Polyploidy can be a critical factor for explaining plant trait variation, niche diversification, or speciation. However, inferring ploidy from silica-dried or historical samples using chromosome counts or flow cytometry is not possible, and scaling up ploidy estimation to population-level fresh contemporary samples can be challenging as well. Thus, we present a new method for estimating ploidy levels directly from sequencing data using machine learning; the Polyploid Population Genomics Tool Kit (PPGTK). The machine-learning approach is advantageous as it relaxes the assumptions of previous probabilistic methods and provides per-sample probabilities, allowing investigators to evaluate uncertainty in their system of interest.. We demonstrate performance and accuracy of the method on simulated and empirical data. Simulations showed above 99% accuracy, even for low coverage data, as long reads were mappable to the reference genome. For empirical analyses, we used target enrichment data from blueberry wild relatives (Vaccinium sect. Cyanococcus) and whole-genome data from sweetpotato wild relatives (Ipomoea ser. Batatas). Ploidy was recovered with 99% accuracy across 70 Vaccinium individuals and 97% across 82 Ipomoea individuals. Analysis of many individuals is fast and requires only a multisample VCF, which is presumably generated for the research anyway, and some samples of known ploidy for training the classifier. The approach implemented in PPGTK is promising for collections-based research as well, enabling ploidy classification of historical specimens based on present-day observations. The method is implemented in a new Python package as a single command that can run on a conventional laptop.
Why it matches plant phenotyping methods植物の倍数性という状態をシーケンスデータから推定する機械学習手法を開発し、シミュレーションおよび実データで精度検証している。Pythonパッケージとして実装され、手法自体が中心である。
abstractwe present a new method for estimating ploidy levels directly from sequencing data using machine learning; the Polyploid Population Genomics Tool Kit (PPGTK).
Reproduction assets foundThe paper's ploidy-classification method is implemented in the authors' public Python package PPGTK, with a specific release (v0.1.0-alpha) used for the manuscript's analyses. The empirical VCF/metadata datasets are promised on Dryad only 'upon acceptance' and thus are not yet actionable.Code · public11
VCFs and metadata needed to reproduce Vaccinium sect. Cyanococcus and Ipomoea ser.
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Batatas analyses with PPGTK will be made available via Dryad upon acceptance. PPGTK is
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manuscript (https://github.com/tileylab/PPGTK/releases/tag/v0.1.0-alpha). PPGTK currently has
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preprint (which was not certified by peer review) is the auOpen asset ↗tileylab/PPGTK · v0.1.0-alphapdf-raw-page:11 lines:1-24Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Published18 Aug 2026TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 0 · OpenAlex ↗
Key message Our scalable two-step method estimates genotypic performance and genetic parameters from ranking data, producing reliable results comparable to quantitative analyses, enabling the integration of ranking data into breeding pipelines. Plant breeding research has chiefly relied on on-station experiments to evaluate varietal performance. Nevertheless, these trials often fail to represent on-farm growing conditions and farmers' preferences, potentially leading to poorly defined breeding targets. Recent work has demonstrated the potential of using on-farm verification trials combined with ranking data to support farmers in evaluating varieties while providing information that is representative of farmers' needs. Despite this potential, scalable methods for quantifying genetic differences and assessing the strength of the genetic signal in such trials remain limited. Here, we present a two-step procedure for analyzing trials based on ranking data, allowing the estimation of genetic parameters. The approach follows a common strategy in quantitative genetics, in which parameters are estimated from tables of genotypic means and their variances. In our framework, these estimates are obtained from Thurstonian and/or Plackett-Luce models, which treat rankings as observations of an underlying continuous trait associated with genotypic performance. Using simulated data, we showed that genotypic mean estimates derived from ranking analyses are linearly related to those obtained from quantitative trait analyses and that their variances adequately capture estimation uncertainty. We further demonstrated that incorporating these estimates and their variances into a second-step mixed-effects model yields accurate estimates of variance components. Analyses of groundnut, maize, and sweetpotato datasets confirmed the applicability of the approach and showed that ranking data can provide reliable estimates of genetic parameters. We argue that this framework can be scaled to obtain genotypic performance estimates from multi-trial on-farm data.
Why it matches plant phenotyping methods作物品種の遺伝型性能をランキングデータから推定する統計的方法そのものが研究の中心であり、育種に再利用可能な植物性能の推定手法を開発・検証している。
abstractHere, we present a two-step procedure for analyzing trials based on ranking data, allowing the estimation of genetic parameters.
Reproduction assets foundThe paper's Data availability statement provides public access to the observed groundnut and sweetpotato ranking/trial datasets (Zenodo 17112492), the authors' R functions and simulation workflow (GitHub hdorado/tricot-ranking-analysis, archived Zenodo 17942919), and supplementary material with methods and figures (ZenDataset · publicThe observed data for groundnut and sweetpotato used in this study are publicly available and can be accessed at: Global multi-crop agricultural trial data supported by citizen science, Zenodo [ https://doi.org/10.5281/zenodo.17112492 ]Open asset ↗Zenodo · 10.5281/zenodo.17112492lines:205-225Code · publicThe R functions and simulation workflow used in this study are publicly available at: - Source code available from: [ https://github.com/hdorado/tricot-ranking-analysis ]Open asset ↗GitHub · hdorado/tricot-ranking-analysislines:205-225Code · public- Archived software available from: [ https://doi.org/10.5281/zenodo.17942919 ] - License: [MIT License]Open asset ↗Zenodo · 10.5281/zenodo.17942919lines:205-225Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published10 Aug 2026International journal of biological macromoleculesCited by 0 · OpenAlex ↗
Starch, a key biological macromolecule accounting for 50-80% of dry weight in sweetpotato (Ipomoea batatas [L.] Lam.) storage roots, underpins food and industrial applications. However, sweetpotato starch characterization is limited by local-sectioning approaches that fail to capture the whole-root granule dynamics. Here, we established a new morphological observation system covering three key root regions based on two representative cultivars: Okinawa 100 (V100), and Yanshu25 (Y25). It was effective and convenient for in situ starch observation and analysis in sweetpotato roots. The whole-root in situ microscopy, starch physicochemical profiling, and transcriptomic correlation were integrated to resolve starch dynamics in Y25 and V100. We identified widespread simple starch granules (SSGs)-compound starch granule (CSG) coexistence across the whole root tissues, with Y25 exhibiting programmed CSG fragmentation driven by ARCs/FtsZ-mediated amyloplast envelope destabilization and concomitant AMY/BMY upregulation. Y25 had a higher amylose content and a higher proportion of medium/long chains, but the average degree of polymerization was slightly lower. Transcriptomic analyses revealed that the differentially expressed genes were annotated in pathways of carbohydrate metabolism, and the differentially expressed genes in the starch metabolism pathway were analyzed. Weighted gene co-expression network analysis further identified the hub genes from different modules and analyzed the co-expression networks. This work will not only advance the understanding of starch granule assembly and remodeling in sweetpotato, but also provide a robust methodological and transcriptome-guided framework for starch-focused germplasm screening and quality improvement.
Why it matches plant phenotyping methodsサツマイモ根全体のデンプン粒形態・動態を観察する新しい形態観察システムを構築し、その有効性を示しており、表現型取得法が研究の中心である。
abstractHere, we established a new morphological observation system covering three key root regions based on two representative cultivars: Okinawa 100 (V100), and Yanshu25 (Y25).
Sweet potato virus disease (SPVD) is one of the most destructive diseases affecting sweet potato production worldwide, causing severe yield losses and posing a significant threat to food security. Vision-based intelligent diagnosis has emerged as a promising solution for large-scale SPVD monitoring due to its low cost and scalability. However, existing publicly available datasets for SPVD are extremely limited and typically focus on a single task, such as disease classification or lesion segmentation, under constrained imaging conditions. This lack of comprehensive, task-oriented datasets significantly restricts the development, evaluation, and fair comparison of advanced computer vision methods for SPVD analysis. In this study, we present SPVD-Field, a task-oriented multi-task visual dataset suite composed of two independently collected sub-datasets optimized for different computer vision tasks. Rather than constructing a single homogeneous dataset, SPVD-Field is deliberately organized into two complementary task-oriented sub-datasets: SPVD-DET, designed for disease detection with bounding-box annotations, and SPVD-SEG, designed for fine-grained lesion segmentation with pixel-level masks. The two sub-datasets were independently collected using different acquisition protocols optimized for their respective tasks, while sharing a unified semantic definition of SPVD symptoms, crop growth stages, and field environments. SPVD-Field captures substantial real-world variability in imaging scale, viewpoint, illumination, background complexity, and symptom manifestation, reflecting the inherent challenges of fieldbased disease diagnosis. We provide detailed documentation of data acquisition, annotation strategies, and quality control procedures, along with baseline benchmark results for both detection and segmentation tasks to demonstrate the usability and difficulty of the dataset. By offering a structured dataset suite rather than a single-task collection, SPVD-Field aims to support diverse research directions, including detection, segmentation, multi-task learning, and disease severity analysis, and to facilitate reproducible and comparable research in SPVD-related plant phenotyping.
Why it matches plant phenotyping methodsサツマイモの病徴を対象とする画像データセットで、検出・病斑セグメンテーション、データ取得・アノテーション・品質管理、ベンチマークを中心的に提供しており、植物病害状態の画像フェノタイピング手法・データ基盤に該当する。
abstractIn this study, we present SPVD-Field, a task-oriented multi-task visual dataset suite composed of two independently collected sub-datasets optimized for different computer vision tasks.
Reproduction assets foundThe paper's core asset is the SPVD-Field dataset (SPVD-DET detection images with bounding-box annotations and SPVD-SEG segmentation images with pixel-level masks), explicitly deposited in a public repository via the data availability statement with a DOI link.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://dx.doi.org/10.21227/hq1q-jp43 .Open asset ↗10.21227/hq1q-jp43lines:664-703Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Sweet potato is a nutritionally valuable crop that contributes to food security, owing to its storage roots rich in starch, sugars, and antioxidants, while requiring minimal cultivation inputs. Estimating its yield based on visible above-ground traits remains challenging due to weak and inconsistent correlations between shoot biomass and storage root development. Therefore, direct assessment of underground biomass is essential. In this study, we demonstrate the field application of ground penetrating radar (GPR) for non-destructive detection and yield estimation of sweet potato. GPR is a geophysical technique that typically transmits ultra high frequency radio waves into the soil and records reflections from subsurface objects. Electromagnetic wave simulations within the soil-root system revealed GPR signals that strongly correlate with root length, forming the basis for yield quantification. We developed an image-processing pipeline comprising static correction, gain adjustment, noise filtering, and hyperbola segmentation via the Hough transform to enable semi-automated storage root detection from GPR data. By integrating detection and quantification approaches, a linear regression model predicting sweet potato yield from GPR signals achieved moderate accuracy ( R 2 = 0.567, normalized RMSE 0.190). We established a non-destructive and low-labor approach for monitoring root systems, providing a foundation for rapid, scalable, and field-ready yield estimation in sweet potato and other root and tuber crops.
Why it matches plant phenotyping methodsGPRによる地下貯蔵根の検出・定量化と収量推定を中心に、信号処理および画像処理パイプラインを開発・評価しているため、植物フェノタイピング手法として収載する。
abstractWe developed an image-processing pipeline comprising static correction, gain adjustment, noise filtering, and hyperbola segmentation via the Hough transform to enable semi-automated storage root detection from GPR data.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe datasets analyzed during the current study consist of GPR line scans of the field at ALRC and list of sweet potato storage root weights. These data are available together with the analysis scripts on GitHub under open access. All data and scripts are the property of NARO and are distributed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). The repository can be accessed at: https://github.com/mtei1/GPRScript.Open asset ↗mtei1/GPRScripthtml-lines:240-264Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published22 Jun 2026Revista edUCA - Revista Multidisciplinar da Faculdade Católica PaulistaCited by 0 · OpenAlex ↗
The development of Low-Cost Phenotyping Platforms Supported by Generative Artificial Intelligence (AI) is part of a recently launched research initiative at Embrapa Vegetables in Brasília, Federal District, aimed at creating a National Platform for Adaptation to Climate Change Applied to Family Farming (Clima AF). Through Prompt Engineering and Command Chaining, this stage was designed for the visual assessment of physiological disorders in sweet potato (Ipomoea batatas) tuberous roots in the context of the Climate Emergency. The pipeline consists of four stages: 1 - Definition of an expert persona; 2 - Phenological contextualization and critical root filling period; 3 - Visual anatomical phenotyping; and 4 - Synthesis of the physiological disorders found, with a focus on heat stress. The methodology is available as open access following FAIR principles. The analysis is conducted using minimal information, such as photos that can be taken with everyday devices like smartphones and information about the harvest season. Because it is available as open access, it democratizes information and contributes to achieving climate justice for a socioeconomically vulnerable audience (family farmers).
Why it matches plant phenotyping methods生成AIとプロンプト連鎖を用い、スマートフォン画像からサツマイモ塊根の生理障害を視覚的に評価する低コスト表現型解析プラットフォームの開発であり、植物状態の取得・抽出法が中心である。
abstractThe development of Low-Cost Phenotyping Platforms Supported by Generative Artificial Intelligence (AI)
Efficient crop health monitoring is crucial for global food security. Supervised deep learning approaches are often impractical due to the scarcity of large, labeled datasets. To address this limitation, this study adapts EfficientAD, an unsupervised, label-free anomaly detection framework originally designed for industrial inspection, for agricultural imagery on small datasets. The method utilizes a Patch Description Network (PDN) for localized feature extraction, a student network for local anomalies, and an autoencoder for global structural constraints. Benchmarked against AnoGAN, Pix2Pix, InTra, and Teacher–Student models, the framework demonstrated superior performance on the MVTec AD, PlantVillage, Coffee Leaf, and a custom real-world Sweet Potato dataset. The model achieved perfect area under the receiver operating characteristic curve (AUROC) scores of up to 100% in categories like “Pongamia”, “Potato”, and “Coffee Leaf”. While image-level classification was exceptionally robust, pixel-level localization (AUPRO) proved sensitive to complex agricultural backgrounds. To overcome this, a background interference analysis was conducted using Background Removed (BGRM) and out-of-distribution Background Replaced-Green (BGRP-G) strategies on the custom dataset. Notably, the BGRP-G strategy remarkably improved the image-level AUROC from 88.9% to 99.5% and substantially boosted the pixel-level AUPRO from 47.1% to 61.9%, successfully preserving the boundary integrity of severe structural defects. Achieving millisecond-level latency without complex data augmentation, this adapted label-free framework offers a versatile, highly efficient solution for real-time crop health diagnostics on resource-constrained Edge AI devices.
Why it matches plant phenotyping methods農作物画像から異常・健康状態を抽出する深層学習手法を開発し、複数データセットで性能比較・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。
abstractthis study adapts EfficientAD, an unsupervised, label-free anomaly detection framework originally designed for industrial inspection, for agricultural imagery on small datasets.
Sweetpotato ( Ipomoea batatas (L.) Lam.) is a crucial crop for global food security. However, its sustainable production is hindered by low nutrient use efficiency. Reliable screening protocols that accurately identify nutrient-efficient germplasm of this crop across developmental stages are still lacking. To bridge this gap, we established a novel two-phase evaluation system integrating hydroponic seedling screening with multi-nutrient field validation. We conducted principal component and regression analyses of 35 germplasms lines under controlled deficiencies of nitrogen (N), phosphorus (P), and potassium (K). Five conserved seedling traits were identified, including leaf number per plant, shoot fresh weight, root fresh weight, shoot dry weight, and net photosynthetic rate (Pn). These traits consistently correlated with tolerance to N, P, or K deficiency, thereby supporting their utility as reliable early indicators of nutrient stress. Field validation further confirmed that storage root fresh and dry weight, nutrient content, accumulation, and use efficiency varied significantly among nutrient treatments and genotypes, serving as key indicators of field performance. This integrated approach successfully identified elite germplasm with specific nutrient use efficiency: XN1985-7 as a low-N-tolerant and N-efficient utilization genotype, XN17104-132 as low-K-tolerant and K-efficient utilization, XN2141-3 as low-P-tolerant and P-efficient utilization, and notably XN2153-5, which exhibited concurrent tolerance to low N, P, and K with broad-spectrum efficiency. Our integrated two-phase framework provides a scalable model for screening nutrient-efficient germplasm in root crops, thereby contributing to sustainable breeding programs.
Why it matches plant phenotyping methods栄養効率遺伝資源を評価するための二段階スクリーニング系を構築し、複数の形態・生理形質を初期指標として検証しているため、植物フェノタイピング手法が中心的です。
abstractReliable screening protocols that accurately identify nutrient-efficient germplasm of this crop across developmental stages are still lacking.
Sweetpotato is a crucial food crop globally, valued for both its economic significance and health benefits. However, the prevalence of sweetpotato virus diseases (SPVD) poses a serious threat to the industry, leading to reduced yields and economic losses for farmers. Efficient diagnostic techniques are essential for ensuring food security and consumer health. Traditional diagnostic methods are effective but suffer from complexity, time consumption, and high costs. To address these challenges, a novel real-time end-to-end detector called SPVD-DETR based on the Transformer architecture is proposed in this study. By utilizing the unmanned aerial vehicle (UAV) orthomosaic image, SPVD can be diagnosed in real-time at the field scale. First, aerial survey tasks are customized with automated drone tools to rapidly scan sweetpotato fields, generating high-resolution orthophotos and a stitched orthomosaic image for analysis. Then, the object detector is enhanced by incorporating efficient backbones and hybrid encoder modules such as cascaded group self-attention, attention-based scale fusion, and dynamic upsampling. Extensive ablation studies and comparative results show that SPVD-DETR achieves a good balance between real-time performance and accuracy. Next, the model is fine-tuned on the SPVD image tiles and achieves a detection accuracy of 31.3% mean average precision (mAP) with the fastest inference speed of 90 frames per second (FPS). Finally, the prediction results are mapped back to the orthomosaic image, estimating an overall SPVD incidence rate of 15% with a misdiagnosis rate of 14%. This study introduces a novel paradigm for detecting SPVD at the field scale, promoting automatic and intelligent plant disease detection for large-scale high-throughput phenotyping in precision agriculture.
Why it matches plant phenotyping methodsUAV画像からサツマイモウイルス病の発生・罹病状態を推定する検出手法を開発し、アブレーション、比較評価、精度・速度・誤診率を検証しており、植物表現型取得が中心である。
abstracta novel real-time end-to-end detector called SPVD-DETR based on the Transformer architecture is proposed in this study.
Limited annotated data often constrain accurate yield prediction in underrepresented crops. To address this challenge, we developed a cross-crop deep transfer learning (TL) framework that leverages potato (Solanum tuberosum L.) as the source domain to predict sweet potato (Ipomoea batatas L.) yield using multi-temporal uncrewed aerial vehicle (UAV)-based multispectral imagery. A hybrid convolutional–recurrent neural network (CNN–RNN–Attention) architecture was implemented with a robust parameter-based transfer strategy to ensure temporal alignment and feature-space consistency across crops. Cross-crop feature migration analysis showed that predictors capturing canopy vigor, structure, and soil–vegetation contrast exhibited the highest distributional similarity between potato and sweet potato. In comparison, pigment-sensitive and agronomic predictors were less transferable. These robustness patterns were reflected in model performance, as all architectures showed substantial improvement when moving from the minimal 3 predictor subset to the 5–7 predictor subsets, where the most transferable indices were introduced. The hybrid CNN–RNN–Attention model achieved peak accuracy (R2≈0.64 and RMSE ≈ 18%) using time-series data up to the tuberization stage with only 7 predictors. In contrast, convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and bidirectional long short-term memory (BiLSTM) baseline models required 11–13 predictors to achieve comparable performance and often showed reduced or unstable accuracy at higher dimensionality due to redundancy and domain-shift amplification. Two-way ANOVA further revealed that cover crop type significantly influenced yield, whereas nitrogen rate and the interaction term were not significant. Overall, this study demonstrates that combining robustness-aware feature design with hybrid deep TL model enables accurate, data-efficient, and physiologically interpretable yield prediction in sweet potato, offering a scalable pathway for applying TL in other underrepresented root and tuber crops.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物収量を推定する深層転移学習法を開発し、複数モデル・予測子構成で性能を比較検証しているため、植物表現型推定法が中心である。
abstractwe developed a cross-crop deep transfer learning (TL) framework that leverages potato (Solanum tuberosum L.) as the source domain to predict sweet potato (Ipomoea batatas L.) yield using multi-temporal uncrewed aerial vehicle (UAV)-based multispectral imagery.
Hyperspectral imaging (HSI) has recently emerged as a valuable tool for various agricultural applications. However, the widespread adoption of hyperspectral imaging is hindered due to the high cost and complexity of collecting and processing hyperspectral images. To address this gap, we introduce Agro-HSR,¹1Link to dataset: Agro-HSR. a large-scale RGB to hyperspectral image reconstruction dataset of sweet potatoes, specifically curated to promote easy access to hyperspectral images for the agricultural community. Agro-HSR comprises 1322 pairs of RGB and hyperspectral image cubes from 790 samples across three sweet potato varieties. For 141 of these samples, the agro-product quality attributes are included in the dataset. Each hyperspectral image cube covers 31 evenly spaced bands within the wavelength range of 400–1000 nm. Benchmarks for hyperspectral image reconstruction were conducted to demonstrate the importance and applicability of Agro-HSR. These benchmarks evaluated the ability to predict critical quality parameters in sweet potatoes, including Brix, dry matter, and firmness, from reconstructed hyperspectral images. Agro-HSR enhances the accessibility of hyperspectral images and promotes opportunities for cross-domain research in deep learning and agricultural science, addressing critical challenges in assessing the quality of agro-products.
Why it matches plant phenotyping methodsサツマイモのハイパースペクトル画像再構成データセットを構築し、再構成画像から品質形質を推定するベンチマークを実施しており、植物形質取得・推定手法が中心である。
abstractBenchmarks for hyperspectral image reconstruction were conducted to demonstrate the importance and applicability of Agro-HSR.
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
The main pathogen of sweet potato black rot, Ceratocystis fimbriata, induces the production of toxic secondary metabolites, leading to significant post-harvest economic losses. Establishing early rapid detection technologies is crucial for ensuring sweet potato food safety and reducing economic losses. This study systematically monitored the spectral image (HSI), electronic nose (E-nose) response signal, and total phenolic content (TPC) reference value of sweet potato samples after artificial inoculation with the pathogen, aiming to utilize TPC as a key biochemical indicator for the early prediction of disease progression. The experiment compared single-source and multi-source data fusion methods. Results showed that the CARS-PCA-MHA-CNN model(Parameters was reduced by 96.58%) based on a feature-level fusion strategy achieved the best predictive performance (R²=0.974, RMSEP=0.041, RPD=6.14). Compared with single-source data, the prediction accuracy was improved by 7.6% and 6.4%, respectively. Furthermore, the model's generalization ability was tested on an independent test set (unenhanced). This study proposes a reliable and non-destructive method for the early prediction of postharvest diseases in root and tuber crops, which has great application potential in the field of intelligent monitoring of agricultural products.
Why it matches plant phenotyping methods病原体接種後のサツマイモの病害進行を、HSI・電子鼻・TPCデータ融合とCNNで非破壊予測する方法が研究の中心であり、植物器官の病害状態を推定する実質的なフェノタイピング手法である。
abstractThe experiment compared single-source and multi-source data fusion methods.
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.
As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato ( Solanum tuberosum L.) and sweet potato ( Ipomoea batatas L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar's leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90-98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90-96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses.
Why it matches plant phenotyping methods植物葉のハイパースペクトル画像からストレス状態・重症度を抽出し、機械学習で分類・空間マッピングする方法が研究の中心であり、独立テストによる性能評価も行っている。
abstractA comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages.
• Leaf curl virus detection in sweetpotato using molecular methods is costly. • Laser 3D scans of sweetpotato plants were used to test this concept. • PointNet++ used 3D point clouds for swift healthy vs diseased classification. • Data augmentation and downsampling enhanced model performance. • Results proved that this method can detect leaf curl virus rapidly and accurately. Sweet potato leaf curl virus (SPLCV) is a systemic viral disease of sweetpotato plants causing significant yield losses and posing a major threat to sweetpotato production. To achieve effective disease management, prompt detection of SPLCV is necessary and crucial. Currently, SPLCV is detected using a variety of molecular techniques, including polymerase chain reaction, loop-mediated isothermal amplification, and enzyme-linked immunosorbent assay, which are laborious, expensive, time-intensive, and limited in practicality for use in rapid, in situ diagnostic applications. Therefore, there is a need to establish alternative, point-of-care techniques for SPLCV detection. The goal of this study was to investigate the potential of using 3D point cloud data and deep learning to detect SPLCV. In this study, 3D point cloud data derived from multispectral laser scanning of healthy and SPLCV-infected sweetpotato plants were modeled with the PointNet++ neural network to discriminate healthy versus diseased plants. Spatial coordinates (X, Y, Z) and color information (R, G, B) of sweetpotato leaf point cloud data were used to develop and improve the efficiency of the classification process. The collected 3D point cloud dataset was first enhanced using the filtering approach to reduce noise and multiple point cloud data augmentation methods to increase robustness, then downsampled to decrease the computational demand followed by randomly split as training and testing sets. Experiments were then conducted to fine-tune the hyperparameters of the PointNet++ algorithm. The results showed that the optimal hyperparameter configuration entailed the adoption of the multi-scale sampling and grouping (MSG) strategy, with the augmented point cloud down sampled to 2048 points and a batch size of 8, yielding a classification accuracy of 87.7 %. The findings of our study showed the feasibility of using 3D imaging and employing the deep learning model PointNet++ for non-destructive, rapid detection of SPLCV in sweetpotato.
Why it matches plant phenotyping methods3Dレーザー画像から植物の病徴状態（健全／感染）を抽出し、PointNet++による分類性能を検証すること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適格。
abstractThe goal of this study was to investigate the potential of using 3D point cloud data and deep learning to detect SPLCV.
Sweetpotato is a major root crop with high yield and nutritional benefits. However, existing methods for evaluating sugars level are inefficient, limiting the breeding and processing of high-quality varieties. This study utilized near-infrared spectroscopy (NIRS) coupled with machine learning algorithms to develop a high-throughput assay for fructose, glucose, sucrose, and maltose in sweetpotatoes across their raw, steamed, and baked states. Leveraging representative samples, characteristic spectral variables, and advanced learning algorithms, twelve optimal models were established for the four sugar indicators under three processing states. These models exhibited outstanding performance in calibration ( R 2 C : 0.941–0.984), cross-validation ( R 2 CV : 0.926–0.976), external validation ( R 2 V : 0.898–0.971), and the ratio of prediction to deviation (RPD: 5.83–10.3), confirming their robust predictive capacity. The findings suggest that these machine learning-enhanced NIRS models enable rapid, high-throughput analysis of sweetpotato sugars, significantly benefiting both breeding programs and food processing applications. • Machine learning enhances NIRS for high-throughput sweetpotato sugar phenotyping. • Robust models quantify sugars across raw, steamed, and baked sweetpotato states. • Optimized models ensure high accuracy and reliability for sugar content prediction. • Efficient NIRS reduces costs and time for postharvest quality assessment. • Insights aid sweetpotato breeding and consumer-oriented product development.
Why it matches plant phenotyping methodsサツマイモの糖含量という植物器官形質を対象に、NIRSと機械学習による高スループット定量法を開発・外部検証しており、表現型取得法が中心である。
abstractThis study utilized near-infrared spectroscopy (NIRS) coupled with machine learning algorithms to develop a high-throughput assay for fructose, glucose, sucrose, and maltose in sweetpotatoes across their raw, steamed, and baked states.
The potential for improving sweetpotato quality remains underutilized due to a lack of comprehensive quality data on germplasm resources. This study evaluated 296 core germplasms, revealing significant phenotypic diversity across 24 quality traits in both stem tips and roots. Landraces had higher sugar content in roots, while wild relatives showed increased total flavonoid and phenol contents. Accessions with red-orange flesh were rich in sugars and carotenoids, whereas those with purple flesh had higher dry matter, flavonoids, and phenols. The accessions were classified into three clusters: high sugars and carotenoids, high phenolic compounds, and high starch. A comprehensive quality scoring model identified SP286 and SP192 as superior for stem tips and roots, respectively. Near-infrared spectroscopy, combined with a random forest algorithm, enabled rapid screening of superior germplasm, achieving prediction accuracies of 97 % for stem tips and 98 % for roots. These findings offer valuable resources and high-throughput models for enhancing sweetpotato quality.
Why it matches plant phenotyping methods近赤外分光法とランダムフォレストによる品質形質の迅速推定・スクリーニングが明示され、植物器官の複数形質を高精度に予測する方法が研究の主要な貢献に含まれる。
abstractNear-infrared spectroscopy, combined with a random forest algorithm, enabled rapid screening of superior germplasm, achieving prediction accuracies of 97 % for stem tips and 98 % for roots.
China is the largest producer of sweetpotato in the world. The unreasonable application of fertilizer in actual production has led to the imbalance of sweetpotato source and sink. Reasonable use of fertilizer requires proper proportion of nitrogen (N) and potassium (K) to balance nutrients of shoots and roots to achieve optimal growth. The changes of critical N dilution curves of shoot, root and whole plant of sweetpotato were analyzed according to different K levels to quantify the effect of K levels on critical N concentration. In this study, two sweetpotato varieties (Yan25 and Shang19), two K application levels (K0 and K1) and five N application levels (N0-N4) were used to construct a dilution model for the critical N concentration of sweetpotato shoot, root and whole plant. Xinxiang was used for model validation. The critical N concentration dilution model of sweetpotato shoot is as follows: K0: Nc = 4.391DM⁻⁰.⁵⁰⁴; K1: Nc = 4.572DM⁻⁰.⁴¹⁸. The critical N concentration dilution model of sweetpotato root is as follows: K0: Nc = 0.93DM⁻⁰.²⁴; K1: Nc = 1.06DM⁻⁰.²⁰⁵. The critical N concentration dilution model of sweetpotato whole plant is as follows: K0: Nc = 3.44DM⁻⁰.⁴¹⁵; K1: Nc = 3.742DM⁻⁰.³⁴⁸. Application of K fertilizer increased the dilution curve of critical N concentration in all parts of sweetpotato. At the K0 level, the optimal N application rate of Yan25 and Shang19 was 120 kg N ha⁻¹ (N2), and at the K1 level, the optimal N application rate was 180 kg N ha⁻¹ (N3). We found that (i) K has greater ability to improve the N absorption of sweetpotato than its ability to increase the dry matter. Differences in critical N curves between different K levels could quantify the effect of K on critical N concentration of sweetpotato. (ii) Under low N conditions, applying K fertilizer will make N more deficient; Under high N conditions, applying K fertilizer will make N suitable and inhibit overgrowth of shoots. (iii) The critical N concentration dilution model based on the whole plant is more suitable for root crops such as sweetpotato and shows high practicability. This study identified changes in the critical N concentration of sweetpotato under different K application levels.
Why it matches plant phenotyping methodsサツマイモの臨界窒素濃度を推定する希釈モデルを構築し、別品種で検証しており、植物の栄養状態を定量化するモデルが研究の中心である。
abstracttwo sweetpotato varieties (Yan25 and Shang19), two K application levels (K0 and K1) and five N application levels (N0-N4) were used to construct a dilution model for the critical N concentration of sweetpotato shoot, root and whole plant.
In sweet potato and potato, sensory traits are critical for acceptance by consumers, growers, and traders, hence underpinning the success or failure of a new cultivar. A quick analytical method for the sensory traits could expedite the selection process in breeding programs. In this paper, the relationship between sensory panel and instrumental color plus texture features was evaluated. Results have shown a high correlation between the sensory panel and instrumental color in both sweet potato (up to r = 0.84) and potato ( r > 0.78), implying that imaging is a potential alternative to the sensory panel for color scoring. High correlations between sensory panel aroma and flavor with instrumental color were detected (up to r = 0.66), although the validity of these correlations needs to be tested. With instrumental color and texture parameters as predictors, low to moderate accuracy was detected in the machine learning models developed to predict sensory panel traits. Overall, the performance of the eXtreme Gradient Boosting (XGboost) was comparable to the radial-based support vector machine (NL-SVM) algorithm, and these could be used for the initial selection of genotypes for aromas and flavors ( r 2 = 0.64-0.72) and texture attributes like moisture or mealiness ( r 2 > 50). Among the chemical properties screened in sweet potato, only starch showed a moderate correlation with sensory features like mealiness ( r = 0.54) and instrumental color ( r = 0.65). From the results, we can conclude that the instrumental scores of color are equivalent to those scored by the sensory panel, and the former could be adopted for quick analysis. Further investigations may be required to understand the association between color and aroma or flavor.
Why it matches plant phenotyping methods育種選抜を目的に、画像由来の色・テクスチャ特徴と機械学習でサツマイモ・ジャガイモの感覚形質を推定し、官能評価との相関・予測精度を検証しているため、表現型取得・推定法が中心である。
abstractA quick analytical method for the sensory traits could expedite the selection process in breeding programs.
Meloidogyne spp. (root-knot nematodes [RKNs]) are a major threat to a wide range of agricultural crops worldwide. Breeding crops for RKN resistance is an effective management strategy, yet assaying large numbers of breeding lines requires laborious bioassays that are time-consuming and require experienced researchers. In these bioassays, quantifying nematode eggs through manual counting is considered the current standard for quantifying establishing resistance in plant genotypes. Counting RKN eggs is highly laborious, and even experienced researchers are subject to fatigue or misclassification, leading to potential errors in phenotyping. Here, we present three automated egg counting models that rely on machine learning and image analysis to quantify RKN eggs extracted from tobacco and sweet potato plants. The first method relied on convolutional neural networks trained using annotated images to identify eggs ( M. enterolobii R 2 = 0.899, M. incognita R 2 = 0.927, M. javanica R 2 = 0.886), whereas a second contour-based approach used image analysis to identify eggs from their morphological characteristics and did not rely on neural networks ( M. enterolobii R 2 = 0.977, M. incognita R 2 = 0.990, M. javanica R 2 = 0.924). A third hybrid model combined these approaches and was able to detect and count eggs nearly as well as human raters ( M. enterolobii R 2 = 0.985, M. incognita R 2 = 0.992, M. javanica R 2 = 0.983). These automated counting protocols have the potential to provide significant time and resource savings annually for breeders and nematologists and may be broadly applicable to other nematode species.
Why it matches plant phenotyping methods植物の抵抗性評価に用いる線虫卵数という表現型を、画像解析・機械学習で自動取得する手法を開発し、複数モデルを比較検証しているため、方法が研究の中心である。
abstractHere, we present three automated egg counting models that rely on machine learning and image analysis to quantify RKN eggs extracted from tobacco and sweet potato plants.
Recent advancements in artificial intelligence and big data analytics introduce new tools that can enhance the packing efficiency of sweetpotatoes (Ipomoea batatas) (SPs). In this study, we focused on the quantification of inventory as early in the packing process as possible to allow for effective storage planning, smarter inventory selection to fulfill orders, and ultimately reduce the need for refrigeration of excess packed SPs. We built and implemented two scanners to quantify phenotype distributions at different stages of the post-harvest pipeline. Testing and validation were conducted through a collaboration with an industry-partner's packing facility in North Carolina, gaining access to their packing methods, warehouse data, and resources. The first scanner imaged all SPs during the conveyance stage, immediately after they are washed but before they are sorted. The second scanner, positioned to view the top bins after harvest, scanned the top layer of bins on harvesting trucks as they entered the storage warehouse for receiving. We compared the output of our first scanner to the output of a commercial optical sorter under a controlled packing simulation, and then compared our two developed scanners against each other in an observational commercial packing operation. We evaluated millions of SPs, assessing length, width, length-to-width ratio (LW ratio), and weight. We computed a pairwise t-test for each phenotype across scanner pairs and evaluated the Cohen's d effect size to interpret our results. We observed no significant differences in the grade distributions across the scanners, except for the “Giant” weight class, which showed variation between the top bin and eliminator table scanners. In summary, both systems demonstrated promising outcomes, suggesting a potential enhancement in packing efficiency through the timely delivery of comprehensive inventory data.
Why it matches plant phenotyping methodsサツマイモの形態・重量形質を取得する2種類のスキャナを開発し、商用光学選別機および相互比較で検証しており、表現型取得法が研究の中心である。
abstractWe built and implemented two scanners to quantify phenotype distributions at different stages of the post-harvest pipeline.
Hyperspectral imaging (HSI) has become a key technology for non-invasive quality evaluation in various fields, offering detailed insights through spatial and spectral data. Despite its efficacy, the complexity and high cost of HSI systems have hindered their widespread adoption. This study addressed these challenges by exploring deep learning-based hyperspectral image reconstruction from RGB (Red, Green, Blue) images, particularly for agricultural products. Specifically, different hyperspectral reconstruction algorithms, such as Hyperspectral Convolutional Neural Network - Dense (HSCNN-D), High-Resolution Network (HRNET), and Multi-Scale Transformer Plus Plus (MST++), were compared to assess the dry matter content of sweet potatoes. Among the tested reconstruction methods, HRNET demonstrated superior performance, achieving the lowest mean relative absolute error (MRAE) of 0.07, root mean square error (RMSE) of 0.03, and the highest peak signal-to-noise ratio (PSNR) of 32.28 decibels (dB). Some key features were selected using the genetic algorithm (GA), and their importance was interpreted using explainable artificial intelligence (XAI). Partial least squares regression (PLSR) models were developed using the RGB, reconstructed, and ground truth (GT) data. The visual and spectra quality of these reconstructed methods was compared with GT data, and predicted maps were generated. The results revealed the prospect of deep learning-based hyperspectral image reconstruction as a cost-effective and efficient quality assessment tool for agricultural and biological applications.
Why it matches plant phenotyping methodsRGB画像からのハイパースペクトル再構成法を比較・評価し、サツマイモの乾物含量を推定する手法を開発・検証しており、植物形質取得が中心である。
abstractexploring deep learning-based hyperspectral image reconstruction from RGB (Red, Green, Blue) images
High-throughput phenotyping technologies successfully employed in plant breeding and precision agriculture could facilitate the screening process for developing consumer-preferred traits. The current study evaluated the potential of near infrared (NIR) spectroscopy to predict visual, aromatic, flavor, taste and texture traits of sweetpotatoes. The focus was to develop predicting models that would be cost-effective, efficient and high throughput. The roots of 207 sweetpotato genotypes from six agroecological zones of Uganda were collected from breeding trials. The spectra were collected in the wavelengths of 400 – 2500 nm at 2 nm intervals. Using the plsR package, the calibrations were carried out using external validation models. The best calibration equation between the sensory and texture reference values (10-point scales) and spectral data was identified based on the highest coefficient of determination (R 2 ) and smallest RMSE in calibration and validation. Of the visual traits, orange color intensity was well calibrated using NIR spectroscopy (r 2 val = 0.92, SEP = 0.92), and the model is sufficient for field application. Pumpkin aroma (r 2 val = 0.67, SEP = 0.33) was the highest predicted among the aromas. The pumpkin flavour model exhibited the highest coefficient of determination in the calibration (r 2 val = 0.52, SEP = 0.45) for the traits considered under flavor and taste. Different models for textural traits exhibited moderate calibration coefficients: mealiness (chalky/floury) by hand (r 2 val = 0.75; SEP = 1.31), crumbliness (r 2 val = 0.73, SEP = 1.21), moisture in mass (r 2 val = 0.73, SEP = 1.26), fracturability (r 2 val = 0.60, SEP = 1.52), hardness by hand (r 2 val = 0.61, SEP = 1.27) and dry matter (r 2 val = 0.70, SEP = 3.10). The range error ratio (RER) values were mostly >6.0. These models could be used for preliminary screening. The predictability of the traits varied among different modes of samples. Models could be improved with an increased range of reference values and/or exploiting the correlations between chemical compounds and sensory traits.
Why it matches plant phenotyping methodsサツマイモ根の感覚・食感形質をNIRスペクトルから推定する予測モデルを開発し、外部検証と性能評価を行っており、形質取得手法が研究の中心である。
abstractThe current study evaluated the potential of near infrared (NIR) spectroscopy to predict visual, aromatic, flavor, taste and texture traits of sweetpotatoes.
Abstract Background The sweet potato whitefly ( Bemisia tabaci ) is a globally important insect pest that damages crops through direct feeding and by transmitting viruses. Current B. tabaci management revolves around the use of insecticides, which are economically and environmentally costly. Host plant resistance is a sustainable option to reduce the impact of whiteflies, but progress in deploying resistance in crops has been slow. A major obstacle is the high cost and low throughput of screening plants for B. tabaci resistance. Oviposition rate is a popular metric for host plant resistance to B. tabaci because it does not require tracking insect development through the entire life cycle, but accurate quantification is still limited by difficulties in observing B. tabaci eggs, which are microscopic and translucent. The goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato. Results We tested a selective staining process originally developed for leafhopper eggs: submerging the leaves in McBryde’s stain (acetic acid, ethanol, 0.2% aqueous acid Fuchsin, water; 20:19:2:1) for three days, followed by clearing under heat and pressure for 15 min in clearing solution (LGW; lactic acid, glycerol, water; 17:20:23). With a less experienced individual counting the eggs, B. tabaci egg counts increased after staining across all five crops. With a more experienced counter, egg counts increased after staining on melons, tomatoes, and cowpeas. For all five crops, there was significantly greater agreement on egg counts across the two counting individuals after the staining process. The staining method worked particularly well on melon, where egg counts universally increased after staining for both counting individuals. Conclusions Selective staining aids visualization of B. tabaci eggs across multiple crop plants, particularly species where leaf morphological features obscure eggs, such as melons and tomatoes. This method is broadly applicable to research questions requiring accurate quantification of B. tabaci eggs, including phenotyping for B. tabaci resistance.
Why it matches plant phenotyping methods植物葉上のコナジラミ卵を染色して定量し、計数値と計数者間一致を改善する方法を評価しており、抵抗性フェノタイピングへの応用が明示された中心的な手法研究。
abstractThe goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's egg-count datasets and the R Markdown analysis code in a Dryad repository, which is a public, paper-specific asset directly reproducing the phenotyping measurements and analysis.Dataset · publicThe datasets generated and analyzed during this study, and an R Markdown document containing the code used to perform these analyses are available in a Dryad repository (DOI: doi: https://doi.org/10.5061/dryad.vmcvdnd1m ).Open asset ↗Dryad · 10.5061/dryad.vmcvdnd1mlines:138-163Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 7 Sept 2026
The lack of an efficient approach for quality evaluation of sweet potatoes significantly hinders progress in quality breeding. Therefore, this study aimed to establish a near-infrared spectroscopy (NIRS) assay for high-throughput analysis of sweet potato root quality, including total starch, amylose, amylopectin, the ratio of amylopectin to amylose, soluble sugar, crude protein, total flavonoid content, and total phenolic content. A total of 125 representative samples were utilized and a dual-optimized strategy (optimization of sample subset partitioning and variable selection) was applied to NIRS modeling. Eight optimal equations were developed with an excellent coefficient of determination for the calibration (R2C) at 0.95–0.99, cross-validation (R2CV) at 0.93–0.98, external validation (R2V) at 0.89–0.96, and the ratio of prediction to deviation (RPD) at 6.33–11.35. Overall, these NIRS models provide a feasible approach for high-throughput analysis of root quality and permit large-scale screening of elite germplasm in future sweet potato breeding.
Why it matches plant phenotyping methodsサツマイモ根の品質形質をNIRSとケモメトリクスでハイスループット推定する測定法を開発し、交差検証・外部検証まで実施しており、形質取得法が研究の中心である。
abstractthis study aimed to establish a near-infrared spectroscopy (NIRS) assay for high-throughput analysis of sweet potato root quality
Breeders have made important efforts to develop genotypes able to resist virus attacks in sweetpotato, a major crop providing food security and poverty alleviation to smallholder farmers in many regions of Sub-Saharan Africa, Asia and Latin America. However, a lack of accurate objective quantitative methods for this selection target in sweetpotato prevents a consistent and extensive assessment of large breeding populations. In this study, an approach to characterize and classify resistance in sweetpotato was established by assessing total yield loss and virus load after the infection of the three most common viruses (SPFMV, SPCSV, SPLCV). Twelve sweetpotato genotypes with contrasting reactions to virus infection were grown in the field under three different treatments: pre-infected by the three viruses, un-infected and protected from re-infection, and un-infected but exposed to natural infection. Virus loads were assessed using ELISA, (RT-)qPCR, and loop-mediated isothermal amplification (LAMP) methods, and also through multispectral reflectance and canopy temperature collected using an unmanned aerial vehicle. Total yield reduction compared to control and the arithmetic sum of (RT-)qPCR relative expression ratios were used to classify genotypes into four categories: resistant, tolerant, susceptible, and sensitives. Using 14 remote sensing predictors, machine learning algorithms were trained to classify all plots under the said categories. The study found that remotely sensed predictors were effective in discriminating the different virus response categories. The results suggest that using machine learning and remotely sensed data, further complemented by fast and sensitive LAMP assays to confirm results of predicted classifications could be used as a high throughput approach to support virus resistance phenotyping in sweetpotato breeding.
Why it matches plant phenotyping methodsウイルス感染に対する抵抗性・耐性を、UAVリモートセンシングと機械学習で大規模分類する方法を中心に開発・適用しており、植物病害状態の表現型取得が主題である。
abstracta lack of accurate objective quantitative methods for this selection target in sweetpotato prevents a consistent and extensive assessment of large breeding populations.
The sweet potato is an essential food and economic crop that is often threatened by the devastating sweet potato virus disease (SPVD), especially in developing countries. Traditional laboratory-based direct detection methods and field scouting are commonly used to rapidly detect SPVD. However, these molecular-based methods are costly and disruptive, while field scouting is subjective, labor-intensive, and time-consuming. In this study, we propose a deep learning-based object detection framework to assess the feasibility of detecting SPVD from ground and aerial high-resolution images. We proposed a novel object detector called SPVDet, as well as a lightweight version called SPVDet-Nano, using a single-level feature. These detectors were prototyped based on a small-scale publicly available benchmark dataset (PASCAL VOC 2012) and compared to mainstream feature pyramid object detectors using a leading large-scale publicly available benchmark dataset (MS COCO 2017). The learned model weights from this dataset were then transferred to fine-tune the detectors and directly analyze our self-made SPVD dataset encompassing one category and 1074 objects, incorporating the slicing aided hyper inference (SAHI) technology. The results showed that SPVDet outperformed both its single-level counterparts and several mainstream feature pyramid detectors. Furthermore, the introduction of SAHI techniques significantly improved the detection accuracy of SPVDet by 14% in terms of mean average precision (mAP) in both ground and aerial images, and yielded the best detection accuracy of 78.1% from close-up perspectives. These findings demonstrate the feasibility of detecting SPVD from ground and unmanned aerial vehicle (UAV) high-resolution images using the deep learning-based SPVDet object detector proposed here. They also have great implications for broader applications in high-throughput phenotyping of sweet potatoes under biotic stresses, which could accelerate the screening process for genetic resistance against SPVD in plant breeding and provide timely decision support for production management.
Why it matches plant phenotyping methods画像からサツマイモのウイルス病状態を推定する深層学習検出器を開発・比較検証しており、植物病害表現型の取得手法が中心である。
abstractwe propose a deep learning-based object detection framework to assess the feasibility of detecting SPVD from ground and aerial high-resolution images.
Root system architecture in storage root crops are an important component of plant growth and yield performance that has received little attention by researchers because of the inherent difficulties posed by in-situ root observation. Sweetpotato ( Ipomoea batatas L.) is an important climate-resilient storage root crop of worldwide importance for both tropical and temperate regions, and identifying genotypes with advantageous root phenotypes and improved root architecture to facilitate breeding for improved storage root yield and quality characteristics in both high and low input scenarios would be beneficial. We evaluated 38 diverse sweetpotato genotypes for early root architectural traits and correlated a subset of these with storage root yield. Early root architectural traits were scanned and digitized using the RhizoVision Explorer software system. Significant genotypic variation was detected for all early root traits including root mass, total root length, root volume, root area and root length by diameter classes. Based on the values of total root length, we separated the 38 genotypes into three root sizes (small, medium, and large). Principal component analysis identified four clusters, primarily defined by shoot mass, root volume, root area, root mass, total root length and root length by diameter class. Average total and marketable yield and number of storage roots, was assessed on a subset of eight genotypes in the field. Several early root traits were positively correlated with total yield, marketable yield, and number of storage roots. These results suggest that root traits, particularly total root length and root mass could improve yield potential and should be incorporated into sweetpotato ideotypes. To help increase sweetpotato performance in challenging environments, breeding efforts may benefit through the incorporation of early root phenotyping using the idea of integrated root phenotypes.
Why it matches plant phenotyping methodsRhizoVision Explorerによる根系形態のスキャン・デジタイズと複数の根形質抽出が研究の中心であり、サツマイモ遺伝子型の早期根系フェノタイピングを実質的に適用している。
abstractEarly root architectural traits were scanned and digitized using the RhizoVision Explorer software system.
The objective was to verify whether convolutional neural networks can help sweet potato phenotyping for qualitative traits. We evaluated 16 families of sweet potato half-sibs in a randomized block design with four replications. We obtained the images at the plant level and used the ExpImage package of the R software to reduce the resolution and individualize one root per image. We grouped them according to their classifications regarding shape, peel color, and damage caused by insects. 600 roots of each class were destined for training the networks, while the rest was used to verify the quality of the fit. We used the python language on the Google Colab platform and the Keras library, considering the VGG-16, Inception-v3, ResNet-50, InceptionResNetV2, and EfficientNetB3 architectures. The InceptionResNetV2 architecture stood out with high accuracy in classifying individuals according to shape, insect damage, and peel color. Image analysis associated with deep learning may help develop applications used by rural producers and improve sweet potatoes, reducing subjectivity, labor, time, and financial resources in phenotyping.
Why it matches plant phenotyping methodsサツマイモ根の形状・皮色・害虫被害という表現型を画像とCNNで分類し、複数アーキテクチャの性能を検証することが中心である。
abstractThe objective was to verify whether convolutional neural networks can help sweet potato phenotyping for qualitative traits.
Sweet potatoMultispectral / hyperspectralPhysiological trait estimationVisualization / data management
This study aimed to achieve the rapid quantification and visualization of the starch content in sweet potato via near-infrared (NIR) spectral and image data fusion. The hyperspectral images of the sweet potato samples containing 900-1700 nm spectral information within every pixel were collected. The spectra were preprocessed, analyzed and the 18 informative wavelengths were finally extracted to relate to the measured starch content using the multiple linear regression (MLR) algorithm, producing a good quantitative prediction accuracy with a correlation coefficient of prediction (r P ) of 0.970 and a root-mean-square error of prediction (RMSE P ) of 0.874 g/100 g by an external validation using a set of dependent samples. The MLR model was further verified in terms of soundness and predictive validity via F-test and t-test, and then transferred to each pixel of the original two dimensional images with the help of a developed algorithm, generating color distribution maps to achieve the vivid visualization of the starch distribution. The study demonstrated that the fusion of the NIR spectral and image data provided a good strategy for the rapidly and nondestructively monitoring the starch content of sweet potato. This technique can be applied to industrial use in the future.
Why it matches plant phenotyping methodsサツマイモ試料のデンプン含量という植物器官形質を、NIRハイパースペクトル画像とデータ融合・回帰モデルで非破壊推定し、外部検証と画素単位可視化まで行っており、形質取得法が研究の中心です。
abstractThis study aimed to achieve the rapid quantification and visualization of the starch content in sweet potato via near-infrared (NIR) spectral and image data fusion.
Occurrence of internal browning in sweet potato tuber has recently been confirmed, and its chemical and bacterial characteristics have been reported. However, the structural characteristics of such tissues are unknown. We investigated the tissue structural characteristics inside a browning sweet potato through magnetic resonance imaging (MRI) and bio-electrochemical impedance spectroscopy (BIS) and the relationship was discussed. The high-resolution proton density-weighted (PDW) and proton spin–spin relaxation time (T₂)-weighted (T2W) images of cutting out samples obtained from micro-imaging revealed changes in the physical structure surrounding the browning tissues. The T₂ distribution maps of the same browning samples assumed the changes in the water distribution and water mobility, which generally changes under the influence of solutes, such as metabolites, starch, protein, and metal ions. BIS further confirmed the variation in the distribution of electrolytes in the tissues. MRI may provide a non-destructive assessment of the internal browning of whole sweet potatoes.
Why it matches plant phenotyping methodsMRIとBISを用いてサツマイモ内部褐変の組織構造・水分/電解質分布を評価し、非破壊的な褐変判定への適用可能性を示すことが中心であり、植物状態の取得手法として該当する。
abstractWe investigated the tissue structural characteristics inside a browning sweet potato through magnetic resonance imaging (MRI) and bio-electrochemical impedance spectroscopy (BIS)
Sweet potatoRootYield / biomass estimationBiomass / plant weightRoot system architecture
ABSTRACT The improvement of sweet potato is a costly job due to the large number of characteristics to be analyzed for the selection of the best genotypes, making it necessary to adopt new technologies, such as the use of images, associated with the phenotyping process. The objective of this research was to develop a methodology for the phenotyping of the root production aiming genetic improvement of half-sib sweet potato progenies through computational analysis of images and to compare its performance to the traditional methodology of evaluation. Sixteen half-sib sweet potato families in a randomized block design with 4 replications were evaluated. At plant level, the weight per root and the total number of roots were evaluated. The images were acquired in a “studio” made of mdf with a digital camera model Canon PowerShotSX400 IS, under artificial lighting. The evaluations were carried out using the R software, where a second-degree polynomial regression model was fitted to predict the root weight (in grams) and the genetic values and expected gains were obtained. It was possible to predict the root weight at plant and plot level, obtaining high coefficients of determination between the predicted and observed weight. Computer vision allowed the prediction of root weight, maintaining the genotype ranking and consequently the similarity between the expected gains with the selection. Thus, the use of images is an efficient tool for sweet potato genetic improvement programs, assisting in the crop phenotyping process.
Why it matches plant phenotyping methodsサツマイモ根重を画像と計算解析で推定する表現型計測手法を開発し、従来法と比較検証しており、フェノタイピング手法が研究の中心である。
abstractThe objective of this research was to develop a methodology for the phenotyping of the root production aiming genetic improvement of half-sib sweet potato progenies through computational analysis of images and to compare its performance to the traditional methodology of evaluation.
Deep learning is a cutting-edge image processing method that is still relatively new but produces reliable results. Leaf disease detection and categorization employ a variety of deep learning approaches. Tomatoes are one of the most popular vegetables and can be found in every kitchen in various forms, no matter the cuisine. After potato and sweet potato, it is the third most widely produced crop. The second-largest tomato grower in the world is India. However, many diseases affect the quality and quantity of tomato crops. This article discusses a deep-learning-based strategy for crop disease detection. A Convolutional-Neural-Network-based technique is used for disease detection and classification. Inside the model, two convolutional and two pooling layers are used. The results of the experiments show that the proposed model outperformed pre-trained InceptionV3, ResNet 152, and VGG19. The CNN model achieved 98% training accuracy and 88.17% testing accuracy.
Why it matches plant phenotyping methodsトマト葉の観察画像から病害を検出・分類するCNN手法を開発・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 article discusses a deep-learning-based strategy for crop disease detection.
Single-target regression can accurately predict the crop’s performance but fails to generalize problems with more than one true and cross-validatable solution. An alternative to output multiple numeric values upon the input, we think, would be multi-target regression (MTR) with either Random Forest (RF) or k-nearest neighbors (KNN). Therefore, we captured the advantages of high-resolution remote sensing and multi-target machine learning into an immersive single framework then analyzed if it could be possible for accurately predicting for the yield of sweet potato by the market class of its tuberous roots (i.e., Extra 0.45 kg) upon imagery data on summer and winter full-scale fields. The remote sensing captured the spectral changes on both fields and enabled the MTR to accurately predict for the yield of sweet potato in total and by the market class of harvestable roots upon normalized difference vegetation index (NDVI) and its derivative version (GreenNDVI) as well as upon soil-adjusted vegetation index (SAVI). The SAVI-RF framework predicted the summer field to yield marketable roots at the proportions of 2.04 t ha⁻¹ Extra, 3.89 t ha⁻¹ Extra AA and 2.08 t ha⁻¹ Extra A, and the spectral data from the mid-stage of cultivation at 296 growing degree days (GDD) minimized its mean absolute error (MAE) to 2.66 t ha⁻¹. The GNDVI-RF framework predicted the winter field to yield 1.64 t ha⁻¹ Extra, 5.02 t ha⁻¹ Extra AA and 3.65 t ha⁻¹ Extra A, with an error of 3.45 t ha⁻¹ upon spectral data from sampling on the late stage at 966 GDD. Our insights are timely an absolutely will open up the horizons for harvesting high-quality roots to commercialization, industrialization and propagation, and scaling up this essentially provocative yet emerging crop for food safety and energy security.
Why it matches plant phenotyping methodsリモートセンシング画像と多目的機械学習を組み合わせ、サツマイモの収量および根の市場区分別収量を推定する方法を中心的に開発・評価している。
abstractwe captured the advantages of high-resolution remote sensing and multi-target machine learning into an immersive single framework
Abstract Sweetpotato is a crucial crop to guarantee food security in sub‐Saharan Africa, and drought events are considered one of the most critical factors affecting sweetpotato productivity in this region. In this study, airborne imagery based on reflectance (NDVI, CI red‐edge ) and canopy temperature minus air temperature (dT) indices was used to characterize sweetpotato genotypes under drought treatments in Mozambique. Two field experiments established in rainy/hot (Trial A) and dry/cool (Trial B) seasons were assessed. In Trial A, 24 genotypes were subjected to early‐ (ESD), mid‐ (MSD) and late‐season (LSD) drought stress treatments and compared against a control. In Trial B, 120 genotypes were subjected to LSD only. The percentage of reduction in vine weight (PR VW ) under drought was related primarily to temporal variation of NDVI and CI, regardless of drought treatment and seasons. dT in relation to control (dT Amp ) was associated with PR VW in ESD‐Trial A and LSD‐Trial B, whereas under LSD‐Trial A, dT Amp was related to total fresh storage root weight (TRW). During the rainy/hot season, higher TRW reduction was promoted under ESD; however, under LSD, it was possible to identify productive genotypes able to withstand drought stress, highlighting their relevance for drought‐tolerance selection purposes.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像を用いてサツマイモ遺伝子型の生産性と干ばつ耐性を表現型評価しており、画像取得・指数抽出が主要な評価ワークフローであるため。
titlePhenotyping of productivity and resilience in sweetpotato under water stress through UAV‐based multispectral and thermal imagery in Mozambique
Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.
Why it matches plant phenotyping methodsジャガイモ・サツマイモ塊茎の品質形質を画像分光と機械学習で評価する手法を中心に扱うレビューであり、植物器官の形質取得・推定に該当する。
abstractThis paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers.
For many horticultural crops, variation in quality (e.g., shape and size) contributes significantly to the crop’s market value. Metrics characterizing less subjective harvest quantities (e.g., yield and total biomass) are routinely monitored. In contrast, metrics quantifying more subjective crop quality characteristics such as ideal size and shape remain difficult to characterize objectively at the production-scale due to the lack of modular technologies for high-throughput sensing and computation. Several horticultural crops are sent to packing facilities after having been harvested, where they are sorted into boxes and containers using high-throughput scanners. These scanners capture images of each fruit or vegetable being sorted and packed, but the images are typically used solely for sorting purposes and promptly discarded. With further analysis, these images could offer unparalleled insight on how crop quality metrics vary at the industrial production-scale and provide further insight into how these characteristics translate to overall market value. At present, methods for extracting and quantifying quality characteristics of crops using images generated by existing industrial infrastructure have not been developed. Furthermore, prior studies that investigated horticultural crop quality metrics, specifically of size and shape, used a limited number of samples, did not incorporate deformed or non-marketable samples, and did not use images captured from high-throughput systems. In this work, using sweetpotato (SP) as a use case, we introduce a computer vision algorithm for quantifying shape and size characteristics in a high-throughput manner. This approach generates 3D model of SPs from two 2D images captured by an industrial sorter 90 degrees apart and extracts 3D shape features in a few hundred milliseconds. We applied the 3D reconstruction and feature extraction method to thousands of image samples to demonstrate how variations in shape features across SP cultivars can be quantified. We created a SP shape dataset containing SP images, extracted shape features, and qualitative shape types (U.S. No. 1 or Cull). We used this dataset to develop a neural network-based shape classifier that was able to predict Cull vs. U.S. No. 1 SPs with 84.59% accuracy. In addition, using univariate Chi-squared tests and random forest, we identified the most important features for determining qualitative shape type (U.S. No. 1 or Cull) of the SPs. Our study serves as a key step towards enabling big data analytics for industrial SP agriculture. The methodological framework is readily transferable to other horticultural crops, particularly those that are sorted using commercial imaging equipment.
Why it matches plant phenotyping methods高スループット画像からサツマイモのサイズ・形状形質を抽出するコンピュータビジョン手法を開発し、3D再構成、特徴抽出、データセット化、分類性能評価まで行っており、フェノタイピング手法が研究の中心である。
abstractIn this work, using sweetpotato (SP) as a use case, we introduce a computer vision algorithm for quantifying shape and size characteristics in a high-throughput manner.
While standard visible-light imaging offers a fast and inexpensive means of quality analysis of horticultural products, it is generally limited to measuring superficial (surface) defects. Using light at longer (near-infrared) or shorter (X-ray) wavelengths enables the detection of superficial tissue bruising and density defects, respectively; however, it does not enable the optical absorption and scattering properties of sub-dermal tissue to be quantified. This paper applies visible and near-infrared interactance spectroscopy to detect internal necrosis in sweetpotatoes and develops a Zemax scattering simulation that models the measured optical signatures for both healthy and necrotic tissue. This study demonstrates that interactance spectroscopy can detect the unique near-infrared optical signatures of necrotic tissues in sweetpotatoes down to a depth of approximately 5±0.5 mm. We anticipate that light scattering measurement methods will represent a significant improvement over the current destructive analysis methods used to assay for internal defects in sweetpotatoes.
Why it matches plant phenotyping methodsサツマイモ内部の壊死という植物器官の状態を、近赤外インタラクタンス分光で非破壊検出し、散乱シミュレーションも開発しているため、表現型取得法が研究の中心である。
abstractThis paper applies visible and near-infrared interactance spectroscopy to detect internal necrosis in sweetpotatoes and develops a Zemax scattering simulation that models the measured optical signatures for both healthy and necrotic tissue.
Visible and near infrared (Vis-NIR) hyperspectral imaging was used for fast detection and visualization of soluble solid content (SSC) in 'Beijing 553' and 'Red Banana' sweet potatoes. Hyperspectral images were acquired from 420 ROIs of each cultivar of sliced sweet potatoes. There were 8 and 10 outliers removed from 'Beijing 553' and 'Red Banana' sweet potatoes by Monte Carlo partial least squares (MCPLS). The optimal spectral pretreatments were determined to enhance the performance of the prediction model. Successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were employed to select characteristic wavelengths. SSC prediction models were developed using partial least squares regression (PLSR), support vector regression (SVR) and multivariate linear regression (MLR). The more effective prediction performances emerged from the SPA-SVR model with R p 2 of 0.8581, RMSEP of 0.2951 and RPD p of 2.56 for 'Beijing 553' sweet potato, and the CARS-MLR model with R p 2 of 0.8153, RMSEP of 0.2744 and RPD p of 2.09 for 'Red Banana' sweet potato. Spatial distribution maps of SSC were obtained in a pixel-wise manner using SPA-SVR and CARS-MLR models for quantifying the SSC level in a simple way. The overall results illustrated that Vis-NIR hyperspectral imaging was a powerful tool for spatial prediction of SSC in sweet potatoes.
Why it matches plant phenotyping methodsサツマイモ切片の可溶性固形分含量をハイパースペクトル画像から画素単位で推定・可視化する手法を開発し、複数モデルの性能評価も行っており、表現型取得法が研究の中心である。
abstractVisible and near infrared (Vis-NIR) hyperspectral imaging was used for fast detection and visualization of soluble solid content (SSC) in 'Beijing 553' and 'Red Banana' sweet potatoes.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · OpenAlex · checked 9 Sept 2026
For many horticultural crops, variation in quality (e.g., shape and size) contribute significantly to the crops market value. Metrics characterizing less subjective harvest quantities (e.g., yield and total biomass) are routinely monitored. In contrast, metrics quantifying more subjective crop quality characteristics such as ideal size and shape remain difficult to characterize objectively at the production-scale due to the lack of modular technologies for high-throughput sensing and computation. Several horticultural crops are sent to packing facilities after having been harvested, where they are sorted into boxes and containers using high-throughput scanners. These scanners capture images of each fruit or vegetable being sorted and packed, but the images are typically used solely for sorting purposes and promptly discarded. With further analysis, these images could offer unparalleled insight on how crop quality metrics vary at the industrial production-scale and provide further insight into how these characteristics translate to overall market value. At present, methods for extracting and quantifying quality characteristics of crops using images generated by existing industrial infrastructure have not been developed. Furthermore, prior studies that investigated horticultural crop quality metrics, specifically of size and shape, used a limited number of samples, did not incorporate deformed or non-marketable samples, and did not use images captured from high-throughput systems. In this work, using sweetpotato (SP) as a use case, we introduce a computer vision algorithm for quantifying shape and size characteristics in a high-throughput manner. This approach generates 3D model of SPs from two 2D images captured by an industrial sorter 90 degrees apart and extracts 3D shape features in a few hundred milliseconds. We applied the 3D reconstruction and feature extraction method to thousands of image samples to demonstrate how variations in shape features across sweetptoato cultivars can be quantified. We created a sweetpotato shape dataset containing sweetpotato images, extracted shape features, and qualitative shape types (U.S. No. 1 or Cull). We used this dataset to develop a neural network-based shape classifier that was able to predict Cull vs. U.S. No. 1 sweetpotato with 84.59% accuracy. In addition, using univariate Chi-squared tests and random forest, we identified the most important features for determining qualitative shape (U.S. No. 1 or Cull) of the sweetpotatoes. Our study serves as the first step towards enabling big data analytics for sweetpotato agriculture. The methodological framework is readily transferable to other horticultural crops, particularly those that are sorted using commercial imaging equipment.
Why it matches plant phenotyping methods高スループット画像からサツマイモのサイズ・形状という植物器官形質を3D再構成と特徴抽出で定量化する手法を開発し、データセット作成と分類性能評価も行っており、表現型取得法が研究の中心である。
abstractIn this work, using sweetpotato (SP) as a use case, we introduce a computer vision algorithm for quantifying shape and size characteristics in a high-throughput manner.
Background Virus diseases caused by co-infection with Sweet potato feathery mottle virus (SPFMV) and Sweetpotato chlorotic stunt virus (SPCSV) are a severe problem in the production of sweetpotato ( Ipomoea batatas L.). Traditional molecular virus detection methods include nucleic acid-based and serological tests. In this study, we aimed to validate the use of a non-destructive imaging-based plant phenotype platform to study plant-virus synergism in sweetpotato by comparing four virus treatments with two healthy controls. Results By monitoring physiological and morphological effects of viral infection in sweetpotato over 29 days, we quantified photosynthetic performance from chlorophyll fluorescence (ChlF) imaging and leaf thermography from thermal infrared (TIR) imaging among sweetpotatoes. Moreover, the differences among different treatments observed from ChlF and TIR imaging were related to virus accumulation and distribution in sweetpotato. These findings were further validated at the molecular level by related gene expression in both photosynthesis and carbon fixation pathways. Conclusion Our study validated for the first time the use of ChlF- and TIR-based imaging systems to distinguish the severity of virus diseases related to SPFMV and SPCSV in sweetpotato. In addition, we demonstrated that the operating efficiency of PSII and photochemical quenching were the most sensitive parameters for the quantification of virus effects compared with maximum quantum efficiency, non-photochemical quenching, and leaf temperature.
Why it matches plant phenotyping methodsウイルス感染による植物の症状・生理状態を、クロロフィル蛍光および熱画像で定量する非破壊表現型プラットフォームの検証が中心である。
abstractwe aimed to validate the use of a non-destructive imaging-based plant phenotype platform
There are only a few studies that have been made on accuracy assessments of Leaf Area Index (LAI) and biomass estimation using three-dimensional (3D) models generated by structure from motion (SfM) image processing. In this study, sweet potato was grown with different amounts of nitrogen fertilization in ridge cultivation at an experimental farm. Three-dimensional dense point cloud models were constructed from a series of two-dimensional (2D) color images measured by a small unmanned aerial vehicle (UAV) paired with SfM image processing. Although it was in the early stage of cultivation, a complex ground surface model for ridge cultivation with vegetation was generated, and the uneven ground surface could be estimated with an accuracy of 1.4 cm. Furthermore, in order to accurately estimate growth parameters from the early growth to the harvest period, a 3D model was constructed using a root mean square error (RMSE) of 3.3 cm for plant height estimation. By using a color index, voxel models were generated and LAIs were estimated using a regression model with an RMSE accuracy of 0.123. Further, regression models were used to estimate above-ground and below-ground biomass, or tuberous root weights, based on estimated LAIs.
Why it matches plant phenotyping methodsUAV-SfMによる3D画像計測と回帰モデルを用いて、サツマイモの草丈、LAI、バイオマス、塊根重を推定し、精度評価まで行うことが研究の中心である。
abstractThree-dimensional dense point cloud models were constructed from a series of two-dimensional (2D) color images measured by a small unmanned aerial vehicle (UAV) paired with SfM image processing.
LEDFLEX is a micro-lidar dedicated to the measurement of vegetation fluorescence. The light source consists of 4 blue Light-Emitting Diodes (LED) to illuminate part of the canopy in order to average the spatial variability of small crops. The fluorescence emitted in response to a 5-μs width pulse is separated from the ambient light through a synchronized detection. Both the reflectance and the fluorescence of the target are acquired simultaneously in exactly the same field of view, as well as the photosynthetic active radiation and air temperature. The footprint is about 1 m 2 at a distance of 8 m. By increasing the number of LEDs longer ranges can be reached. The micro-lidar has been successfully applied under full sunlight conditions to establish the signature of water stress on pea (Pisum Sativum) canopy. Under well-watered conditions the diurnal cycle presents an M shape with a minimum (Fmin) at noon which is Fmin > Fo. After several days withholding watering, Fs decreases and Fmin < Fo. The same patterns were observed on mint (Menta Spicata) and sweet potatoes (Ipomoea batatas) canopies. Active fluorescence measurements with LEDFLEX produced robust fluorescence yield data as a result of the constancy of the excitation intensity and its geometry fixity. Passive methods based on Sun-Induced chlorophyll Fluorescence (SIF) that uses high-resolution spectrometers generate only flux data and are dependent on both the 3D structure of vegetation and variable irradiance conditions along the day. Parallel measurements with LEDFLEX should greatly improve the interpretation of SIF changes.
Why it matches plant phenotyping methods植物キャノピーの蛍光・反射を測定するマイクロライダーを開発し、ストレス検出への適用と蛍光データの頑健性を示しており、植物表現型取得法が研究の中心である。
abstractLEDFLEX is a micro-lidar dedicated to the measurement of vegetation fluorescence.
In recent decades, some photogrammetric methods for 3D monitoring of plant growth and structure parameters have been studied by the automated feature extraction and matching. In this study on growth analysis of sweet potato (Ipomoea batatas L.) plants at different fertilizer conditions, we proposed a convenient solution of 3D reconstruction by a single camera photography system based on Structure from Motion (SfM) method. Also, we handled effectively the noise problem by minimizing re-projection errors. The results of 3D models demonstrated that the average percentage error was a constant about 4.8% for plant height or decreased from 13% to 8% (leaf area index, LAI=3.5) for leaf number and from 19% to 12% (LAI=3.5) for leaf area with increasing in LAI, although each percentage error fluctuated, especially at low LAI. In contrast, the average percentage error in 2D image processing was 20% to 45% for leaf number and 60% to 90% for leaf area, and the leaf height was immeasurable. Comparing with the errors of 3D results, the errors of 2D images were much larger because 2D imaging had some problems, such as not being robust against occlusion of plant organs, and the ambiguity between object size and distance from the camera. On the other hand, we examined a method to calibrate the estimates from 3D models using the regression model between the measured value and the value estimated from 3D model. The regression models showed the linear and good estimation for leaf height (R2=0.97 and RMSE=0.71 cm), leaf number (R2=0.99 and RMSE=4.03) and leaf area (R2=0.98 and RMSE=0.12 m2), in spite of use of data across a wide range in fertilizer supply and growth stage of sweet potato plants. The results demonstrated that 3D imaging technique in the study has the potential to remotely monitor plant growth status and estimate growth and structure parameters at various environmental factors outdoors.
Why it matches plant phenotyping methods単一カメラSfMによる植物の3D再構成、器官形質抽出、誤差評価と校正を中心に開発・検証しており、植物フェノタイピング手法が明確に中心である。
abstractwe proposed a convenient solution of 3D reconstruction by a single camera photography system based on Structure from Motion (SfM) method
The potential of chemical imaging for rapid measurement of dry matter concentration (DMC) and starch concentration (SC) in both potato and sweet potato tubers was investigated. The time series images of tuber samples were acquired, then the resulting reflectance spectra (RS) were corrected and transformed into absorbance spectra (AS), and exponent spectra (ES). Full wavelength regression models including multiple linear regression (MLR), partial least squares regression (PLSR) and locally weighted partial least squares regression (LWPLSR) were established based on spectral profiles with measured DMC and SC values. The best calibration model for measuring DMC and SC was LWPLSR based on ES and RS where the coefficients of determination in cross-validation (R2CV) were 0.987 and 0.985, and the root mean squared errors in cross-validation (RMSECV) were 0.015 and 0.014, respectively. After, six groups of eight feature wavelengths were chosen from RS, AS and ES based on wavelength selection methods including β-coefficient (βC) of PLSR and the first derivative and mean centering iteration algorithm (FMCIA), and were successively used to build simplified models. The acquired FMCIA-RS-LWPLSR and βC-RS-LWPLSR models showed better accuracy than other simplified models, with R2P of 0.985 and RMSEP of 0.016 for DMC prediction, and R2P of 0.983 and RMSEP of 0.015 for SC prediction, respectively. Besides, the optimal models for MLR and PLSR were obtained using FMCIA on the basis of the ES. After further reducing the number of feature wavelengths, only six wavelengths (1028, 1068, 1135, 1208, 1262 and 1460nm) were selected and utilized to develop the simplest FMCIA-Es-MLR model for determining DMC and FMCIA-Es-PLSR model for detecting SC, yielding a reasonable level of accuracy with R2P of 0.962 and 0.963 as well as RMSEP of 0.025 and 0.023, respectively. Furthermore, the time series variations of DMC and SC on tuber samples were visualized based on an equation to apply the simplest models to the spectral images.
Why it matches plant phenotyping methods化学イメージングとスペクトル回帰モデルを開発・評価し、塊茎の乾物濃度とデンプン濃度を画像から推定・可視化しており、植物形質取得手法が研究の中心である。
abstractThe potential of chemical imaging for rapid measurement of dry matter concentration (DMC) and starch concentration (SC) in both potato and sweet potato tubers was investigated.
Background Endoreduplication, the process of DNA replication in the absence of cell division, is associated with specialized cellular function and increased cell size. Genes controlling endoreduplication in tomato fruit have been shown to affect mature fruit size. An efficient method of estimating endoreduplication is required to study its role in plant organ development. Flow cytometry is often utilized to evaluate endoreduplication, yet some tissues and species, among them the tubers of Solanum tuberosum , remain intractable to routine tissue preparation for flow cytometry. We aimed to develop a method through the use of protoplast extraction preceding flow cytometry, specifically for the assessment of endoreduplication in potato tubers. Results We present a method for appraising endoreduplication in potato ( Solanum tuberosum ) tuber tissues. We evaluated this method and observed consistent differences between pith and cortex of tubers and between different cultivars, but no apparent relationship with whole tuber size. Furthermore, we were able to observe distinct patterns of endoreduplication in 16 of 20 wild potato relatives, with mean endoreduplication index (EI) ranging from 0.94 to 2.62 endocycles per cell. The protocol was also applied to a panel of starchy root crop species and, while only two of five yielded reliable flow histograms, the two (sweet potato and turnip) exhibited substantially lower EIs than wild and cultivated potato accessions. Conclusions The protocol reported herein has proven effective on tubers of a variety of potato cultivars and related species, as well as storage roots of other starchy crops. This method provides an important tool for the study of potato morphology and development while revealing natural variation for endoreduplication which may have agricultural relevance.
Why it matches plant phenotyping methodsジャガイモ塊茎の倍数化状態（内倍加）を測定するため、プロトプラスト抽出とフローサイトメトリーを組み合わせた方法を開発・評価しており、植物表現型の取得が研究の中心です。
abstractWe aimed to develop a method through the use of protoplast extraction preceding flow cytometry, specifically for the assessment of endoreduplication in potato tubers.
Near-infrared spectroscopy (NIRS) is an alternative analytical method that can be used to quantify protein content in sweetpotato. It is relatively cheaper and efficient than other methods. This study was conducted to develop NIRS-based models for quantifying protein content of sweetpotato for selection or wide-area production of recommended varieties. A pool of 104 sweetpotato varieties were sampled and roots scanned using NIR spectrometer. Calibration models were developed by subjecting spectral and reference datasets to partial least squares regression. Several pre-processing methods were investigated. Models that yielded the highest coefficient of determination (R2), residual predictive deviation (RPD) and lowest root mean square error of calibration (RMSEC) and prediction (RMSEP) were selected. Optimal model performances were obtained using second derivative pre-processing, showing the highest values of R2v, RMSEP and RPDv of 0.98, 0.29, and 4.0, respectively. The regression analysis indicated that informative NIR bands for quantifying protein content of sweetpotatoes ranged between 1600 and 2200 nm. The results demonstrated that NIRS is capable of predicting protein content on sweetpotatoes, rapidly and accurately. Therefore, the NIRS model developed in this study may help to quantify protein composition of sweetpotato for rapid screening of germplasm in breeding programs with high throughput and accuracy.
Why it matches plant phenotyping methodsサツマイモ根のタンパク質含量という植物形質をNIRSで推定するモデルを開発し、前処理・回帰モデル・予測性能を比較検証しており、表現型取得法が中心である。
abstractThis study was conducted to develop NIRS-based models for quantifying protein content of sweetpotato
PotatoSweet potatoMultispectral / hyperspectralClassificationWater status / transpiration
The reliability and veracity of hyperspectral imaging integrated with multivariate analyses were investigated for authentication of sliced organic potato (OP) from non-organic tubers and rapid grading of tubers on the basis of different moisture levels. Hyperspectral images of all the tuber samples were obtained and their spectral data were extracted and pre-processed. Then, partial least squares discriminant analysis (PLSDA) model was established for recognition of the tested samples. Loading plots of the second derivative (SD) and principal component analysis (PCA) were used for selecting characteristic wavelengths. The OP samples were identified correctly (100% accuracy) from non-organic tubers by MC-PLSDA model using only characteristic wavelengths, with predicted sensitivities of 1.000, specificities of ⩾0.944, classification error of ⩽0.028, coefficient of determination (R2P) of no more than 0.979 and the root mean square error of prediction (RMSEP) of ⩽0.532 for each adulterate type. Another simplified PLSDA model was applied for grading tuber moisture levels, resulting in a correct classification of ⩾91.6%. The visualization results shown on classification maps achieved a rapid and convenient interpretation of tuber varieties and moisture levels. These results indicated that hyperspectral imaging has a great potential for discrimination of OP and identification of tuber moisture levels.
Why it matches plant phenotyping methodsハイパースペクトル画像と多変量解析を用いて、ジャガイモ塊茎の水分レベルという植物器官形質を推定・分類し、分類精度も検証している。画像取得・特徴抽出・モデル化が中心であり、単なる生物学実験のルーチン測定ではない。
abstractThe reliability and veracity of hyperspectral imaging integrated with multivariate analyses were investigated for authentication of sliced organic potato (OP) from non-organic tubers and rapid grading of tubers on the basis of different moisture levels.
Detecting sun-induced chlorophyll fluorescence (SIF) offers a new approach for remote sensing photosynthesis. However, to analyse the response characteristics of SIF under different stress states, a long-term time-series comparative observation of vegetation under different stress states must be carried out at the canopy scale, such that the similarities and differences in SIF change law can be summarized under different time scales. A continuous comparative observation system for vegetation canopy SIF is designed in this study. The system, which is based on a high-resolution spectrometer and an optical multiplexer, can achieve comparative observation of multiple targets. To simultaneously measure the commonly used vegetation index and SIF in the O₂-A and O₂-B atmospheric absorption bands, the following parameters are used: a spectral range of 475.9 to 862.2 nm, a spectral resolution of approximately 0.9 nm, a spectral sampling interval of approximately 0.4 nm, and the signal-to-noise ratio (SNR) can be as high as 1000:1. To obtain data for both the upward radiance of the vegetation canopy and downward irradiance data with a high SNR in relatively short time intervals, the single-step integration time optimization algorithm is proposed. To optimize the extraction accuracy of SIF, the FluorMOD model is used to simulate sets of data according to the spectral resolution, spectral sampling interval and SNR of the spectrometer in this continuous observation system. These data sets are used to determine the best parameters of Fraunhofer Line Depth (FLD), Three FLD (3FLD) and the spectral fitting method (SFM), and 3FLD and SFM are confirmed to be suitable for extracting SIF from the spectral measurements. This system has been used to observe the SIF values in O₂-A and O₂-B absorption bands and some commonly used vegetation index from sweet potato and bare land, the result of which shows: (1) the daily variation trend of SIF value of sweet potato leaves is basically same as that of photosynthetically active radiation (PAR); and (2) the bare land is a non-fluorescent emitter, the SIF of which is significantly smaller than that of sweet potato; and (3) analysis result based on the measured data is basically same as that based on simulated data. The above results verified the reliability of the SIF extracted from the measured data and the feasibility of comparatively observing the SIF value and the commonly used vegetation index of multiple vegetation canopy with this continuous observation system. This approach is beneficial for comprehensively analysing the stress response characteristics of vegetation canopies.
Why it matches plant phenotyping methods植生キャノピーのSIFを連続・比較観測するセンサーシステムを設計し、SIF抽出アルゴリズムと精度・実現可能性を検証しており、植物生理状態の取得方法が研究の中心である。
abstractA continuous comparative observation system for vegetation canopy SIF is designed in this study.