The intrinsic properties of plants offer numerous opportunities for scientific and technological advancement. Considerable efforts have been directed toward developing plant-on-chip platforms to investigate cellular responses to external stimuli, including chemical, mechanical, and electrical cues. In this study, we present a fluidic platform using polydimethylsiloxane (PDMS) and a printed circuit board (PCB), integrated with electrochemical impedance spectroscopy (EIS) detection. Various experimental conditions were examined, including ionic and pH stimulation, as well as membrane dimensions, with the onion inner membrane treated as a black-box system. The measurement results are presented as Nyquist plots, and a resistance model incorporating multifactorial influences is proposed. Impedance variations in plant cells serve as a basis for electrical modulation. To explore these properties, we converted acoustic signals into electrical inputs and recorded the outputs after being modulated by onion inner epidermal cells. A transfer function analysis was subsequently performed. Our results indicate that the plant cell-on-chip (PCOC) platform holds promise for further investigations into plant cell properties. The impedance results suggest that plant cells can respond to different external stimuli, enabling modulation of the electrical properties. These findings lay the groundwork for future studies on cellular electrical characteristics and the development of preliminary bioelectrical circuits.
Why it matches plant phenotyping methods植物細胞の電気的生理状態を測定・解析するEISベースのオンチップ基盤を開発しており、植物状態の取得方法が研究の中心である。
abstractwe present a fluidic platform using polydimethylsiloxane (PDMS) and a printed circuit board (PCB), integrated with electrochemical impedance spectroscopy (EIS) detection
Traditional visual inspection is replaced by plant disease detection (PDD), which integrates plant pathology, image processing, remote sensing, agronomy and machine learning. Remote sensing tools and high-resolution drone imagery enhance real-time disease monitoring. However, challenges like data scarcity and algorithm generalization make accurate and automated disease diagnosis more complex. In this manuscript, Improved Agricultural Monitoring through Automated Detection and Classification of Purple Blotch and Yellow Blotch Diseases on Onion Leaves utilizing Dual Branch Graph Neural Network (ADC-PBYBD-OL-DBGNN) is proposed. The onion leaf dataset was first collected in the Karnataka village of Chilwadigi, which has a variety of climates. Then the collected images are preprocessed using Distributed Adaptive Spatial Filtering (DASF) is used for resizing and cropping. The preprocessed images are fed to Onion Leaf Disease Detection utilizing Dual Branch Graph Neural Network (DBGNN) for detecting and classifying as Healthy, Iris Yellow Virus, Purple Blotch and Leaf Blight. DBGNN generally doesn't show any adaptation of optimization techniques for figuring out the best parameters to guarantee precise onion leaf detection. Hence, Superb Fairy-wren Optimization Algorithm (SFOA) is utilized to optimize DGRGNN for precisely classifying the onion leaves. Then the proposed ADC-PBYBD-OL-DBGNN is implemented and the performance metrics like Accuracy, Precision, Recall, F1 score, Computational time are analyzed. Finally, the performance of proposed ADC-PBYBD-OL-DBGNN method provides 26.68%, 25.75%, and 26.16% higher accuracy and 27.49%, 24.75%, and 25.85% higher precision while compared with existing methods such as the onion plant leaf image dataset for classification and detection (OPLM-CD-Xpection), the onion and maize image datasets for creating AI-based classification models for pests and diseases (OMI-PD-CNN) and the meta-learning based neural architecture search for automated generation of deep neural networks for plant disease detection tasks (SAG-PDD-DNN) correspondingly.
Why it matches plant phenotyping methodsタマネギ葉の画像から病害状態を検出・分類する画像ベースの植物フェノタイピング手法を提案し、性能比較まで行っており、方法開発が中心である。
Background Accurate pre-harvest yield estimation of underground bulb crops such as onion and garlic is important for precision agriculture, harvest planning, and food-security-oriented decision-making. However, their harvestable organs develop below ground and cannot be directly observed using conventional remote sensing methods. This study aimed to develop a non-destructive yield estimation framework by integrating UAV-based hyperspectral imaging with hybrid machine learning models. Method Field experiments were conducted in Muan-gun, Korea, using onion and garlic as representative underground bulb crops. UAV-based hyperspectral images, crop growth traits, and destructive live bulb weight measurements were collected during the growing period. Hyperspectral images were processed through geometric correction, radiometric correction, and Savitzky-Golay spectral smoothing. Three dimensionality reduction methods, including genetic algorithm (GA), principal component analysis (PCA), and clustering, were used to reduce spectral redundancy. Five prediction models, including random forest (RF), XGBoost, partial least squares regression (PLSR), multilayer perceptron (MLP), and residual network (ResNet), were then evaluated for live bulb weight prediction. Result Significant spectral differences were observed in the 550-680 nm and 730-800 nm bands, which were closely associated with crop yield and below-ground bulb development. GA was the most effective feature selection method for extracting yield-related spectral bands. For onion yield prediction, the GA + RF model achieved the highest predictive accuracy, with an R 2 of 0.9656 and an NRMSE of 18.55%. For garlic yield prediction, PLSR showed the best performance, with an R 2 of 0.9260 and an NRMSE of 27.20%. Conclusion The proposed UAV-based hyperspectral framework enables accurate, real-time, and non-destructive yield estimation for underground bulb crops. This approach reduces reliance on labor-intensive destructive sampling and provides a practical tool for precision crop monitoring and data-driven agricultural management.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習による地下球根の収量（生体球重）推定フレームワークの開発・比較評価が研究の中心であり、植物形質の取得・推定手法に該当する。
abstractThis study aimed to develop a non-destructive yield estimation framework by integrating UAV-based hyperspectral imaging with hybrid machine learning models.
Plant diseases are one of the major challenges faced in modern agriculture, as they directly impact crop productivity, food quality, and the overall economic stability of farmers. Various environmental factors such as climate change, excessive moisture, poor soil conditions, and pest attacks contribute to the rapid spread of plant diseases. Traditional methods of disease identification mainly rely on manual inspection by agricultural experts, which is time-consuming, costly, and often inaccurate during the early stages of infection. Therefore, there is a growing need for an automated, fast, and reliable plant disease detection system that can assist farmers in identifying diseases at an early stage and taking appropriate preventive actions. This project presents an intelligent plant disease detection system that utilizes image processing, Machine learning, and machine learning techniques for accurate disease identification in crops such as Onion, Brinjal, Mango, Papaya, and Guava. The system is designed to analyze images of plant leaves captured through cameras or mobile devices. Using advanced image preprocessing methods, the captured leaf images are enhanced and processed to extract important features such as color, texture, and disease patterns. These features are then analyzed using a Convolutional Neural Network (CNN) model, which classifies the plant as either healthy or diseased with high accuracy. If a disease is detected, the system further identifies the specific type of disease affecting the plant and provides suitable recommendations for treatment and prevention. These recommendations include appropriate fertilizers, pesticides, organic supplements, and preventive agricultural practices customized for each crop type. The system also helps farmers understand the severity of the disease and suggests measures to minimize its spread to nearby plants. By providing real-time analysis and accurate predictions, the proposed solution reduces dependency on manual monitoring and expert consultation. The main objective of this project is to support precision agriculture by enabling early disease diagnosis, improving crop management efficiency, and increasing agricultural productivity. The automated detection process saves time, reduces crop losses, minimizes excessive pesticide usage, and promotes sustainable farming practices. Furthermore, this system can be integrated into smart farming applications and mobile-based agricultural support systems, making it accessible and beneficial for farmers in rural and urban areas alike
Why it matches plant phenotyping methods植物葉画像から健康・罹病状態と病害種を推定する画像処理・CNN手法が研究の中心であり、植物病害状態の表現型取得に該当する。ただし処置推奨は付随的である。
abstractThis project presents an intelligent plant disease detection system that utilizes image processing, Machine learning, and machine learning techniques for accurate disease identification in crops such as Onion, Brinjal, Mango, Papaya, and Guava.
Effectively imaging the variation of heavy metal induce stress (HMIS) in plant is significantly important for stress resistance research in the fields of environmental and plant biology. However, due to the absence of distinctive parameter to reveal the relationship between HMIS and plant homeostasis, the reported fluorescence sensors fail to assess HMIS. Herein, a new fluorescent sensor (quinoline-based viscosity probe, QVP) with prominent-responsive viscosity was first developed for evaluating HMIS in plants. Spectral experiments indicate that QVP exhibited selectivity, sensitive, photochemical stability, and pH adaptability for viscosity detection. Motivated by the robust detection capacities, QVP was further applied for clear fluorescence imaging of viscosity changes of plant cell (onion epidermis and scallion bulb) induced by HMIS (Cu 2+ , Au 3+ and Ag + ). Notably, the cellular viscosity was positively correlated with Cu 2+ concentration. More importantly, the sensor QVP had good penetration within plant tissues and enabled viscosity imaging of root hairs, leaves and other tissues. This work not only provides a novel molecular tool for understanding HMIS resistance of the plant by investigating the dynamic change of intracellular viscosity, but also provides an additional dimension for evaluating crop stress resistance.
Why it matches plant phenotyping methods植物細胞・組織の細胞内粘度を蛍光イメージングで測定し、金属イオンストレスを評価する新規センサーを開発・適用しており、植物状態の取得方法が研究の中心である。
abstractHerein, a new fluorescent sensor (quinoline-based viscosity probe, QVP) with prominent-responsive viscosity was first developed for evaluating HMIS in plants.
Accurate identification of Allium seed genotypes is essential for cultivar authentication, breeding, and fraud prevention, yet remains challenging due to morphological similarities. This study evaluates the potential of a visible and near-infrared (Vis-NIR) spectrometer and a hyperspectral camera for non-destructive classification of seven closely related Allium genotypes, including shallot, red, white, and yellow onions, bon-sorkh, and two leek varieties. A total of 700 spectra and 70 images were acquired using the Vis-NIR spectrometer and hyperspectral camera, respectively, under controlled conditions and spectral preprocessing was applied to enhance signal quality. For spectrometer data, classification models were developed using soft independent modelling of class analogy (SIMCA), artificial neural networks (ANN), and histogram-based gradient boosting (HisGB). For hyperspectral data, pixel-level spectra were used to train ANN, HisGB, and deep convolutional neural networks (1D and 2D CNNs). Among the spectrometer models, the combination of second derivative preprocessing with HisGB achieved the highest performance (F1-score: 98.52%). For HSI, HisGB yielded the highest pixel-level classification accuracy (F1-score: 97.83%; error: 2.49%), followed by 1D CNN (F1-score: 96.85%). Spatial analysis revealed that HisGB and 1D CNN produced consistent classification maps across genotypes, whereas ANN and 2D CNN exhibited higher misclassification rates, particularly for morphologically similar classes such as shallot and bon-sorkh. At image level, the hyperspectral camera outperformed the Vis-NIR spectrometer, achieving perfect classification across all models. These results demonstrate the potential of hyperspectral imaging, especially when combined with ensemble and deep learning approaches, for high-throughput, non-destructive seed sorting and genotype purity assessment. The study also emphasizes the trade-off between the lower cost but reduced precision of the Vis-NIR spectrometer and the superior accuracy offered by the hyperspectral camera.
Why it matches plant phenotyping methodsAllium種子の遺伝型識別を対象に、Vis-NIR分光およびハイパースペクトル画像取得と分類ワークフローを比較・評価しており、非破壊的な表現型取得・判別手法が研究の中心です。
abstractThis study evaluates the potential of a visible and near-infrared (Vis-NIR) spectrometer and a hyperspectral camera for non-destructive classification of seven closely related Allium genotypes
본 연구는 UAV 기반 다중분광 영상을 활용하여 양파 및 마늘 재배지에서 작물 분할 기법이 식생지수 기반 생산량 예측 성능에 미치는 영향을 분석하는 것을 목적으로 한다. 기존 연구에서는 처리구(plot)별 ROI 평균을 이용한 식생지수 산출이 주로 활용되었으나, 작물 분할 전략 자체가 예측 성능에 미치는 영향은 충분히 검토되지 않았다. 이에 본 연구에서는 비지도 학습 기반작물 분할(none, otsu-NDVI, k-means, GMM)을 적용하고, 분할 방법에 따른 처리구 단위 식생지수 특성과 예측 성능 차이를 비교하였다. 6개 생육 시점의 UAV 다중분광 영상으로부터 분할된 식생 영역 대상으로 9개 식생지수를 산출하고, PLS 기반으로 차원 축소하고 머신러닝 모델을 이용해 생산량을 예측하였다. 모델 성능은 처리구 단위 Leave-one-out 교차검증으로 평가하였다. 분석결과, 작물 분할 적용 시 식생지수 분포가 유의하게 변화하였으며, 반복측정 분산분석을 통해 분할 방법과 생육 시점 간 상호작용 효과가 확인되었다. 이러한 입력 변수 특성의 개선은 생산량 예측 성능 향상으로 이어졌고, 특히 군집 기반 분할에서 RMSE 및 MAPE가 가장 크게 감소하였다.
Why it matches plant phenotyping methodsUAV多波長画像における作物分割手法を比較し、植生指数から収量を推定する方法と性能を評価しており、表現型取得・抽出が研究の中心である。
abstract작물 분할 전략 자체가 예측 성능에 미치는 영향은 충분히 검토되지 않았다.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Despite their relevance to postharvest engineering and cultivar improvement, the genotype- and environment-dependent variation of the physical and geometrical traits of onion bulb remains poorly characterized in the Republic of Korea. The study evaluated these traits in six commercial onion cultivars grown across two distinct production regions (Muan and Changnyeong), using a randomized complete block design with three replications. A standardized phenotyping workflow combined with image acquisition and imageJ-based trait extraction was employed to measure linear dimensions, including polar and equatorial diameters, neck and bulb thickness. Linear mixed models were used to partition genotype (G), location (L), and G x L interaction effects. Most traits exhibited significant G, L, and G×L effects, indicating strong environmental sensitivity alongside genetic control Combined heritability estimates were high for bulb thickness (0.83), bulb weight (0.66), and diameter- and area-related traits (0.54–0.68), while broad-sense heritability across locations was consistently high (0.71–0.99), particularly for single bulb weight and size traits. Spring Breeze, Katamaru, and Healthy Q consistently produced larger bulbs, while Cheonjujeok and Eomji Nara exhibited smaller bulb dimensions. Trait responses varied markedly between environments, with changes ranging from reduction of approximately 80% to increases exceeding 160%, highlighting pronounced genotype × environment interactions. Hierarchical cluster heatmap analysis revealed strong associations among bulb size–related traits and distinct genotype groupings, with clear location-dependent differences in trait expression between Muan and Changnyeong. These findings demonstrate the utility of image-based phenotyping for robust environment-aware assessment of onion bulb geometry and provides a quantitative basis for region-specific cultivar selection, postharvest system design, and future multi-site breeding evaluations.
Why it matches plant phenotyping methods画像取得とImageJによる形質抽出を組み合わせた標準化フェノタイピングワークフローが、タマネギ球の形態形質測定の中心的手法として明示されているため。
abstractA standardized phenotyping workflow combined with image acquisition and imageJ-based trait extraction was employed to measure linear dimensions
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Precise, non-destructive phenotyping of saffron during vegetative growth is critical for optimizing corm yield and accelerating breeding programs, yet systematic 3D measurements have remained elusive due to extreme morphological challenges: ultra-narrow leaves, severe mutual occlusion, and prostrate growth architecture. Traditional single-view imaging systems fail to resolve individual leaves under such conditions, limiting phenotypic analysis to whole-canopy descriptors. Here, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction. RESULTS: > 0.94, MAPE < 6%), achieving accuracy benchmarks established for broad-leaved crops using commercial-grade hardware at 100 × lower cost. Systematic voxel sensitivity analysis across nine scales identified optimal preprocessing parameters (2 cm voxel size) balancing measurement precision with computational efficiency, addressing a critical reproducibility gap in plant phenotyping. Exploratory longitudinal tracking revealed that above-ground biomass was correlated with final corm yield (r = 0.68, P < 0.001), with mid-vegetative canopy volume also showing strong correlation (r = 0.52, P < 0.01), suggesting potential resource allocation trade-offs between vegetative expansion and storage organ development. CONCLUSIONS: This work demonstrates that organ-level 3D phenotyping of narrow, overlapping leaves is achievable using low-cost imaging hardware and transparent methodological workflows. Complete documentation of algorithmic parameters and hardware specifications enables direct replication and adaptation to other narrow-leaved crops (wheat, rice, onion, leek), democratizing access to high-throughput phenotyping in resource-limited settings. The workflow advances plant phenomics by demonstrating that methodological transparency and cost-effectiveness need not compromise measurement precision, opening new avenues for phenotype-to-genotype mapping and predictive breeding in underutilized crops.
Why it matches plant phenotyping methods低コストの双眼カメラとSfM-MVSによるサフラン葉の器官レベル3D形質取得ワークフローを開発し、精度検証、再現性、パラメータ最適化まで扱っており、植物フェノタイピング手法が研究の中心である。
abstractHere, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction.
The integration of unmanned aerial vehicle (UAV)-assisted remote sensing with the Internet of Things (IoT) and Internet of Everything (IoE) offers a robust platform for optimizing precision agriculture by capturing spatiotemporal variability in crop growth. In this context, the present study aimed to predict the bulb yield of rainy-season onion crops across four staggered planting dates using UAV-based multispectral imagery. Canopy reflectance mosaics acquired at key growth stages, along with vegetation indices (VIs), viz. NDVI, NDRE, SAVI, LAI, NORM2, and GNDVI, were extracted for yield modeling. Yield prediction models at three onion growth stages were developed and assessed using five machine learning algorithms: linear regression (lm), random forest (rf), support vector machine with radial kernel (svmRadial), gradient boosting (gbm), and elastic net regression (glmnet), with model training and evaluation performed using 10-fold cross-validation. Among these, random forest consistently outperformed the other models at all growth stages, showing promising results at the bulb development stage, with a training R 2 = 0.944, RMSE = 1.919 t ha -1 , MAE = 1.523 t ha -1 , and a validation R² = 0.755, RMSE = 3.824 t ha -1 , and MAE = 3.11 t ha -1 . The support vector machine also demonstrated strong generalization (training R² = 0.787; validation R 2 = 0.716), highlighting its predictive capability. Year-wise evaluation revealed notable interannual variability in model performance, with models trained on data from 2024 outperforming those from 2023. Overall, these results demonstrate the efficacy of UAV-derived multispectral sensing, combined with machine learning, as an effective, scalable, and timely approach for reliable onion yield prediction and decision support in rainy-season onion crops under varying agronomic conditions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からタマネギの収量を推定する取得・解析ワークフローと機械学習モデルを開発・評価しており、植物表現型の測定が中心である。
abstractthe present study aimed to predict the bulb yield of rainy-season onion crops across four staggered planting dates using UAV-based multispectral imagery.
Endogenous ethylene production occurs across biological kingdoms, yet its pathophysiological roles remain incompletely defined. In situ detection of ethylene is impeded by its inherent volatility and chemical inertness. Here, we report BORh, a new turn-on fluorescent probe that operates via ethylene-triggered displacement of a rhodium quencher from a BODIPY-rhodacycle scaffold, liberating the intensely fluorescent BOET. BORh exhibits high sensitivity and selectivity, broad pH tolerance, and negligible cytotoxicity. In mammalian PC12 cells, it permits real-time visualization of both exogenously supplied and in situ generated ethylene. Within photosynthetic systems, BORh overcomes cell wall barriers and chlorophyll autofluorescence, enabling in situ monitoring of ethylene dynamics in algae ( Chlamydomonas reinhardtii ) and higher plant ( Arabidopsis thaliana and Allium cepa ) tissues. Remarkably, BORh-enabled fluorescence imaging revealed synchronous upregulation of ethylene and reactive oxygen species (ROS) under plant abiotic stress. H 2 O 2 exhibited concentration-dependent biphasic regulation of ethylene biosynthesis, whereas ethylene exerted no reciprocal effect on ROS generation. These findings establish ROS as upstream regulators of ethylene biosynthesis within plant stress signaling cascades. BORh emerges as a robust chemical tool for spatiotemporal dissection of ethylene biochemistry, offering new insights into ROS-ethylene crosstalk during plant stress responses and paving the way for future investigations of ethylene function in mammalian pathophysiology.
Why it matches plant phenotyping methods植物組織内のエチレン動態を可視化・測定する蛍光プローブを開発し、植物での性能を実証しているため、植物生理状態の取得法が中心である。
abstractHere, we report BORh, a new turn-on fluorescent probe
Unmanned aerial vehicles (UAVs) are transforming agriculture through enhanced data acquisition, improved monitoring efficiency, and support for data-driven decision-making. Complementing this, AI-driven platforms provide intuitive and reliable tools for advanced UAV analytics. However, their integration remains underexplored, particularly in specialty crops. Therefore, in this study, we evaluated the performance of an AI-driven web platform (Solvi) for automated plant counting and biometric trait estimation in two contrasting systems: pecan, a perennial nut crop, and onion, an annual vegetable. Ground-truth measurements included pecan tree number, tree height, and canopy area, as well as onion bulb number and diameter, the latter used for market class classification. Counting performance was assessed using precision, recall, and F1 score, while trait estimation was evaluated with linear regression analysis. UAV-based counts showed strong agreement with ground-truth data, achieving precision, recall, and F1 scores above 97% for both crops. For pecans, UAV-derived estimates of tree height (R2 = 0.98, error = 11.48%) and canopy area (R2 = 0.99, error = 23.16%) demonstrated high accuracy, while errors were larger in young trees compared with mature trees. For onions, UAV-derived bulb diameters achieved an R2 of 0.78 with a 6.29% error, and market class classification (medium, jumbo, colossal) was predicted with
Why it matches plant phenotyping methodsUAV画像とAIプラットフォームによる植物個体数・樹高・樹冠面積・球根径の自動推定を評価しており、表現型取得手法の性能評価が研究の中心です。
abstractwe evaluated the performance of an AI-driven web platform (Solvi) for automated plant counting and biometric trait estimation
Accurate estimation of total leaf area (TLA) is essential for assessing plant growth, photosynthetic activity, and transpiration, but remains a challenge for bushy plants like dwarf tomatoes. Traditional destructive methods and imaging-based techniques often fall short due to labor intensity, plant damage, or the inability to capture complex canopies. This study evaluated a non-destructive method combining sequential 3D reconstructions from RGB images and machine learning to estimate TLA for three dwarf tomato cultivars—Mohamed, Hahms Gelbe Topftomate, and Red Robin—grown under controlled greenhouse conditions. Two experiments, conducted in spring–summer and autumn–winter, included 73 plants, yielding 418 TLA measurements using an “onion” approach, where layers of leaves were sequentially removed and scanned. High-resolution videos were recorded from multiple angles for each plant, and 500 frames were extracted per plant for 3D reconstruction. Point clouds were created and processed, four reconstruction algorithms (Alpha Shape, Marching Cubes, Poisson’s, and Ball Pivoting) were tested, and meshes were evaluated using seven regression models: Multivariable Linear Regression (MLR), Lasso Regression (Lasso), Ridge Regression (Ridge-Reg), Elastic Net Regression (ENR), Random Forest (RF), extreme gradient boosting (XGBoost), and Multilayer Perceptron (MLP). The Alpha Shape reconstruction (α = 3) combined with XGBoost yielded the best performance, achieving an R² of 0.80 and MAE of 489 cm², with significant results across other model combinations. Results were lower when using data from different experiments as train and test datasets (R² = 0.56 and MAE = 579 cm²). Feature importance analysis identified height, width, and surface area as the most predictive features. These findings demonstrate the robustness of our approach across variable environmental conditions and canopy structures. This scalable, automated TLA estimation method is particularly suited for urban farming and precision agriculture, offering practical implications for automated pruning, improved resource efficiency, and sustainable food production.
Why it matches plant phenotyping methodsRGB画像からの3D再構成と機械学習により植物の総葉面積を推定する手法を開発・評価しており、表現型取得が研究の中心です。
abstractThis study evaluated a non-destructive method combining sequential 3D reconstructions from RGB images and machine learning to estimate TLA
Background Phenotypic characterization of onion germplasm is requisite for designing breeding programs, and for meeting industrial processing, and marketing demands. Onion bulb morphology, and geometrical properties, which are the physical and spatial dimensions and shape characteristics influence consumer and market demand, as well as suitability for processing and mechanizing post-harvest handling. Many previous studies employed manual tools such as Vernier calipers for measurement of onion bulb parameters, which is time-consuming. The emergence and application of phenomics tools such as digital cameras are more convenient for rapid phenotypic characterization. Aim This study aimed to investigate the phenotypic variability of 29 onion accessions based on ten qualitative and twelve quantitative bulb characteristics. Methodology Freshly harvested onion bulbs ( n = 10/accession) were obtained from the Allium Vegetable Research Institute (AVRI), at Muan-Gun, Republic of Korea. A digital camera was used to capture images of the bulbs. The images were saved in JPEG file format, and uploaded into ImageJ software for measurement of linear dimensions, including polar diameter, equatorial diameter, transverse diameter or thickness. To ensure accurate measurement, images were first calibrated, using the straight line tool and the "Set scale" function in the software. Results of the linear dimensions were then used for estimating other geometrical properties, such as aspect ratio, sphericity, and geometric and arithmetic mean diameters. Results Our findings revealed a broad range of phenotypic variation within the germplasm. Polar and equatorial diameters ranged from 4.731 to 11.998 cm, and from 4.54 to 10.196 cm, with mean values of 9.213 and 7.472 cm, respectively. Also, geometric and arithmetic mean diameters ranged from 4.224 to 10.484 cm, and from 4.257 to 10.569 cm, with corresponding mean of 7.901 and 7.980 cm, respectively. Principal component analysis grouped the accessions into three distinct clusters, with cluster three composing the highest number of accessions. Strong significant positive associations were observed among several traits. For instance, polar diameter correlated strongly with polar diameter and transverse diameter ( r > 0.97), geometric and arithmetic mean diameters ( r > 0.98), surface area ( r > 0.96), frontal surface area ( r > 0.94), cross sectional area ( r > 0.96), equatorial diameter ( r > 0.83), and thickness of neck ( r > 0.84). High to moderate broad sense heritability and genetic gain were estimated for several traits. Conclusion Overall, the significant variability within the onion germplasm provides a potential for breeding new cultivars to meet consumer and industrial requirements. The results also provide information vital for future genomic and metabolite studies.
Why it matches plant phenotyping methodsタマネギ球の形態・幾何形質をデジタル画像とImageJで取得・算出するワークフローが、遺伝資源の表現型評価の中心であるため。
abstractThe emergence and application of phenomics tools such as digital cameras are more convenient for rapid phenotypic characterization.
Onion crops are affected by many diseases at different stages of growth, resulting in significant yield loss. The early detection of diseases helps in the timely incorporation of management practices, thereby reducing yield losses. However, the manual identification of plant diseases requires considerable effort and is prone to mistakes. Thus, adopting cutting-edge technologies such as machine learning (ML) and deep learning (DL) can help overcome these difficulties by enabling the early detection of plant diseases. This study presents a cross layer integration of YOLOv8 architecture for detection of onion leaf diseases viz .anthracnose, Stemphylium blight, purple blotch (PB), and Twister disease. The experimental results demonstrate that customized YOLOv8 model YOLO-ODD integrated with CABM and DTAH attentions outperform YOLOv5 and YOLO v8 base models in most disease categories, particularly in detecting Anthracnose, Purple Blotch, and Twister disease. Proposed YOLOv8 model achieved the highest overall 77.30% accuracy, 81.50% precession and Recall of 72.10% and thus YOLOv8-based deep learning approach will detect and classify major onion foliar diseases while optimizing for accuracy, real-time application, and adaptability in diverse field conditions.
Why it matches plant phenotyping methodsタマネギ葉の病害状態を画像から検出・分類するYOLOv8改良手法の開発と比較評価が中心であり、植物病害フェノタイピング手法に該当する。
abstractThis study presents a cross layer integration of YOLOv8 architecture for detection of onion leaf diseases viz .anthracnose, Stemphylium blight, purple blotch (PB), and Twister disease.
Early detection of plant diseases is critical for sustainable agriculture and reducing crop losses. This study presents a real-time monitoring system integrating IoT and machine learning for the early detection of basal bulb rot disease in shallots. The system combines image data captured by an ESP32-CAM and soil pH data from a sensor to provide timely alerts to farmers. The images undergo preprocessing using Gaussian filtering and histogram equalization, while pH data is smoothed using a moving average filter. Features such as color, texture, shape, and pH dynamics are extracted and analyzed using a hybrid classification model comprising MobileNetV2 for image-based disease identification and Random Forest for soil pH classification, fused at the decision level. The models were optimized for edge deployment using TensorFlow Lite and field-tested under solar-powered conditions. Experimental results demonstrate a hybrid model accuracy of 93.7%, with recall and specificity of 94.1% and 92.8%, respectively. The system responds within 450 milliseconds, making it suitable for real-time applications. This solution offers a low-cost, scalable, and accurate method for precision agriculture, reducing dependence on manual inspections and enabling proactive disease management.
Why it matches plant phenotyping methodsタマネギの病徴を画像から分類・検出する手法と、リアルタイム監視システムの開発・評価が研究の中心であり、植物の病害状態を直接推定するため含める。土壌pH分類は補助的だが、画像による病害表現型推定が明確である。
abstractThis study presents a real-time monitoring system integrating IoT and machine learning for the early detection of basal bulb rot disease in shallots.
Main conclusion Our method can simultaneously quantify cell wall elasticity and turgor pressure of live plant cells through AFM measurements and calculations based on elastic shell theory. Abstract The morphological behavior of plant cells depends on their mechanical properties. Cell wall elasticity ( E ) and turgor pressure ( P ) are main factors that dominate the behavior. A method to simultaneously quantify them in live cells has yet to be established, hindering progress in plant mechanobiology. Recently, atomic force microscopy (AFM) has been used to analyze single cells based on Hertz’s contact theory (HCT). However, HCT cannot evaluate P . Several groups have attempted to evaluate P by adapting elastic shell theory (EST), but it is still difficult to estimate both E and P from the indentation data and EST alone. Herein an analytical method is proposed based on EST using the cell indentation and surface geometry from the AFM measurements. We also demonstrate the reliability of our approach under various osmotic pressure conditions and simultaneously determine the values of P and E in epidermal monolayer cells of an Allium cepa L. Supplementary Information The online version contains supplementary material available at 10.1007/s00425-025-04683-4.
Why it matches plant phenotyping methodsAFM測定と弾性殻理論に基づき、生細胞の細胞壁弾性と膨圧を定量する解析手法を開発し、浸透圧条件下で信頼性を検証しており、植物表現型取得が研究の中心です。
abstractOur method can simultaneously quantify cell wall elasticity and turgor pressure of live plant cells through AFM measurements and calculations based on elastic shell theory.
Background Plant growth and morphogenesis is a mechanical process controlled by genetic and molecular networks. Measuring mechanical properties at various scales is necessary to understand how these processes interact. However, obtaining a device to perform the measurements on plant samples of choice poses technical challenges and is often limited by high cost and availability of specialized components, the adequacy of which needs to be verified. Developing software to control and integrate the different pieces of equipment can be a complex task. Results To overcome these challenges, we have developed a computer automated micro-extensometer combined with low-cost optical tracking (Camelot) that facilitates measurements of elasticity, creep, and yield stress. It consists of three primary components: a force sensor with a sample attachment point, an actuator with a second attachment point, and a camera. To monitor force, we use a parallel beam sensor, commonly used in digital weighing scales. To stretch the sample, we use a stepper motor with a screw mechanism moving a stage along linear rail. To monitor sample deformation, a compact digital microscope or a microscope camera is used. The system is controlled by MorphoRobotX, an integrated open-source software environment for mechanical experimentation. We first tested the basic Camelot setup, equipped with a digital microscope to track landmarks on the sample surface. We demonstrate that the system has sufficient accuracy to measure the stiffness in delicate plant samples, the etiolated hypocotyls of Arabidopsis, and were able to measure stiffness differences between wild type and a xyloglucan-deficient mutant. Next, we placed Camelot on an inverted microscope and used a C-mount microscope camera to track displacement of cell junctions. We stretched onion epidermal peels in longitudinal and transverse directions and obtained results similar to those previously published. Finally, we used the setup coupled with an upright confocal microscope and measured anisotropic deformation of individual epidermal cells during stretching of an Arabidopsis leaf. Conclusions The portability and suitability of Camelot for high-resolution optical tracking under a microscope make it an ideal tool for researchers in resource-limited settings or those pursuing exploratory biomechanics work.
Why it matches plant phenotyping methods植物試料の力学的形質（弾性、クリープ、降伏応力、剛性、変形）を光学追跡で測定する装置とソフトウェアを開発しており、植物表現型の取得手法が研究の中心である。
abstractwe have developed a computer automated micro-extensometer combined with low-cost optical tracking (Camelot) that facilitates measurements of elasticity, creep, and yield stress.
Shallot plant diseases can reduce yields by up to 50% of total land area. Currently, shallot plant disease identification relies on direct observation, which is less effective and efficient due to varying intensities of disease and large cultivation areas. This study aims to develop a predictive model for shallot disease severity using multispectral drone imagery, apply Artificial Neural Network (ANN) algorithm to analyze multispectral band data, and evaluate the model's performance. The study used ANN algorithm with multi-layer perceptron regressor, involving following stages such as dataset acquisition, dataset stitching, dataset filtering and feature extraction, model development, and model evaluation. Multispectral data were taken using DJI Mavic 3 Multispectral drone, resulting 696 images per bands that were stitched into orthophoto map. The filtering process of plant objects yielded better model training results compared to unfiltered data. The optimal ANN model structure was identified as 4-6-2-1, with R² value of 0.9194 and MAE value of 0.0618. Model testing results demonstrated that using four input bands (G, R, RE, NIR) provided the best performance with R² value of 0.9194, followed by combination of two bands (R, RE) with R² value of 0.8883. This indicated that the R and RE bands were most strongly correlated with shallot disease severity. Keywords: Drone, Multi-layer perceptron, Multispectral imagery, Plant disease, Shallot.
Why it matches plant phenotyping methodsドローン multispectral画像から植物病害の重症度を推定するANNモデルを開発・評価しており、植物状態の取得・推定手法が研究の中心である。
abstractThis study aims to develop a predictive model for shallot disease severity using multispectral drone imagery, apply Artificial Neural Network (ANN) algorithm to analyze multispectral band data, and evaluate the model's performance.
Viscosity and hypochlorite (ClO - ) are two crucial microenvironmental species that play significant roles in biological activities. Their abnormal levels are closely associated with numerous common diseases. Therefore, accurate and real-time detection of hypochlorite and viscosity related to inflammatory microenvironment conduces to elucidate the pathogenesis and further diagnose the disease. In this work, based on the strategy of the phenothiazine (PTZ)-dicyanoisophorone (DCO) dyad system, a new dual-response fluorescent sensor (PBI) was successfully constructed for the simultaneous detection and visualization of viscosity and hypochlorite (ClO - ) both in vitro and in vivo. The free sensor emits weak fluorescence in aqueous solution thanks to twisted intramolecular charge transfer (TICT) and photoinduced electron transfer (PET). However, in a high-viscosity system, the fluorescence emission of the sensor at 459 nm was significantly enhanced. Upon introduction of ClO - in aqueous buffer solution, the PBI exhibited apparent fluorescence enhancement at 577 nm, and showed large Stokes shift (177 nm). The fluorescence responsive mechanism was confirmed using HRMS, 1 H NMR and DFT calculation analysis. Onion and lotus root cells imaging of PBI towards ClO - was implemented. Furthermore, PBI has been successfully applied to the fluorescence imaging of viscosity and exogenous/endogenous hypochlorite in zebrafish.
Why it matches plant phenotyping methods植物細胞内の粘度と次亜塩素酸を可視化する蛍光センサーの開発が中心であり、植物の生理状態を画像計測する方法として植物細胞で実証されている。
abstracta new dual-response fluorescent sensor (PBI) was successfully constructed for the simultaneous detection and visualization of viscosity and hypochlorite (ClO - ) both in vitro and in vivo.
Background: The dark green coloration of bunching onion leaf blades is a key determinant of market value, nutritional quality, and visual appeal. This trait is regulated by a complex network of pigment interactions, which not only determine coloration but also serve as critical indicators of plant growth dynamics and stress responses. This study aimed to elucidate the mechanisms regulating the dark green trait and develop a predictive model for accurately assessing pigment composition. These advancements enable the efficient selection of dark green varieties and facilitate the establishment of optimal growth environments through plant growth monitoring. Methods: Seven varieties and lines of heat-tolerant bunching onions were analyzed, including two commercial F1 cultivars, along with two purebred varieties and three F1 hybrid lines bred in Yamaguchi Prefecture. The analysis was conducted on visible spectral reflectance data (400-700 nm at 20 nm intervals) and pigment compounds (chlorophyll a , chlorophyll b and pheophytin a , lutein, and β-carotene), whereas primary and secondary metabolites were assessed by using widely targeted metabolomics. In addition, a random forest regression model was constructed by using spectral reflectance data and pigment compound contents. Results: Principal component analysis based on spectral reflectance data and the comparative profiling of 186 metabolites revealed characteristic metabolite accumulation associated with each green color pattern. The "green" group showed greater accumulation of sugars, the "gray green" group was characterized by the accumulation of phenolic compounds, and the "dark green" group exhibited accumulation of cyanidins. These metabolites are suggested to accumulate in response to environmental stress, and these differences are likely to influence green coloration traits. Furthermore, among the regression models for estimating pigment compound contents, the one for chlorophyll a content achieved high accuracy, with an R2 value of 0.88 in the test dataset and 0.78 in Leave-One-Out Cross-Validation, demonstrating its potential for practical application in trait evaluation. However, since the regression model developed in this study is based on data obtained from greenhouse conditions, it is necessary to incorporate field trial results and reconstruct the model to enhance its adaptability. Conclusions: This study revealed that cyanidin is involved in the characteristics of dark green varieties. Additionally, it was demonstrated that chlorophyll a can be predicted using visible spectral reflectance. These findings suggest the potential for developing markers for the dark green trait, selecting high-pigment-accumulating varieties, and facilitating the simple real-time diagnosis of plant growth conditions and stress status, thereby enabling the establishment of optimal environmental conditions. Future studies will aim to elucidate the genetic factors regulating pigment accumulation, facilitating the breeding of dark green varieties with enhanced coloration traits for summer cultivation.
Why it matches plant phenotyping methods可視スペクトル反射データから葉のクロロフィルa含量を推定する回帰モデルを構築・検証しており、植物形質の取得・推定法が中心的です。
abstractFurthermore, among the regression models for estimating pigment compound contents, the one for chlorophyll a content achieved high accuracy, with an R2 value of 0.88 in the test dataset and 0.78 in Leave-One-Out Cross-Validation, demonstrating its potential for practical application in trait evaluation.
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。Dataset · publicthe raw MS data can be downloaded from DROP Met database ( https://prime.psc.riken.jp/menta.cgi/prime/drop_index#DM0069 , accessed on 14 February 2025).Open asset ↗DROP Met · DM0069lines:156-172Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The advancement of digital technologies has significantly impacted plant pest and disease management, yet gaps remain, especially in developing regions. This paper introduces the TOM2024 dataset, a comprehensive collection of high-resolution images designed to enhance pest and disease identification of maize, tomato, and onion crops. The dataset encompasses 25,844 raw images and over 12,000 labeled images, categorized into 30 classes (healthy crop, infested crop, and pest) across the three cropping systems. Acquired through meticulous fieldwork in Burkina Faso using high-resolution cameras, the dataset includes diverse environmental conditions and crop stages, ensuring a robust resource for AI model training and validation. The dataset is segmented into three categories: processed images (Category A), selected images with augmentation (Category B), and an online repository with over 25,000 raw images (Category C). Category A and B features images of crops affected by 21 distinct pests and diseases. This dataset addresses critical gaps in existing collections by offering extensive coverage and high-resolution imagery that can be used to developed AI models for automatic identification and classification of pests and diseases that affects crops. TOM2024's versatility extends to research, educational purposes, and the practical application of digital tools in agriculture thereby contributes to the advancement of precision agriculture, sustainable agricultural practices, and food security globally.
Why it matches plant phenotyping methods植物の健全・感染状態を含む画像データセットを構築し、病害・害虫状態の自動分類モデル開発用リソースとして提供することが中心であり、再利用可能な画像ベース表現型データセットに該当する。
abstractThis paper introduces the TOM2024 dataset, a comprehensive collection of high-resolution images designed to enhance pest and disease identification of maize, tomato, and onion crops.
Reproduction assets foundThe paper is a Data in Brief article describing the TOM2024 dataset of tomato, onion, and maize pest/disease images, publicly deposited on Mendeley Data with an explicit direct URL and DOI. This is a paper-specific public image dataset (phenotyping-style plant image asset) directly produced by this paper.Dataset · publicrce location
West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL)
6 BP 9507
Ouagadougou, Burkina Faso
Tel: +226 25375423
Email: secretariat_cc@wascal.org
Website: www.wascal.org .
Data accessibility
Repository name: TOM2024
Data identification number: doi: 10.17632/3d4yg89rtr.1
Direct URL to data: https://data.mendeley.com/datasets/3d4yg89rtr/1
Related research articleOpen asset ↗10.17632/3d4yg89rtr.1lines:1-43Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published10 Jan 2025Advances and Applications in Discrete MathematicsCited by 0 · OpenAlex ↗
Detecting disorders in crops at an initial phase is important for improved agricultural productivity. Different diseases like purple blotch in onions affect crop quality worldwide. Traditional approaches for identifying purple blotch require time, wide examination, and frequent farm observation. With technical improvements in recent years, agriculturalists have been able to discover optimal solutions that have caused higher harvests. This article presents compact Convolutional Neural Network (CNN) architecture for onion disease (purple blotch) classification from crop images. This Onion Crop Disease Dataset (OCDD) contains 1000 images of healthy and infected crops. Four distinct architectures InceptionV3, Xception, EfficientNetB7, and DenseNet201 are compared. Subsequent trials for assessment are applied. DenseNet201 delivers 94.54% accuracy in comparison to other models.
Why it matches plant phenotyping methodsタマネギ画像から病害状態を分類するCNN手法の開発・比較・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 article presents compact Convolutional Neural Network (CNN) architecture for onion disease (purple blotch) classification from crop images.
Three-dimensional (3D) information on crops and soil undulations is important for precision agriculture.Light detection and ranging (LiDAR) has previously been used to obtain highly accurate 3D information.However, with recent advancements in digital technology, photogrammetry, which does not require special sensors, has attracted considerable attention.In this study, models using these two 3D reconstruction methods are compared and their corresponding applications are summarized.The results show that the standard deviation of the height difference between the 3D models of the two methods is 4.1 cm in a 1.7 ha onion field.However, it is inferred that LiDAR is more suitable than photogrammetry for understanding the 3D growth information of slender upright onion plants.
Why it matches plant phenotyping methodsタマネギの3D生育情報を取得するLiDARと写真測量の精度比較・評価が中心であり、植物形態のフェノタイピング手法の検証に該当する。
abstractIn this study, models using these two 3D reconstruction methods are compared and their corresponding applications are summarized.
Agricultural monitoring systems must provide timely and standardized information on crop production, status, and yield, from sub-regional to national scales. Accurate monitoring and mapping of vegetation condition and health are vital for managing crops, assessing damage, and predicting yields. Crop health monitoring is one of the important items for tracking the general health status of any crop. In this regard, remote sensing and GIS play a crucial role for monitoring crop health, providing current information that traditional methods like field surveys and sampling questionnaires struggle to obtain. Effective cropland mapping techniques are essential for regular crop monitoring. This type of monitoring demands frequents continuous data with high time and space resolution. Near real-time crop monitoring uses technologies like the Sentinel-2 satellite mission, offering a consistent 5-day revisit cycle and freely accessible data. This opens new doors for delivering timely updates and monitoring parcel-based crop health and conditions in real-time. Therefore, this study used satellite images, Global Positioning System (GPS) collected data, and parcel-based socioeconomic data. GPS and socioeconomic data were employed to validate the satellite-based near real-time crop monitoring results. Vegetation Condition Index (VCI) and the Normalized Difference Vegetation Index (NDVI) were used to evaluate crop health at different stages of the growing season and to generate time series data for crop phenology respectively. NDVI time series data was used to generate crop phenology information for four main crops: Teff, wheat, onion, and sorghum. The crop type maps for these crops at the study sites were validated with an overall accuracy of 79.26% and a Kappa value of 0.737. Additionally, the results from the current research and the field-collected data were consistent in providing information about the onset, greening, maturity, and senescence dates of each crop. These findings highlight the effectiveness of the satellite-based system for real-time agricultural crop monitoring using Sentinel-2 observations across various sites and time frames. Moreover, it helps to fill the gaps of traditional crop monitoring methods with those based on satellite technology. This system is particularly valuable for early warning purpose in areas like the current study site, where conventional crop monitoring methods and inputs are limited.
Why it matches plant phenotyping methodsSentinel-2衛星画像を用いて作物の健康状態と生育フェノロジーを圃場・区画レベルで推定し、現地データで検証する監視手法が研究の中心であるため、植物状態のリモートセンシング型フェノタイピングとして含める。
abstractremote sensing and GIS play a crucial role for monitoring crop health
Canopy coverage-based crop growth monitoring is highly dependent on the performance of crop segmentation algorithms. Under field conditions, crop segmentation for unmanned aerial vehicle (UAV) imagery should be sophisticated considering geometric distortion of images by wind and illumination variations. Under Korean cultivation conditions, a plastic mulch used to restrict weeds and prevent cold weather damage increases the complexity of the image background. In particular, on-site monitoring of onion and garlic growth has been limited by their morphology because they have long narrow leaves. The ultimate goal of this study was to quantify the growth parameters of onion and garlic at multiple growth stages using red, green, and blue (RGB) imagery obtained with UAVs. Canopy coverage and plant height were used as predictor variables to develop mathematical models to estimate the fresh weights of onion and garlic. The use of a CIE L*a*b* color space and mean shift (MS) algorithm enhanced the extraction of the canopy coverage of onion and garlic from complex backgrounds, including plastic mulch, soil, and shadows under varying illumination conditions. Multiple linear regression models consisting of the a* band-based vegetation fraction (VF) and structure from motion (SfM)-based plant height (PH) fitted the fresh weight data of onion and garlic well with high coefficients of determination (R²) ranging from 0.82 to 0.92. The validation results showed an almost 1:1 slope with highly linear relationships (R² > 0.82) between the onion and garlic fresh weights obtained with the UAV RGB imagery and actual fresh weights, confirming that the UAV-RGB imagery based on the use of the a*band and PH can be used to quantify the spatial and temporal variability of onion and garlic growth parameters during the growing season.
Why it matches plant phenotyping methodsUAV RGB画像の分割・SfM解析により、タマネギとニンニクのキャノピー被覆、草丈、成長量・新鮮重を推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractThe ultimate goal of this study was to quantify the growth parameters of onion and garlic at multiple growth stages using red, green, and blue (RGB) imagery obtained with UAVs.
This study presents PlanteSaine, a novel mobile application powered by Artificial Intelligence (AI) models explicitly designed for maize, tomato, and onion farmers in Burkina Faso. Agriculture in Burkina Faso, like many developing nations, faces substantial challenges from plant pests and diseases, posing threats to both food security and economic stability. PlanteSaine addresses these challenges by offering a comprehensive solution that provides farmers with real-time identification of pests and diseases. Farmers capture images of affected plants with their smartphones, and PlanteSaine's AI system analyzes these images to provide accurate diagnoses. The application's offline functionality ensures accessibility even in remote areas with limited internet connectivity, while its messaging feature facilitates communication with agricultural authorities for guidance and support. Additionally, PlanteSaine includes an emergency alert mechanism to notify farmers about pest and disease outbreaks, enhancing their preparedness to deal with these threats. An AI-driven framework, featuring an image feature extraction phase with EfficientNetB3 and an artificial neural network (ANN) classifier, was developed and integrated into PlanteSaine. The evaluation of PlanteSaine demonstrates its superior performance compared to baseline models, showcasing its effectiveness in accurately detecting diseases and pests across maize, tomato, and onion crops. Overall, this study highlights the potential of PlanteSaine to revolutionize agricultural technology in Burkina Faso and beyond. Leveraging AI and mobile computing, PlanteSaine provides farmers with accessible and reliable pest and disease management tools, ultimately contributing to sustainable farming practices and enhancing food security. The success of PlanteSaine underscores the importance of interdisciplinary approaches in addressing pressing challenges in global agriculture
Why it matches plant phenotyping methods画像から作物の病害状態を推定するAI手法とアプリを開発・評価しており、植物病害の観測・分類が中心的な技術貢献である。害虫管理機能も含むが、植物画像に基づく病害診断は対象範囲に該当する。
abstractFarmers capture images of affected plants with their smartphones, and PlanteSaine's AI system analyzes these images to provide accurate diagnoses.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
This article presents the chili and onion leaf (COLD) dataset, which focuses on the leaves of chili and onion plants, scientifically known as Allium cepa and capsicum. The presence of various diseases such as Purple blotch, Stemphylium leaf blight, Colletotrichum leaf blight, and Iris yellow spot virus in onions, as well as Cercospora leaf spot, powdery mildew, Murda complex syndrome, and nutrition deficiency in chili, have had a significant negative effect on onion and chili production. As a consequence, farmers have incurred financial losses. Computer vision and image-processing algorithms have been widely used in recent years for a range of applications, such as diagnosing and categorizing plant leaf diseases. In this paper we introduced a detailed chilli and onion leaf dataset gathered from Chilwadigi village with varying climatic conditions in Karnataka. The dataset contains a variety of chili and onion leaf categories carefully selected to tackle the complex challenges of categorizing leaf images taken in natural environments. Dealing with challenges such as subtle inter-class similarities, changes in lighting, and differences in background conditions like different foliage arrangements and varying light levels. We carefully documented chilli and onion leaves from various angles using high resolution camera to create a diverse and reliable dataset. The dataset on chilli leaves is set to be a valuable resource for enhancing computer vision algorithms, from traditional deep learning models to cutting-edge vision transformer architectures. This will help in creating advanced image recognition systems specifically designed for identifying chilli plants. By making this dataset publicly accessible, our goal is to empower researchers to develop new computer vision techniques to tackle the unique challenges of chilli and onion leaf recognition. You can access the dataset for free at the following DOI number: http://doi.org/10.17632/7nxxn4gj5s.3 and http://doi.org/10.17632/tf9dtfz9m6.3.
Why it matches plant phenotyping methods植物葉画像から病害状態を識別する公開データセットの構築が中心であり、画像ベース植物フェノタイピング用の再利用可能な資源に該当する。
abstractThis article presents the chili and onion leaf (COLD) dataset
Real-time tracking of dynamic changes in the three-dimensional morphology of the cell plasma membrane is of great importance for a deeper understanding of physiological processes related to the cell plasma membrane. However, there is a lack of imaging dyes that can specifically be used for a long term labelling of plasma membranes, especially for plant cells. Here, we have used molecular engineering strategies to develop a series of target-activated multicolour fluorescent dyes that can be used for long-term and three-dimensional imaging of plant cell plasma membranes. By combining different electron acceptors and donors, four molecular backbones with different emission colours from green to NIR have been obtained. In the designed styrene-based dyes, referred to as the SD dyes, several functional groups were introduced into the backbones to achieve the properties of target-activated fluorescence, rapid and wash-free staining, high plasma membrane targeting ability and long-term imaging function. Using onion epidermal cells as a platform, these dye molecules can provide high-quality imaging of the plasma membrane for up to 6 hours, providing a powerful tool for long-term monitoring of plasma membrane-related biological events. Calcium-mediated apoptosis of plant cells has been tracked for the first time by monitoring the morphological changes of the plasma membrane in real time using SD dyes. These dyes also exhibit excellent 3D imaging performance of the plasma membrane and were further used to track in real time the 3D morphological changes of the plasma membrane during plasmolysis of plant cells, providing a powerful imaging tool for three-dimensional (3D) biology. This work provides a set of multi-colour dye tools for long-term and three-dimensional imaging of plant cell plasma membranes, and also provides molecular design principles for guiding the transmembrane transport of small molecules.
Why it matches plant phenotyping methods植物細胞膜の3D形態を長時間・リアルタイムに取得する蛍光色素とイメージング手法を開発しており、植物の形態・アポトーシス・原形質分離状態の測定が中心である。
abstractHere, we have used molecular engineering strategies to develop a series of target-activated multicolour fluorescent dyes that can be used for long-term and three-dimensional imaging of plant cell plasma membranes.
Gas chromatography ion-mobility spectrometry (GC-IMS) technology is drawing increasing attention due to its high sensitivity, low drift, and capability for the identification of compounds. The noninvasive detection of plant pests and pathogens is an application area well suited to this technology. In this work, we employed GC-IMS technology for early detection of Fusarium basal rot in brown onion, red onion, and shallot bulbs and for tracking disease progression during storage. The volatile profiles of the infected and healthy control bulbs were characterized using GC-IMS and gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS). GC-IMS data combined with principal component analysis and supervised methods provided discrimination between infected and healthy control bulbs as early as 1 day after incubation with the pathogen, classification regarding the proportion of infected to healthy bulbs in a sample, and prediction of the infection's duration with an average R 2 = 0.92. Furthermore, GC-TOF-MS revealed several compounds, mostly sulfides and disulfides, that could be uniquely related to Fusarium basal rot infection.
Why it matches plant phenotyping methodsGC-IMSによるVOCセンシングと解析で、タマネギ球の感染状態・感染進行を非侵襲的に推定し、早期検出性能を評価しているため、植物病害フェノタイピング手法が中心である。
abstractIn this work, we employed GC-IMS technology for early detection of Fusarium basal rot in brown onion, red onion, and shallot bulbs and for tracking disease progression during storage.
Plant epidermal cell walls maintain the mechanical integrity of plants and restrict organ growth. Mechanical analyses can give insights into wall structure and are inputs for mechanobiology models of plant growth. To better understand the intrinsic mechanics of epidermal cell walls and how they may accommodate large deformations during growth, we analyzed a geometrically simple material, onion epidermal strips consisting of only the outer (periclinal) cell wall, ~7 μm thick. With uniaxial stretching by >40%, the wall showed complex three-phase stress-strain responses while cyclic stretching revealed reversible and irreversible deformations and elastic hysteresis. Stretching at varying strain rates and temperatures indicated the wall behaved more like a network of flexible cellulose fibers capable of sliding than a viscoelastic composite with pectin viscosity. We developed an analytic framework to quantify nonlinear wall mechanics in terms of stiffness, deformation, and energy dissipation, finding that the wall stretches by combined elastic and plastic deformation without compromising its stiffness. We also analyzed mechanical changes in slightly dehydrated walls. Their extension became stiffer and more irreversible, highlighting the influence of water on cellulose stiffness and sliding. This study offers insights into the structure and deformation modes of primary cell walls and presents a framework that is also applicable to tissues and whole organs.
Why it matches plant phenotyping methods植物細胞壁の力学特性を伸展試験で測定し、非線形力学を定量化する解析フレームワークを開発しており、植物の機械的状態の取得・抽出が研究の中心である。
abstractWe developed an analytic framework to quantify nonlinear wall mechanics in terms of stiffness, deformation, and energy dissipation
Foliar feeding has been confirmed to be the fastest way of dealing with nutrient deficiencies and increasing the yield and quality of crop products. The synthesis of chlorophyll and photosynthesis are directly related to magnesium (Mg), which operates in the improvement of plant tissues and enhances the appearance of plants. This study aimed to analyze the correlation between two biophysical variables, including the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (FAPAR), and seven spectral vegetation indices. The spectral indices under investigation were Atmospherically Resistant Vegetation Index (ARVI), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Disease–Water Stress Index (DSWI), Modified Chlorophyll Absorption Ratio Index (MCARI), the Red-Edge Inflection Point Index (REIP), and Pigment-Specific Simple Ratio (PSSRa). These indices were derived from Sentinel-2 data to investigate the impact of applying foliar applications of Mg from various sources in the production of green-onion crops. The biophysical variables were derived using field measurements and Sentinel-2 data under the effects of different sources of Mg foliar sprays. The correlation coefficient between field-measured LAI and remotely sensed, calculated LAI was 0.72 in two seasons. Concerning FAPAR, it was found that the correlation between remotely sensed calculated FAPAR and field-measured FAPAR was 0.66 in the first season and 0.89 in the second season. The magnesium oxide nanoparticle (nMgO) treatments resulted in significantly higher yields than the different treatments of foliar applications. The LAI and FAPAR variables showed a positive correlation with yield in the first season (October) and in the second season (March). Yield in treatment by nMgO varied significantly from that in the other treatments, ranging from 69-ton ha−1 in the first season to 74.9-ton ha−1 in the second season. Linear regression between LAI and PSSRa showed the highest correlation coefficient (0.90) compared with other vegetation indices in the first season. In the same season, the highest correlation coefficient (0.94) was found between FAPAR and PSSRa. In the second season, the highest accuracy to the estimate LAI was found in the correlation between MCARI and PSSRa, with correlation coefficients of 0.9 and 0.91, respectively. In the second season, the highest accuracy to the estimate FAPAR was found with the correlation between PSSRa, ARVI, and NDVI, with correlation coefficients 0.97 and 0.96, respectively. The highest correlation coefficients between vegetation indices and yield were found with ARVI and NDVI in the first season, and only with NDVI in the second season.
Why it matches plant phenotyping methodsSentinel-2と圃場測定を用いてLAI・FAPARを推定し、実測値との相関で検証しており、植物形質取得法が研究の中心的要素である。
abstractThe biophysical variables were derived using field measurements and Sentinel-2 data under the effects of different sources of Mg foliar sprays.
The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on the onion crop and the challenges presented throughout its phenological cycle. Aerial monitoring using unmanned aerial vehicles (UAV) and digital image processing were used to identify patterns in the onion crop, including humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 ton/ha, but only 25.00 ton/ha was reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being of medium size. It is estimated that approximately 20% of the production was lost due to blight and unfavorable weather conditions.
Why it matches plant phenotyping methodsUAV画像とデジタル画像処理を用いてタマネギの生育不良、植生欠損、葉の枯死、球肥大不良などの植物状態を抽出するアルゴリズムが中心であり、単なる収量測定ではない。
abstractAerial monitoring using unmanned aerial vehicles (UAV) and digital image processing were used to identify patterns in the onion crop
Currently, the concept of plant capture efficiency is not quantitatively considered in the evaluation of off-target drift for the purposes of pesticide risk assessment in the United States. For on-target pesticide applications, canopy capture efficiency is managed by optimizing formulations or tank-mixing with adjuvants to maximize retention of spray droplets. These efforts take into consideration the fact that plant species have diverse morphology and surface characteristics, and as such will retain varying levels of applied pesticides. This work aims to combine plant surface wettability potential, spray droplet characteristics, and plant morphology into describing the plant capture efficiency of drifted spray droplets. In this study, we used wind tunnel experiments and individual plants grown to 10-20 cm to show that at two downwind distances and with two distinct nozzles capture efficiency for sunflower (Helianthus annuus L.), lettuce (Lactuca sativa L.), and tomato (Solanum lycopersicum L.) is consistently higher than rice (Oryza sativa L.), peas (Pisum sativum L). and onions (Allium cepa L.), with carrots (Daucus carota L.) showing high variability and falling between the two groups. We also present a novel method for three-dimensional modeling of plants from photogrammetric scanning and use the results in the first known computational fluid dynamics simulations of drift capture efficiency on plants. The mean simulated drift capture efficiency rates were within the same order of magnitude of the mean observed rates of sunflower and lettuce, and differed by one to two orders for rice and onion. We identify simulating the effects of surface roughness on droplet behavior, and the effects of wind flow on plant movement as potential model improvements requiring further species-specific data collection.
Why it matches plant phenotyping methods植物の形態をフォトグラメトリで3次元モデル化し、ドリフト散布液の植物捕捉効率を推定・検証する手法が研究の中心であるため、植物フェノタイピング手法として採用する。
abstractWe also present a novel method for three-dimensional modeling of plants from photogrammetric scanning and use the results in the first known computational fluid dynamics simulations of drift capture efficiency on plants.
Cryo-electron tomography (cryo-ET) is a formidable technique to observe the inner workings of vitrified cells at a nanometric resolution in near-native conditions and in three-dimensions. One consequent drawback of this technique is the sample thickness, for two reasons: i) achieving proper vitrification of the sample gets increasingly difficult with sample thickness, and ii) cryo-ET relies on transmission electron microscopy (TEM), requiring thin samples for proper electron transmittance ( This protocol was validated in: Curr Biol (2022), DOI: 10.1016/j.cub.2022.04.024.
Why it matches plant phenotyping methodsタマネギ細胞壁の三次元構造を観察するためのFIBミリングとクライオ電子線トモグラフィーの手法・プロトコルが中心であり、植物の形態・構造状態を取得する方法を扱う。
titleFocused Ion Beam Milling and Cryo-electron Tomography Methods to Study the Structure of the Primary Cell Wall in Allium cepa .
Abstract The stiffness of a plant cell in response to an applied force is determined not only by the elasticity of the cell wall but also by turgor pressure and cell geometry, which affect the tension of the cell wall. Although stiffness has been investigated using atomic force microscopy (AFM) and Young’s modulus of the cell wall has occasionally been estimated using the contact-stress theory (Hertz theory), the existence of tension has made the study of stiffness more complex. Elastic shell theory has been proposed as an alternative method; however, the estimation of elasticity remains ambiguous. Here, we used finite element method simulations to verify the formula of the elastic shell theory for onion ( Allium cepa ) cells. We applied the formula and simulations to successfully quantify the turgor pressure and elasticity of a cell in the plane direction using the cell curvature and apparent stiffness measured by AFM. We conclude that tension resulting from turgor pressure regulates cell stiffness, which can be modified by a slight adjustment of turgor pressure in the order of 0.1 MPa. This theoretical analysis reveals a path for understanding forces inherent in plant cells.
Why it matches plant phenotyping methodsAFMで測定した細胞の見かけの剛性と細胞曲率から、有限要素シミュレーションで検証した弾性殻理論により、植物細胞の膨圧と弾性を定量化する方法を開発・検証しており、表現型取得が中心です。
abstractHere, we used finite element method simulations to verify the formula of the elastic shell theory for onion ( Allium cepa ) cells.
The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae , which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae , to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops.
Why it matches plant phenotyping methods電子鼻によるタマネギ・シャロットの感染状態と感染進行の非破壊検出が研究の中心であり、植物病害状態を直接推定するセンサー手法を評価している。
abstractSensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Single-cell phase-contrast tomography promises to become decisive for studying 3D intracellular structures in biology. It involves probing cells with light at wide angles, which unfortunately requires complex systems. Here we show an intriguing concept based on an inherent natural process for plants biology, i.e., dehydration, allowing us to easily obtain 3D-tomography of onion-epidermal cells’ nuclei. In fact, the loss of water reduces the turgor pressure and we recognize it induces significant rotation of cells’ nuclei. Thanks to the holographic focusing flexibility and an ad-hoc angles’ tracking algorithm, we combine different phase-contrast views of the nuclei to retrieve their 3D refractive index distribution. Nucleolus identification capability and a strategy for measuring morphology, dry mass, biovolume, and refractive index statistics are reported and discussed. This new concept could revolutionize the investigation in plant biology by enabling dynamic 3D quantitative and label-free analysis at sub-nuclear level using a conventional holographic setup.
Why it matches plant phenotyping methods植物細胞の脱水による核回転と位相差トモグラフィーを利用し、核の3D形態・乾燥質量・体積・屈折率を定量する新規手法を開発しており、植物フェノタイピング手法が研究の中心である。
abstractHere we show an intriguing concept based on an inherent natural process for plants biology, i.e., dehydration, allowing us to easily obtain 3D-tomography of onion-epidermal cells’ nuclei.
The primary plant cell wall is a complex matrix composed of interconnected polysaccharides including cellulose, hemicellulose, and pectin. Changes of this dynamic polysaccharide system play a critical role during plant cell development and differentiation. A better understanding of cell wall architectures can provide insight into the plant cell development. In this study, a Raman spectroscopic imaging approach was developed to visualize the distribution of plant cell wall polysaccharides. In this approach, Surface-enhanced Raman scattering (SERS through self-assembled silver nanoparticles) was combined with Raman labels (4-Aminothiophenol. 4ATP) and targeted enzymatic hydrolysis to improve the sensitivity, specificity, and throughput of the Raman imaging technique, and to reveal the distribution of pectin and its co-localization with xyloglucan inside onion epidermal cell (OEC) wall. This technique significantly decreased the required spectral acquisition time. The resulted Raman spectra showed a high Raman signal. The resulted Raman images successfully revealed and characterized the pectin distribution and its co-localization pattern with xyloglucan in OEC wall.
Why it matches plant phenotyping methods植物細胞壁多糖類の分布を可視化・特徴づけるラマン分光イメージング法を開発しており、フェノタイピング手法の開発が研究の中心である。
abstractIn this study, a Raman spectroscopic imaging approach was developed to visualize the distribution of plant cell wall polysaccharides.
In comparison to field crops such as cereals, cotton, hay and grain, specialty crops often require more resources, are usually more sensitive to sudden changes in growth conditions and are known to produce higher value products. Providing quality and quantity assessment of specialty crops during harvesting is crucial for securing higher returns and improving management practices. Technical advancements in computer and machine vision have improved the detection, quality assessment and yield estimation processes for various fruit crops, but similar methods capable of exporting a detailed yield map for vegetable crops have yet to be fully developed. A machine vision-based yield monitor was designed to perform size categorization and continuous counting of shallots in-situ during the harvesting process. Coupled with a software developed in Python, the system is composed of a video logger and a global navigation satellite system. Computer vision analysis is performed within the tractor while an RGB camera collects real-time video data of the crops under natural sunlight conditions. Vegetables are first segmented using Watershed segmentation, detected on the conveyor, and then classified by size. The system detected shallots in a subsample of the dataset with a precision of 76%. The software was also evaluated on its ability to classify the shallots into three size categories. The best performance was achieved in the large class (73%), followed by the small class (59%) and medium class (44%). Based on these results, the occasional occlusion of vegetables and inconsistent lighting conditions were the main factors that hindered performance. Although further enhancements are envisioned for the prototype system, its modular and novel design permits the mapping of a selection of other horticultural crops. Moreover, it has the potential to benefit many producers of small vegetable crops by providing them with useful harvest information in real-time.
Why it matches plant phenotyping methodsシャロットの収穫時画像から個体数・サイズを抽出し、収量・品質マッピングを行う機械視覚システムの開発と性能評価が主題であり、植物形質取得手法が中心である。
abstractA machine vision-based yield monitor was designed to perform size categorization and continuous counting of shallots in-situ during the harvesting process.
The world’s growing population leads the government of Pakistan to increase the supply of food for the coming years in a well-organized manner. Feasible agriculture plays a vital role for sustain food production and preserves the environment from any unnecessary chemicals by the use of technology for good management. This research presents the design and development of a multi-spectral imaging system for precision agriculture tasks. This imaging system includes an RGB camera and Pi NoIR camera controlled by a raspberry pi in a drone. The images are captured by Unmanned Aerial Vehicle (UAV) and then send images to the Java application. Images are processed to sharp, resize by application. The Normalized Difference Vegetation Index (NDVI) is calculated to determine the crop health status based on real-time data. The Deep Learning (DL) technique is used to recognize the onion crop growth stage using the captured dataset. We express how to implement a progressive model for the deep neural network to recognize the onion crop growth stage. The performance accuracy of the system for batch size 16 is 96.10% and for batch size 32 is 93.80%.
Why it matches plant phenotyping methodsUAVマルチスペクトル撮像システムと深層学習によるタマネギの生育段階・健全性推定を開発しており、植物表現型の取得・抽出が研究の中心である。
abstractThis research presents the design and development of a multi-spectral imaging system for precision agriculture tasks.
Abstract The plant cell wall is a complex network of polysaccharides. A better understanding of the plant cell wall polysaccharide content, distribution, and interactions among them could be instrumental for our further understanding of plant cell wall in general. Confocal Raman microscopy (CRM) is a powerful tool that could reveal details about chemical landscape of the plant cell wall with micrometer resolution. However, the low signal‐to‐noise (S/N) ratio of Raman spectral signal led to low throughput of Raman imaging, which limited its application in plant cell wall study. In this study, the interaction between the cell wall polysaccharides and the pectin enzyme endo‐polygalacturonase (EPG) was characterized by analyzing Raman images collected before and after EPG treatment at high scanning speed. The obtained low S/N ratio Raman spectra were processed with principal component analysis (PCA) and hierarchical clustering analysis (HCA) to recover information‐rich signature changes in Raman signal dataset that reflected the changes in the polysaccharides caused specifically by the enzymatic hydrolysis. The PCA reconstruction technique significantly improved the S/N ratio of the spectra dataset while kept the Raman signal intact. Further analysis of principal components (PCs) revealed the pectin distribution and its interaction with the enzyme, which provided organizational details of pectin inside the onion plant cell wall. The technique could be used to reveal polysaccharide organization and distribution inside plant cell walls.
Why it matches plant phenotyping methods植物細胞壁内のペクチン分布を取得するラマン分光イメージングと、低S/Nデータを改善するPCA再構成・HCA解析が研究の中心であり、植物組織の状態・化学的分布を画像化する方法開発に該当する。
abstractConfocal Raman microscopy (CRM) is a powerful tool that could reveal details about chemical landscape of the plant cell wall with micrometer resolution.
Epicuticular waxes on the surface of plant leaves are important for the tolerance to abiotic stresses and plant-parasite interactions. In the onion ( Allium cepa L.), the variation for the amounts and types of epicuticular waxes is significantly associated with less feeding damage by the insect Thrips tabaci (thrips). Epicuticular wax profiles are measured using used gas chromatography mass spectrometry (GCMS), which is a labor intensive and relatively expensive approach. Biochemical spectroscopy is a non-destructive tool for measurement and analysis of physiological and chemical features of plants. This study used GCMS and full-range biochemical spectroscopy to characterize epicuticular waxes on seven onion accessions with visually glossy (low wax), semi-glossy (intermediate wax), or waxy (copious wax) foliage, as well as a segregating family from the cross of glossy and waxy onions. In agreement with previous studies, GCMS revealed that the three main waxes on the leaves of a wild type waxy onion were the ketone hentriacontanone-16 (H16) and fatty alcohols octacosanol-1 (Oct) and triacontanol-1 (Tri). The glossy cultivar "Odourless Greenleaf" had a unique phenotype with essentially no H16 and Tri and higher amounts of Oct and the fatty alcohol hexacosanol-1 (Hex). Hyperspectral reflectance profiles were measured on leaves of the onion accessions and segregating family, and partial least-squares regression (PLSR) was utilized to generate a spectral coefficient for every wavelength and prediction models for the amounts of the three major wax components. PLSR predictions were robust with independent validation coefficients of determination at 0.72, 0.70, and 0.42 for H16, Oct, and Tri, respectively. The predicted amounts of H16, Oct, and Tri are the result of an additive effect of multiple spectral features of different intensities. The variation of reflectance for H16, Oct, and Tri revealed unique spectral features at 2259 nm, 645 nm, and 730 nm, respectively. Reflectance spectroscopy successfully revealed a major quantitative trait locus (QTL) for amounts of H16, Oct, and Tri in the segregating family, agreeing with previous genetic studies. This study demonstrates that hyperspectral signatures can be used for non-destructive measurement of major waxes on onion leaves as a basis for rapid plant assessment in support of developing thrips-resistant onions.
Why it matches plant phenotyping methodsタマネギ葉のエピクチクラワックス量を非破壊的に推定するハイパースペクトル反射分光法を開発・検証しており、植物形質の取得手法が研究の中心である。
abstractPLSR predictions were robust with independent validation coefficients of determination at 0.72, 0.70, and 0.42 for H16, Oct, and Tri, respectively.
The effective crop management is major issue in recent agriculture because the cultivation area per farmer is increasing consistently while the aging-related reductions in the labor force. To manage crop cultivation effectively, it needs automatic monitoring in farmland. This paper presents an image-based field monitoring system for automatically crop monitoring and consists of constructing field monitoring system for periodic capturing of onion field images, training the deep neural network model for detecting the disease symptom, and evaluating performance of the developed system. The field monitoring system was composed of a PTZ camera, a motor system, wireless transceiver, and image logging module. The deep learning model was trained based on weakly supervised learning method that can classify and localize objects only with image-level annotation. It is effective to recognize crop disease symptom which has ambiguous boundary. The model was trained using captured onion images using the filed monitoring system, and 6 classes including the disease symptom were classified. The detected disease symptom was localized from background through thresholding of the class activation map. The 60% of maximum value in class activation map was determined as an Optimal threshold for disease symptom localization. Identification performance of disease symptom was evaluated using mAP metric by IoU. The results show that the mAP at IoU criteria 0.5, which should have over 50% overlap, was the highest in all models from 74.1 to 87.2. The results showed that the developed field monitoring system could automatically detect onion disease symptoms in real-time.
Why it matches plant phenotyping methodsタマネギ葉の病徴を画像から検出・局在化する撮像システムと深層学習手法を開発・評価しており、植物病害状態の表現型取得が中心である。
abstractThis paper presents an image-based field monitoring system for automatically crop monitoring and consists of constructing field monitoring system for periodic capturing of onion field images, training the deep neural network model for detecting the disease symptom, and evaluating performance of the developed system.
Plant cell walls (CWs) with complex macromolecular structures can surround and protect cells from a variety of harsh environmental conditions such as pathogens, herbivores, and trace metals. Here, a novel strategy for in situ imaging of plant cell walls was developed to evaluate heavy metal pollution via thiolated full-color emissive carbon-dots (F-CDs) targeting Pb(ii)-adsorbed onion cell walls. The thiolated F-CDs with excellent optical properties from red light to blue light were synthesized through a facile electrochemical approach using new precursors of luminol and l-tryptophan and further modified with l-cysteine. Based on a strong covalent interaction of Pb(ii) and thiolated F-CDs, we achieved in situ fluorescence imaging for the Pb(ii) adsorbed on CWs, which showed enhanced red, blue and green multi-color fluorescence (FL) on CWs with increased Pb(ii)-ion content. In contrast, multi-color fluorescence on cytoplasm diminished, attributed to F-CDs targeting and accumulating on the cytoskeleton which thus limited F-CD diffusion into protoplasm. Therefore, in situ fluorescent images for CWs can demonstrate heavy metal contamination degrees in plant cells. This facile and undamaging protocol will be beneficial for investigating heavy metal migration into the protoplast and fast evaluation of food quality and safety.
Why it matches plant phenotyping methods植物細胞壁への重金属蓄積を蛍光画像から評価する新規なin situ画像法を開発しており、植物の汚染状態の取得が研究の中心である。
abstractHere, a novel strategy for in situ imaging of plant cell walls was developed to evaluate heavy metal pollution via thiolated full-color emissive carbon-dots (F-CDs) targeting Pb(ii)-adsorbed onion cell walls.
Biomass monitoring is one of the main pillars of precision farm management as it involves deeper knowledge about pest and weed status, soil quality, water stress, and yield prediction, among others. This research focuses on estimating crop biomass from high-resolution red, green, blue imaging obtained with an unmanned aerial vehicle. Onion, as one of the most cultivated vegetables, was studied for two seasons under non-controlled conditions in two commercial plots. Green canopy cover, crop height, and canopy volume (Vcₐₙₒₚy) were the predictor variables extracted from the geomatic products. Strong relationships were found between Vcₐₙₒₚy and dry leaf biomass and dry bulb biomass. Adjusted coefficient of determination ([Formula: see text]) values were 0.76 and 0.95, respectively. Nevertheless, crop management practices and leaf depletion at vegetative stages significantly affect the accuracy of the canopy model. These results suggested that obtaining biomass using aerial images are a good alternative to other sensors and platforms as they have high spatial and temporal resolution to perform high-quality biomass monitoring.
Why it matches plant phenotyping methodsUAV RGB画像からキャノピー形質を抽出し、タマネギの乾物バイオマスを推定・検証することが研究の中心であり、植物フェノタイピング手法の実質的な応用に該当する。
abstractThis research focuses on estimating crop biomass from high-resolution red, green, blue imaging obtained with an unmanned aerial vehicle.
Abstract Cellulose microfibrils are crucial for many of the remarkable mechanical properties of primary cell walls. Nevertheless, many structural features of cellulose microfibril organization in cell walls are not yet fully described. Microscopy techniques provide direct visualization of cell wall organization, and quantification of some aspects of wall microstructure is possible through image processing. Complementary to microscopy techniques, scattering yields structural information in reciprocal space over large sample areas. Using the onion epidermal wall as a model system, we introduce resonant soft X-ray scattering (RSoXS) to directly quantify the average interfibril spacing. Tuning the X-ray energy to the calcium L-edge enhances the contrast between cellulose and pectin due to the localization of calcium ions to homogalacturonan in the pectin matrix. As a consequence, RSoXS profiles reveal an average center-to-center distance between cellulose microfibrils or microfibril bundles of about 20 nm.
Why it matches plant phenotyping methods植物細胞壁の微細構造（セルロース微小繊維間隔）をRSoXSで定量する測定法を導入しており、植物表現型の取得法が研究の中心である。
abstractUsing the onion epidermal wall as a model system, we introduce resonant soft X-ray scattering (RSoXS) to directly quantify the average interfibril spacing.
Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R 2 lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R 2 of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (< 30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops.
Why it matches plant phenotyping methods作物キャノピー水分含量という植物生理状態を、ハイパースペクトルデータから推定する新規指標を開発・検証しており、フェノタイピング手法が中心である。
abstractWe propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale.
ArabidopsisCottonOnionMicroscopyRaman / spectroscopyCell / cellular structureTissueVisualization / data management
Sum frequency generation (SFG) vibrational spectroscopy can selectively detect and analyze noncentrosymmetric components interspersed in amorphous matrices; this principle has been used for studies of nanoscale structure and mesoscale assembly of cellulose in plant cell walls. However, the spectral information averaged over a large area or volume cannot provide regiospecific or tissue-specific information of different cells in plants. This study demonstrates spatially resolved SFG analysis and imaging by combining a broad-band SFG spectroscopy system with an optical microscope. The system was designed to irradiate both narrow-band 800 nm and broad-band tunable IR beams through a single reflective objective lens, but from opposite sides of the surface normal direction of the sample. The developed technique was used to reveal inhomogeneous distributions of cellulose microfibrils within single cell walls, such as cotton fibers and onion epidermis as well as among different tissues in Arabidopsis inflorescence stems and bamboo culms. SFG microscopy can be used for vibrational spectroscopic imaging of other biological systems in complement to conventional Fourier transform infrared spectroscopy and confocal Raman microscopy.
Why it matches plant phenotyping methods植物細胞壁内のセルロース微小 fibril 分布という構造的植物形質を、空間分解SFG分光・顕微鏡で取得する手法を開発・実証しており、測定法が研究の中心である。
abstractThis study demonstrates spatially resolved SFG analysis and imaging by combining a broad-band SFG spectroscopy system with an optical microscope.
Cell walls shape and protect plant cells and proper wall assembly is crucial for normal growth and development. Textbook diagrams of primary (growing) cell walls show that they are made up of cellulose in a matrix of xyloglucan, pectins, and various proteins; secondary cell walls have distinctive matrix polymers, including lignin. Despite years of research by numerous groups, there are still many unanswered questions about how cell walls are organized and how they incorporate new materials for growth, and different experimental techniques sometimes gave contrasting results (Cosgrove, 2014). To better understand the organization of the components of cell walls, better imaging techniques are required. Fluorescence microscopy lacks the resolution to see individual cellulose microfibrils or single polymer chains. Atomic force microscopy (AFM) and Field Emission Scanning Electron Microscopy (FESEM) offer vast improvements in resolution. Daniel Cosgrove has worked on various aspects of cell growth since he was a graduate student and has studied cell walls per se since 1990. For example, in 1992 his group discovered expansins (McQueen-Mason et al., 1992), a group of proteins that catalyze cell wall extension (Cosgrove, 2016) and are now known also from the microbial world (Cosgrove, 2017). Expansins are not enzymes that cleave polysaccharides, but instead disrupt noncovalent bonding between polysaccharides and thereby initiate wall loosening. Daniel mentioned that although there is a crystal structure for an expansin, its detailed mechanism of action remains enigmatic, in part because of gaps in our understanding of primary wall structure. Since 2009, he has been the director of a DOE-funded Center for Lignocellulose Structure and Formation at Penn State, which is part of a 6-campus consortium that studies plant cell walls with advanced physical and imaging techniques. For example, AFM enabled the Cosgrove group to image cellulose microfibril organization and movements in onion cells (Zhang et al., 2016, 2017). In the highlighted paper (Zheng et al., 2018), they turned their attention to xyloglucan. Although there have been many models for xyloglucan's role(s) in cell wall structure, it had not been experimentally established, at the nanometer scale, where exactly xyloglucan was located in the wall. Furthermore, an Arabidopsis mutant line that lacked detectable xyloglucan (Cavalier et al., 2008) had only subtle phenotypic defects, raising some questions about its importance for cell wall structure. The first author, Yunzhen, works for the DOE Center; she carried out all the FESEM experiments. Xuan is a 5th year graduate student who developed the methods they used for analyzing cellulose organization. Yuning, a former postdoc, and Edward, who started in the Cosgrove lab as an undergraduate and then stayed on as a technician after graduation, generated the CBM-nanogold probes used to detect binding to xyloglucan and cellulose. They wanted to know if xyloglucan was really in an extended form and bound to cellulose microfibril surfaces, as shown in textbooks, or was it coiled, the conformation it assumes in solution? As shown in the schematic in Figure 1, they used onion epidermal cells to image newly deposited cell wall components. To prepare onion epidermal peels for FESEM, the tissue must be dehydrated and superficial pectin molecules, which might obscure the cellulose microfibrils, must be removed by treatment with pectate lyase. By comparing the images of the cellulose microfibrils obtained with AFM and FESEM, they noticed that there was some distortion of the microfibrils in the FESEM images, likely due to the required dehydration step and pectate lyase treatment, whereas AFM images are from cell walls that are in water, and so presumably close to the in vivo situation. Nevertheless, the microfibrils were easily visualized with FESEM. They next treated the samples with substrate-specific endoglucanases, and quantified their effects on fiber bundle widths and pore sizes. Statistically significant changes were noted, indicating that the endoglucanase treatments had some consequences, but they could not unambiguously identify xyloglucan location or form. They next tried directly probing for xyloglucan, using an anti-xyloglucan antibody (LM15) and a secondary antibody conjugated to nanogold, but there were technical challenges in detection with standard FESEM. However, by using FESEM with backscattered electron detection, which highlights the nanogold signal, they were able to conclude that xyloglucan was located only partly on the surface of cellulose microfibrils. Why is backscattering an improvement? Daniel offered an analogy – regular FESEM is like shooting a bullet (a high-energy electron) at a brick wall and the detector senses the brick fragments that scatter in many directions, whereas backscattered electron detection is like shooting a bullet at a wall and the detector senses the bullet that ricochets directly backwards. Gold nuclei do this much better than carbon and oxygen and so gold particles are bright in such images. Finally, they used nanogold conjugates of carbohydrate binding module (CBM) proteins, namely CBM76, a small protein that binds xyloglucan, and CBM3, a protein that binds the hydrophobic face of cellulose. Small proteins are better probes than bulky antibody probes; the distance from xyloglucan to CBM76 should be 3 nanometers or less. By using these CBM-nanogold probes, in combination with xyloglucanase treatments, they concluded that at least some xyloglucan indeed assumes an extended conformation that binds to the hydrophobic surfaces of cellulose microfibrils, and that this binding appreciably prevents CBM3 binding to cellulose. They will expand their FESEM analyses to walls other than epidermal walls, as the orientations of cellulose microfibrils in the walls of other types of cell are transverse, and there might be differences. They hope to use FESEM and the CBM-nanogold probes to compare the walls of wild type Arabidopsis and the xyloglucan-deficient mutant line (Cavalier et al., 2008). They also want to develop nanogold probes for other wall components, such as pectins and glycoproteins. Using such techniques, researchers will be able to test quantitative models based on experimentally-derived nanoscale organization of wall components (and as a bonus, improve those textbook diagrams).
Why it matches plant phenotyping methodsAFM/FESEMとナノゴールドプローブを用いて植物細胞壁の微細構造を可視化・定量する手法が中心であり、単なる細胞壁生物学の測定ではない。
abstractTo better understand the organization of the components of cell walls, better imaging techniques are required.
The growing plant cell wall is comprised of long, thin cellulose microfibrils embedded in a hydrated matrix of polysaccharides and glycoproteins. These components are typically constructed in layers (lamellae) on the inner surface of the cell wall, i.e ., between the existing wall and the plasma membrane. The organization of these components is an important feature for plant cell growth and mechanics. To directly visualize the nano-scale structure of the newly-deposited surface of primary plant cell walls without dehydration or chemical extraction, a protocol of cell wall preparation for AFM imaging the most recently-synthesized cell wall surface in aqueous solutions was developed. Although the method was developed for onion scale epidermal peels, it can also be adapted to other organs, such as Arabidopsis hypocotyls, as well as ground samples of cell walls from the leaf petioles or hypocotyls of Arabidopsis and cucumber, maize coleoptiles and onion parenchyma. Potential artifacts of AFM imaging of plant cell walls are also discussed.
Why it matches plant phenotyping methods植物細胞壁のナノスケール構造をAFMで直接可視化するための試料調製プロトコルを開発しており、植物形態・構造の取得法が中心である。
abstracta protocol of cell wall preparation for AFM imaging the most recently-synthesized cell wall surface in aqueous solutions was developed
OnionCell / cellular structurePhysiological trait estimationGrowth / development / phenology
Here we describe two experimental protocols to measure the biomechanical properties of primary (growing) plant cell walls, with a focus on analyzing cell wall epidermal strips of onion scales. The first protocol measures cell wall creep (time-dependent irreversible extension) under constant force. Such creep is often mediated by the wall-loosening action of expansin or selective endoglucanases. The second protocol is based on two consecutive stretches of the wall and measures the wall's elastic and plastic compliances, which depend on cell wall structure. These two assays provide complementary information that may be linked to cell wall structure and expansive growth of cells.
Why it matches plant phenotyping methods植物細胞壁の力学的性質を測定する2つの実験プロトコル自体が中心であり、細胞壁構造や伸長成長に関わる植物状態を定量化するため、方法論文として収録する。
abstractHere we describe two experimental protocols to measure the biomechanical properties of primary (growing) plant cell walls
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract We have used field emission scanning electron microscopy (FESEM) to study the high-resolution organization of cellulose microfibrils in onion epidermal cell walls. We frequently found that conventional “rule of thumb” conditions for imaging of biological samples did not yield high-resolution images of cellulose organization and often resulted in artifacts or distortions of cell wall structure. Here we detail our method of one-step fixation and dehydration with 100% ethanol, followed by critical point drying, ultrathin iridium (Ir) sputter coating (3 s), and FESEM imaging at a moderate accelerating voltage (10 kV) with an In-lens detector. We compare results obtained with our improved protocol with images obtained with samples processed by conventional aldehyde fixation, graded dehydration, sputter coating with Au, Au/Pd, or carbon, and low-voltage FESEM imaging. The results demonstrated that our protocol is simpler, causes little artifact, and is more suitable for high-resolution imaging of cell wall cellulose microfibrils whereas such imaging is very challenging by conventional methods.
Why it matches plant phenotyping methods植物細胞壁のセルロース微繊維構造を高解像度で取得するFESEM試料調製・撮像法を開発し、従来法と比較検証しており、植物形態・構造の表現型取得が中心である。
abstractHere we detail our method of one-step fixation and dehydration with 100% ethanol, followed by critical point drying, ultrathin iridium (Ir) sputter coating (3 s), and FESEM imaging at a moderate accelerating voltage (10 kV) with an In-lens detector.
Abstract. In this paper we investigate the performance of new light-weight multispectral sensors for micro UAV and their application to selected tasks in agronomical research and agricultural practice. The investigations are based on a series of flight campaigns in 2014 and 2015 covering a number of agronomical test sites with experiments on rape, barley, onion, potato and other crops. In our sensor comparison we included a high-end multispectral multiSPEC 4C camera with bandpass colour filters and reference channel in zenith direction and a low-cost, consumer-grade Canon S110 NIR camera with Bayer pattern colour filters. Ground-based reference measurements were obtained using a terrestrial hyperspectral field spectrometer. The investigations show that measurements with the high-end system consistently match very well with ground-based field spectrometer measurements with a mean deviation of just 0.01-0.04 NDVI values. The low-cost system, while delivering better spatial resolutions, expressed significant biases. The sensors were subsequently used to address selected agronomical questions. These included crop yield estimation in rape and barley and plant disease detection in potato and onion cultivations. High levels of correlation between different vegetation indices and reference yield measurements were obtained for rape and barley. In case of barley, the NDRE index shows an average correlation of 87% with reference yield, when species are taken into account. With high geometric resolutions and respective GSDs of down to 2.5 cm the effects of a thrips infestation in onion could be analysed and potato blight was successfully detected at an early stage of infestation.
Why it matches plant phenotyping methods軽量マルチスペクトルUAVセンサーの性能比較・地上計測との検証が中心で、収量推定や植物病害検出という植物表現型への適用も評価している。
abstractIn this paper we investigate the performance of new light-weight multispectral sensors for micro UAV
Summary We used atomic force microscopy ( AFM ), complemented with electron microscopy, to characterize the nanoscale and mesoscale structure of the outer (periclinal) cell wall of onion scale epidermis – a model system for relating wall structure to cell wall mechanics. The epidermal wall contains ~100 lamellae, each ~40 nm thick, containing 3.5‐nm wide cellulose microfibrils oriented in a common direction within a lamella but varying by ~30 to 90° between adjacent lamellae. The wall thus has a crossed polylamellate, not helicoidal, wall structure. Montages of high‐resolution AFM images of the newly deposited wall surface showed that single microfibrils merge into and out of short regions of microfibril bundles, thereby forming a reticulated network. Microfibril direction within a lamella did not change gradually or abruptly across the whole face of the cell, indicating continuity of the lamella across the outer wall. A layer of pectin at the wall surface obscured the underlying cellulose microfibrils when imaged by FESEM , but not by AFM . The AFM thus preferentially detects cellulose microfibrils by probing through the soft matrix in these hydrated walls. AFM ‐based nanomechanical maps revealed significant heterogeneity in cell wall stiffness and adhesiveness at the nm scale. By color coding and merging these maps, the spatial distribution of soft and rigid matrix polymers could be visualized in the context of the stiffer microfibrils. Without chemical extraction and dehydration, our results provide multiscale structural details of the primary cell wall in its near‐native state, with implications for microfibrils motions in different lamellae during uniaxial and biaxial extensions.
Why it matches plant phenotyping methodsAFMを中核として植物細胞壁の微細構造、硬さ、接着性を画像・マッピングし、植物組織の状態を定量的に評価しているため、単なる生物学的測定ではなく実質的な表現型取得手法の応用に該当する。
titleSpatial organization of cellulose microfibrils and matrix polysaccharides in primary plant cell walls as imaged by multichannel atomic force microscopy