Abstract The diseases on sesame leaves have a huge implication on the production and earnings of farmers particularly in the developing areas. It is important to ensure that the disease is properly managed by identifying it early and correctly. The purpose of this study is to create a deep learning-based framework that could be used to categorize four of the most common scenarios involving sesame leaves, i.e. Healthy Leaf, Leaf Spot Disease, Yellowing Leaf Syndrome, and Leaf Damage by Insects using high-resolution images acquired in Pabna, Bangladesh. The preprocessing, augmentation and split of a set of 3,540 images were performed into training and validation sets. The pre-trained convolutional neural networks models were trained and tested on five models inceptionV3, EfficientNetB3, ResNet50, MobileNet, and DenseNet121 by measuring the metrics such as accuracy, precision, recall, and F1-score. MobileNet achieved the highest accuracy of 96.33%, precision of 96%, recall of 96%, and the F1-score of 96%, which is the best amongst them. The findings indicate that deep learning architectures are capable of classifying the sesame leaf diseases in a reliable and precision-oriented way that is not affected by different environmental circumstances. The study facilitates the creation of the automated, efficient methods of detecting the disease at an early stage, cutting down the number of pesticides used and enhancing crop control. Future direction will be to enlarge the dataset, add temporal data and to implement lightweight models so that it can be deployed to real-time field projects
Why it matches plant phenotyping methodsセサマ葉画像から病害状態をCNNで分類する手法の開発・比較評価が研究の中心であり、植物の病害表現型を直接推定している。
abstractThe purpose of this study is to create a deep learning-based framework that could be used to categorize four of the most common scenarios involving sesame leaves
Early-generation sesame (Sesamum indicum L.) breeding requires high-throughput phenotyping of large unreplicated populations across contrasting environments. A DJI Phantom 4 Multispectral UAV was flown at 40, 80, and 120 m above ground level (AGL) over 588 M2 genotypes under full irrigation (ENV1) and terminal drought (ENV2; irrigation withheld from reproductive onset) on four dates (July–September 2025). Structure-from-motion canopy height models were compared with ground measurements, and four spectral reflectance indices—Normalised Difference Vegetation Index (NDVI), Normalised Difference Red Edge (NDRE), Green Normalised Difference Vegetation Index (GNDVI), and Leaf Chlorophyll Index (LCI)—were derived from 40 m imagery. Ordinary least squares (OLS), Random Forest, and Gradient Boosting were evaluated under leave-one-genotype-out (LOGO), leave-one-environment-out (LOEO), and leave-one-date-out (LODO) cross-validation; genotypic repeatability was quantified by intraclass correlation (ICC), and drought performance was ranked by a composite Stress Tolerance Index (STI) validated against an independent breeder assessment. The 40 m altitude gave the highest height accuracy (R2 = 0.812 in ENV1; 0.663 in ENV2). LOGO accuracy (R2 ≈ 0.83) fell to R2 ≈ 0.55 under LODO—the operationally relevant figure for a new phenological stage—and the full structural–spectral OLS model collapsed (R2 = −0.203) where tree ensembles remained stable. Spectral-index repeatability was up to ~2-fold higher under stress (ICC(3,4) > 0.84). The composite STI flagged 38 elite genotypes (7.6% of 498); 10 of its top 30 were confirmed in the breeder’s 48-best selection from all 588 rows—a 4.1-fold enrichment over chance (hypergeometric p = 4.5 × 10−5).
Why it matches plant phenotyping methodsUAV画像から草冠高・スペクトル形質を抽出し、飛行高度、予測モデル、再現性、交差検証を体系的にベンチマークしているため、植物表現型取得法が中心である。
abstractEarly-generation sesame (Sesamum indicum L.) breeding requires high-throughput phenotyping of large unreplicated populations across contrasting environments.
Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy provides a rapid, reproducible, and non-destructive analytical platform for profiling biochemical variation in biological samples. In this study, we demonstrate its application for diversity assessment in sesame (Sesamum indicum L.) seed oils, highlighting its potential as a methodological tool for high-throughput biochemical phenotyping. Spectral fingerprints were acquired from 64 genotypes and analysed using principal component analysis, hierarchical clustering, and K-means clustering. The first two principal components captured 73% of the total spectral variance, while clustering methods consistently separated genotypes into distinct groups, reflecting underlying biochemical polymorphisms. Key wavenumbers, 3888, 3757, 3564, 3294, 3132, 2902, 2470, 1850, 1685, 1436, 1350, 989, 888, and 788 cm -1 , were identified as major contributors to diversity, serving as spectral markers for oil quality and compositional analysis. The clustering of genotypes was further supported by band ratio analysis highlighting differences in unsaturation and esterification patterns among clusters. Beyond sesame, the workflow established here underscores the analytical capacity of ATR-FTIR, coupled with chemometric approaches, for capturing subtle biochemical variation across complex biological matrices. These results position ATR-FTIR as a broadly applicable method for biochemical screening and diversity studies in plant-derived and other biological systems.
Why it matches plant phenotyping methodsATR-FTIRスペクトルとケモメトリクスを用いたセサミ種子油の生化学的多様性評価を、ハイスループットな植物フェノタイピング手法として実証しており、測定・解析ワークフローが中心である。
abstracthighlighting its potential as a methodological tool for high-throughput biochemical phenotyping
Efficient and non-destructive extraction of organ-level phenotypic parameters of sesame ( Sesamum indicum L.) plants is a key bottleneck in current sesame phenotyping research. To address this issue, this study proposes a method for organ segmentation and phenotypic parameter extraction based on CAVF-PointNet++ and geometric clustering. First, this method constructs a high-precision 3D point cloud using multi-view RGB image sequences. Based on the PointNet++ model, a CAVF-PointNet++ model is designed to perform feature learning on point cloud data and realize the automatic segmentation of stems, petioles, and leaves. Meanwhile, different leaves are segmented using curvature-density clustering technology. Based on the results of segmentation, this study extracted a total of six organ-level phenotypic parameters, including plant height, stem diameter, leaf length, leaf width, leaf angle, and leaf area. The experimental results show that in the segmentation tasks of stems, petioles, and leaves, the overall accuracy of CAVF-PointNet++ reaches 96.93%, and the mean intersection over union is 82.56%, which are 1.72% and 3.64% higher than those of PointNet++, demonstrating excellent segmentation performance. Compared with the results of manual segmentation of different leaves, the proposed clustering method achieves high levels in terms of precision, recall, and F1-score, and the segmentation results are highly consistent. In terms of phenotypic parameter measurement, the coefficients of determination between manual measurement values and algorithmic measurement values are 0.984, 0.926, 0.962, 0.942, 0.914, and 0.984 in sequence, with root-mean-square errors of 5.9 cm, 1.24 mm, 1.9 cm, 1.2 cm, 3.5°, and 6.22 cm 2 , respectively. The measurement results of the proposed method show a strong correlation with the actual values, providing strong technical support for sesame phenotyping research and precision agriculture. It is expected to provide reference and support for the automated 3D phenotypic analysis of other crops in the future.
Why it matches plant phenotyping methods3D画像・点群分割と幾何クラスタリングにより、ゴマの器官分割および6種類の表現型形質抽出法を開発し、手動測定との精度検証も行っているため、方法が中心的である。
abstractthis study proposes a method for organ segmentation and phenotypic parameter extraction based on CAVF-PointNet++ and geometric clustering.
This study presents an ensemble-based approach for detecting and classifying sesame diseases using deep convolutional neural networks (CNNs). Sesame is a crucial oilseed crop that faces significant challenges from various diseases, including phyllody and bacterial blight, which adversely affect crop yield and quality. The objective of this research is to develop a robust and accurate model for identifying these diseases, leveraging the strengths of three state-of-the-art CNN architectures: ResNet-50, DenseNet-121, and Xception. The proposed ensemble model integrates these individual networks to enhance classification accuracy and improve generalization across diverse datasets. A comprehensive dataset of sesame leaf images, representing healthy, phyllody, and bacterial blight conditions was utilized to train and evaluate the models. The ensemble approach achieved an impressive overall accuracy of 96.83%, demonstrating superior performance in accurately classifying the different leaf conditions. The results highlight the effectiveness of combining multiple deep learning models, which allows for the extraction of diverse feature representations and decision-making strategies. This thesis also discusses the advantages of the ensemble methodology, including improved robustness to variations in disease symptoms and enhanced adaptability to changing agricultural practices. The findings of this research have significant implications for precision agriculture. They offer a reliable tool for the early detection and classification of sesame diseases. By enabling timely interventions, this ensemble-based framework can contribute to the sustainability and productivity of sesame cultivation, ultimately supporting food security and agricultural resilience.
Why it matches plant phenotyping methodsゴマ葉画像から病害状態を推定するCNN分類手法の開発と評価が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThis study presents an ensemble-based approach for detecting and classifying sesame diseases using deep convolutional neural networks (CNNs).
Monitoring sesame plant health and detecting disease early are essential to reducing disease spread and facilitate effective management practices. In this research, we developed an image classification model to detect bacterial blight-infected, phyllody-infected, and healthy sesame crops. Since images were necessary to carry out this study, we collected 2300 images at the Gondar and Humera Agriculture Research Centers and directly from the field in Metema. Since the collected images were limited, to increase the number of images in the dataset, we used image augmentation with different variations. In the image preprocessing step, we used a median filter for noise filtering, and contrast stretching techniques were used for image contrast and brightness enhancement. SegNet semantic segmentation, which is deep convolution neural network-based architecture, was used to segment the leaf part of the image from the background. In the feature extraction and classification steps, a deep convolutional neural network was used. Finally, we evaluated the proposed model and compared it with two recent deep convolution neural network models, namely, Xception and InceptionV3. The proposed model for the classification of sesame diseases achieved better accuracy, with 96.67% testing accuracy, 97.78% validation accuracy, and 98% training accuracy.
Why it matches plant phenotyping methodsゴマ葉の画像から病害状態をセグメンテーション・分類する手法を開発し、複数モデルとの比較検証を行っており、植物病害表現型の取得が中心である。
abstractwe developed an image classification model to detect bacterial blight-infected, phyllody-infected, and healthy sesame crops
Sesame (Sesamum indicum) is an important oilseed crop with rising demand owing to its nutritional and health benefits. There is an urgent need to develop and integrate new genomic-based breeding strategies to meet these future demands. While genomic resources have advanced genetic research in sesame, the implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11, conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.
Why it matches plant phenotyping methods高スループット表現型プラットフォームによる時系列の植物形質取得が研究の中心的データ基盤であり、複数形質の縦断測定と予測への応用を評価している。
abstractwe combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons
Reproduction assets foundThe paper's Data Availability Statement points to a public figshare deposit containing all phenotypic data (temporal HTP traits: plant height, LAI, spectral vegetation indices), genomic data, and GWAS results for this sesame study. No author analysis code repository is explicitly deposited.Dataset · publicfor longitudinal traits derived from single time points (green) and random regression (orange) analysis.
Figure S3 . Phenotypic (green) and genetic (orange) correlations between longitudinal traits at each time point and seed‐yield.
Data Availability Statement
All phenotypic data, genomic data, and GWAS results can be found at https://doi.org/10.6084/m9.figshare.24961491 .Open asset ↗figshare · 10.6084/m9.figshare.24961491lines:1055-1061Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 7 Sept 2026
Abstract Sesame ( Sesamum indicum ) is an important oilseed crop with rising demand due to its high oil quality. To meet these future demands, there is an urgent need to develop and integrate new breeding strategies. While genomic resources have advanced genetic research in sesame, implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11 conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.
Why it matches plant phenotyping methodsセサマの時系列高スループット表現型計測と解析を用い、複数の植物形質を縦断的に取得して遺伝予測・関連解析へ活用しており、表現型プラットフォームの実質的な適用が研究の中心です。
abstractHere, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel.
Published1 Jan 2024Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Sesame is a crucial oilseed crop that contains vital fatty acids. The objective of this study was to build calibration equations using near-infrared reflectance spectroscopy for quality screening of sesame. A total of 136 sesame samples were scanned in the reflectance mode and their wet chemistry was determined by n-hexane extraction and gas chromatography mass spectroscopy. Models for oil and four fatty acids were developed with 110 samples and had an acceptable value of calibration coefficient of determination (R²c), with suitable one minus the ratio of unexplained variance divided by variance (1-VR) value were found except palmitic acid. The prediction of an external validation with 26 datasets revealed a blameless correlation between reference values and NIRS values based on the coefficient of determination of validation (R²ᵥ) and relative prediction deviation (RPDᵥ). The models for oil, stearic, oleic, and linoleic acids had suitable values of coefficient of determination of validation and relative prediction deviation, which were more than 2.0 and 0.8, respectively. As a result of this research, it was discovered that the NIRS technology could be used to examine the oil and fatty acid contents of sesame seed qualities directly in breeding program, standard agency and commodity exchanges.
Why it matches plant phenotyping methodsゴマ種子の油分・脂肪酸含量を推定するNIRS校正式を開発し、外部検証まで実施しており、育種で利用可能な植物形質の非破壊測定法が研究の中心である。
abstractThe objective of this study was to build calibration equations using near-infrared reflectance spectroscopy for quality screening of sesame.
Summary One of the most significant factors that influence the globalized economy is agriculture. In order to address the requirement of increasing populations in terms of food necessities, modernizations and technological progressions in agriculture, it is necessary to implement a smart agricultural system. Various traditional techniques are still utilized by the farmers and their intuition in agriculture is not enough to furnish and deliver various issues namely soil management, plant disease identification, weed management, irrigation management, and so forth. Therefore, in this paper, a low‐cost smart farming automation system is evaluated and presented. Since the development of a smart farming automation system minimizes the labor cost and enhances agricultural production level, this paper proposes a deep neural network based Interactive fuzzy Bayesian search (DNN‐IFBS) algorithm for a low‐cost smart farming automation system. In addition to this, the crop water stress index (CWSI) is computed to determine the water status of the plant by employing solar irradiation, canopy temperature, and so forth. The data for analysis is collected simultaneously from three cameras containing image resolutions of about 650 × 460 pixels each. Two different plants namely the melon and sesame are utilized as datasets for experimentation. Finally, the performances of the proposed approach are determined according to the statistical performance measures namely accuracy, specificity as well as F ‐measure. The comparative analysis is carried out to evaluate the effectiveness of the proposed system. From the evaluation results, the accuracy rate obtained for the proposed approach is 98.7%.
Why it matches plant phenotyping methods低コスト農業自動化システムの開発が中心で、画像データとCWSIから植物の水分状態を推定する技術を評価しているため、植物フェノタイピング手法として含める。
abstractthis paper proposes a deep neural network based Interactive fuzzy Bayesian search (DNN‐IFBS) algorithm for a low‐cost smart farming automation system
On-farm genotype screening is at the core of every breeding scheme, but it comes with a high cost and often high degree of uncertainty. Phenomics is a new approach by plant breeders, who use optical sensors for accurate germplasm phenotyping, selection and enhancement of the genetic gain. The objectives of this study were to: (1) develop a high-throughput phenotyping workflow to estimate the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge index (NDRE) at the plot-level through an active crop canopy sensor; (2) test the ability of spectral reflectance indices (SRIs) to distinguish between sesame genotypes throughout the crop growth period; and (3) identify specific stages in the sesame growth cycle that contribute to phenotyping accuracy and functionality and evaluate the efficiency of SRIs as a selection tool. A diversity panel of 24 sesame genotypes was grown at normal and late planting dates in 2020 and 2021. To determine the SRIs the Crop Circle ACS-430 active crop canopy sensor was used from the beginning of the sesame reproductive stage to the end of the ripening stage. NDVI and NDRE reached about the same high accuracy in genotype phenotyping, even under dense biomass conditions where “saturation” problems were expected. NDVI produced higher broad-sense heritability (max 0.928) and NDRE higher phenotypic and genotypic correlation with the yield (max 0.593 and 0.748, respectively). NDRE had the highest relative efficiency (61%) as an indirect selection index to yield direct selection. Both SRIs had optimal results when the monitoring took place at the end of the reproductive stage and the beginning of the ripening stage. Thus, an active canopy sensor as this study demonstrated can assist breeders to differentiate and classify sesame genotypes.
Why it matches plant phenotyping methodsゴマ遺伝子型の表現型を推定するための作物群落センサーによる高スループット測定ワークフローを開発・評価しており、センサー取得と選抜性能の検証が中心である。
abstractdevelop a high-throughput phenotyping workflow to estimate the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge index (NDRE) at the plot-level through an active crop canopy sensor