents the results of a survey campaign carried out in October 2023 on two citrus fields located in south-eastern Sicily (Italy) to highlight the health status of crops just before the harvesting period. By using multispectral and thermal sensors installed on a drone, different vegetation indices have been calculated to identify, in each field, the areas with the highest photosynthetic activity and the zones characterised by a lack of water or other nutrients, on which targeted agronomic interventions should be planned as a priority.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像を用いて、柑橘作物の健康状態、光合成活性、水ストレスを推定するセンシング手法の適用が中心であり、単なるルーチン測定ではない。

titleestimating crop health and water stress by comparing UAV Multispectral and Thermal Imagery

Screening drought-resistant and water-saving winter wheat varieties by predicting yields with multi-source UAV remote sensing data ↗

The uneven spatial and temporal distribution of precipitation poses significant challenges to the growth and development of winter wheat. Screening drought-resistant and water-saving winter wheat varieties in water-limited regions is crucial for increasing crop production. However, quickly screening suitable cultivars remains a challenge. Utilizing unmanned aerial vehicles (UAVs) for remote sensing (RS) offers a solution by enabling the prediction of yields, overcoming issues such as the labor-intensive process of manual yield data collection and the difficulty of screening during the growing season. In this study, three types of water treatments were applied to 48 varieties screened in the North China Plain, with each water treatment repeated three times using a randomized block design. The aim is to explore the potential of UAVs for non-destructive yield prediction at various crop growth stages by integrating UAVs-based RS with machine learning, while also screening for drought-resistant and water-saving variety based on predicted yields, actual evapotranspiration (ET) derived from soil water balance and water use efficiency (WUE) at grain yield level. The results indicate that the random forest regression (RFR) model achieved the best prediction results. The optimal data combination of RS, canopy temperature, and data of variety by using RFR yielded the highest coefficient of determination (R²). Additionally, the RFR performs best when using data from the mid-filling stage (single-stage data) and the entire growth stage data (multi-stage data), with R² 0.58 and 0.69, respectively. Among the varieties, Malan 1 and Jimai 765 ranked first and second in both predicted and measured yield assessments, indicating the reliability of the yield prediction model for top-performing varieties. By combining predicted yields from RFR with ET, the screening results demonstrated high consistency between predicted and measured yields. Notably, even yield prediction models with lower R² can still provide satisfactory screening results. These findings will contribute to screening drought-resistant and water-saving winter wheat varieties by UAV. This research accelerates the variety screening process and addresses the conflict between agricultural production and water scarcity in the North China Plain.

Why it matches plant phenotyping methodsUAVリモートセンシングと機械学習による冬コムギの収量予測を中心的に開発・評価し、予測収量を品種スクリーニングに利用しているため、植物フェノタイピング手法に該当する。

abstractThe aim is to explore the potential of UAVs for non-destructive yield prediction at various crop growth stages by integrating UAVs-based RS with machine learning

A gradient-based nonlinear multi-pixel physical method for simultaneously separating component temperature and emissivity from nonisothermal mixed pixels with DART ↗

Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multi-component analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (e.g., spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m²·sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUST-DART is distributed together with DART (https://dart.omp.eu).

Why it matches plant phenotyping methods植物を含む混合画素から温度・放射率という生理状態関連の形質を抽出する熱赤外リモートセンシング手法を開発し、植生シーンで精度検証しているため、都市監視用途を含むが方法論的中心性が高い。

abstractWe propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas.

Leveraging UAV spectral and thermal traits for the genetic improvement of resistance to Dothistroma needle blight in Pinus radiata D.Don. ↗

Introduction Phenotyping is critical in tree breeding, but traditional methods are often labour-intensive and not easily scalable. Resistance to biotic and abiotic stress is a key focus in tree breeding programmes. While heritable traits derived from spectral remote sensing have been identified in trees, their application to tree phenotyping remains unexplored. This study investigates in-situ high-throughput hyperspectral and thermal imaging for assessing Dothistroma needle blight (DNB) resistance in Pinus radiata D.Don. Methods Using UAV-based hyperspectral and thermal imaging during a severe DNB outbreak in a clonal trial in New Zealand, we computed narrow-band hyperspectral indices (NBHIs), canopy temperature indices, radiative transfer inverted plant traits, and solar-induced fluorescence. Visual severity scores and remote sensing indices were modelled using spatially explicit mixed-effect linear models integrating pedigree and genomic data in a single-step genomic evaluation. Multi-trait models and sampling simulations were used to evaluate the potential of remote sensing indices to supplement or replace traditional phenotyping. Results Remote sensing indices exhibited narrow-sense heritability values comparable to severity scores (up to 0.37) and high absolute correlation coefficients with severity scores (up to 0.79). Carotenoid and chlorophyll-related NBHIs were the most informative, reflecting physiological impacts of DNB. Combining partial visual scoring with NBHIs maintained high estimated breeding value (EBV) accuracy (0.68) at 50% scoring and moderate accuracy (0.59) at 20% scoring. EBV correlation with full scoring was above 0.8 even at 20% scoring. Using solely the most heritable NBHI achieved 0.71 breeding value accuracy and 0.79 absolute EBV correlation with severity scores, suggesting NBHIs can replace visual scoring with minimal precision loss. Discussion By utilising UAV-based hyperspectral and thermal imaging to capture single-tree phenotypes related to disease in a forestry trial and pairing the data to genomic evaluation, this study establishes that remote sensing data offers an efficient, scalable alternative to traditional phenotyping. Our approach constitutes a major step towards characterising specific physiological responses, facilitating the discovery of the genetic architecture of physiological traits, and significantly enhancing genetic improvement.

Why it matches plant phenotyping methodsUAVハイパースペクトル・熱画像を用いて単木の病害関連形質を取得し、従来の視覚評価との比較・代替可能性まで検証しており、フェノタイピング手法が中心である。

abstractThis study investigates in-situ high-throughput hyperspectral and thermal imaging for assessing Dothistroma needle blight (DNB) resistance in Pinus radiata D.Don.

Assessing Crop Water Status and Stress Response in Precision Agriculture: The Role of CCATD and CTD ↗

Crop temperature regulation is a fundamental aspect of plant physiology, especially under fluctuating environmental conditions. Temperature-based indices such as Crop Canopy Air Temperature Difference (CCATD) and Canopy Temperature Depression (CTD) are vital indicators of plant water status, transpiration efficiency, and drought response. CCATD, defined as the difference between canopy temperature (Tc) and air temperature (Ta), provides insights into water stress, with higher values indicating limited transpiration and increased canopy heat accumulation. In contrast, CTD—calculated as the difference between Ta and Tc—reflects the plant’s evaporative cooling capacity, where higher values denote active transpiration and efficient water use. The inverse relationship between CCATD and CTD enhances their utility in crop stress monitoring, precision irrigation, and the selection of stress-resilient genotypes in breeding programs. Advanced technologies such as infrared thermometry, UAV-mounted thermal imaging, and satellite-based remote sensing support accurate assessment of these indices at multiple scales. Environmental variables—including solar radiation, vapor pressure deficit (VPD), wind speed, and soil moisture—significantly influence CCATD and CTD, highlighting the need for their integration with multispectral and physiological data for more effective stress detection. This review emphasizes the critical role of CCATD and CTD in optimizing water management, guiding climate-resilient crop selection, and advancing precision agriculture. Future research should focus on integrating these indices with AI-driven analytics and high-throughput phenotyping to enhance their predictive value and support sustainable crop production under increasing climate variability.

Why it matches plant phenotyping methods作物の水分状態・ストレスを熱画像や赤外線計測で評価する指標を中心に扱うレビューであり、植物表現型の取得・評価手法が主題である。

abstractTemperature-based indices such as Crop Canopy Air Temperature Difference (CCATD) and Canopy Temperature Depression (CTD) are vital indicators of plant water status, transpiration efficiency, and drought response.

Rapid diagnosis of herbicidal activity and mode of action using spectral image analysis and machine learning ↗

Herbicide screening requires a substantial amount of time, effort, and cost, making a new herbicide discovery expensive and time-consuming. Various diagnostic methods have been developed, but most of them are destructive and require significant time and effort to identify herbicide activity. Therefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs). RGB, chlorophyll fluorescence (CF), and infrared (IR) thermal images were acquired after treating herbicides with different MOAs to a model plant, oilseed rape ( Brassica napus ), and analyzed using MATLAB 2021b to quantify NDI, ExG, F d /F m , and plant leaf temperature. NDI, ExG and F d /F m decreased, while plant leaf temperature increased after herbicide treatment. Distinctive spectral responses were found depending on the herbicide MOAs. PSII and PPO inhibitors showed rapid responses in IR thermal and CF images within 1 day after herbicide treatment. HPPD inhibitor showed a continuous decrease in F d /F m , while EPSPS inhibitor showed gradual changes in all spectral indices. Machine learning by Subspace Discriminant algorithm of spectral indices acquired at 6 ​h enabled the diagnosis of herbicide MOAs with 89.6 ​% accuracy, which gradually increased by adding new spectral indices acquired later time points until 3 DAT, when validation accuracy scored 100 ​%. The indices acquired at 6 ​h, and F d /F m and leaf temperature data were shown to contribute to higher accuracies of identifying herbicide MOAs. Overall test accuracy scored 87.5 ​%, verifying the possibility of diagnosing herbicide MOAs based on spectral indices. Therefore, we could conclude that herbicide activity and MOAs can be diagnosed by analyzing spectral images combined with machine learning, suggesting the possibility of high-throughput screening of herbicide MOAs using plant image analysis.

Why it matches plant phenotyping methods植物への除草剤処理を目的とするが、スペクトル画像から葉温度や蛍光などの植物状態を抽出し、機械学習で作用機序を診断する画像解析手法が中心であるため、植物フェノタイピング手法として含める。

abstractTherefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs).

Semantic Segmentation for Early Detection of Plant Stress and Disease using Multispectral and Thermal Imaging Through U-Net based Model ↗

Early detection of plant stress and sickness is more and more critical for sustainable agriculture, as diverse stress elements including drought, pests, nutrient deficiencies, and sicknesses-threaten plant fitness and productiveness. Traditional detection methods are often manual and time- in depth, main to delays in intervention. This paper presents a novel framework for early detection of plant stress and disease that combines U-Net based semantic segmentation with multispectral and thermal imaging. Using excessive-resolution multispectral and thermal imagery, U-Net can accurately section stress-affected regions in plant leaves and stems, distinguishing them from healthful tissue and taking into consideration well timed, focused intervention. And also this challenge is enhanced with disease prediction abilities, this version can also analyze environmental information, which include humidity, temperature, and soil great, to understand patterns that correlate with precise diseases. By integrating ancient data on preceding ailment outbreaks, U-Net's prediction layer identifies early signs and symptoms of recurring issues approximately the plant healthful or now not healthy by means of checking it is having any ailment or not. By reading stress patterns and distributions inside the segmented regions, the model provides a strong tool for early detection and unique intervention, probably decreasing yield losses and resource wastage in agriculture.

Why it matches plant phenotyping methods植物のマルチスペクトル・熱画像からストレス／病害領域を抽出するU-Net手法が研究の中心であり、植物状態の画像ベース表現型推定に該当する。

abstractThis paper presents a novel framework for early detection of plant stress and disease that combines U-Net based semantic segmentation with multispectral and thermal imaging.

Prediction of harvest-related traits in barley using high-throughput phenotyping data and machine learning ↗

Abstract Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines ( Hordeum vulgare ) were grown in a greenhouse environment with well-watered and drought treatments, and phenotyped using RGB, thermal infrared, chlorophyll fluorescence and hyperspectral imaging sensors. Temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (R 2 ≥ 0.97) even when exclusively using predictors only from the early phase after drought induction. Canopy temperature depression at the early stage and RGB-derived plant size estimates at the late stage were identified as key classification features. Temporal phenomic prediction model of harvest-related traits achieved particularly high mean R 2 values for total biomass dry weight (0.97) and total spike weight (0.93), with RGB plant size estimators emerging as important predictors. Prediction accuracy for these traits remained high (R 2 ≥ 0.84) when using only predictors from the first half of the experiment. Models trained on pooled drought and control data outperformed single-treatment models and retained high accuracy when applied across treatments. These findings support the integration of high-throughput phenotyping and temporal modelling to enable timely and more cost-effective selection of drought-resilient genotypes, and illustrate the broader potential of phenomics-driven approaches in accelerating crop improvement under stress-prone conditions.

Why it matches plant phenotyping methodsRGB・熱赤外・蛍光・ハイパースペクトルによる高スループット表現型取得と、時系列モデルによる干ばつ状態および収穫形質の予測が研究の中心である。

abstractwe investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants

A lightweight model for early perception of rice diseases driven by photothermal information fusion ↗

Rice blast disease poses a significant threat to rice yield. The disease progresses rapidly once symptoms appear, making timely control challenging. Moreover, once lesions form, the damage becomes irreversible. Existing detection methods often suffer from delays and lack effective strategies for identifying the disease at its asymptomatic stage, hindering early diagnosis. In this study, we collected thermal and optical data from rice canopies at different infection stages and integrated physiological and biochemical analyses to investigate the infection mechanism during the early, asymptomatic phase. Additionally, we employed the SURF feature extraction algorithm to fuse thermal and optical images, developing a preliminary method for identifying asymptomatic rice regions based on thermal signatures. This approach effectively captured the spectral responses of asymptomatic rice and mitigated the limitations of single-sensor detection in early disease identification. By analyzing spectral and temperature characteristics, we applied feature dimensionality reduction techniques to construct early detection models at both the canopy and leaf levels. The models achieved overall classification accuracies (OA) of 92 % and 97 %, respectively, enabling detection 72 h prior to lesion formation. Finally, we designed fixed-point IP and multi-level register-cascade pipeline architecture, implementing low-power FPGA-based edge computing system. The leaf-level detection model deployed on the FPGA achieved an accuracy of 92 %, with a power consumption of 0.076 W and an inference speed of 0.11 ms. This study proposes an effective real-time detection method for identifying early asymptomatic rice blast, thereby facilitating timely disease monitoring and prevention.

Why it matches plant phenotyping methodsイネの無症状病害を熱画像・光学画像から抽出し、検出モデルとFPGA実装まで開発・評価しており、植物状態の取得手法が中心である。

abstractwe employed the SURF feature extraction algorithm to fuse thermal and optical images, developing a preliminary method for identifying asymptomatic rice regions based on thermal signatures.

MADI: A multispectral automated dynamic imager to monitor plant health ↗

High-throughput phenotyping has a tremendous capacity to advance our understanding of plant biology. Integrating growth parameters with information on a plant's physiology through multispectral imaging can provide a holistic picture of its health status and its responses to environmental stressors. Furthermore, the screening of large-scale populations of genotypes or germplasms, using such platforms, can identify lines with desirable traits to help feed a growing world population in the background of climate change. Here, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging, and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency. Additionally, we have integrated and validated a fluorescence-based parameter to non-destructively assess chlorophyll content. The utility of the MADI system was demonstrated through four case studies in which lettuce and Arabidopsis plants were exposed to various abiotic stress conditions. We demonstrate that plant compactness is a useful marker for stress responses, including drought, and could serve as a biomarker to study plant hormones. Additionally, we observed the phenomenon of chlorophyll hormesis under salt stress, a rather poorly understood process. In conclusion, the MADI is a multifunctional, adaptable system that can be employed to gain insights into plant stress responses and help to improve agricultural practices. It can be used primarily for rosette-growing species, such as leafy greens, which represent a significant portion of cultivated crops worldwide.

Why it matches plant phenotyping methods植物の成長・生理形質を取得するマルチスペクトル自動イメージング基盤を開発し、蛍光パラメータを検証してストレス事例で実証しているため、フェノタイピング手法が中心である。

abstractHere, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging, and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency.

Evaporative cooling signals for wound healing in plants ↗

Repairing damaged tissues is essential for the survival of all organisms. In plants, tissue injury rapidly triggers defense and repair programs. However, the molecular mechanisms linking early injury cue to the later stages of wound repair remain unclear. Here, we show that wounding of Arabidopsis leaves induces localized low temperature at the injury site, likely caused by evaporative cooling, which is accompanied by an activation of cold-responsive genes. Using thermal imaging combined with computer vision and deep learning, we developed a workflow to monitor the dynamics of wound healing in a quantitative, non-invasive and real-time manner. Mechanistically, we show that C-repeat Binding Factor (CBF) transcription factors are required for the activation of injury-associated cold response and downstream salicylic acid (SA) signaling. The CBF–SA module promotes lignin deposition and wound repair. Together, these findings reveal a link between a wound-induced biophysical cue and the tissue repair program.

Why it matches plant phenotyping methods熱画像とコンピュータビジョン・深層学習を組み合わせ、植物の創傷修復動態を定量・非侵襲・リアルタイムに測定するワークフローを開発しており、フェノタイピング手法が中心的である。

abstractUsing thermal imaging combined with computer vision and deep learning, we developed a workflow to monitor the dynamics of wound healing in a quantitative, non-invasive and real-time manner.

Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials ↗

Accurate grain yield (GY) prediction is essential in wheat breeding to enhance selection and accelerate breeding cycles. This study explored whether high-throughput phenotyping (HTP) data collected from small plot (SP) trials can effectively predict GY outcomes in later-stage big plot (BP) trials. Genomic (G) data were combined with hyperspectral (H) and multispectral + thermal (M) imaging across the 2022 and 2023 growing seasons at the Plant Science Research and Education Unit, Citra, Florida. A panel of 312 wheat genotypes was analyzed using GBLUP-based models, integrating G + H and G + M data from SP to predict BP yield. SP models demonstrated promising predictive ability, with G + H models achieving moderate within-year (0.43 to 0.51) and across-year (0.43) prediction accuracies, while G + M models reached 0.53 to 0.58 and 0.45, respectively. The Random Forest Regression (RFR) model produced an accuracy of 0.47 when M data from the 2022 SP, combined with G, was used to predict BP yield in 2023. Additionally, the top 25% specificity (coincide index) was evaluated, with models showing up to 47–51% within a year and 43–45% between years overlap in the highest predicted-yielding lines between SP and BP trials, further emphasizing the potential of SP data for early selection. These findings suggest that SP trials can provide meaningful predictions for BP yields, enabling earlier selection and faster breeding cycles.

Why it matches plant phenotyping methods小区試験の高スループット画像データを用いて大区画の穀粒収量を予測し、複数年・モデル間の予測精度を評価しており、フェノタイピングデータの解析ワークフローが中心的です。

abstractThis study explored whether high-throughput phenotyping (HTP) data collected from small plot (SP) trials can effectively predict GY outcomes in later-stage big plot (BP) trials.

Mapping field-scale crop water stress for wheat using satellite remote sensing data by formulating lower baseline using a novel approach ↗

The quantification of crop water stress is very crucial for efficient irrigation water management and sustainable agriculture. The empirically derived crop water stress index (CWSI) is a widely used method for quantifying the crop water status. However, developing lower baseline is a prerequisite for estimating the crop water stress using the empirical approach. Traditionally, the lower baseline is formulated by taking in-situ observations of a well-watered crop canopy using infrared radiometers. In this study, a novel methodology is formulated for estimating the lower baseline using land surface temperature (LST) and normalized difference vegetation index (NDVI) for the wheat crops using Landsat-8, Landsat-9 and Sentinel-2 satellite data. This study is conducted during the 2021-22 and 2022-23 wheat crop seasons, covering approximately 630 acres of agricultural fields, managed by local farmers in the western part of Uttar Pradesh, India. The entire analysis is conducted on Google Earth Engine. Initially, multi-temporal image classification is performed, employing the synergetic use of Sentinel-2 and machine learning algorithms, to distinguish the wheat and non-wheat fields. The manually collected ground truth data are used to train and test the random forest model. Subsequently, the candidate pixels are selected based on the maximum NDVI range, from (NDVImax - 0.1) to NDVImax, which represents dense and healthy wheat patches. These candidate pixels are further refined by selecting the pixels having less than 10th percentile of the LST values, which account for relatively higher evapotranspiration. The lower baseline is derived using LST values of the refined candidate pixels along with concurrent air temperature (Ta) and relative humidity measurements recorded by an automatic weather station. Finally, CWSI is mapped for the study area using the empirical approach.Classification accuracy of 96% and 95% was achieved for the classification of wheat and non-wheat fields during the 2021-22 and 2022-23 seasons, respectively, with corresponding Kappa coefficients of 0.85 and 0.80. For the classified wheat pixels, the lower baseline equation formulated by the proposed methodology are (LST – Ta) = -1.864VPD + 1.325 for 2021-22 season and (LST – Ta) = -4.92VPD + 3.14 for 2022-23 season, where VPD is vapour pressure deficit. The fixed upper baseline of (LST – Ta) = 4°C is taken for empirically deriving and mapping CWSI for both seasons. The minimum and maximum values of the CWSI ranged from 0 to 0.89 during the 2021-22 season and from 0 to 0.78 during the 2022-23 season. The 2021-22 cropping season observed increased CWSI values as compared to 2022-23, primarily due to the heatwave that occurred in the study area from during the latter part of the 2021-22crop season. Significant spatial and temporal variability is obtained in the CWSI values within the study area. The results suggest that the proposed methodology can be effectively used for mapping crop water stress at field scale without the requirement of tedious in-situ canopy temperature observations.

Why it matches plant phenotyping methods衛星リモートセンシングによる小麦の水分ストレス（CWSI）の推定・マッピング手法を開発し、精度評価まで行っており、植物の生理状態取得が研究の中心である。

abstracta novel methodology is formulated for estimating the lower baseline using land surface temperature (LST) and normalized difference vegetation index (NDVI) for the wheat crops using Landsat-8, Landsat-9 and Sentinel-2 satellite data.

Synergistic 3D, multispectral, and thermal image analysis via supervised machine learning for improved detection of root rot symptoms in hydroponically-grown flat-leaf parsley ↗

Root rot in hydroponically-grown leafy vegetables is difficult to detect via conventional manual and machine vision-based approaches as symptoms of infection are not clearly visible on the canopy at earlier stages of infection. Hence, the present study investigates the potential of using machine learning for assessing canopy information obtained from multiple imaging platforms synergistically to improve root rot detection. Herein, flat-leaf parsley seedlings were grown in an experimental hydroponic vertical farm and inoculated with Pythium irregulare and Phytophthora nicotianae . Subsequently, the seedlings were imaged via 3D, multispectral, and thermal sensors at various stages of growth to obtain twenty-six image-based plant features. Following a preliminary screening of redundant features via regression analysis, data for seventeen image features associated with morphometric, spectral, and thermal attributes was co-analyzed using supervised machine learning by Support Vector Machines (SVM). Exhaustive feature selection using different SVM kernels and maximum feature thresholds was performed to identify optimal feature subsets. It was observed that combining parameters obtained from all three imaging platforms enabled better identification of infected samples (>99%) than using a higher number of attributes from individual imaging systems. In addition, model performance was improved considerably by including temporal information during model training. Hence, it may be inferred that fusion of data from multiple imaging systems and using it with temporal information can enable better real-time high-throughput monitoring of root rot.

Why it matches plant phenotyping methods複数の画像センサーから植物形質を取得し、機械学習で根腐病症状を推定する統合的フェノタイピング手法の開発・評価が研究の中心である。

abstractthe present study investigates the potential of using machine learning for assessing canopy information obtained from multiple imaging platforms synergistically to improve root rot detection

Thermal Imaging and Disease Detection in Hibiscus Plant ↗

Abstract: The threat of plant diseases poses a significant challenge to agricultural productivity, especially in developing countries where small-scale farmers are highly vulnerable. To avoid crop loss and guarantee food security, early identification of plant stress and disease is crucial. Even if they work well, traditional diagnostic techniques take a lot of time and effort. This study explores the potential of thermal imaging as a non-invasive and efficient solution for early stress detection in Hibiscus plants. The experiment was conducted on a single potted hibiscus plant at Bikaner Technical University over a period of approximately two months (14th December 2024 to 5th February 2025). Initially kept outdoors with regular watering, the plant was moved to a closed indoor setting without sunlight and water from Day 9, allowing for observation of stress progression and disease emergence. Although the thermal dataset was recorded for 16 days, intermediate day observations confirmed consistent stress behavior. Four visual diseases—Leaf Spot, Rust Disease, Botrytis Blight, and Mosaic Virus—were noted, but due to the limited dataset, the prototype focuses on classifying plant health into four thermal stress categories: healthy, mild, significant, and critical. Thermal images were captured from top and front views, and a deep learning model based on MobileNetV2 was developed using a multi-view classification approach. The model was trained using Leave-One-Out Cross-Validation (LOOCV) to ensure robustness with constrained data. Instead of relying solely on traditional performance metrics, a confidence-based interpretation method was adopted to improve decision reliability. The prototype demonstrates the feasibility of using thermal imaging and deep learning for early, non-destructive plant stress classification, paving the way for smarter and more sustainable agricultural monitoring.

Why it matches plant phenotyping methods熱画像と深層学習による植物ストレス状態の取得・分類手法が研究の中心であり、植物の健康状態・病害ストレスを直接推定しているため含める。

abstractThis study explores the potential of thermal imaging as a non-invasive and efficient solution for early stress detection in Hibiscus plants.

Thermal Imaging and Disease Detection in Patharchatta Plant ↗

Abstract: Plant diseases cause significant agricultural losses, affecting both crop yield and quality. Early detection is crucial for effective disease management. This study explores thermal imaging as a non-invasive method for identifying plant stress in Patharchatta (Kalanchoe pinnata). Two cases were analyzed: wilting due to dehydration and black spot disease from overwatering.Thermal thresholds of 16°C (early stress) and 18°C (critical damage) were experimentally identified, particularly in fungal-infected Patharchatta plants.A thermal image-based classification model was developed to support detection, achieving over 91% accuracy.The findings demonstrate that thermal imaging is a promising, real-time toolfor early disease detection, enabling proactive plant health management.

Why it matches plant phenotyping methods植物のストレス・病害状態を熱画像から推定する分類モデルと閾値を開発・評価しており、表現型取得手法が研究の中心である。

abstractThis study explores thermal imaging as a non-invasive method for identifying plant stress in Patharchatta (Kalanchoe pinnata).

UAV-based multispectral and thermal indexes for estimating crop water status and yield on super-high-density olive orchards under deficit irrigation conditions ↗

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像による作物水分状態と収量の推定が題名の中心であり、植物状態・収量を抽出するセンシング手法の応用に該当する。

titleUAV-based multispectral and thermal indexes for estimating crop water status and yield on super-high-density olive orchards under deficit irrigation conditions

Multimodal Deep Learning Framework for Proactive Plant Disease Diagnosis ↗

Early diagnosis, the correct diagnosis of plant diseases is important to ensure sustainable agriculture and the minimalization of the loss of production. Traditional approaches of plant disease detection, which involve manual inspection and single modal imaging, are highly cumbersome, erroneous and lack in capturing the niche characteristics of the disease. Some recent achievements of deep learning advocate for possible automatic plant disease diagnosis; however, still most of the current models are plagued from low generalization capability, high computational cost and the issue of real time implementation. To alleviate these difficulties, this article introduces a brand-new multiple-mode deep learning framework, that combines RGB, hyperspectral and thermal imaging to take on the task of setting up precision and efficiency for plant disease detection. The described framework makes use of EfficientNet-based CNN for spatial feature extraction from RGB images, 1D-CNN for hyperspectral spectral feature learning and Vision Transformers (ViT) for learning long-range contextual dependencies. Above sensor- features are fused by Means of weighted summation methodology, dynamically adjusts contribution of per modality to Obtain endurance and accurate. To achieve real-time performance, the model is optimized via quantization, knowledge distillation and model pruning, with a substantial decrease in its computational load. The final optimal model is implemented in NVIDIA Jetson Nano to allow low-latency inference supporting high precision agriculture. The results of the experimental results show, the proposed multi-modal framework has achieved 97.8% accuracy, 96.5% precision, 95.7% recall and 96.1% score of F, all far exceed traditional deep learning models of ResNet-50, VGG-16, EfficientNet and Vision Transformers (ViT). Moreover, the framework offers inferences in 20 milliseconds, which makes it really suitable for real-time applications. Accomplishing a successful integration of multi-modal data fusion and model optimization not only increase classification performance, but also makes the solution/matter practical and deployable in real-world agricultural environment. The proposed framework provides a hopeful solution to smart farming, which provides a possibility of detecting disease early and managing effectively the crops.

Why it matches plant phenotyping methodsRGB・ハイパースペクトル・熱画像から植物病害を推定するマルチモーダル手法を開発し、精度・推論速度を評価しているため、植物フェノタイピング手法が中心である。

abstractthis article introduces a brand-new multiple-mode deep learning framework, that combines RGB, hyperspectral and thermal imaging to take on the task of setting up precision and efficiency for plant disease detection.

Infrared Thermography in Plant Factories: Solving Spatiotemporal Variations Via Machine Learning ↗

ABSTRACT Infrared thermography (IRT) for real‐time stress detection in plant factories (PFs) remains largely unexplored. Hence, this study investigates the feasibility of implementing IRT in PFs, using machine learning (ML) to address the challenges in information processing. Herein, purple basil plantlets were subjected to root dehydration within a pilot‐scale PF, and canopy temperature was monitored at regular intervals using a thermal camera. Subsequently, eight ML models using the ‘support vector machines’ algorithm were tested for stress detection. Our findings revealed that differences in canopy temperature due to microenvironmental variations led to inaccurate representation of stress. Nonetheless, binary classification models trained using plants at medial and high stress overcame this issue by identifying stressed samples with 81%–94% accuracy. However, although models trained with medially stressed samples performed well for all stress levels, models trained using highly stressed samples failed to identify medial stress reliably. Additionally, ternary and quaternary classification models were able to identify unstressed samples but could not distinguish between different levels of stress. Hence, binary classification models trained using medially stressed samples overcame spatiotemporal variations in canopy thermal profile most effectively and provided probabilistic estimates of plant stress within the PF most consistently.

Why it matches plant phenotyping methods植物工場での赤外線サーモグラフィーと機械学習による植物ストレス推定が研究の中心であり、手法の実装・評価と精度検証を行っている。

abstracteight ML models using the ‘support vector machines’ algorithm were tested for stress detection

Deep learning based abiotic crop stress assessment for precision agriculture: A comprehensive review. ↗

Abiotic stresses are a leading cause of crop loss and a severe peril to global food security. Precise and prompt identification of abiotic stresses in crops is crucial for effective mitigation strategies. In recent years, Deep learning (DL) techniques have demonstrated remarkable promise for high-throughput crop stress phenotyping using remote sensing and field data. This study offers a comprehensive review of the applications of DL models like artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), vision transformers (ViT), and other advanced deep learning architectures for abiotic crop stress assessment using different modalities like IoT sensor data, thermal, spectral, RGB with field, UAV and satellite based imagery. The study comprehensively analyses the abiotic stress conditions due to (a) water (b) nutrients (c) salinity (d) temperature and (e) heavy metal. Key contributions in the literature on stress classification, localization, and quantification using deep learning approaches are discussed in detail. The study also covers the principles of deep learning models, and their unique capabilities for handling complex, high-dimensional datasets inherent in abiotic crop stress assessment. The review also highlights important challenges and future directions in deep learning based abiotic crop stress assessment like limited labelled data, model interpretability, and interoperability for robust stress phenotyping. This study critically examines the research pertaining to the abiotic crop stress assessment, and provides a comprehensive view of the role deep learning plays in advancing abiotic crop stress assessment for data-driven precision agriculture.

Why it matches plant phenotyping methods作物の非生物的ストレスを深層学習と各種センシングで分類・局在化・定量するフェノタイピング研究を包括的にレビューしており、方法論が中心である。

abstractThis study offers a comprehensive review of the applications of DL models like artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), vision transformers (ViT), and other advanced deep learning architectures for abiotic crop stress assessment using different modalities like IoT sensor data, thermal, spectral, RGB with field, UAV and satellite based imagery.

MADI: A multispectral automated dynamic imager to monitor plant health. ↗

High-throughput phenotyping has a tremendous capacity to advance our understanding of plant biology . Integrating growth parameters with information on a plant's physiology through multispectral imaging can provide a holistic picture of its health status and its responses to environmental stressors. Furthermore, the screening of large-scale populations of genotypes or germplasms , using such platforms, can identify lines with desirable traits to help feed a growing world population in the background of climate change . Here, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging , and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency . Additionally, we have integrated and validated a fluorescence-based parameter to non-destructively assess chlorophyll content. The utility of the MADI system was demonstrated through four case studies in which lettuce and Arabidopsis plants were exposed to various abiotic stress conditions. We demonstrate that plant compactness is a useful marker for stress responses, including drought, and could serve as a biomarker to study plant hormones. Additionally, we observed the phenomenon of chlorophyll hormesis under salt stress, a rather poorly understood process. In conclusion, the MADI is a multifunctional, adaptable system that can be employed to gain insights into plant stress responses and help to improve agricultural practices. It can be used primarily for rosette-growing species, such as leafy greens, which represent a significant portion of cultivated crops worldwide.

Why it matches plant phenotyping methods植物の成長・葉温・光合成効率・クロロフィル含量を取得するマルチスペクトル自動計測プラットフォームの開発と検証が中心であり、植物フェノタイピング手法として適格。

abstractHere, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging , and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency .

Predicting stomatal conductance of chili peppers using TPE-optimized LightGBM and SHAP feature analysis based on UAVs’ hyperspectral, thermal infrared imagery, and meteorological data ↗

Effective water management is crucial for ensuring the healthy growth and high yield of crops, and it relies on accurate monitoring of plant water status. As a core indicator of plant gas exchange capacity, stomatal conductance (Gs) directly determines the efficiency of photosynthesis and transpiration, significantly impacting crop growth and yield formation. Therefore, timely and accurate prediction of stomatal conductance is essential for optimizing water management strategies and improving crop yield and quality. However, stomatal conductance is influenced by a variety of environmental factors and plant physiological traits, making its variability complex and dynamic. These challenges result in difficulties in selecting key features, insufficient prediction accuracy, and a lack of transparency in model decision-making processes. To address these issues, this study proposes a novel approach that combines a random forest (RF) feature selection method with a Tree-structured Parzen Estimator (TPE)-optimized light gradient boosting machine (LightGBM) model (TPE-LightGBM). This approach leverages UAV-based hyperspectral, thermal infrared imagery, and meteorological data to improve predictive performance. Additionally, SHAP (SHapley Additive exPlanations) analysis is incorporated to offer insights into the model’s decision-making process by revealing feature dependencies. In the feature selection process, we compared four common methods, including mutual information (MI), successive projection algorithm (SPA), recursive feature elimination (RFE), and least absolute shrinkage and selection operator (LASSO) to ensure the significance and effectiveness of the selected features. To comprehensively evaluate model performance, we also compared five predictive models: ridge regression (RR), partial least squares regression (PLSR), random forest regression (RFR), random search-optimized LightGBM (Random-LightGBM), and grid search-optimized LightGBM (Grid-LightGBM). The experimental results revealed that the combination of RF and TPE-optimized LightGBM significantly outperformed all other models, achieving the highest prediction accuracy. The optimal number of features was determined to be N = 15, with a coefficient of determination (R²) of 0.862, a root mean square error (RMSE) of 0.037, and a mean absolute error (MAE) of 0.029. Through SHAP analysis, the study not only identifies key influencing factors such as photosynthetically active radiation (PAR), canopy temperature (CT), and red-edge spectral bands, but also sheds light on how these factors interact with each other to influence stomatal conductance. The proposed model provides an innovative approach to effectively predicting stomatal conductance, enabling agricultural managers to better understand and regulate chili pepper’s water status, thereby promoting healthy chili pepper growth and efficient resource management.

Why it matches plant phenotyping methodsUAVのハイパースペクトル・熱赤外画像からチリ pepper の気孔コンダクタンスという生理形質を推定するモデルを開発・比較・解釈しており、表現型取得・推定手法が研究の中心です。

abstractThis approach leverages UAV-based hyperspectral, thermal infrared imagery, and meteorological data to improve predictive performance.

Determining water status of walnut orchards using the crop water stress index and canopy temperature measurements. ↗

Background Accurately evaluating the water status of walnuts in different growth stages is fundamental to implementing deficit irrigation strategies and improving the yield of walnuts. The crop water stress index (CWSI) based on the canopy temperature is one of the most commonly used tools for current research on plant water monitoring. However, the suitability and effectiveness of using the CWSI as an indicator of the walnut water status under field conditions are still unclear. This paper focuses on walnut orchards in Northwest China using synchronous monitoring of the canopy temperature, meteorological parameters, and water physiological parameters of walnut trees under both full irrigation and deficit irrigation treatments. The aim is to test the effectiveness of the simplified crop water stress index (CWSI s ) and the theoretical crop water stress index (CWSI t ) in tracking the diurnal and daily variations of the water conditions in walnut orchards. Results The CWSI s can reflect the diurnal and daily changes in the water status of walnut orchards. It was found that the CWSI s at 12:00 local time had the best performance in tracking the daily changes in the water status. Compared to the daily averaged CWSI calculated using the measured transpiration (CWSI Tr_day ), the correlation coefficient, index of agreement, and root mean squared error between the CWSI s and CWSI Tr_day were 0.82, 0.94, and 0.11, respectively. However, due to the calculation errors of the aerodynamic resistance in walnut trees, the CWSI t was unable to track the diurnal variations in the water status in walnut orchards and the degree of water stress was underestimated. In addition, the variations in minimum canopy resistance in the various growth stages of walnut orchards may also affect the accuracy of the CWSI t in terms of indicating the seasonal changes in the water status. Conclusions The CWSI s provides a non-destructive, quickly and effective method for monitoring the water status of walnuts. However, the results of this study suggest that the effects of aerodynamic resistance parameterization and variations in minimum canopy resistance in the various growth stages of walnut orchards in the CWSI t calculation should be noted.

Why it matches plant phenotyping methodsクルミ樹の水分状態という植物生理状態を、樹冠温度に基づくCWSIで非破壊推定し、簡易法・理論法の有効性と誤差を比較検証しているため、フェノタイピング手法が中心である。

abstractThe aim is to test the effectiveness of the simplified crop water stress index (CWSI s ) and the theoretical crop water stress index (CWSI t ) in tracking the diurnal and daily variations of the water conditions in walnut orchards.

A practical data-driven approach for precise stem water potential monitoring in pistachio and almond orchards using supervised machine learning algorithms ↗

The advent of machine learning technologies in conjunction with the advancements in UAV-based remote sensing pioneered a new era of research in agriculture. The escalating concern for water management in drought-prone areas such as California underscores the urgent need for sustainable solutions. Stem water potential (SWP) measurement using pressure chambers is one of the most common methods used to directly determine tree water status and the optimal timing for irrigation in orchards. However, this approach is inefficient due to its labor-intensive nature. To address this problem, we used weather, thermal and multispectral data as inputs to the machine learning (ML) algorithms to predict the SWP of pistachio and almond trees. For each crop, we first deployed six supervised ML classification models: Random Forest (RF), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Tree (DT), K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN). All classifiers provided more than 79% of accuracy while RF showed high performance in both pistachio and almond orchards at 88% and 89%, respectively. The feature importance results by the RF model revealed that the weather features were the most influential factors in the decision-making process. In both crops, canopy temperature Tc was the next important feature closely followed by OSAVI in pistachios and NDVI in almonds. RF regression model predicted SWPs with R2 of 0.70 in pistachio and R2 of 0.55 in the almond orchard. Our results demonstrate that ML models are practical tools for irrigation scheduling decisions. This study offered a data-driven approach that effectively balances minimal data requirements with accuracy to facilitate optimal water management for end-users.

Why it matches plant phenotyping methods気象・熱・マルチスペクトルデータから樹体の水分状態という生理形質（SWP）を機械学習で推定する手法開発・評価が中心であり、灌漑判断への応用も検証している。

abstractwe used weather, thermal and multispectral data as inputs to the machine learning (ML) algorithms to predict the SWP of pistachio and almond trees.

Principles of high-throughput phenotyping and sensor platforms for non-invasive research in plant protection ↗

Climate change and the increasing resistance of pathogens are driving the need for innovative methods of plant disease diagnostics, particularly for high-risk pathogens such as Fusarium graminearum, which causes significant wheat yield losses. Traditional visual inspection methods suffer from low throughput and subjectivity, limiting their effectiveness in large-scale monitoring. This study aimed to explore the principles of high-throughput phenotyping and to evaluate the effectiveness of a sensor platform for the non-invasive investigation of Fusarium ear blight under field conditions in Ukraine (Kyiv region). A combination of multispectral imaging, thermal imaging, and machine learning algorithms was applied in a 1-hectare experimental field with 20% of the plots artificially infected. The results demonstrated that the proposed system achieved a 92% accuracy rate in early pathogen detection, representing a 37% improvement over visual assessment methods. Spectral indices showed a strong correlation with pathogen concentration: a decrease in the normalised difference vegetation index from 0.72 to 0.35 corresponded with an 80% increase in fungal biomass. Thermal imaging revealed a rise in leaf temperature of 2.5°C as early as 5-7 days after infection. The integration of all methods enabled an accuracy of 96% in processing one hectare within 2.5 hours, which is three times faster than traditional approaches. Polymerase chain reaction analysis confirmed the specificity of the techniques: 95% of infected samples contained Fusarium deoxyribonucleic acid, while sequencing revealed a 100% match for β-tubulin. Automated data processing required 2.5 hours per hectare, compared with 8 hours per hectare for visual inspection, and scaling up to 10 hectares reduced time expenditure by a factor of 12. The study confirmed the effectiveness of high-throughput phenotyping for precision plant protection and highlighted the need for further refinement of the methods in line with local climatic conditions. The practical significance of this research lies in the potential to reduce fungicide use through targeted treatment of infected areas, minimise crop losses in regions with high infection pressure, and establish a foundation for automated monitoring systems compatible with precision agriculture technologies

Why it matches plant phenotyping methods植物病害の早期検出を目的に、マルチスペクトル・熱画像と機械学習を統合した非侵襲的ハイスループット表現型解析プラットフォームを開発・評価しており、植物の病害状態を直接推定する方法が中心である。

abstractThis study aimed to explore the principles of high-throughput phenotyping and to evaluate the effectiveness of a sensor platform for the non-invasive investigation of Fusarium ear blight under field conditions in Ukraine (Kyiv region).

Advanced Plant Phenotyping Technologies for Enhanced Detection and Mode of Action Analysis of Herbicide Damage Management ↗

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 methods, plant phenotyping enables the 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雑草の除草剤損傷・抵抗性評価へ応用する方法論レビューであり、センサー、撮像プラットフォーム、モデリング手法を中心に扱っている。

abstractThis review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods

Monitoring maize phenology using multi-source data by integrating convolutional neural networks and Transformers ↗

Abstract：Maize is an essential grain crop in China, playing a crucial role in safeguarding in national food security. However, the increasing instability of the maize cultivation environment caused by global climate change, along with various adverse stress factors, presents significant challenges to maintaining yield stability. Effective monitoring of maize phenology under stress conditions is crucial for optimizing agricultural management and mitigating yield losses. This study proposes an innovative phenological monitoring model utilizing near-ground remote sensing technology. High-resolution imagery of maize fields was collected using unmanned aerial vehicles (UAVs) equipped with multispectral and thermal infrared cameras. By integrating these datasets with Convolutional Neural Network (CNN) and Transformer, the study developed a robust and efficient model that fuses multispectral, thermal infrared, and accumulated temperature datasets. The proposed model enables accurate inversion and quantitative analysis of maize phenological traits, offering critical insights to support agricultural management strategies and enhance crop yield stability under stress conditions. The results showed that the integration of multispectral imagery and accumulated temperature achieved an accuracy of 92.9%, while the inclusion of thermal infrared imagery further improved the accuracy to 97.5%. Additionally, UAV-based remote sensing offers superior spatial resolution and operational efficiency compared to manual observation methods in precision and scalability. This study highlights the potential of UAV-based remote sensing, combined with CNN and Transformer as a transformative approach for precision agriculture. It provides a valuable framework for advancing agricultural informatization and enhancing crop management.Key words: Maize; Crop phenology; Deep learning; UAV；Multi-source data

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外画像とCNN/Transformerを用いて、トウモロコシの生育（フェノロジー）形質を定量推定する手法を開発・評価しており、フェノタイピング手法が中心である。

abstractThis study proposes an innovative phenological monitoring model utilizing near-ground remote sensing technology.

Linking Citrus Fruit Cracking Intensity to Plant Water Status: Insights from UAV-Derived Metrics Validated by Ground-Based Data ↗

Citrus fruit cracking, a physical failure of the peel, causes yield losses of 10% to 35%, peaking during October-November. Water status of the tree and water flow into the fruit influence this phenomenon. with excessive irrigation during critical fruit development stages exacerbates cracking. As part of the EU-Horizon CrackSense project, this study is aimed to link citrus tree plant water status (PWS) to fruit cracking, emphasizing how deficit irrigation can reduce yield loss due to cracking. Using UAV and eco-physiological measurements, we developed models to predict PWS and its relationship with cracking intensity early in the season. The study, conducted in 2023-2024 in a commercial orchard near Kfar Chabad, Israel, tested four irrigation treatments: control, defined as the standard irrigation, two deficits irrigations regimes (50% of control) early and late in the season, and excessive irrigation (150% of control) throughout the season. Ground-based measurements included fruit and trunk diameter, stem water potential (SWP), stomatal conductance, plant area index (PAI), and growth rate (TG). UAV flights integrated multispectral, thermal, and LiDAR sensors to capture spatial-temporal variability in PWS. Canopy metrics, such as height, volume, LiDAR-based PAI, and spectral and thermal indices, were incorporated into PWS models. Results revealed significant differences in TG, SWP, and stomatal conductance for 50% of early and late deficit irrigation treatments compared to other treatments. Random forest models demonstrated strong predictive performance for SWP (R² > 0.77) and TG (R² > 0.76). LiDAR-derived PA correlated highly with field optical measurements (R² = 0.92), yield (R² = 0.67), and cracked fruit percentages (R² > 0.50). This study underscores the importance of precise irrigation management in reducing fruit cracking. It highlights the potential of remote sensing systems for predicting cracking and managing water status at the tree level. The developed models equip farmers with tools to apply controlled water stress, minimizing cracking and improving yield.

Why it matches plant phenotyping methodsUAVのマルチスペクトル・熱・LiDAR計測から樹体の水分状態、成長、樹冠形質、裂果状態を推定するモデルを開発し、地上計測で検証しており、表現型取得手法が研究の中心である。

abstractUsing UAV and eco-physiological measurements, we developed models to predict PWS and its relationship with cracking intensity early in the season.

Effectiveness of drone-based thermal sensors in optimizing controlled environment agriculture performance under arid conditions. ↗

Controlled environmental agriculture (CEA), integrated with internet of things and wireless sensor network (WSN) technologies, offers advanced tools for real-time monitoring and assessment of microclimate and plant health/stress. Drone applications have emerged as transformative technology with significant potential for CEA. However, adoption and practical implementation of such technologies remain limited, particularly in arid regions. Despite their advantages in agriculture, drones have yet to gain widespread utilization in CEA systems. This study investigates the effectiveness of drone-based thermal imaging (DBTI) in optimizing CEA performance and monitoring plant health under arid conditions. Several WSN sensors were deployed to track microclimatic variations within the CEA environment. A novel method was developed for assessing canopy temperature (Tc) using thermocouples and DBTI. The crop water stress index (CWSI) was computed based on Tc extracted from DBTI. Findings revealed that DBTI effectively distinguished between all treatments, with Tc detection exhibiting a strong correlation (R 2 = 0.959) with sensor-based measurements. Results confirmed a direct relationship between CWSI and Tc, as well as a significant association between soil moisture content and CWSI. This research demonstrates that DBTI can enhance irrigation scheduling accuracy and provide precise evapotranspiration (ETc) estimates at specific spatiotemporal scales, contributing to improved water and food security.

Why it matches plant phenotyping methodsドローン熱画像から作物の樹冠温度と水ストレス指数を抽出する手法を開発し、センサー測定との相関で検証しており、植物状態の取得法が中心である。

abstractA novel method was developed for assessing canopy temperature (Tc) using thermocouples and DBTI.

Enhanced flight planning and calibration for UAV based thermal imaging: implications for canopy temperature and transpiration analysis ↗

Leaf and canopy temperature have long been recognized as important indicators of plant water status because leaves cool when water is transpired and warm up when leaf stomata close and transpiration is reduced. Unmanned aerial vehicles (UAVs) open up the possibility to capture high resolution thermal images of forest canopies at the leaf scale. However, a careful calibration procedure is required to convert the thermal images to absolute temperatures, in addition, at high spatial resolution, the complexity of forest canopies leads to challenges in stitching overlapping thermal images into an orthomosaic of the forest site. In this study, we present a novel flight planning approach in which the locations of ground temperature references are directly integrated in the flight plan. Six UAV flight campaigns were conducted over a tropical dry forest in Costa Rica. For each flight five different calibration methods were tested. The most accurate calibration was used to analyze the tree canopy temperature distributions of five tree species. From the distribution we correlated its mean, variance, 5th and 95th percentile against individual tree transpiration estimates derived from sapflow measurements. Our results show that the commonly applied calibration provided by the cameras manufacturer (factory calibration) and empirical line calibration were less accurate than the novel repeated empirical line calibration and the factory calibration including drift correction (MAE 3.5°C vs. MAE 1.5°C). We show that the orthomosaic is computable by directly estimating the thermal image orientation from the visible images during the structure from motion step. We found the 5th percentile of the canopy temperature distribution, corresponding to the shaded leaves within the canopy, to be a better predictor of tree transpiration than the mean canopy temperature (R 2 0.85 vs. R 2 0.60). Although these shaded leaves are not representative of the whole canopy, they may be the main transpiration site in the heat of the day. Spatially high-resolution, validated temperature data of forest canopies at the leaf scale have many applications for ecohydrological questions, e.g., the estimation of transpiration, for comparing plant traits and modeling of carbon and water fluxes by considering the entire canopy temperature distribution in mixed-species forests.

Why it matches plant phenotyping methodsUAV熱画像の飛行計画、温度校正、オルソモザイク生成を開発・比較検証し、樹冠温度という植物生理形質を推定しているため、フェノタイピング手法が中心である。

abstractwe present a novel flight planning approach in which the locations of ground temperature references are directly integrated in the flight plan.

Improved estimation of stomatal conductance by combining high-throughput plant phenotyping data and weather variables through machine learning ↗

Stomatal conductance (g s ) quantifies the rate of exchange of carbon dioxide for photosynthesis and water vapor for transpiration between plant leaves and the atmosphere. g s is usually measured by handheld devices like porometers , and readings are manually taken in the field, which is time-consuming and labor-intensive. In this study, we investigated the use of high-throughput phenotyping (HTP) data combined with weather data to estimate g s through machine-learning (ML) modeling. The experiment was conducted in a research field equipped with an HTP platform in 2020 and 2021 involving maize, sorghum, soybean, sunflower , and winter wheat . Weather variables including dew point temperature, wind speed , air temperature, solar radiation, and relative humidity were collected by an onsite weather station . Plot-level canopy temperature, soil temperature , and seven vegetation indices were acquired using a thermal infrared camera, a multispectral camera, and a visible near-infrared spectrometer integrated on the HTP platform. Three supervised ML methods (Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and Support Vector Regression (SVR)) were employed to train the estimation models for g s , and model performance was evaluated by Coefficient of Determination (R 2 ) and Root Mean Squared Error (RMSE). The result showed that RFR and SVR outperformed PLSR in g s modeling. The RFR model achieved R 2 of 0.63 and RMSE of 0.16 mol m −2 ·s −1 with the combination of phenotyping data and weather data. It outperformed the model using only the weather data (R 2 =0.35 and RMSE=0.21 mol m −2 ·s −1 ), or the model using only the phenotyping data (R 2 =0.46 and RMSE=0.19 mol m −2 ·s −1 ). This result suggested that high-throughput plant phenotyping data effectively complement weather data in estimating g s rapidly and non-destructively through ML. With the wide adoption of HTP technologies in aerial and ground-based platforms, this research provides a practical framework to estimate g s at large scale for crop breeding and irrigation management .

Why it matches plant phenotyping methodsHTPセンサーデータと機械学習を用いて、植物の生理形質である気孔コンダクタンスを大規模・非破壊推定する方法が研究の中心であり、モデル性能も評価している。

abstractIn this study, we investigated the use of high-throughput phenotyping (HTP) data combined with weather data to estimate g s through machine-learning (ML) modeling.

Configuration of a multisensor platform for advanced plant phenotyping and disease detection: Case study on Cercospora leaf spot in sugar beet ↗

Plant phenotyping, which involves measuring and analysing plant traits, has seen significant advances in recent years by integrating autonomous platforms and sophisticated sensor systems. In contrast to traditional methods, modern unmanned ground vehicles (UGVs) provide robust and accurate phenotyping capabilities by enabling close, detailed and continuous monitoring of crops under different environmental conditions. This study presents the configuration and validation of a multi-sensor platform (MSP) integrated with a UGV to improve plant phenotyping through advanced data fusion and co-registration techniques. The platform incorporates red, green, and blue channel (RGB), hyperspectral from visible light (VIS) and near-infrared light (NIR) spectrum, thermal sensors, and a three-dimensional (3D) light detection and ranging (LiDAR), all subjected to extensive calibration to ensure precise temporal and spatial alignment. Intrinsic calibration was applied, including correcting the spectral signatures of VIS and NIR. Additionally, timestamps were synchronised using the VIS sensor as the primary reference due to its central position and higher data acquisition frequency. Homography matrices were computed using checkerboard patterns for geometric alignment across sensors, and motion corrections accounted for UGV movement and ground sample distance. LiDAR point clouds were transformed into depth-maps (DMs) using radial basis function interpolation, enriching the spatial data for further analysis. The co-registered and synchronised MSP was tested for detecting Cercospora leaf spot (CLS) in sugar beet plants during a field experiment. Two models were implemented: (1) a soil and plant segmentation model based on the DeepLabV3+ architecture, achieving an F1-score of 0.85 and an accuracy of 0.95, and (2) a CLS severity scoring model using a custom convolutional neural network (CNN). The severity model, leveraging NIR and DM channels, achieved an F1-score of 0.7066, accuracy of 0.7104, and recall of 0.7167, with NIR wavelengths between 814 and 851 contributing significantly to performance. These results highlight the importance of accurate data fusion and synchronisation in multi-sensor systems for plant phenotyping. Moreover, the study demonstrates the potential of integrating multiple sensors on a UGV for precision agriculture, thereby enhancing MSP effectiveness in crop monitoring and disease detection. • Multi-sensor platform supports detailed plant phenotyping using data fusion. • Effective synchronization ensured accurate temporal alignment across sensors. • RGB, hyperspectral, thermal, and LiDAR sensors calibrated for accurate alignment. • Soil-plant and segmentation Cercospora leaf spot disease severity estimated using neural network. • NIR and depth map sensor fusion enhance plant phenotyping accuracy for Cercospora leaf spot disease severity.

Why it matches plant phenotyping methodsマルチセンサーUGVプラットフォームの構成、校正、同期、データ融合を開発・検証し、植物病害の重症度という表現型を推定しているため、方法が研究の中心である。

abstractThis study presents the configuration and validation of a multi-sensor platform (MSP) integrated with a UGV to improve plant phenotyping through advanced data fusion and co-registration techniques.

Leveraging synergies between UAV and Landsat 8 sensors to evaluate the impact of pale lichen biomass on land surface temperature in heath tundra ecosystems. ↗

Pale terricolous lichens are a vital component of Arctic ecosystems, significantly contributing to carbon balance, energy regulation, and serving as a primary food source for reindeer. Their characteristically high albedo also impacts land surface temperature (LST) dynamics across various spatial scales. However, remote sensing of lichens is challenging due to their complex spectral signatures and large spatial variations in coverage and biomass even within local landscape scales. This study evaluates the influence of pale lichens on LST at local and landscape scales by integrating RGB, multispectral, and thermal infrared imagery from an Unmanned Aerial Vehicle (UAV) with multi-temporal Landsat 8 thermal data. An Extreme Gradient Boosting algorithm was employed to map pale lichen biomass, areal extent, and the occurrence of major plant functional types in the sub-arctic heath tundra landscape in the Jávrrešduottar and Sieiddečearru areas on the Finland-Norway border. Generalized Additive Models (GAMs) were used to elucidate the factors affecting LST. The UAV model accurately predicted pale lichen biomass (R 2 0.63) and vascular vegetation cover (R 2 0.70). GAMs revealed that pale lichens significantly influence thermal regimes, with increased biomass leading to decreased LST, an effect more pronounced at the landscape scale (deviance explained 47.26 % and 65.8 % for local and landscape models, respectively). Pale lichen biomass was identified as the second most important variable affecting LST at both scales, with elevation being the most important variable. This research demonstrates the capability of UAV-derived models to capture the heterogeneous and fine-scale structure of tundra ecosystems. Furthermore, it underscores the effectiveness of combining high spatial resolution UAV and high temporal resolution satellite platforms. Finally, this study highlights the pivotal role of pale lichens in Arctic thermal dynamics and showcases how advanced remote sensing techniques can be used for ecological monitoring and management.

Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル・熱赤外画像と衛星データを統合し、機械学習で地衣類 biomass と植生被覆を推定し、精度を検証しているため、植物状態の取得手法が中心です。

abstractintegrating RGB, multispectral, and thermal infrared imagery from an Unmanned Aerial Vehicle (UAV) with multi-temporal Landsat 8 thermal data

Harnessing AI for agriculture utilizing color-condition camera sensors and thermal imaging drones for crop color-condition detection and predictive yield analysis with inventory management system ↗

Technological Gaps and Challenges for Agriculture in the Philippines Such advancements may be relevant to other countries as well but we should also consider that there is greater potential of technology adoption with these purposes than ever befo re, especially those small-scale farmers from local areas who have less accessibility on technological trendsetting. In this study, we attempt to solve these problems using Artificial Intelligence (AI) in order to improve crop monitoring and predictive yiel d amidst the climate change. Color-condition detection using Convolutional Neural Networks (CNN) with 76.97% accuracy and predictive analysis by Artificial Neural Network(ANN). This optimizes the timing of planting and harvest, depending on the combination of these algorithms.

Why it matches plant phenotyping methodsカメラセンサーと熱画像ドローンを用いた作物状態検出、およびCNNによる色状態推定が中心で、植物の状態・収量に関する表現型推定手法を扱っている。

titlecolor-condition camera sensors and thermal imaging drones for crop color-condition detection and predictive yield analysis

Mapping plant-scale variation in crop physiological traits and water fluxes ↗

Nitrogen (N) is a vital plant element, affecting plant physiological processes, carbon and water fluxes and ultimately crop yields. However, N uptake by crops can vary over fine spatiotemporal scales, and optimising the application of N-fertiliser to maximise crop performance is challenging. To investigate the potential of spatially mapping the impact of N fertiliser application on crop physiological performance and yield, we leverage both optical and thermal data sampled from drone platforms and ground-level leaf measurements, across a range of different N, Sulphur (S) and sucrose treatments in winter wheat. Using leaf level hyperspectral reflectance data, leaf chlorophyll content was accurately modelled across fertiliser treatments via partial least squares regression (PLSR; R2= 0.93, P < 0.001). Leaf photosynthetic capacity (Vcmax) exhibited a strong linear relationship with leaf chlorophyll (R2 = 0.77; P < 0.001). Using drone-acquired MERIS terrestrial chlorophyll index (MTCI) values as a proxy for leaf chlorophyll (R2 = 0.76; P < 0.001), Vcmax was spatially mapped at the centimetre-scale. Thermal drone and ground measurements demonstrated that N application leads to cooler leaf temperatures, which led to a strong relationship with ground-measured leaf stomatal conductance (R2= 0.6; P < 0.01). Final grain yield was most accurately predicted by optical reflectance (MTCI, R2 = 0.94; P < 0.001). Precise retrieval of leaf-level crop performance indicators from drones establishes significant potential for optimising fertiliser application, to reduce environmental costs and improve yields.

Why it matches plant phenotyping methodsドローンの光学・熱画像と回帰モデルを用いて、葉クロロフィル、Vcmax、気孔コンダクタンス、収量などの植物形質を空間推定・検証しており、形質取得手法が研究の中心である。

abstractUsing leaf level hyperspectral reflectance data, leaf chlorophyll content was accurately modelled across fertiliser treatments via partial least squares regression

UAV hyperspectral-thermal-lidar fusion in phenotyping: genetic trait differences among Fremont cottonwood populations ↗

Abstract Context Climate change is causing landscape shifts and locally-adapted plants are becoming increasingly maladapted. As a foundation species, Fremont cottonwood facilitates adaptation to changing climate for the whole community. Populations within this species, however, have varying adaptive responses and facilitative capacity due to genetic variation. It is important to identify these differences to inform landscape restoration and management. Objectives UAV hyperspectral, thermal, and lidar images might reveal genetic trait differences within a single tree species. This study tests and demonstrates: (1) UAV hyperspectral images in detecting differences among populations in canopy leaf area, water content, carbon, and nitrogen content as indicators of population-level productivity, fitness, adaptability, and biodiversity they can support, and (2) UAV hyperspectral-thermal-lidar fusion in detecting and classifying 16 populations sourced from different environments across Arizona, USA. Methods UAV hyperspectral, thermal, and lidar images were acquired from a common garden with 16 different Fremont cottonwood populations growing together. The UAV hyperspectral image was used to calculate spectral indices for canopy leaf area (LAI), canopy water content, nitrogen, carbon, and carbon-to-nitrogen ratio (C:N). The hyperspectral indices (EVI, LAI, PRI, MSI, NDWI, NDNI, NDLI, and C:N) were also examined with the UAV thermal image-derived canopy temperature data for potential correlations. Finally, all hyperspectral bands (n = 487 bands), thermal image-derived canopy temperature, and lidar-derived maximum canopy height estimates were stacked into a single image and then classified to detect 16 different populations of Fremont cottonwood using a random forest classification. Results The UAV hyperspectral indices and canopy temperature were significantly different among populations suggesting that the productivity, fitness, and adaptability of varying populations are significantly different. Many of the UAV hyperspectral indices were strongly correlated with canopy temperature. Populations with greater canopy cover, lower canopy temperature, and greater canopy height were well detected in the UAV hyperspectral-thermal-lidar fusion-based classification (producer’s accuracies of > 75%), whereas populations at low abundance were poorly classified (producer’s accuracies of Conclusions This study demonstrates the first application of UAV hyperspectral-thermal-lidar data fusion in phenotyping. The machine learning-based classification detects various populations within a single tree species. Future studies can use similar UAV data sources, derived variables, and data fusion to detect populations that have better fitness and adaptability to changing environments. Such populations can be strategically managed to sustain healthy landscapes that support diverse communities and species.

Why it matches plant phenotyping methodsUAVのハイパースペクトル・熱・LiDAR融合により、樹冠形態・生理形質を推定し、集団差を分類する手法を中心的に開発・実証しているため。

abstractThis study tests and demonstrates: (1) UAV hyperspectral images in detecting differences among populations in canopy leaf area, water content, carbon, and nitrogen content

Remote sensing reveals inter- and intraspecific variation in riparian cottonwood (Populus spp) response to drought ↗

O_LIUnderstanding how vegetation responds to drought is fundamental for understanding the broader implications of climate change on foundation tree species that support high biodiversity. Leveraging remote sensing technology provides a unique vantage point to explore these responses across and within species. C_LIO_LIWe investigated interspecific drought responses of two Populus species (P. fremontii, P. angustifolia) and their naturally occurring hybrids using leaf-level visible through shortwave infrared (VSWIR; 400-2500 nm) reflectance. As F1 hybrids backcross with either species, resulting in a range of backcross genotypes, we heretofore refer to the two species and their hybrids collectively as "cross types." We additionally explored intraspecific variation in P. fremontii drought response at the leaf and canopy levels using reflectance data and thermal unmanned aerial vehicle (UAV) imagery. We employed several analyses to assess genotype-by-environment (GxE) interactions concerning drought, including principal component analysis, support vector machine, and spectral similarity index. C_LIO_LIFive key findings emerged: (1) Spectra of all three cross types shifted significantly in response to drought. The magnitude of these reaction norms can be ranked from hybrids>P. fremontii>P. angustifolia, suggesting differential variation in response to drought; (2) Spectral space among cross types constricted under drought, indicating spectral--and phenotypic--convergence; (3) Experimentally, populations of P. fremontii from cool regions had different responses to drought than populations from warm regions, with source population mean annual temperature driving the magnitude and direction of change in VSWIR reflectance. (4) UAV thermal imagery revealed that watered, warm-adapted populations maintained lower leaf temperatures and retained more leaves than cool-adapted populations, but differences in leaf retention decreased when droughted. (5) These findings are consistent with patterns of local adaptation to drought and temperature stress, demonstrating the ability of leaf spectra to detect ecological and evolutionary responses to drought as a function of adaptation to different environments. C_LIO_LISynthesis. Leaf-level spectroscopy and canopy-level UAV thermal data captured inter- and intraspecific responses to water stress in cottonwoods, which are widely distributed in arid environments. This study demonstrates the potential of remote sensing to monitor and predict the impacts of drought on scales varying from leaves to landscapes. C_LI

Why it matches plant phenotyping methods葉面分光とUAV熱画像を用いて、植物の乾燥応答や葉温・葉保持を測定し、リモートセンシングによる表現型評価の有効性を実証しているため。

abstractWe additionally explored intraspecific variation in P. fremontii drought response at the leaf and canopy levels using reflectance data and thermal unmanned aerial vehicle (UAV) imagery.

Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models. ↗

Crop health assessment and early yield predictions are highly crucial under biotic stress conditions for crop management and market planning by farmers and policy planners. The objective of this study was, therefore, to assess the impact of different levels of wilt disease on the biophysical parameters of chickpea and developing machine learning (ML) models for early yield prediction. Field experiments were carried out over three years at the Indian Agricultural Research Institute research farm in New Delhi. Thermal and visible images were collected alongside the measurement of crop biophysical parameters, including leaf area index (LAI), photosynthesis, transpiration rate, stomatal conductance, relative leaf water content (RWC), membrane stability index (MSI), and NDVI, for 85 chickpea genotypes with varying levels of wilt resistance. ML models were developed for early yield prediction by combining visible and thermal image indices with biophysical parameters. The results showed that the canopy temperatures were directly correlated with increasing levels of wilt severity. Crop photosynthesis, stomatal conductance, transpiration, LAI, RWC, MSI, and NDVI dropped significantly with increasing levels of wilt severity. Yield reductions of 44-69% were observed in susceptible genotypes. Machine learning models were able to give accurate early yield predictions. The accuracy of the models increases as we move closer to the harvest. Ranking of the model's performances indicated that XGB is the best model to predict chickpea yield under wilt conditions. NDVI was identified as most important variable for yield prediction. The findings of the study quantified the impacts of wilt on important crop biophysical parameters and highlighted the suitability of ML models in early yield prediction under different levels of disease severity.

Why it matches plant phenotyping methods可視・熱画像と生物物理形質を統合した機械学習による、萎凋病条件下の遺伝子型別早期収量予測を開発・評価しており、形質推定手法が中心である。

abstractML models were developed for early yield prediction by combining visible and thermal image indices with biophysical parameters.

Potential and Limitations of Computer Vision for Crop Water Stress Detection in Irrigation Scheduling ↗

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.

Modelling crop traits and fluxes under multiple abiotic stressors ↗

Agriculture is the largest consumer of freshwater, accounting for approximately 70% of the total global usage. As the human population continues to grow, demand for water will be exacerbated by a changing climate and shifting temperature and precipitation regimes. Dynamically modelling crop physiological function will be crucial to optimising crop management strategies. In this study we synergise hyperspectral and thermal remotely-sensed data to model plant traits and water fluxes in spring wheat (Triticum aestivum) in growth chambers within a controlled environment experiment under water and/or nitrogen stress conditions. Results showed that plants which had first received nitrogen fertiliser and were subsequently droughted presented the lowest water fluxes, and the lowest leaf chlorophyll content and photosynthetic capacity (Vcmax) values. Partial least squares regression (PLSR) analysis of hyperspectral reflectance data revealed key wavelengths sensitive to six different plant traits and fluxes (including relative water content, leaf nitrogen, stomatal conductance), with strong correlations between measured and modelled values (R2 = 0.84; p

Why it matches plant phenotyping methodsハイパースペクトル・熱画像データとPLSRを用いて、複数の植物形質および水フラックスを推定する手法が研究の中心であり、測定値とモデル値の相関も評価しているため。

abstractwe synergise hyperspectral and thermal remotely-sensed data to model plant traits and water fluxes in spring wheat

Site-specific determination of plant water status in a steep sloped vineyard using a microclimatic monitoring system in combination with a water balance model and UAV-based thermal and multispectral imagery ↗

Climate change poses fundamental challenges to viticulture, such as more frequent droughts in Central Europe. This development requires precise, site-specific methods to determine plant water status. Especially in steep sloped vineyards, the spatial variability of drought stress can be high and depends on different factors such as slope, aspect and soil characteristics. Most established methods for determining plant water status are destructive, labor-intensive, or provide point-in-time measurements, or e.g. non-destructive modeling approaches need to be well referenced. UAV campaigns using thermal and multispectral imagery, as well as in-field sensor networks, provide non-destructive solutions with high spatio-temporal resolution. This study aims to combine both solutions to measure the high spatial and temporal variability of drought stress in a steep sloped vineyard. The goal is to develop a continuous, cross-scale, and resource-efficient method that can be used directly for irrigation scheduling or as a reference method for cross-scale modeling approaches at high spatial resolution. During the growing season of 2022, UAV campaigns were conducted every two weeks to generate thermal and multispectral imagery over a vineyard of 1 ha in Saxony, Germany. The vineyard was divided into five management zones (MZ), which differ in terms of slope, aspect, soil characteristics and grape varieties. A monitoring system has been established in each management zone to continuously collect data on local climate, as well as soil and plant water properties. Simultaneously with the UAV campaigns, the water status and physiological stage of the vines were determined as reference measurements. Therefore, predawn leaf water potential (Ψpd) was measured using a Scholander pressure chamber. Based on the processed aerial images and the in-situ sensor-based measurements the Crop Water Stress Index (CWSI) was computed and then validated by comparing it’s values to in-field reference measurements such as soil water status and Ψpd. Weather and plant physiological in-situ measurements were also integrated into a grapevine water balance model to derive quantitative information on plant and soil water status. In-situ measurements of plant and soil water potentials correlated well with the results of the modeling approach. This was a good representation of the spatial heterogeneity of the vineyard, especially the differences in plant water availability between MZs. CWSI values from the UAV campaigns will be compared with the in-situ measurements in terms of spatial variability, and also temporal variability to reproduce drought and other seasonal events. The combination of sensor data, simulation modeling and UAV-based thermal and multispectral imagery offers great potential to provide site-specific information with high spatio-temporal resolution about the plant water status. In particular, the inclusion of UAV campaigns can help to optimize the implemented sensor network and minimize the number of in-situ reference measurements. However, this cross-scale method also depends on a large number of influencing factors that need to be considered and discussed in depth in order to allow a valid assessment of drought stress dynamics and to set thresholds for irrigation or other management measures.

Why it matches plant phenotyping methodsUAV熱・マルチスペクトル画像、センサーネットワーク、水収支モデルを統合してブドウの水分状態を推定する手法を開発し、圃場基準測定で検証している。植物表現型の取得・推定が研究の中心である。

abstractThe goal is to develop a continuous, cross-scale, and resource-efficient method that can be used directly for irrigation scheduling or as a reference method for cross-scale modeling approaches at high spatial resolution.

Evaluation of Crop Water Stress Using Drone Images and Numerical Weather Prediction Model Data ↗

Water conditions in soil are measured with soil moisture sensors such as tensiometer and time-domain reflectometry. However, installed soil moisture sensors may not fully represent the entire cultivation area due to factors such as topography, meteorological conditions, and irrigation systems.The purpose in this study is to identify spatial variations of crop growth and moisture conditions using drone images and weather data. The drone, equipped with multi-spectral, hyper-spectral, and infrared cameras, captured images, and precipitation information up to 3 days later was automatically collected from numerical weather prediction model. Thermal images of crops and soil responded immediately depending on the presence or absence of irrigation. In irrigated crops, leaf temperature decreased due to transpiration. The hyper-spectral images, including short-wave infrared wavelengths, proved sensitive to soil water conditions. However, reflectance-based water indices showed no immediate differences for crops unless soil moisture fell below the wilting point. There was a difference in crop growth depending on the level of irrigation, which was clearly revealed in the vegetation index. Crop growth was poor in areas where irrigation was low. When soil moisture sensor values decrease and no rainfall is expected in the near future, drone images can be utilized to identify specific areas experiencing crop moisture stress. This suggests the potential for drones to support irrigation decision-making.Acknowledgments: This research was funded by the Rural Development Administration, grant number RS-2022-RD009999.

Why it matches plant phenotyping methodsドローンのマルチスペクトル・ハイパースペクトル・熱画像を用いて、作物生育、水分状態、蒸散に伴う葉温、乾燥ストレスを空間的に推定する方法が研究の中心であり、単なる灌漑試験の routine 測定ではない。

abstractThe purpose in this study is to identify spatial variations of crop growth and moisture conditions using drone images and weather data.

Modelling crop productivity, water fluxes and yield in winter wheat from remotely-sensed drone data under differential sulphur, nitrogen and or sugar application. ↗

Understanding how crops contribute to carbon, water and nitrogen cycling under different fertiliser regimes will be crucial for improving ecosystem models and predicting future yields. Synthetic fertilisers hugely boost crop yields, but excessive application often leads to negative environmental impacts including increased nitrous oxide emissions (about c. 300x more potent than CO2). To maximise crop yields and optimise fertiliser and water application, rapid retrieval of plant traits and fluxes will be critical. Here, we explore the effectiveness of optical (trait-based) and thermal (flux-based) remotely-sensed data collected from ground-based and drone platforms for quantifying differences in plant physiological performance and overall yield in field-grown wheat under different nitrogen, sulphur and or sugar treatments.Research was undertaken at a winter wheat variable nutrient field trial in North Yorkshire, UK during June, 2021. Across 24 treatment plots (3 plot replicates per treatment), leaf level hyperspectral reflectance data was obtained using a Spectral Evolution PSR+ 3500 Spectroradiometer which was paired with stomatal conductance (gsw) measurements (collected using a LI-COR LI-600 porometer) and photosynthetic capacity (Vcmax) measurements (collected using a Li-6800 portable infra-red gas analyser). Plant thermal images were captured using a handheld FLIR T650-C thermal imaging camera (640x480). Field-assessed leaves were destructively harvested for leaf chlorophyll and nitrogen content analysis. Drone flights were conducted using a DJI Matrice M200 with a MicaSense RedEdge-Mx multispectral imaging sensor (1456 x 1088) and a Parrot Analfi thermal drone (160 x 120) at a 10 m altitude above ground.Results show that plants fertilised with sulphur and nitrogen had the highest or equal-highest leaf chlorophyll values (c. 60-70 µg/cm2), followed by plants that only received nitrogen (c. 40-55 µg/cm2), with unfertilised controls having the lowest chlorophyll values (c. 15-20 µg/cm2). Sugar did not significantly affect leaf chlorophyll values but an interaction was detectable between sugar and fertiliser at the plot level (Two-way ANOVA, p

Why it matches plant phenotyping methodsドローン・地上の光学および熱画像データを用いて、植物の生理性能と収量を定量化する手法の有効性を検討しており、表現型取得が中心的である。

abstractwe explore the effectiveness of optical (trait-based) and thermal (flux-based) remotely-sensed data collected from ground-based and drone platforms for quantifying differences in plant physiological performance and overall yield

Potential of a Remotely Piloted Aircraft System with Multispectral and Thermal Sensors to Monitor Vineyard Characteristics for Precision Viticulture. ↗

Grapevines are subjected to many physiological and environmental stresses that influence their vegetative and reproductive growth. Water stress, cold damage, and pathogen attacks are highly relevant stresses in many grape-growing regions. Precision viticulture can be used to determine and manage the spatial variation in grapevine health within a single vineyard block. Newer technologies such as remotely piloted aircraft systems (RPASs) with remote sensing capabilities can enhance the application of precision viticulture. The use of remote sensing for vineyard variation detection has been extensively investigated; however, there is still a dearth of literature regarding its potential for detecting key stresses such as winter hardiness, water status, and virus infection. The main objective of this research is to examine the performance of modern remote sensing technologies to determine if their application can enhance vineyard management by providing evidence-based stress detection. To accomplish the objective, remotely sensed data such as the normalized difference vegetation index (NDVI) and thermal imaging from RPAS flights were measured from six commercial vineyards in Niagara, ON, along with the manual measurement of key viticultural data including vine water stress, cold stress, vine size, and virus titre. This study verified that the NDVI could be a useful metric to detect variation across vineyards for agriculturally important variables including vine size and soil moisture. The red-edge and near-infrared regions of the electromagnetic reflectance spectra could also have a potential application in detecting virus infection in vineyards.

Why it matches plant phenotyping methodsRPASのマルチスペクトル・熱画像を用いてブドウの水分ストレス、低温ストレス、サイズ、ウイルス感染を推定・検証することが研究の中心であり、植物形質・状態の取得手法を評価している。

abstractThe main objective of this research is to examine the performance of modern remote sensing technologies to determine if their application can enhance vineyard management by providing evidence-based stress detection.

Design and application of a multi-source sensor data fusion system based on a robot phenotype platform ↗

The compact, high-throughput phenotyping platform, characterized by its portability and small size, is well-suited for crop phenotyping across diverse environments. However, integrating multi-source sensors to achieve synchronized data acquisition and analysis poses significant challenges due to constraints in load capacity and available space. To address these issues, we developed a robotic platform specifically designed for phenotyping greenhouse strawberries. This system integrates an RGB-D camera, a multispectral camera, a thermal camera, and a LiDAR sensor, enabling the unified analysis of data from these sources. The platform accurately extracted key phenotypic parameters, including canopy width (R² = 0.9864, RMSE = 0.0185 m) and average temperature (R2 = 0.8056, RMSE = 0.1732 °C), with errors maintained below 5%. Furthermore, it effectively distinguished between different strawberry varieties, achieving an Adjusted Rand Index of 0.94, underscoring the value of detailed phenotyping in variety differentiation. Compared to conventional UGV-LiDAR systems, the proposed platform is more cost-effective, efficient, and scalable, with enhanced data consistency, making it a promising solution for agricultural applications.

Why it matches plant phenotyping methods複数センサーを統合したロボット植物フェノタイピング基盤を開発し、キャノピー幅と平均温度の抽出精度を検証しているため、フェノタイピング手法が中心である。

abstractwe developed a robotic platform specifically designed for phenotyping greenhouse strawberries.

Morphophysiological Plant Phenotyping for the Development of Plant Breeding Under Drought and Heat Conditions: A Practical Approach ↗

ABSTRACT Currently, the breeding programs focus their efforts on identifying and developing tolerant genotypes to adverse conditions, such as drought and high temperatures. In this context, the physiological approach, which involves phenotyping several traits, is useful for breeding programs. Leaf photosynthetic traits have become one of the main objectives to be evaluated for breeders due to their relationship with improving grain yield and biomass production. Gas exchange ( Ge ) and chlorophyll “a” fluorescence ( Chf ) are the main tools to characterize the photosynthetic activity in real time at the leaf level. Consequently, several association studies using proximal and nonproximal sensing (e.g., RGB, thermography) have been developed. However, for the correct application of this breeding approach, it is essential to have a basic knowledge of both the physiological principles involved in the readings and the limitations of phenotyping due to the characteristics of the devices available on the market. This revision also covers other traits, such as the morphological and anatomical characteristics of leaves and roots, and the use of isotopes complementing Ge and Chf measurements.

Why it matches plant phenotyping methods植物フェノタイピング手法のレビューであり、光合成測定、蛍光、近接・非近接センシングの原理と限界を扱うため、方法論が中心です。

titleMorphophysiological Plant Phenotyping for the Development of Plant Breeding Under Drought and Heat Conditions: A Practical Approach

Exploring multi-features in UAV based optical and thermal infrared images to estimate disease severity of wheat powdery mildew ↗

Remote sensing based on unmanned aerial vehicle (UAV) is a non-destructive way for wheat powdery mildew (WPM) detection in the field management and crop protection. However, WPM causes complex symptoms and impacts on wheat plants, such as reducing pigment, losing biomass and hindering normal growth. There are great challenges on UAV-based estimation on the disease index (DI) of WPM stress with such kinds of different symptoms during the infected status. Thus, this study aimed to explore the potential multi-features in the UAV-based optical and thermal infrared information to indicate the WPM impact and estimate DI. UAV multispectral and thermal imagery data were acquired continuously in the field during the early, middle, and late infection stages after artificially inoculated with fungal pathogens at the Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Xinxiang, China in 2022. The multi-features between healthy and infected plots were analyzed, mainly including (i) spectral reflectance calculated by mean (labeled as CS) and percentile methods (labeled as LS) from all pixels; (ii) vegetation indices (VIs) constructed from CS and LS; (iii) three-band texture combination indices (TTCI); and (iv) canopy temperature (CT). The optimal variables for DI estimation were determined by the Pearson correlation analysis and a recursive feature elimination algorithm. Multiple linear regression was used to construct DI estimation models based on single and fused types features. Results showed spectral features calculated by LS were more suitable for detecting WPM impacts than CS because more marked differences were observed. Only normalized difference red edge index, plant senescence reflectance index, and Meris terrestrial chlorophyll index could consistently capture changes induced by the disease in the middle to late stages. The proposed TTCI were able to distinguish infection changes compared to spectral and CT features as early as 9-30 days. The combination of spectral features calculated by LS, TTCI2, and CT provided the highest estimation accuracy of DI, with 0.90 of coefficient of determination (R²) and 8.28 % of root mean square error (RMSE). Compared with CS, LS, TTCI2, LS + TTCI2, the R² was increased by 36 %, 27 %, 7 %, and 3 %, and the RMSE was decrease by 44 %, 39 %, 18 %, and 10 %, respectively. This study analyzed the ability of optical and thermal infrared information to detect the WPM impact and compared the potential of features in estimating DI, which provided a UAV-based remote sensing method for disease prevention and control in the field.

Why it matches plant phenotyping methodsUAVの光学・熱赤外画像からコムギうどんこ病の病勢指数を推定する特徴量選択とモデル構築が研究の中心であり、植物病害状態の定量的フェノタイピング手法を開発・評価している。

abstractThus, this study aimed to explore the potential multi-features in the UAV-based optical and thermal infrared information to indicate the WPM impact and estimate DI.

Accurate estimation of winter-wheat leaf water content using continuous wavelet transform-based hyperspectral combined with thermal infrared on a UAV platform ↗

Timely and accurate assessment of crop water status using unmanned aerial vehicle (UAV) imagery is helpful for precision irrigation and field management. The aim of this study is to investigate the application potential of continuous wavelet transform (CWT)-based hyperspectral combined with thermal infrared image data for the estimation of leaf water content (LWC) in winter wheat. This study evaluates the performance of convolutional neural networks (CNN) for feature extraction and long short-term memory (LSTM) networks for sequential data processing in LWC estimation. A UAV platform carrying hyperspectral and thermal infrared sensors was used to collect high spatial resolution images of winter wheat under different water treatments over two years. The LWC was collected simultaneously. The original (OR) and CWT-transformed canopy spectral and textural features, as well as canopy temperature indicators, were extracted from the UAV-based images. On this basis, the LWC estimation model was established using the CNN and LSTM model. The results showed that the combination of thermal features with spectral and texture features significantly improves model performance compared to models built on a single data. The CWT-transformed spectral features improved LWC estimation compared to the original spectrum, with the third scale (CWT3) yielding the best results. Moreover, the CWT-transformed texture at multi-decomposition scales proved to be effective for estimating LWC. Compared to other models, the LSTM model (T-STCWT₃-LSTM), built by thermal feature fusion with CWT3-based spectral and texture features, achieved the best LWC estimation results, with R² of 0.827 and 0.836, RMSE of 2.575% and 1.822%, and MAE of 2.041% and 1.434% for 2022 and 2023, respectively. In addition, the robustness of the T-STCWT₃-LSTM model was successfully verified at different growth stages. Overall, the CWT technique and multi-feature fusion approach provide a valuable technical reference for real-time crop water status monitoring, supporting improved precision irrigation practices and sustainable crop management.

Why it matches plant phenotyping methodsUAVのハイパースペクトル・熱赤外画像から冬コムギの葉水分含量を推定する特徴抽出とCNN/LSTMモデルを中心に、性能評価・異なる生育段階での頑健性検証を行っているため、植物フェノタイピング手法として含める。

abstractThe aim of this study is to investigate the application potential of continuous wavelet transform (CWT)-based hyperspectral combined with thermal infrared image data for the estimation of leaf water content (LWC) in winter wheat.

Performance of stacking machine learning and volume model for improving corn above ground biomass prediction ↗

The aboveground biomass (AGB) of crops is an essential metric for monitoring crop growth, making timely and accurate AGB forecasting critical for effective agricultural management. The introduction of Unmanned Aerial Vehicles (UAVs) and advanced sensor technologies has revolutionized traditional AGB prediction techniques. Currently, machine learning (ML) combined with UAV data are commonly utilized, along with the Vegetation Index Weighted Canopy Volume Model (CVMVI) for AGB prediction. Nevertheless, there is limited investigation into how these methods perform across different agricultural conditions. This study aims to fill this gap by creating specific methodologies for estimating corn AGB under diverse fertilization and irrigation treatments. We utilized LiDAR, multispectral (MS), thermal infrared (TIR), along with measured AGB and Leaf Area Index (LAI) data from various growth stages to develop a stacking ensemble learning model. This model effectively integrates data from multiple sources, resulting in a strong prediction performance with R² of 0.86, Mean Absolute Error (MAE) of 1.54 t/ha, and Root Mean Square Error (RMSE) of 2.06 t/ha. Meanwhile, the analysis of the accuracy of CVMVI revealed its efficacy during the early-stage when corn is short, with its predictive capability diminishing as AGB increases. Consequently, we recommend the CVMVI for early-stage AGB prediction, which can streamline data collection and computational efforts. In contrast, the ML approach, which benefits from data fusion, is more appropriate for predicting AGB during the mid to late growth stages. This study enhances AGB prediction accuracy and speed, providing critical understanding of regional AGB dynamics and supporting better agricultural decision-making.

Why it matches plant phenotyping methodsUAVのLiDAR・マルチスペクトル・熱赤外データを用いてトウモロコシの地上部バイオマスを推定する機械学習モデルと体積モデルを開発・比較し、精度を評価しているため、植物形質取得手法が研究の中心である。

abstractThis study aims to fill this gap by creating specific methodologies for estimating corn AGB under diverse fertilization and irrigation treatments.

Relating spatial turfgrass quality to actual evapotranspiration for precision golf course irrigation ↗

Golf courses are increasingly affected by water scarcity and climate change. An understanding of spatial variability of actual evapotranspiration (ETₐ) and turfgrass quality (TQ) site-specific management zones (SSMZ) is important for the implementation of precision turfgrass management. Therefore, the main objectives of this study were to quantify the relationship between remotely sensed TQ and ETₐ estimates and to evaluate the spatial variations of TQ and ETₐ at a golf course in Utah. Ground-based normalized difference vegetation index was collected using a TCM-500 sensor, and aerial multispectral and thermal imagery data were acquired from unpiloted aircraft systems (UAS) in 2021, 2022, and 2023. A remote sensing TQ-random forest (RF) model was developed using six datasets of UAS spectral indices and the RF algorithm. The spatial data were analyzed to determine the correlation between TQ and ETₐ estimates. The TQ and ETₐ SSMZ were created and integrated with irrigation heads on the golf course using the Thiessen polygons tool. Results demonstrated that TQ-RF model was accurate within a root mean square error of 0.05. The correlation between TQ-RF and ETₐ was stronger for fairways (R² = 0.74), tees (R² = 0.66), and roughs (R² = 0.75) as compared to greens (R² = 0.25) and the driving range (R² = 0.36) on July 20, 2022. Actual evapotranspiration SSMZ, in combination with TQ-RF SSMZ, is useful for irrigation scheduling, addressing the question of how much and where to irrigate. This study demonstrates the ability of TQ-RF and ETₐ SSMZ to identify spatial variation for the purpose of landscape irrigation management in semi-arid areas.

Why it matches plant phenotyping methodsUAS画像とRFモデルにより芝草品質という植物状態を推定する手法を開発・検証しており、表現型取得が研究の中心である。

abstractA remote sensing TQ-random forest (RF) model was developed using six datasets of UAS spectral indices and the RF algorithm.

Chapter Five - Use of artificial intelligence in soybean breeding and production ↗

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.

Optimizing Crop Production With Plant Phenomics Through High‐Throughput Phenotyping and AI in Controlled Environments ↗

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).

Drone-Based Sensing and Imaging for Fruit Crop Monitoring: A Review ↗

The global fruit production industry has been suffering from numerous problems in the yield, quality, and food safety context. The use of drone-based sensing and imaging technologies has emerged as a promising approach for monitoring fruit crops, enabling real-time assessment of crop health, growth, and development. Monitoring fruit crops helps identify areas of improvement and makes decisions based on data. This review focuses on the various sensor technologies utilized in drone-based fruit crop monitoring, including RGB, multispectral, hyperspectral, thermal, and LiDAR sensors. The applications of these sensors are discussed, including yield estimation and prediction, crop growth monitoring, disease detection and diagnosis, pest detection and management, nutrient deficiency detection, and water stress monitoring. The review highlights the advantages and limitations of each sensor technology, as well as the challenges associated with data processing and analysis. Case studies demonstrate the effectiveness of drone-based sensing and imaging in fruit crop monitoring, and future directions are discussed, including the integration of sensor technologies with other precision agriculture tools and the development of specialized sensors and cameras. Standardization and best practices are emphasized as crucial for the widespread adoption of drone-based sensing and imaging in fruit crop monitoring.

Why it matches plant phenotyping methods果樹の生育・収量・病害・水分ストレスなどの植物状態を対象に、ドローン搭載センサーと画像処理の技術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 focuses on the various sensor technologies utilized in drone-based fruit crop monitoring, including RGB, multispectral, hyperspectral, thermal, and LiDAR sensors.

A Crop Water Stress Index for Hazelnuts Using Low-Cost Infrared Thermometers ↗

Incorporating data-driven technologies into agriculture presents a promising approach to optimizing crop production, especially in regions dependent on irrigation, where escalating heat waves and droughts driven by climate change pose increasing challenges. Recent advancements in sensor technology have introduced diverse methods for assessing irrigation needs, including meteorological sensors for calculating reference evapotranspiration, belowground sensors for measuring plant available water, and plant sensors for direct water status measurements. Among these, infrared thermometry stands out as a non-destructive remote sensing method for monitoring transpiration, with significant potential for integration into drone- or satellite-based models. This study applies infrared thermometry to develop a crop water stress index (CWSI) model for European hazelnuts (Corylus avellana), a key crop in Oregon, the leading hazelnut-producing state in the United States. Utilizing low-cost, open-source infrared thermometers and data loggers, we aim to provide hazelnut farmers with a practical tool for improving irrigation efficiency and enhancing yields. The CWSI model was validated against plant water status metrics such as stem water potential and gas exchange measurements. Our results show that when stem water potential is below −6 bar, the CWSI remains under 0.2, indicating low plant stress, with corresponding leaf conductance rates ranging between 0.1 and 0.4 mol m2 s−1. Additionally, un-irrigated hazelnuts were stressed (CWSI > 0.2) from mid-July through the end of the season, while irrigated plants remained unstressed. The findings suggest that farmers can adopt a leaf conductance threshold of 0.2 mol m2 s−1 or a water potential threshold of −6 bar for irrigation management. This research introduces a new CWSI model for hazelnuts and highlights the potential of low-cost technology to improve agricultural monitoring and decision-making.

Why it matches plant phenotyping methods低コスト赤外線サーモメータでヘーゼルナッツの水ストレス指標を開発し、植物水分状態およびガス交換で検証しており、植物生理状態の取得手法が中心である。

abstractThis study applies infrared thermometry to develop a crop water stress index (CWSI) model for European hazelnuts (Corylus avellana)

Malt Barley Yield and Quality Response to Crop Water Stress Index ↗

Malt barley is a crucial irrigated crop in the semi-arid Western United States, where the states of Idaho, Colorado, Wyoming, and Utah account for 92% of the irrigated production acreage and 30% of total U.S. production. In this region, spring malt barley’s seasonal evapotranspiration ranges from 400 to 650 mm, and competition for limited water supplies, coupled with drought, is straining regional water resources. This study aimed to investigate the use of canopy temperature for deficit irrigation scheduling of malt barley. Specifically, the objectives were to use data-driven models to estimate well-watered (TLL) and non-transpiring (TUL) canopy temperatures, correlate the crop water stress index (CWSI) with malt barley yield and quality measures, and assess the applicability of CWSI for malt barley irrigation scheduling in a semi-arid climate. A 3-year field study was conducted with five irrigation treatments relative to estimated crop evapotranspiration (full, 75%, 50%, 25%, and no irrigation) and four replicates each. Continuous canopy temperature measurements and meteorological data were collected, and a feedforward neural network model was used to predict TLL, while a physical model was used to estimate TUL. The neural network model accurately predicted TLL, with a strong correlation (R2 = 0.99), a root mean square error of 0.89 °C, and a mean absolute error of 0.70 °C. Significant differences in calculated season-average CWSI were observed between the irrigation treatments, and relative evapotranspiration, malt barley relative yield, test weight, and plump kernels were negatively correlated with the season-average CWSI, while seed protein was positively correlated. The relationship between daily CWSI and fraction of available soil water was well described by an exponential decay function (R2 = 0.72). These results demonstrate the applicability of data-driven models for computing CWSI of irrigated spring malt barley in a semi-arid environment and their ability to assess plant water stress and predict crop yield and quality response from CWSI.

Why it matches plant phenotyping methodsキャノピー温度とCWSIを用いた植物水ストレス推定法が中心で、ニューラルネットワークおよび物理モデルの開発・検証と、収量・品質との関連評価を行っている。

abstractthe objectives were to use data-driven models to estimate well-watered (TLL) and non-transpiring (TUL) canopy temperatures, correlate the crop water stress index (CWSI) with malt barley yield and quality measures, and assess the applicability of CWSI for malt barley irrigation scheduling

Detection of a vascular wilt disease in potato (‘Blackleg’) based on UAV hyperspectral imagery: Can structural features from LiDAR or SfM improve plant-wise classification accuracy? ↗

Ensuring plant health is a key factor to maximize crop yield. Despite that, the current field scouting and disease monitoring approaches often rely on visual evaluations and are, therefore, subjective and time demanding. New methods to assist in disease detection and severity assessment are required to allow better crop management and higher throughput in field phenotyping studies. With this objective, techniques involving the use of multi- and hyperspectral imagery for retrieval of plant traits and assessment of general crop health status are increasingly being proposed as alternatives to conventional disease monitoring approaches. Conversely, research focusing on specific pathogens are still lacking in many cases, in particular studies investigating multi-source sensing approaches, which have the potential to improve retrieval/classification accuracy. In this study, hyperspectral imagery and point clouds obtained with LiDAR or through Structure from Motion algorithm (SfM) applied to high resolution RGB images were evaluated as possible alternatives to detect Blackleg (caused by bacteria of the genera Pectobacterium and Dickeya ) in potato. It was demonstrated that all the different datasets have potential to discriminate healthy from diseased plants. The combination of Vegetation Indices (VIs) derived from hyperspectral images with structural features from LiDAR resulted in the best validation results (Balanced Accuracy – BA = 0.915). Small improvements were also achieved by combining VIs with SfM features (BA = 0.876) in comparison to VIs alone (BA = 0.846). Evaluation of feature importance for classification models derived from the different datasets indicated that after structural features derived from LiDAR or RGB imagery were added as predictor variables the relative importance of VIs for the predictions decreased, in particular for VIs related to LAI or other traits describing canopy properties. Finally, analysis of false negatives and positives indicated some limitations to the predictive potential of the different datasets, with diseased and healthy plants eventually presenting atypical structural and spectral characteristics in comparison to those expected for their classes. Therefore, multi-source sensing, including additional modalities (e.g., thermal or fluorescence), might be required to further improve detection of pathogens with complex symptoms, as those affecting roots, tubers and stems.

Why it matches plant phenotyping methodsUAVハイパースペクトル、LiDAR、SfMによる植物単位の病害状態判別を開発・比較検証しており、植物フェノタイピング手法が中心である。

abstractNew methods to assist in disease detection and severity assessment are required to allow better crop management and higher throughput in field phenotyping studies.

A spatial machine-learning model for predicting crop water stress index for precision irrigation of vineyards ↗

Optimization of water inputs is possible through precision irrigation based on prescription maps. The crop water stress index (CWSI) is an indicator of spatial and dynamic changes in plant water status that can serve irrigation management decision-making. The driving hypothesis was that in-season CWSI maps based on combined static and spatial-dynamic variables could be used to delineate irrigation MZs. A primary incentive was to minimize thermal-imaging campaigns and to complement CWSI maps between campaigns with cost-effective multi-spectral imaging campaigns producing normalized difference vegetative index (NDVI) maps. A spatial machine-learning model based on a random-forest (RF) algorithm combined with spatial statistical methods was developed to predict the spatial and temporal variability in CWSI of single vines in a vineyard. Model criteria and objectives included the reduction of sample data and input variables to a minimum without impacting prediction accuracy, consideration of only variables readily available to farmers, and accounting for spatial location and spatial processes. The model was developed and tested on data from a ‘Cabernet Sauvignon’ vineyard in Israel over two years. Prediction of CWSI was driven by terrain parameters, slope, aspect and topographical wetness index, soil apparent electrical conductivity (ECa), and NDVI. Spatial models based on RF were found to support CWSI prediction. Adding a geospatial component significantly improved model performance and accuracy, particularly when raw data was represented as z-scores or when z-scores were used as weights. NDVI, followed by ECa, aspect, or slope, was the most important variable predicting CWSI in the non-spatial models. The stronger the variable importance of NDVI, the better the model performed. The weaker the effect of NDVI in predicting CWSI, the stronger the effect of terrain and soil variables. In the spatial models, based on z-transformed values or on weighted values, the most important variable in predicting CWSI was either NDVI or location. The model, based on a limited and readily accessible number of variables, can serve as the basis for user-friendly decision support tools for precision irrigation. Additional research is needed to evaluate alternative prediction variables and to account for case studies in more geographical locations to address overfitting specific input data. Socio-economic and cost-benefit considerations should be integrated to examine whether precision irrigation management based on such models has the desired effects on water consumption and yield.

Why it matches plant phenotyping methods単一ブドウ樹の水分状態（CWSI）を推定する空間機械学習モデルを開発・検証しており、植物表現型の取得・推定手法が研究の中心である。

abstractA spatial machine-learning model based on a random-forest (RF) algorithm combined with spatial statistical methods was developed to predict the spatial and temporal variability in CWSI of single vines in a vineyard.

Detecting Xylella fastidiosa in a machine learning framework using Vcmax and leaf biochemistry quantified with airborne hyperspectral imagery ↗

The bacterium Xylella fastidiosa (Xf) is a plant pathogen that can block the flow of water and nutrients through the xylem. Xf symptoms may be confounded with generic water stress responses. Here, we assessed changes in biochemical, biophysical and photosynthetic traits, inferred using biophysical models, in Xf-affected almond orchards under rainfed and irrigated conditions on the Island of Majorca (Balearic Islands, Spain). Recent research has demonstrated the early detection of Xf-infections by monitoring spectral changes associated with pigments, canopy structural traits, fluorescence emission and transpiration. Nevertheless, there is still a need to make further progress in monitoring physiological processes (e.g., photosynthesis rate) to be able to efficiently detect when Xf-infection causes subtle spectral changes in photosynthesis. This paper explores the ability of parsimonious machine learning (ML) algorithms to detect Xf-infected trees operationally, when considering a proxy of photosynthetic capacity, namely the maximum carboxylation rate (Vcₘₐₓ), along with carbon-based constituents (CBC, including lignin), and leaf biochemical traits and tree-crown temperature (Tc) as an indicator of transpiration rates. The ML framework proposed here reduced the uncertainties associated with the extraction of reflectance spectra and temperature from individual tree crowns using high-resolution hyperspectral and thermal images. We showed that the relative importance of Vcₘₐₓ and leaf biochemical constituents (e.g., CBC) in the ML model for the detection of Xf at early stages of development were intrinsically associated with the water and nutritional conditions of almond trees. Overall, the functional traits that were most consistently altered by Xf-infection were Vcₘₐₓ, pigments, CBC, and Tc, and, particularly in rainfed-trees, anthocyanins, and Tc. The parsimonious ML model for Xf detection yielded accuracies exceeding 90% (kappa = 0.80). This study brings progress in the development of an operational ML framework for the detection of Xf outbreaks based on plant traits related to photosynthetic capacity, plant biochemistry and structural decay parameters.

Why it matches plant phenotyping methods植物の生理・生化学形質を航空ハイパースペクトル／熱画像から推定し、Xylella感染を検出する機械学習フレームワークの開発が中心であるため。

abstractThe ML framework proposed here reduced the uncertainties associated with the extraction of reflectance spectra and temperature from individual tree crowns using high-resolution hyperspectral and thermal images.

Predicting inbred parent synchrony at flowering for maize hybrid seed production by integrating crop growth model with whole genome prediction ↗

One of the challenges of maize hybrid seed production is to ensure synchrony at flowering of the two inbred parents of a hybrid, which depends on the specific parental combination and environmental conditions of the production field. Maize flowering can be simulated using a mechanistic crop growth model that converts thermal time accumulation to leaf numbers based on inbred specific physiological parameter values. Heretofore, these inbred specific physiological parameters need to be measured or assigned based on prior knowledge. Here, we leverage genetic, environmental and management data to predict physiological parameters and simulate flowering phenotypes by using whole genome prediction methodology combined with a crop growth model (CGM-WGP) as part of in-field in-season inbred growth development. We use two estimation sets that differ in terms of management and weather information to test the robustness of our approach. As part of our findings, we demonstrate the importance of defining informative priors to generate biologically meaningful predictions of unobserved physiological parameters. Our CGM-WGP infrastructure is efficient at simulating flowering phenotypes. An important practical application of our method is the ability to recommend differential planting intervals for male and female maize inbreds used in commercial seed production fields to synchronize male and female flowering. Core ideas Synchrony at flowering of maize inbred parents is crucial for optimal pollination and consequently seed yield. Integrating WGP with CGM can accurately predict physiological parameters and simulate maize flowering phenotypes. CGM-WGP infrastructure can be used to optimize field operations for large scale maize hybrid seed production.

Why it matches plant phenotyping methodsCGMと全ゲノム予測を統合し、トウモロコシの開花表現型をシミュレーション・予測する計算手法が研究の中心であり、植物形質推定法として該当する。

abstractwe leverage genetic, environmental and management data to predict physiological parameters and simulate flowering phenotypes by using whole genome prediction methodology combined with a crop growth model (CGM-WGP)

A Review of CNN Applications in Smart Agriculture Using Multimodal Data ↗

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.

An attention-based deep network for plant disease classification ↗

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method.

Why it matches plant phenotyping methods植物の葉画像から病変部位を捉えて病害状態を分類する深層学習手法の開発が中心であり、植物病害の表現型推定に該当する。

abstractA deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work.

Determination of Salt Stress by Plant Leaf Temperature and Thermal Imaging in Black Cumin Grown Under Different Salt Sources ↗

Thermal imaging has been used in recent years to determine salinity stress in certain crops. However, no information is available on this regarding black cumin (Nigella sativa L.). The main purpose of this study was to investigate the determinability of the salt stress on black cumin by thermal imaging. The leaf temperature values were obtained by acquiring the thermal images for black cumin grown under different irrigation water salinity levels (0.6 as control, 1.5, 2.5, and 5.0 dS/m) prepared for different salt sources (CaCl2, MgCl2, NaCl, Ca(NO3)2, MgSO,4 and Na2SO4). Plant leaf temperatures averaged over all irrigation water salinity levels did not show a significant difference among salt sources. On the other hand, plant leaf temperatures independent of the salt sources significantly increased with increasing salinity levels. The order for salinity levels in terms of their effect on the leaf temperature of black cumin was determined as 5.0 dS/m > 2.5 dS/m > 1.5 dS/m ≥ 0.6 dS/m. The highest leaf temperature was obtained under 5.0 dS/m salinity level with CaCl2, MgCl2, Ca(NO3)2, MgSO4, and Na2SO4 salt sources, but the values for MgSO4 and Na2SO4 salt sources were not significantly different from those under 2.5 dS/m salinity. In addition, a strong-negative relationship between plant water consumption and leaf temperature under CaCl2 and MgSO4 and a moderate-negative relationship under MgCl2 and NaCl salt sources were determined. The results of this study showed that the plant leaf temperature values obtained by thermal imaging reflected salt stress conditions, especially under high salinity levels.

Why it matches plant phenotyping methods熱画像から葉温を取得し、黒クミンの塩ストレス状態を判定できるかを主目的として評価しており、植物表現型取得法の実質的な適用研究である。

abstractThe main purpose of this study was to investigate the determinability of the salt stress on black cumin by thermal imaging.

Research Progress of Spectral Imaging Techniques in Plant Phenotype Studies ↗

Spectral imaging technique has been widely applied in plant phenotype analysis to improve plant trait selection and genetic advantages. The latest developments and applications of various optical imaging techniques in plant phenotypes were reviewed, and their advantages and applicability were compared. X-ray computed tomography (X-ray CT) and light detection and ranging (LiDAR) are more suitable for the three-dimensional reconstruction of plant surfaces, tissues, and organs. Chlorophyll fluorescence imaging (ChlF) and thermal imaging (TI) can be used to measure the physiological phenotype characteristics of plants. Specific symptoms caused by nutrient deficiency can be detected by hyperspectral and multispectral imaging, LiDAR, and ChlF. Future plant phenotype research based on spectral imaging can be more closely integrated with plant physiological processes. It can more effectively support the research in related disciplines, such as metabolomics and genomics, and focus on micro-scale activities, such as oxygen transport and intercellular chlorophyll transmission.

Why it matches plant phenotyping methods植物表現型に用いるスペクトル画像技術を体系的にレビューし、各手法の適用性や比較を扱っているため、方法論レビューとして中心的です。

abstractThe latest developments and applications of various optical imaging techniques in plant phenotypes were reviewed, and their advantages and applicability were compared.

Multi-source data fusion for estimating maize leaf area index over the whole growing season under different mulching and irrigation conditions ↗

Leaf area index (LAI) is a vital indicator to identify the crop growth condition and to infer crop yield and water consumption. The generality of empirical models for estimating LAI based on vegetation indices (VI) is often questioned due to their reduced sensitivity in dense vegetation cover or the influence of external disturbances. We aimed to investigate the potential of multi-source data fusion for improving the LAI estimation accuracy in various crop growth stages (e.g., high soil back ground disturbance in seedling stage, and dense crop coverage in mid-stage). The field experiments of two varieties of spring maize under three drip irrigation levels and two film mulching conditions were conducted in the Shiyang River Basin of Northwest China in 2021 and 2022. We collected multispectral images, thermal infrared images, hyperspectral reflectance data, photosynthetically active radiation (PAR) and LAI for each year. After data processing, we first evaluated the performance of the six VIs, i.e., normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), nonlinear vegetation index (NLI), anti-atmospheric vegetation index (ARVI) and the optimized NLI (VI0), for LAI estimation. We then utilized partial least squares regression (PLSR) on the 2022 dataset. Inputs included canopy temperature (Tc), fraction of canopy PAR interception (FPAR), the best VI, and their products (VI*FPAR, VI*Tc, FPAR*Tc and VI*FPAR*Tc). Ultimately, the performance of the PLSR was evaluated using the 2021 dataset. The six VIs could provide accurate estimation of LAI when LAI 4.0 (R²: 0–0.5, RMSE: 0.64–0.90 m² m⁻²), indicating VI alone failed to accurately estimate LAI at dense crop cover stage. VARI was the best among the six VIs, being soil-noise resistant but highly sensitive to leaf properties. PLSR showed higher LAI prediction accuracy than the VI-based models. Especially when LAI> 4.0, the R² of the PLSR increased by 0.23–0.46 and RMSE decreased by 0.08–0.29 m² m⁻² compared with the VI-based model. Besides, the fusion of multi-source data estimating method removed the soil background disturbance, showing high prediction accuracy at the seedling stage. These findings indicate that combining multi-source field data could reduce the saturation of dense vegetation cover and the soil noise, providing an efficient way to map the spatial and temporal distribution of LAI.

Why it matches plant phenotyping methodsマルチソースの画像・熱・スペクトル・PARデータを融合し、作物のLAIを推定する方法を開発・評価しており、植物形質取得が研究の中心である。

abstractWe aimed to investigate the potential of multi-source data fusion for improving the LAI estimation accuracy in various crop growth stages

Advancing lettuce physiological state recognition in IoT aeroponic systems: A meta-learning-driven data fusion approach ↗

Automatically identifying key physiological factors in plants, such as leaf relative humidity (LRH), chlorophyll content (Chl), and nitrogen levels (N), is vital for effective aeroponic management and improving growth, yield, quality, and sustainability. Meta-learning (MetaL) solutions utilize data fusion and intelligent processing, ensuring fast and consistent outcomes. This paper aims to develop a novel MetaL framework that leverages multimodal data sources—including spectral, thermal, and IoT environmental data—to enable real-time, non-invasive identification of LRH, Chl, and N content in aeroponically grown lettuce. The research examined various spectral reflectance indices (SRIs) and thermal indicators from plant characteristics. Model-based feature selection was implemented using back-propagation neural networks (BPNN), decision trees (DT), and gradient boosting machines (GBM) to identify key attributes and optimize hyperparameters. The experimental findings indicated that deploying GBM-based top variables as the foundational model, combined with BPNN as the meta-model, significantly improved the accuracy of analyzing the assigned factors. The prediction scores (R²) for LRH, Chl, and N increased to 0.875 (RMSE=0.879), 0.886 (RMSE=0.694), and 0.930 (RMSE=0.184), respectively, compared to applying BPNN-based features alone as a standalone model. Overall, the designed methodology contributes to more accurate predictions of plant physiological states, enabling proactive steps toward sustainable aeroponic agriculture.

Why it matches plant phenotyping methodsスペクトル・熱画像・IoTデータを融合し、レタスの生理状態を非破壊推定するMetaL手法の開発が中心であり、植物表現型の取得・推定方法に該当する。

abstractThis paper aims to develop a novel MetaL framework that leverages multimodal data sources—including spectral, thermal, and IoT environmental data—to enable real-time, non-invasive identification of LRH, Chl, and N content in aeroponically grown lettuce.

Proxy detection of wheat water stress from photochemical reflectance index and land surface temperature data ↗

In semi-arid and arid regions, crops face elevated atmospheric demands and endure prolonged periods of moderate to severe water scarcity. In this context, this study investigated the effectiveness of the photochemical reflectance index (PRI) and a normalized surface temperature index (Tₙₒᵣₘ) for proxy detection of the water stress of winter wheat crops. Furthermore, the potential of PRI for characterizing water, atmospheric or photo-inhibition stress, and wheat transpiration was assessed over experimental drip-irrigated crop fields in the Haouz plain, central Morocco. In practice, PRI observations were compared to agro-environmental variables such as Leaf Area Index (LAI), Available Water Content (AWC) at a root zone depth, net Radiation (Rₙ), Vapor Pressure Deficit (VPD) and the wheat transpiration derived from sap flows, lysimeters and a crop water balance model. Due to the strong relationship between PRI and LAI (R² = 0.91), another index named PRIⱼ was derived to correct for this effect. The PRIⱼ was found to be independent of structural effects related to LAI and significantly correlated with AWC (R² = 0.85). Using the PRIⱼ index, we can reflect the level of water stress experienced by the wheat field throughout the experiment with an R² of 0.69 for a FAO-56 water stress coefficient (Kₛ) of less than 1. Under dry conditions, for an AWC below 30%, the correlation between AWC and Tₙₒᵣₘ gives an R² of 0.29. However, comparison of PRIⱼ with the Tₙₒᵣₘ index showed that PRIⱼ is an early water stress index and provides information on the state of the vegetation cover at all stages of wheat development. The study's findings can have a significant impact on the use of the PRI as a water stress indicator, helping in the optimal irrigation of crops.

Why it matches plant phenotyping methodsPRIと地表面温度からコムギの水ストレスを推定する指標を開発・補正し、LAIや水ストレス係数などで検証しており、植物状態の取得手法が中心である。

abstractthis study investigated the effectiveness of the photochemical reflectance index (PRI) and a normalized surface temperature index (Tₙₒᵣₘ) for proxy detection of the water stress of winter wheat crops.

Phenotyping for the Real-time Detection of Plant Leaf Stress Using Infrared Thermal Imaging Sensors ↗

Plant stress in California has become a significant issue in recent years due to a combination of drought, malnutrition, and infections. There is an urgent need to develop a cost-effective, time-efficient, and reliable method to address this issue. This paper aims to develop a method of using infrared thermal imaging techniques to detect stress in plant leaves. The design of experiment (DOE) is divided into two phases. Phase one involved selecting three types of plants that represent significant stress factors in California - drought, infections, and malnutrition. The plants selected were raspberry, cherries, corn tomato, eggplant, and oleander. For each type of plant, three areas were chosen that each represented a stage of the plant’s stress: no stress (healthy), early stress, and fully stressed. Twenty points of surface thermal temperature were taken from each area of the plant leaf, and t-tests were conducted to calculate the p-value. The experiment indicates that thermal imaging techniques can be used for early detection in raspberry (drought and malnutrition) (p< 0.0001), cherries (drought) (p= 0.2996), corn (drought) (p< 0.0001), tomato (infections) (p< 0.0001), and eggplant (infections) (p< 0.0001), oleander (infections) (p< 0.0001). Phase two focused on developing a method to monitor the progressive development of plant stress throughout the entire drought process. A corresponding thermal model was built to understand the stress mechanism better for management of irrigation scheduling and plant phenotyping. The plants chosen were gardenia, tomato, and cucumber. In addition to recognizing thermal patterns throughout the process, an image processor was also created by code to calculate the percentage of healthy versus diseased area of the plant. The immediate application for this research is that it offers an advanced, non-invasive method for early detection of various plant stressors. This provides an effective solution for farmers to combat climate change and reduce plant losses due to infections, promoting much more sustainable agriculture.

Why it matches plant phenotyping methods植物葉のストレス状態を赤外線熱画像で検出し、さらに画像処理で健全・罹病領域を定量化する手法の開発が主題であり、植物フェノタイピング手法が中心である。

abstractThis paper aims to develop a method of using infrared thermal imaging techniques to detect stress in plant leaves.

A multispectral camera in the VIS-NIR equipped with thermal imaging and environmental sensors for non invasive analysis in precision agriculture. ↗

Multispectral imaging (MSI) is a technique used to inspect materials properties in different domains, ranging from industrial to medical and cultural heritage and, recently, precision agriculture. Even though several MSI solutions are already commercially available, the research community is working to optimize multispectral cameras in terms of performance and cost. Systems for the agricultural field are usually very compact, combined with drones for large areas acquisition. In this work, we detail the implementation of an innovative, modular and low-cost solution of a multispectral camera based on three core camera systems in the optical (VIS-NIR) and thermal (LWIR) range. Multispectral imaging is performed with a rotating wheel of interchangeable band-pass filters. The system is also equipped with a set of environmental sensors to acquire CO 2 concentration values, light intensity, temperature, and relative humidity of the surrounding environment. The technology and the measurement protocol were experimentally validated in laboratory and in open field. Advantages with respect to the available MSI cameras mounted on UAV is the integrated imaging in both the reflectance and the thermal emissive band in a close-up imaging setup and the use of environmental sensors. From the multispectral stack the spectral signature of the plants can be obtained and various vegetation indices (e.g., NDVI, NDRE) can be calculated for investigating the health status of the plant, while thermography provide additional monitoring. Close-up multispectral imaging is expected to tackle the new challenges of precision agriculture by enabling the acquisition of high-quality dataset on single plants.

Why it matches plant phenotyping methods植物のマルチスペクトル・熱画像カメラと環境センサーを開発し、測定プロトコルを実験的に検証して、植物のスペクトル特性・植生指数・健康状態を取得する方法が中心である。

abstractIn this work, we detail the implementation of an innovative, modular and low-cost solution of a multispectral camera based on three core camera systems in the optical (VIS-NIR) and thermal (LWIR) range.

Estimation of common bean (cultivar BRS Estilo) crop evapotranspiration by remote sensing using different albedo sources ↗

Evapotranspiration (ET) estimation by remote sensing is an innovative and promising option, due to its low cost and operation. It is an important tool for estimating ET and can be used to support decision-making. The origin and quality of images are fundamental for quality of information, as low spatial and temporal resolution of satellites directly impacts these customers. In this context, the objective of this study was to estimate the evapotranspiration of common bean crop using the SAFER algorithm in three different sources of albedo. The study was carried out in a bean cultivation area irrigated by central pivot, located in Itaberaí-GO Brazil in 2021. Images from a MicaSense Altum multispectral and thermal camera coupled to a drone and albedo images from Landsat 8 and Sentinel 2A satellites were used for ETa estimation. The data were compared with ET met by FAO method, Embrapa and climatological water balance by statistical indices. The correlation with standard methods was satisfactory, especially with FAO, and in general, the MSE (mean square error) and MAE (mean absolute error) adopted values smaller than 0.4mm day-1. The confidence index ranges from 0.91 to 0.97. The comparison of the ET values calculated from the multispectral and thermal camera and the three ways of calculating the surface albedo was considered satisfactory. Thus, the adaptation adopted in the SAFER algorithm for obtaining the albedo was efficient. The use of multispectral and thermal camera images with SAFER is an efficient tool in estimating the evapotranspiration of bean crop, and is capable of replacing the use of orbital images, which are limited by meteorological conditions and imaging frequency.

Why it matches plant phenotyping methodsマルチスペクトル・熱画像とSAFERアルゴリズムを用いて豆作物の蒸発散量を推定し、FAO等の標準法と比較検証しているため、作物の生理状態を測定する手法が中心です。

abstractThe data were compared with ET met by FAO method, Embrapa and climatological water balance by statistical indices.

Thermographic Analysis of Green Wall and Green Roof Plant Types under Levels of Water Stress ↗

Urban green infrastructure (UGI) plays a vital role in mitigating climate change risks, including urban development-induced warming. The effective maintenance and monitoring of UGI are essential for detecting early signs of water stress and preventing potential fire hazards. Recent research shows that plants close their stomata under limited soil moisture availability, leading to an increase in leaf temperature. Multi-spectral cameras can detect thermal differentiation during periods of water stress and well-watered conditions. This paper examines the thermography of five characteristic green wall and green roof plant types (Pachysandra terminalis, Lonicera nit. Hohenheimer, Rubus tricolor, Liriope muscari Big Blue, and Hedera algeriensis Bellecour) under different levels of water stress compared to a well-watered reference group measured by thermal cameras. The experiment consists of a (1) pre-test experiment identifying the suitable number of days to create three different levels of water stress, and (2) the main experiment tested the suitability of thermal imaging with a drone to detect water stress in plants across three different dehydration stages. The thermal images were captured analyzed from three different types of green infrastructure. The method was suitable to detect temperature differences between plant types, between levels of water stress, and between GI types. The results show that leaf temperatures were approximately 1–3 °C warmer for water-stressed plants on the green walls, and around 3–6 °C warmer on the green roof compared to reference plants with differences among plant types. These insights are particularly relevant for UGI maintenance strategies and regulations, offering valuable information for sustainable urban planning.

Why it matches plant phenotyping methods熱画像・ドローンを用いて植物の水ストレスを検出する方法の適用性を実験的に評価しており、植物状態の取得・判定が研究の中心である。

abstractthe main experiment tested the suitability of thermal imaging with a drone to detect water stress in plants across three different dehydration stages.

Remote detection of water stress in cotton using a center pivot irrigation system-mounted sensor package. ↗

Much research has been invested in infrared temperature (IRT)-based methods for cotton (Gossypium hirsutism L.) water stress detection using in-field sensors, but adoption of these is low, perhaps due to logistical challenges. Alternatively, the Water Deficit Index (WDI) was developed for crop water stress assessment using remote sensors not embedded in the canopy. The objective of this research was to evaluate the performance of a sensor package-including modern IRT and normalized difference vegetation index (NDVI) sensors facing downward at 45˚, and a mini weather station-attached unintrusively to a center pivot irrigation system for detecting cotton water stress using WDI. Sensor packages were evaluated in a two-year field study that included four irrigation treatments (0, 30, 60, and 90% ET replacement) and in two production cotton fields. Overall, the tested system was effective at distinguishing crop water stress among irrigation rates. Comparison of the results to a ground-based station and simulations indicated that WDI overestimated water stress at the highest irrigation rate, but performed well otherwise. Accuracy of the system could be improved by measuring canopy coverage (Fc) from the same vantage point as the IRT and NDVI sensors (from the pivot, downward at a 45˚ angle).

Why it matches plant phenotyping methods綿花の水ストレスという植物状態を、センターピボット搭載センサーパッケージとWDIで検出・評価する方法が研究の中心であり、技術性能の比較検証も行っている。

abstractThe objective of this research was to evaluate the performance of a sensor package-including modern IRT and normalized difference vegetation index (NDVI) sensors facing downward at 45˚, and a mini weather station-attached unintrusively to a center pivot irrigation system for detecting cotton water stress using WDI.

Rapid monitoring of tea plants under cold stress based on UAV multi-sensor data ↗

In the context of climate change, extreme weather events, represented by frost injury, are increasingly having a negative impact on the growth of tea plants. This has brought huge losses to the tea industry. Traditionally, the freezing injury of tea plants in the field was assessed by vision. This is labor-intensive and subjective. In this research, multimodal remote sensing data from different periods of natural overwintering tea plantations were collected by using unmanned aerial vehicles (UAV) equipped with multispectral (MS), thermal infrared (TIR) and RGB sensors. And the physiological data of tea leaves on the same day were obtained to construct a tea cold injury score (TCIS). Then, a convolutional neural networks-gate recurrent unit (CNN-GRU) model was improved for estimating TCIS. To better compare the performance of CNN-GRU, a single GRU model and three classical machine learning models were also used for comparison. The study found that: (1) The multimodal data fusion was superior to the unimodal data. The best prediction results were achieved for the combined bimodal MS + RGB data (Rp² = 0.862, RMSEP = 0.138, RPD = 2.220); (2) The CNN-GRU hybrid model was superior to the other four baseline models. The best effect was achieved based on the multivariate input of MS + RGB (Rp² = 0.862) or MS + RGB + TIR (Rp² = 0.850); (3) The accuracy of the model after removing soil features was lower than that of the model without background removal. Therefore, the TCIS-CNN-GRU model combined with multi-source remote sensing data can objectively and accurately evaluate the cold injury phenotype of tea plants, making the CNN-GRU model more scientific and promising.

Why it matches plant phenotyping methodsUAVマルチセンサー画像から茶樹の凍害表現型を推定する取得・計算手法を開発し、複数モデルと比較検証しており、フェノタイピング手法が中心である。

abstractmultimodal remote sensing data from different periods of natural overwintering tea plantations were collected by using unmanned aerial vehicles (UAV) equipped with multispectral (MS), thermal infrared (TIR) and RGB sensors.

Spectrum imaging for phenotypic detection of greenhouse vegetables: A review ↗

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

Evaluating the utility of combining high resolution thermal, multispectral and 3D imagery from unmanned aerial vehicles to monitor water stress in vineyards ↗

PURPOSE: High resolution imagery from unmanned aerial vehicles (UAVs) has been established as an important source of information to perform precise irrigation practices, notably relevant for high value crops often present in semi-arid regions such as vineyards. Many studies have shown the utility of thermal infrared (TIR) sensors to estimate canopy temperature to inform on vine physiological status, while visible-near infrared (VNIR) imagery and 3D point clouds derived from red–green–blue (RGB) photogrammetry have also shown great promise to better monitor within-field canopy traits to support agronomic practices. Indeed, grapevines react to water stress through a series of physiological and growth responses, which may occur at different spatio-temporal scales. As such, this study aimed to evaluate the application of TIR, VNIR and RGB sensors onboard UAVs to track vine water stress over various phenological periods in an experimental vineyard imposed with three different irrigation regimes. METHODS: A total of twelve UAV overpasses were performed in 2022 and 2023 where in situ physiological proxies, such as stomatal conductance (gₛ), leaf (Ψₗₑₐf) and stem (Ψₛₜₑₘ) water potential, and canopy traits, such as LAI, were collected during each UAV overpass. Linear and non-linear models were trained and evaluated against in-situ measurements. RESULTS: Results revealed the importance of TIR variables to estimate physiological proxies (gₛ, Ψₗₑₐf, Ψₛₜₑₘ) while VNIR and 3D variables were critical to estimate LAI. Both VNIR and 3D variables were largely uncorrelated to water stress proxies and demonstrated less importance in the trained empirical models. However, models using all three variable types (TIR, VNIR, 3D) were consistently the most effective to track water stress, highlighting the advantage of combining vine characteristics related to physiology, structure and growth to monitor vegetation water status throughout the vine growth period. CONCLUSION: This study highlights the utility of combining such UAV-based variables to establish empirical models that correlated well with field-level water stress proxies, demonstrating large potential to support agronomic practices or even to be ingested in physically-based models to estimate vine water demand and transpiration.

Why it matches plant phenotyping methodsUAV搭載の熱・マルチスペクトル・3D画像を組み合わせ、ブドウの水ストレス、生理指標、LAIを推定する手法を評価・検証しており、表現型取得とモデル性能評価が研究の中心である。

abstractthis study aimed to evaluate the application of TIR, VNIR and RGB sensors onboard UAVs to track vine water stress over various phenological periods

Crop water stress index characterizes maize productivity under water and salt stress by using growth stage-specific non-water stress baselines ↗

The use of non-destructive, continuous, and rapid canopy temperature (Tc) indices for crop stress diagnosis is of significant importance for improving crop water productivity (WP). However, the comprehensive applicability of the crop water stress index (CWSI), grounded in Tc, in diagnosing both single and combined water and salt stress, as well as characterizing physiological and growth traits, remains inadequately explored. We aim to investigate the ability of CWSI to diagnose single and combined water and salt stress and to test whether a non-water stress baseline (NWSB) with or without growth stage and genotype differences influences CWSI to characterise maize leaf physiological and growth traits. Here, we measured the Tc using infrared radiation thermometers of two maize genotypes (XY335 and ZD958) under both single and combined water and salt stress over two growing seasons, compared the differences of NWSB in three growth stages, and established CWSI. Our analysis involved scrutinizing the differences in characterizing crop physiology and growth traits between CWSI calculated using NWSB with and without growth stage differentiations. Our findings indicated that Tc is modulated by an interplay of soil water content, VPD, and soil salinity. The NWSB exhibited variations with both growth stage (pₛₗₒₚₑ < 0.001) and genotype (pₛₗₒₚₑ or pᵢₙₜₑᵣcₑₚₜ < 0.01). The CWSI can diagnose single and combined water and salt stress suffered by maize. Under no stress, and single and combined water and salt stress, CWSI was significantly correlated with stomatal conductance (R² ≥ 0.31, p < 0.1) and net photosynthetic rate (R² ≥ 0.38, p < 0.1), rather than with hydraulic traits. The mean CWSI across the entire growth period closely correlated with leaf area index (LAI), canopy photosynthetically active radiation interception, biomass, yield, and evapotranspiration across varying treatments (R² ≥ 0.54, p < 0.1). Contrary to CWSI derived from NWSB without growth stage variations, utilizing CWSI with growth stage distinctions better characterized physiological traits, while the former was more suitable for delineating yield and WP. This research underscores the efficacy of CWSI for stress diagnosis and the evaluation of gas exchange and productivity in maize under both single and combined soil water-salt stress. This investigation significantly propels forward the implementation of crop-centric irrigation strategies aimed at optimizing water utilization efficiency.

Why it matches plant phenotyping methods赤外線によるキャノピー温度からCWSIを算出し、成長段階・遺伝子型別の基準線を比較して、水・塩ストレスおよび生理・成長形質の評価性能を検証しているため、フェノタイピング手法が中心である。

abstractWe aim to investigate the ability of CWSI to diagnose single and combined water and salt stress and to test whether a non-water stress baseline (NWSB) with or without growth stage and genotype differences influences CWSI to characterise maize leaf physiological and growth traits.

Detection of bean damage caused by Epilachna varivestis (Coleoptera: Coccinellidae) using drones, sensors, and image analysis. ↗

The Mexican bean beetle, Epilachna varivestis Mulsant (Coleoptera: Coccinellidae), is a key pest of beans, and early detection of bean damage is crucial for the timely management of E. varivestis. This study was conducted to assess the feasibility of using drones and optical sensors to quantify the damage to field beans caused by E. varivestis. A total of 14 bean plots with various levels of defoliation were surveyed aerially with drones equipped with red-blue-green (RGB), multispectral, and thermal sensors at 2 to 20 m above the canopy of bean plots. Ground-validation sampling included harvesting entire bean plots and photographing individual leaves. Image analyses were used to quantify the amount of defoliation by E. varivestis feeding on both aerial images and ground-validation photos. Linear regression analysis was used to determine the relationship of bean defoliation by E. varivestis measured on aerial images with that found by the ground validation. The results of this study showed a significant positive relationship between bean damages assessed by ground validation and those by using RGB images and a significant negative relationship between the actual amount of bean defoliation and Normalized Difference Vegetation Index values. Thermal signatures associated with bean defoliation were not detected. Spatial analyses using geostatistics revealed the spatial dependency of bean defoliation by E. varivestis. These results suggest the potential use of RGB and multispectral sensors at flight altitudes of 2 to 6 m above the canopy for early detection and site-specific management of E. varivestis, thereby enhancing management efficiency.

Why it matches plant phenotyping methodsドローン搭載センサーと画像解析により、植物の食害・葉面積減少（defoliation）を定量化し、地上検証と比較評価しているため、植物表現型取得手法が中心である。

abstractThis study was conducted to assess the feasibility of using drones and optical sensors to quantify the damage to field beans caused by E. varivestis.

Recent Methods for Evaluating Crop Water Stress Using AI Techniques: A Review. ↗

This study systematically reviews the integration of artificial intelligence (AI) and remote sensing technologies to address the issue of crop water stress caused by rising global temperatures and climate change; in particular, it evaluates the effectiveness of various non-destructive remote sensing platforms (RGB, thermal imaging, and hyperspectral imaging) and AI techniques (machine learning, deep learning, ensemble methods, GAN, and XAI) in monitoring and predicting crop water stress. The analysis focuses on variability in precipitation due to climate change and explores how these technologies can be strategically combined under data-limited conditions to enhance agricultural productivity. Furthermore, this study is expected to contribute to improving sustainable agricultural practices and mitigating the negative impacts of climate change on crop yield and quality.

Why it matches plant phenotyping methods作物の水ストレスという植物状態を、RGB・熱・ハイパースペクトルリモートセンシングとAIで評価する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レビューが中心です。

abstractThis study systematically reviews the integration of artificial intelligence (AI) and remote sensing technologies

Detection of Bean Rust (Uromyces appendiculatus) Disease Under Field Conditions Using Thermal Imaging ↗

Among the factors causing yield losses in agricultural fields, plant diseases are known to be one of the most significant. For many years, pesticides have been used to combat these diseases. However, due to the unintended toxic effects of pesticides on non-target organisms in recent years, there have been restrictions on their usage. Therefore, there has been an increased interest in alternative methods to chemical control in combating plant diseases. Among these alternative methods, thermal imaging, widely used within the scope of precision agriculture practices, holds a significant position. This study aims to detect bean rust disease (Agent: Uromyces appendiculatus) at an early stage using thermal imaging methods. According to the obtained results, it has been determined that leaves infected with the pathogen have a temperature approximately 2 ºC lower than healthy leaves. Surface temperatures of healthy and infected leaves were measured at 60-minute intervals for three weeks. Throughout this three-week period, it was observed that the average daily temperatures of infected leaves and healthy leaves were below ambient temperatures. Thermal imaging is considered to play a crucial role in the potential early detection of plant diseases.

Why it matches plant phenotyping methods熱画像を用いて感染葉と健全葉の温度差から植物病害を早期検出する方法を評価しており、植物状態の取得・判定が研究の中心です。

abstractThis study aims to detect bean rust disease (Agent: Uromyces appendiculatus) at an early stage using thermal imaging methods.

Fruit Water Stress Index of Apple Measured by Means of Temperature-Annotated 3D Point Cloud. ↗

In applied ecophysiological studies related to global warming and water scarcity, the water status of fruit is of increasing importance in the context of fresh food production. In the present work, a fruit water stress index ( FWSI ) is introduced for close analysis of the relationship between fruit and air temperatures. A sensor system consisting of light detection and ranging (LiDAR) sensor and thermal camera was employed to remotely analyze apple trees ( Malus x domestica Borkh. "Gala") by means of 3D point clouds. After geometric calibration of the sensor system, the temperature values were assigned in the corresponding 3D point cloud to reconstruct a thermal point cloud of the entire canopy. The annotated points belonging to the fruit were segmented, providing annotated fruit point clouds. Such estimated 3D distribution of fruit surface temperature ( T Est ) was highly correlated to manually recorded reference temperature ( r 2 = 0.93). As methodological innovation, based on T Est , the fruit water stress index ( FWSI Est ) was introduced, potentially providing more detailed information on the fruit compared to the crop water stress index of whole canopy obtained from established 2D thermal imaging. FWSI Est showed low error when compared to manual reference data. Considering in total 302 apples, FWSI Est increased during the season. Additional diel measurements on 50 apples, each at 6 measurements per day (in total 600 apples), were performed in the commercial harvest window. FWSI Est calculated with air temperature plus 5 °C appeared as diel hysteresis. Such diurnal changes of FWSI Est and those throughout fruit development provide a new ecophysiological tool aimed at 3D spatiotemporal fruit analysis and particularly more efficient, capturing more samples, insight in the specific requests of crop management.

Why it matches plant phenotyping methodsLiDAR・熱画像を幾何較正して温度注釈付き3D点群を構築し、果実表面温度と水ストレス指数を抽出・検証する方法が研究の中心である。

abstractA sensor system consisting of light detection and ranging (LiDAR) sensor and thermal camera was employed to remotely analyze apple trees

Field heterogeneity of soil texture controls leaf water potential spatial distribution in non-irrigated vineyards ↗

Abstract. Grapevine water status exhibits substantial variability even within a single vineyard. Understanding how edaphic, topographic and climatic conditions impact grapevine water status heterogeneity at the field scale, in non-irrigated vineyards, is essential for winemakers as it significantly influences wine quality. This study aimed to quantify the spatial distribution of grapevine leaf water potential (Ψleaf) within vineyards and to assess the influence of soil properties heterogeneity, topography and weather on this intra-field variability, in two non-irrigated vineyards during two viticultural seasons. By combining multilinearly vegetation indices from very-high spatial resolution multispectral, thermal and LiDAR imageries collected with unmanned aerial systems, we efficiently and robustly captured the spatial distribution of Ψleaf across both vineyards, at different dates. Our results demonstrated that in non-irrigated vineyards, the spatial distribution of Ψleaf was mainly governed by the within-vineyard soil hydraulic conductivity heterogeneity (R² up to 0.81), and was particularly marked when the evaporative demand and the soil water deficit increased, since the range of Ψleaf was greater, up to 0.73 MPa, in these conditions. However, topographic attributes (elevation and slope) were less related to grapevine Ψleaf variability. These findings show that soil properties within-field spatial distribution and weather conditions are the primary factors governing Ψleaf heterogeneity observed in non-irrigated vineyards, and their effects are concomitants.

Why it matches plant phenotyping methodsUAVのマルチスペクトル・熱・LiDAR画像を統合し、ブドウの葉水ポテンシャルという生理形質の空間分布を推定する手法を実質的に適用しているため、植物フェノタイピング手法の応用として含める。

abstractBy combining multilinearly vegetation indices from very-high spatial resolution multispectral, thermal and LiDAR imageries collected with unmanned aerial systems, we efficiently and robustly captured the spatial distribution of Ψleaf across both vineyards, at different dates.

New Approaches to Plant Pathogen Detection and Disease Diagnosis. ↗

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.

Direct and indirect technical guide for the early detection and management of fungal plant diseases. ↗

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.

Machine learning-based detection of frost events in wheat plants from infrared thermography ↗

Frost is an extreme temperature event that significantly impacts crops, particularly in Mediterranean-type climates. Current frost damage assessment techniques are heavily dependent on traditional temperature logger data and manual inspection of the crops after a suspected frost event, an approach that can be erroneous, labour-intensive and can lead to delayed management decisions. This study investigates a new technique to automatically detect two crucial stages of frost in on-field plants, i.e., exposure to freezing temperatures with and without ice formation (crystallisation and supercooling), using machine learning (ML) models trained on infrared thermal (IRT) images. Our dataset consists of IRT images of on-field wheat plants collected during the winter growing season. We demonstrate that our approach based on classification accuracy curves, can detect ice nucleation and freezing point temperatures with four ML models, extreme gradient boosting (XGBoost), random forest (RF), convolutional neural networks (CNN) and ResNet-50. We find that RF detects frost events, i.e., crystallisation for frost and supercooling for non-frost night from the accuracy curves with fastest classification time (approx. 17 ms per image). Our study provides important insights into a primary building block for the future development of automatic and real-time on-field plant frost monitoring systems.

Why it matches plant phenotyping methods赤外線熱画像と機械学習により、圃場のコムギ植物の凍結・過冷却状態を自動検出する手法を開発・比較しており、植物状態の取得が中心的です。

abstractThis study investigates a new technique to automatically detect two crucial stages of frost in on-field plants

High-throughput proximal ground crop phenotyping systems – A comprehensive review ↗

Current crop phenotyping mainly relies on manual measurements and visual inspection for data collection and crop assessment, which is labor-intensive, subjective, and inefficient. Hence, modern methods depend primarily on using sensors for phenotypic data collection to replace labor vision, developing algorithms for decision-making to replace human domain knowledge, and integrating autonomous phenotyping systems to improve efficiencies in the past decades. Despite the research progress in phenotyping, there is a lack of extensive review on this topic that will be useful to various stakeholders interested in this field. Therefore, this study was conducted to perform a comprehensive review of multiple methodologies and techniques used in high-throughput ground crop phenotyping systems. A Web of Science literature search was conducted with appropriate keywords for the recent past, and the research trends in this field were captured. The current review categorizes the progress of technology in terms of phenotyping platform, sensing, data processing, and system integration. Platforms have evolved from manual-based to autonomous. Manual-based platforms require workers for data collection, while autonomous platforms involve new technologies for navigation and data collection. Different sensing techniques are used for phenotyping data collection. This study mainly discusses the mainstream sensors, including RGB, multi/hyperspectral, thermal, stereo, and light detection and ranging, and concludes that multi-source sensors could provide more accurate phenotypic information. Algorithms are applied to collected data to extract useful phenotyping information at different scales (organ, individual plant, and community). Both machine learning (ML) and deep learning (DL) have been used for phenotyping information extraction, and the DL is gradually replacing ML due to its superior performance. A case study of integrated high-throughput proximal phenotyping robot was presented, showing how different sensors and navigation systems come together to achieve on-site and real-time measurements. Advancements in high-throughput proximal ground phenotyping systems through new information, communication, sensing, and autonomous technologies in agriculture are anticipated to be more integrated and efficient phenotyping. It is anticipated that autonomous robots would finally replace workers from laborious phenotyping work.

Why it matches plant phenotyping methods植物フェノタイピングの高スループット地上システムについて、プラットフォーム、センサー、データ処理、統合技術を体系的にレビューしており、方法論が中心である。

abstractTherefore, this study was conducted to perform a comprehensive review of multiple methodologies and techniques used in high-throughput ground crop phenotyping systems.

Edge compute algorithm enabled localized crop physiology sensing system for apple (Malus domestica Borkh.) crop water stress monitoring ↗

Elevated air temperature (>35 ℃) combined with intense solar radiation can cause heat stress related damage to apple (Malus domestica Borkh.) fruits (e.g., sunburn) and increase tree evapotranspiration demand. Current heat stress mitigation techniques (e.g., evaporative cooling and netting) may protect fruits but can skew the tree evapotranspiration rates, preventing precision under-tree irrigation. A detailed understanding of heat stress mitigation techniques on tree fruit water status is critical for optimized irrigation scheduling and reduced crop losses. This study aimed to quantify water stress using a localized edge-compute-enabled crop physiology sensing system (CPSS), developed previously for fruit heat stress management. The CPSS is capable of acquiring thermal infrared and RGB images of the scene at predetermined interval. In this study, the edge compute algorithm on CPSS was amended to estimate crop water stress index (CWSI). Developed algorithm was validated for its accuracy in predicting the crop water stress under four different heat stress mitigation techniques namely: conventional overhead sprinklers, foggers, netting, and combinations of foggers and netting. A CPSS node was deployed in each treatment for acquiring thermal infrared and RGB images. Acquired imagery data were used to estimate CWSI using the modified algorithm. The algorithm-estimated CWSI showed significant negative correlation with stem water potential measurements (r = -0.8, p < 0.01). The heat stress mitigation techniques had varying effects on sensitivity of estimated CWSI. Algorithm estimated CWSI was most sensitive to changes in water stress under fogging (r = 0.76) and least sensitive under neeting (r = -0.65). Overall, the use of real-time CWSI estimates in conjunction with heat stress monitoring could help improve precision irrigation management, enabling timely actuation of the under tree drip irrigation in apple orchards.

Why it matches plant phenotyping methods熱画像・RGB画像を用いて作物水分ストレス指標（CWSI）を推定するエッジ計算アルゴリズムとセンシングシステムを開発・改修し、茎水ポテンシャルで検証しており、植物生理状態の取得手法が研究の中心である。

abstractThis study aimed to quantify water stress using a localized edge-compute-enabled crop physiology sensing system (CPSS), developed previously for fruit heat stress management.

Integrated sensing and machine learning: Predicting saccharine and bioenergy feedstocks in sugarcane ↗

Predicting saccharine and bioenergy feedstocks in sugarcane enables growers and industries to determine the precise time and location for harvesting a better-quality product in the field. On one hand, Brix, Purity, and total recoverable sugars (TRS) can provide meaningful and reliable indicators of high-quality raw materials for first-generation (1 G) bioethanol. Conversely, Cellulose, Hemicellulose, and Lignin are the primary constituents of straw, directly contributing to second-generation (2G) bioethanol. However, analyzing these materials in the laboratory is a time-consuming and non-scalable task. Therefore, we propose an approach based on a multi-sensor framework, which includes multispectral unmanned aerial vehicle (UAV) imagery, thermal, photosynthetic active radiation (PAR), and chlorophyll fluorescence (ChlF) data, along with machine learning (ML) algorithms namely random forest (RF), multiple linear regression (MLR), decision tree (DT), and support vector machine (SVM), to develop a non-invasive and predictive framework for mapping sugarcane feedstocks. We collected samples of stalks and leaves/straw during the maturity stage while simultaneously collecting remote sensing data. The ML models played a crucial role in predicting 1 G (R² = 0.88–0.93) and 2 G (R² = 0.56–0.82) feedstocks. Notably, remote sensing data could serve as important features for the models, mainly through the spectral bands (Blue, Green, and RedEdge), DTemp and ChlF. Hence, the best features can be further implemented within a framework to predict sugarcane feedstocks. Our study marks a significant advancement in the industrial-scale prediction of sugarcane feedstocks, providing stakeholders with invaluable prescriptive harvesting strategies for both primary products and by-products.

Why it matches plant phenotyping methodsサトウキビの茎・葉由来の飼料成分を、複数センサーと機械学習で非侵襲的に推定・マッピングする方法を中心に開発しており、植物形質の取得・推定が主題である。

abstractwe propose an approach based on a multi-sensor framework, which includes multispectral unmanned aerial vehicle (UAV) imagery, thermal, photosynthetic active radiation (PAR), and chlorophyll fluorescence (ChlF) data, along with machine learning (ML) algorithms namely random forest (RF), multiple linear regression (MLR), decision tree (DT), and support vector machine (SVM), to develop a non-invasive and predictive framework for mapping sugarcane feedstocks.

An intelligent deep augmented model for detection of banana leaves diseases. ↗

One of the most popular fruits worldwide is the banana. Accurate identification and categorization of banana diseases is essential for maintaining global fruits security and stakeholder profitability. Four different types of banana leaves exist Healthy, Cordana, Sigatoka, and Pestalotiopsis. These types can be analyzed using four types of vision: RGB, night vision, infrared vision, and thermal vision. This paper presents an intelligent deep augmented learning model composed of VGG19 and passive aggressive classifier (PAC) to classify the four diseases types of bananas under each type of vision. Each vision consisted of 1600 images with a size of (224 × 224). The training-testing approach was used to evaluate the performance of the hybrid model on Kaggle dataset, which was justified by various methods and metrics. The proposed model achieved a remarkable mean accuracy rate of 99.16% for RGB vision, 98.02% for night vision, 96.05% for infrared vision, and 96.10% for thermal vision for training and testing data. Microscopy employed in this research as a validation tool. The microscopic examination of leaves confirmed the presence and extent of the disease, providing ground truth data to validate and refine the proposed model. RESEARCH HIGHLIGHTS: The model can be helpful for internet of things -based drones to identify the large scale of banana leaf-disease detection using drones for images acquisition. Proposed an intelligent deep augmented learning model composed of VGG19 and passive aggressive classifier (PAC) to classify the four diseases types of bananas under each type of vision. The model detected banana leaf disease with a 99.16% accuracy rate for RGB vision, 98.02% accuracy rate for night vision, 96.05% accuracy rate for infrared vision, and 96.10% accuracy rate for thermal vision The model will provide a facility for early disease detection which minimizes crop loss, enhances crop quality, timely decision making, cost saving, risk mitigation, technology adoption, and helps in increasing the yield.

Why it matches plant phenotyping methodsバナナ葉の病徴・病害状態を画像から分類する深層学習モデルを開発し、複数の撮像方式と顕微鏡による検証を含むため、植物病害フェノタイピング手法が中心である。

abstractThis paper presents an intelligent deep augmented learning model composed of VGG19 and passive aggressive classifier (PAC) to classify the four diseases types of bananas under each type of vision.

Field collaborative recognition method and experiment for thermal infrared imaging of damaged potatoes ↗

If damaged potatoes are detected promptly during harvest, the spread of potato decay in potato storage warehouses is reduced and timely loss control is achieved. Therefore, damaged potatoes must be identified and removed during harvest. A field collaborative recognition method for thermal infrared imaging of damaged potatoes is proposed in this work. An experimental device was developed to detect and identify three commonly damaged potatoes using this method. First, a heat transfer model for damaged potatoes based on the field synergy theory was established and heat transfer analysis was conducted. The reason for the temperature difference between the damaged and intact parts of potatoes was the difference in convective heat transfer intensity between the two and the hot air, as well as the thermal properties of the potato skin and flesh. Then, the surface temperature distribution of the damaged potato under various hot air inlet angles was obtained using finite element simulation tests. According to the imaging effect of the thermal image, the optimal inlet angle was determined to be 90° vertically. Finally, the operating parameters of the potato-screening device were optimized and analyzed. The optimal parameter combination obtained is as follows: The conveyor belt speed was 0.37 m/s, the hot air speed was 3.5 m/s, and the hot air temperature was 45 °C. Actual experiments were conducted on this device. The experimental results indicated that the accuracy, precision, recall, and F-score evaluation values of detecting damaged potatoes were 96%, 94.6%, 97.6%, and 0.961 respectively. The potato thermal infrared damage detection method can meet the technical requirements of grading detection during potato harvest.

Why it matches plant phenotyping methods熱赤外画像を用いてジャガイモの損傷状態を検出する装置・手法を開発し、実験で性能評価しており、植物状態の取得が中心的です。

abstractA field collaborative recognition method for thermal infrared imaging of damaged potatoes is proposed in this work.

High-throughput physiological phenotyping of crop evapotranspiration at the plot scale ↗

Platforms and instrumentation for Field High-Throughput Plant Phenotyping (FHTPP) are well developed to measure important traits for crop breeding and agronomic studies. However, the research has focused on morphological and spectral traits; and approaches to estimate major physiological processes such as evapotranspiration (ET) for small experimental plots are lacking. In this study, we put forward a new analytical framework to estimate plot-scale ET by integrating frequent phenotyping data (multispectral and thermal infrared images, canopy reflectance, and LiDAR point clouds) from a FHTPP system (known as NU-Spidercam), the weather data, a simplified two-source energy balance model, and reference ET and crop coefficient calculation. The new plot-scale ET method was tested on five field experiments involving maize and soybean crops over two growing seasons, with the different treatment levels of irrigation water. Estimated plot-scale ET was accumulated across the growing reason for each plot, and its association with grain yield was investigated with regression analysis. The result showed that plot-scale accumulated ET captured the seasonal trend of plot water use and clearly differentiated the irrigation treatments. Strong linear correlations were observed between plot-scale ET and grain yield, with R² values ranging from 0.35 to 0.93 (average R² = 0.71). Plot-scale ET appeared to be a more steady and stronger predictor of grain yield across the seasons than several other morphological and spectral traits including crop height, green pixel fraction, canopy temperature depression, and red-edge normalized difference vegetation index. High spatial and temporal resolution of the field phenotyping data, along with the new analytical framework reported, successfully estimated ET at small plot scale, which is difficult to achieve with other systems or methods. Our work of estimating ET at the plot-scale can be adopt to other ground-based platforms and drones, thus empowers physiologists, breeders, and agronomists for high-throughput phenotyping of water-use related traits and drought response evaluation.

Why it matches plant phenotyping methods圃場高スループット表現型データを統合した、作物プロット単位の蒸発散量推定手法を開発し、複数の圃場実験で検証している。水利用形質・干ばつ応答評価のための再利用可能な生理的表現型取得法が中心である。

abstractwe put forward a new analytical framework to estimate plot-scale ET by integrating frequent phenotyping data (multispectral and thermal infrared images, canopy reflectance, and LiDAR point clouds) from a FHTPP system

Multi-scale advanced approaches to high-throughput phenotyping in crop improvement ↗

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

Thermal imaging from UAS for estimating crop water status in a Merlot vineyard in semi-arid conditions ↗

Abstract Thermal remote sensing indicators of crop water status can help to optimize irrigation across time and space. The Crop Water Stress Index (CWSI), calculated from thermal data, has been widely used in irrigation management as it has a proven association with evapotranspiration ratios. However, different approaches can be used to calculate the CWSI. The aim of this study is to identify the most robust method for estimating the CWSI in a commercial Merlot vineyard using high-resolution thermal imaging from Unoccupied Aerial Systems (UAS). To that end, three different methods were used to estimate the CWSI: Jackson’s model (CWSIj), Wet Artificial Reference Surface (WARS) method (CWSIw), and the Bellvert approach (CWSIb). A simpler indicator calculated as the difference between canopy and air temperature (Tc–Ta) was the benchmark to beat. The water status of a vine cultivar with anisohydric behavior (Merlot) in a vineyard in central Spain was assessed for two years with different agroclimatic conditions. Canopy temperature (Tc) was obtained from UAS flights at 9:00 h and 12:00 h solar hour over eight days during the irrigation period (June–August), and from vines under five different irrigation treatments. Stem water potential (SWP), stomatal conductance (gs), and leaf temperature (TL) were recorded at the time of the flights and compared with the thermal indices (CWSIj, CWSIw, CWSIb) and the benchmark indicator (Tc–Ta). Results show that the simpler indicator of water stress, Tc–Ta, performed better at identifying varying levels of crop hydration than CWSIb or CWSIw at 12:00 h. Under conditions of extreme aridity, the latter indices were less accurate than the physically-based CWSIj at 12:00 h, which had the highest correlation with SWP (r = 0.84), followed by the benchmark index Tc–Ta (r = 0.70 at 12:00). Considering the current climatic trends towards aridification, the CWSIj emerges as a useful operational tool, with robust performance across days and times of day. These results are important for irrigation management and could contribute to improving water use efficiency in agriculture.

Why it matches plant phenotyping methodsUAS熱画像からブドウの水分状態を推定する複数の熱指標を比較・検証しており、植物生理状態の取得方法が研究の中心である。

abstractThe aim of this study is to identify the most robust method for estimating the CWSI in a commercial Merlot vineyard using high-resolution thermal imaging from Unoccupied Aerial Systems (UAS).

Development of a UAV-Based Multi-Sensor Deep Learning Model for Predicting Napa Cabbage Fresh Weight and Determining Optimal Harvest Time ↗

Accurate and timely prediction of Napa cabbage (Brissica rapa subsp. Perkinensis) fresh weight is crucial for optimizing harvest timing, crop management, and supply chain logistics, contributing to food security and price stabilization. Traditional manual sampling methods are labor-intensive and imprecise. This study addresses this challenge by developing a comprehensive (artificial intelligence) AI-powered model for predicting Napa cabbage fresh weight using unmanned aerial vehicle (UAV)-based multi-sensor data. High-resolution RGB, multispectral, and thermal infrared (TIR) imagery were collected over a Napa cabbage field throughout the 2020 growing season. Various vegetation indices, crop features (vegetation fraction, crop height model), and water stress indi-cators (CWSI) were extracted from the imagery. Three AI algorithms&mdash;deep neural network (DNN), support vector machine (SVM), and random forest (RF)&mdash;were trained and evaluated, with the DNN model consistently outperforming the others. The DNN model achieved the highest accuracy (R&sup2; = 0.86 for training, 0.82 for testing; root mean square error (RMSE) = 0.432 kg for training, 0.465 kg for testing) during the mid-to-late rosette growth stage (DAP 35-42), highlighting this period as crucial for fresh weight estimation due to stable leaf area and well-developed canopy structure. The model tended to underestimate the weight of Napa cabbages exceeding 5 kg, potentially due to limited samples and saturation effects of vegetation indices. However, the overall error rate was less than 5%, demonstrating the feasibility and effectiveness of this approach. Spatial analysis revealed that the model accurately captured the variability in Napa cabbage growth across different soil types and irrigation conditions, particularly reflecting the positive impact of drip irrigation on the sandy loam plot. Bias analysis indicated the DNN model's tendency to overestimate smaller Napa cabbages (2 kg), suggesting areas for future refinement. This study demonstrates the potential of UAV-based multi-sensor data and AI algorithms for accurate and non-invasive prediction of Napa cabbage fresh weight. The developed DNN model offers a promising tool for optimizing harvest timing, improving crop management practices, and en-hancing supply chain efficiency. Future research should focus on refining the model for specific weight ranges and diverse environmental conditions, as well as extending its application to other crops, to further advance precision agriculture and contribute to sustainable food production.

Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル・熱画像からキャベツの生体重を推定する手法を開発・評価しており、植物形質取得が研究の中心です。

abstractThis study addresses this challenge by developing a comprehensive (artificial intelligence) AI-powered model for predicting Napa cabbage fresh weight using unmanned aerial vehicle (UAV)-based multi-sensor data.

High-throughput physiological phenotyping of crop evapotranspiration at the plot scale ↗

Platforms and instrumentation for Field High-Throughput Plant Phenotyping (FHTPP) are well developed to measure important traits for crop breeding and agronomic studies. However, the research has focused on morphological and spectral traits; and approaches to estimate major physiological processes such as evapotranspiration (ET) for small experimental plots are lacking. In this study, we put forward a new analytical framework to estimate plot-scale ET by integrating frequent phenotyping data (multispectral and thermal infrared images, canopy reflectance, and LiDAR point clouds) from a FHTPP system (known as NU-Spidercam), the weather data, a simplified two-source energy balance model, and reference ET and crop coefficient calculation. The new plot-scale ET method was tested on five field experiments involving maize and soybean crops over two growing seasons, with the different treatment levels of irrigation water. Estimated plot-scale ET was accumulated across the growing reason for each plot, and its association with grain yield was investigated with regression analysis. The result showed that plot-scale accumulated ET captured the seasonal trend of plot water use and clearly differentiated the irrigation treatments. Strong linear correlations were observed between plot-scale ET and grain yield, with R2 values ranging from 0.35 to 0.93 (average R2 = 0.71). Plot-scale ET appeared to be a more steady and stronger predictor of grain yield across the seasons than several other morphological and spectral traits including crop height, green pixel fraction, canopy temperature depression, and red-edge normalized difference vegetation index. High spatial and temporal resolution of the field phenotyping data, along with the new analytical framework reported, successfully estimated ET at small plot scale, which is difficult to achieve with other systems or methods. Our work of estimating ET at the plot-scale can be adopt to other ground-based platforms and drones, thus empowers physiologists, breeders, and agronomists for high-throughput phenotyping of water-use related traits and drought response evaluation.

Why it matches plant phenotyping methods作物個体・プロットの蒸発散量という生理形質を推定する新しい解析フレームワークを開発し、複数の圃場実験で検証しており、フェノタイピング手法が中心である。

abstractwe put forward a new analytical framework to estimate plot-scale ET by integrating frequent phenotyping data

Recognition Method of Crop Disease Based on Image Fusion and Deep Learning Model ↗