In situ monitoring of plant responses to stress is one of the most challenging aspects of precision agriculture, and the dynamic control of crop growth according to fluctuating environmental factors. Although fluorescence imaging provides a nondestructive approach for monitoring stress-related biomarkers, its performance is often hindered by the low abundance of endogenous signaling molecules and strong tissue autofluorescence. Here, we report a microneedle-integrated sensing platform that enables sensitive detection of endogenous hydrogen peroxide (H 2 O 2 ) in living plants. The platform incorporates a second near-infrared fluorescent nanoprobe composed of Er 3+ -doped lanthanide nanoparticles emitting at 1550 nm and Mo-doped polymetallic oxomolybdates serving as the H 2 O 2 -responsive unit. Embedding the nanoprobe into custom-fabricated microneedles allows precise positioning on plant midribs for continuous monitoring of H 2 O 2 dynamics. Under stress conditions, the system successfully visualized spatiotemporal fluctuations of H 2 O 2 in living tomato, spinach, and tobacco plants. This work establishes a strategy for early stress diagnosis and developing universal plant health monitoring technologies.
Why it matches plant phenotyping methods植物ストレス状態を生体内H2O2として連続取得・可視化するマイクロニードル統合センシング基盤の開発が中心であり、単なる生物学的測定ではない。
abstractHere, we report a microneedle-integrated sensing platform that enables sensitive detection of endogenous hydrogen peroxide (H 2 O 2 ) in living plants.
Plant leaf diseases must be detected and treated early to improve crop yield and reduce agricultural losses. However, pixel-level representations and the inability to be read limit the applicability of existing deep learning approaches to the agricultural sector. A graph neural network termed the Attention-Enhanced Graph Neural Network (AE-GNN) may explain and diagnose multi-plant leaf disease. The proposed framework models leaf pictures as a graph with nodes representing discriminative leaf areas and edges representing their spatial connection. Before creating the global context vector and classification, graph features are aggregated, and an attention weighting method is applied to refocus on disease-relevant nodes obscured by less informative background characteristics. Final disease prediction uses a multilayer perceptron classifier. A curated dataset of half-spinach and curry leaf pictures is used to assess the proposed method for fifteen illnesses and their healthy classifications. Grad-CAM-based explainable AI methods make the model predictions' most important areas clearer. The dataset and source code from this work are available on GitHub for reproducibility and openness. Experimental results reveal that the proposed AE-GNN outperforms convolutional neural networks and graph-based models in classification. Graph-structured learning, attention enhancement, and explainability create a robust and interpretable framework for multi-plant leaf disease diagnosis.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類・診断する画像解析手法を提案し、既存モデルとの比較評価と説明可能性解析を行っているため、植物フェノタイピング手法が中心である。
abstractA graph neural network termed the Attention-Enhanced Graph Neural Network (AE-GNN) may explain and diagnose multi-plant leaf disease.
Reproduction assets foundThe paper's Data availability section explicitly links a public GitHub repository containing the paper's spinach/curry leaf fungal disease image dataset used for the AE-GNN phenotyping/classification analysis.Dataset · publicData availability
The dataset is available at the link below. https://github.com/MeganathanE1990/FINAL-DISEASE-DATA-SET/tree/mainOpen asset ↗MeganathanE1990/FINAL-DISEASE-DATA-SETlines:413-463Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Hydrogen sulfide (H 2 S) is a key gaseous regulator in plant stress responses, but its spatiotemporal dynamics in living plants remain poorly understood due to the lack of noninvasive sensing tools. Here, we report a stomata-infiltratable SERS nanosensor based on Au@Ag@SiO 2 core-shell nanoparticles for real-time monitoring of endogenous H 2 S. The sensor, with an enhancement factor of ∼6.07 × 10 9 and a detection limit of 15 nM, efficiently infiltrates leaves of diverse species (Arabidopsis, spinach, and tomato). Real-time monitoring revealed that H 2 S accumulation kinetics are stress-specific and occur within 20 min of stress onset, preceding visible phenotypic damage. Notably, the nanosensor enabled visualization of stress-induced H 2 S transmission between neighboring plants, suggesting a role for H 2 S as an airborne signal in plant-to-plant communication. Furthermore, a species-dependent kinetic framework describing systemic signal propagation was established. This work demonstrates a versatile SERS-based platform for noninvasive monitoring of gaseous signaling molecules in plants.
Why it matches plant phenotyping methods植物内のH₂S動態という生理状態をリアルタイム・非侵襲的に測定するSERSセンサーを開発し、複数種で性能と適用性を示した研究であり、測定手法が中心的である。
abstractHere, we report a stomata-infiltratable SERS nanosensor based on Au@Ag@SiO 2 core-shell nanoparticles for real-time monitoring of endogenous H 2 S.
Using nanosensors in living plants allows the real-time detection of internal reactive oxygen species (e.g., hydrogen peroxide) signaling in response to environmental stressors. The time-dependent pulse of hydrogen peroxide (H 2 O 2 ) constitutes a signaling waveform ; however, standardized methods of extracting and analyzing such waveforms quantitatively remain elusive. Here, we develop a reference-less framework to extract stress-induced H 2 O 2 waveforms in planta directly from active nanosensors for the first time. We show that waveforms extracted for 3-week-old spinach across different experimental configurations, including 2D nIR imaging and 1D spectroscopy, are identical. Using this standardized approach, we systematically validate an analytical waveform model based on H 2 O 2 reaction-diffusion transport with a large waveform data set and extract the wave velocities and propagation rate constants from different waveforms. A wave-velocity-rate constant map is created for comparative studies. Results suggest that nanosensors can identify distinct waveforms associated with specific plant stressors with the proposed framework, providing opportunities for new diagnostic tools.
Why it matches plant phenotyping methods植物内H2O2シグナル波形をナノセンサーから抽出・定量解析する標準化手法を開発し、異なる画像・分光構成と大規模データで検証しているため、植物状態の測定法が中心である。
abstractHere, we develop a reference-less framework to extract stress-induced H 2 O 2 waveforms in planta directly from active nanosensors for the first time.
BACKGROUND: Root biomass serves as a critical indicator of plant eco-physiological status and crop productivity, yet its non-destructive monitoring remains challenging because of its underground location. The use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible. In this study, we investigated and compared the performance of RGB and hyperspectral imaging for predicting root dry weight in hydroponically grown spinach (Spinacia oleracea L.). RESULTS: Using 430 root segments divided from 60 plants, three models were developed: (1) an area-based regression based on root coverage, (2) a convolutional neural network (CNN) using RGB images, and (3) a partial least squares regression (PLSR) model using hyperspectral data (450-950 nm). The area-based regression exhibited limited accuracy (R² = 0.446) because of saturation at high root coverage. The CNN model improved predictive performance (R² = 0.739) but tended to overestimate sparse roots as a result of resolution constraints. The PLSR model achieved the highest accuracy (R² = 0.822, RMSE = 0.019 g/segment), with significantly lower error than RGB-based approaches (P < 0.01). Variable importance in projection analysis indicated that PLSR effectively exploited spectral signatures at 450 nm (background contrast) and 750 nm (tissue scattering), thereby maintaining stable accuracy across the full biomass range. When validated using 104 independent plants, the PLSR model achieved high predictive accuracy. Furthermore, as a proof of concept, this model successfully visualized the spatiotemporal dynamics of root biomass accumulation over 50 days, with only a 7.70% relative error at harvest. CONCLUSIONS: To our knowledge, this study is among the first to demonstrate the non-destructive monitoring of biomass distribution within entire root systems under production conditions. Hyperspectral imaging combined with PLSR outperforms RGB-based approaches by capturing spectral signatures that reflect internal tissue properties of roots, thereby overcoming limitations caused by morphological occlusion. This approach provides a robust tool for precision agriculture and high-throughput phenotyping, enabling continuous assessment of root growth through simple modifications to the existing hydroponic systems.
Why it matches plant phenotyping methodsRGB・ハイパースペクトル画像と機械学習／PLSRを用いて根乾物重を非破壊推定・検証する方法研究であり、植物表現型の取得と定量化が中心である。
abstractThe use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible.
Reproduction assets foundThe paper's Data availability statement deposits the paper-specific phenotyping assets (raw hyperspectral images, RGB images, and root dry weight measurements) in a Zenodo record. The provided URL includes a token and 'preview=1', suggesting the record may not yet be fully open, but it is the authors' stated public URLDataset · publicThe datasets generated and analyzed during the model construction of the current study are available in the Zenodo repository: [https://zenodo.org/records/18072801?preview=1&token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImFiYTkzMzY2LTIzZjktNDlkMy1iZTBjLTk3M2E5YTUyOTFmZCIsImRhdGEiOnt9LCJyYW5kb20iOiIzNGE1ZjMxNDZhYjhiYjlhZWRiOWFjNzBkNzcwY2I3NyJ9.uR4HfosoSaVWhtSblMOS1v9bJFA5MvHwXvcW9uoNbcTWRDU4RNxZpVHjXTC3ulBM1JTlBbeHp_4T5EcILawxdg].The dataset includes:
- Raw hyperspectral images and data- RGB images
- Root dry weight measurementsOpen asset ↗Zenodo · 18072801lines:176-248Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Ensuring the post-harvest quality and health of leafy vegetables is critical for minimizing economic loss, enhancing food security, and promoting sustainable agricultural practices. Spinach, a highly nutritious yet perishable crop, is particularly susceptible to rapid freshness degradation and foliar diseases. While computer vision and deep learning have shown promise for automated quality assessment, existing models often lack the robustness to handle the dual-task classification of both freshness and disease states across diverse local spinach varieties. To bridge this gap, this paper introduces a novel hybrid Convolutional Neural Network (CNN) architecture specifically designed for the multi-class detection of freshness and visual disease symptoms in local spinach leaves. The proposed model synergistically integrates a powerful feature extraction backbone with a tailored attention and fusion mechanism, enhancing its ability to capture discriminative spatial and textural features critical for fine-grained classification. It was trained and validated on a curated dataset comprising high-resolution images of three prominent local varieties (Malabar, Water, and Red spinach) in both fresh and non-fresh conditions. The proposed hybrid model achieved a classification accuracy of 98.36%, significantly outperforming benchmark state-of-the-art models including DenseNet121, ResNet50, and EfficientNetB0. Furthermore, explainable AI (XAI) techniques visually validated the model’s decision-making process, confirming its focus on biologically relevant leaf regions. The results demonstrate that the proposed hybrid framework offers a highly accurate, reliable, and interpretable tool for non-destructive, real-time quality monitoring. This work provides a significant contribution towards intelligent post-harvest management systems, capable of reducing waste and supporting the value chain for local spinach cultivation.
Why it matches plant phenotyping methods葉画像から鮮度および視覚的な病徴を分類するCNN手法の開発・検証が中心であり、植物の状態を直接推定している。
abstractthis paper introduces a novel hybrid Convolutional Neural Network (CNN) architecture specifically designed for the multi-class detection of freshness and visual disease symptoms in local spinach leaves.
Reproduction assets foundThe paper's Data Availability statement points to a public Mendeley Data deposit of the local spinach leaf image dataset used for the CNN freshness/disease classification, matching the paper's phenotyping inputs.Dataset · publicThe datasets analyzed during the current study are publicly available in the Mendeley Data repository at: [https://data.mendeley.com/datasets/skf6w2s2h2/2](https:/data.mendeley.com/datasets/skf6w2s2h2/2).Open asset ↗Mendeley Datahtml-lines:660-689Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Malabar spinach is a nutrient-dense leafy vegetable widely cultivated and consumed in Bangladesh. Its productivity is often compromised by Alternaria leaf spot and straw mite infestations. This work proposes an efficient and interpretable deep learning framework for automatic Malabar spinach leaf disease classification. A curated dataset of Malabar spinach images collected from Habiganj Agricultural University and supplemented with public samples was categorized into three classes: Alternaria, straw mite, and healthy leaves. A lightweight SpinachCNN established a strong baseline, while Spinach-ResSENet, enhanced with squeeze-and-excitation modules, improved channel-wise attention and feature discrimination. A customized Vision Transformer (SpinachViT) and SwinV2-Base were further investigated to assess the benefits of transformer-based architectures under limited data. To mitigate annotation scarcity, we employed SimSiam-based self-supervised pretraining on unlabeled images, followed by supervised fine-tuning with cross-entropy or a hybrid objective combining cross-entropy and supervised contrastive loss. The best-performing domain-optimized model, SimSiam-CBAM-ResNet-50, incorporated Convolutional Block Attention Modules and achieved 97.31% test accuracy, 0.9983 macro ROC-AUC, and low calibration error, while maintaining robustness to Gaussian and salt-and-pepper noise. Although a SwinV2-Base benchmark pretrained on ImageNet-22k reached slightly higher accuracy (97.98%, 98.99% with test-time augmentation), its 86.9M parameters and reliance on large-scale pretraining reduce feasibility for edge deployment. In contrast, the SimSiam-CBAM model offers a more parameter-efficient and deployment-friendly solution for real-world agricultural applications. Model decisions are interpretable via Grad-CAM, Grad-CAM++, and LayerCAM, which consistently highlight biologically relevant lesion regions. The spinach dataset used in this study is publicly available on: https://huggingface.co/datasets/saifullah03/malabar_spinach_leaf_disease_dataset.
Why it matches plant phenotyping methods葉画像から病害状態を分類する深層学習手法を開発・比較し、公開データセットと解釈可能性・頑健性も評価しており、植物表現型取得が中心である。
abstractThis work proposes an efficient and interpretable deep learning framework for automatic Malabar spinach leaf disease classification.
Reproduction assets foundThe paper's Malabar spinach leaf disease image dataset (the phenotyping input used for all measurements) is explicitly stated as publicly available on Hugging Face, with the URL given in the abstract and Data Availability Statement. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicThe spinach dataset used in this study is publicly available on: https://huggingface.co/datasets/saifullah03/malabar_spinach_leaf_disease_dataset.Open asset ↗huggingface · saifullah03/malabar_spinach_leaf_disease_datasethtml-lines:585-614Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Jan 2026Computers and Electronics in Agriculture.
Monitoring plant weight during its development cycle is crucial for effective growth monitoring; it provides valuable information on plant’s health and development. Weight data are essential to determine the optimal harvest time and ensure that plants are harvested when they are at their best. This work aimed to design and implement a workflow that allows the study of growth and development variables in a spinach crop cycle using high spatial resolution multispectral images acquired with an Unmanned Aerial Vehicle (UAV). We based this workflow on applying a multitask attention U-Net model for plant segmentation and advanced statistical analysis, including regression methods and hierarchical analysis, to build and evaluate a Random Forest (RF) model and a Generalized Linear Model (GLM) for spinach fresh weight estimation. The segmentation model achieved a mean Intersection over Union (mIoU) of 0.90 and an F-score of 0.93 against manually drawn labels. Experimental validation of the estimation of the fresh weight of spinach plants from geometric and spectral characteristics with an R2 of 0.90 and RMSE = 23.48 g for the RF model constructed from explanatory variables found with hierarchical analysis. Results demonstrate the utility of a novel hybrid approach for analysis of multispectral imagery from UAVs in crop monitoring.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植物セグメンテーションと幾何・スペクトル特徴を抽出し、ホウレンソウの生体重を推定するワークフローを開発・検証しており、表現型取得・推定手法が中心である。
abstractThis work aimed to design and implement a workflow that allows the study of growth and development variables in a spinach crop cycle using high spatial resolution multispectral images acquired with an Unmanned Aerial Vehicle (UAV).
Achieving reliable and accurate detection of heavy metals in vegetables remains a critical challenge in food safety. While spectroscopic techniques enable rapid and nondestructive measurements, their practical application is often hindered by insufficient accuracy. Here, this study presents a hyperspectral imaging approach guided by the physiological response mechanisms of spinach to accurately quantify cadmium content in leaves. Cadmium-induced disruption of the chlorophyll-flavonoid system and impairment of cellular integrity established the physiological basis for selecting two characteristic spectral regions (388.34-430 nm and 1000-1036.34 nm). Leveraging these insights, we designed Cd-SpiNet with multiple convolutional branches to specifically extract cadmium-specific spectral features, thereby enhancing model sensitivity to cadmium-related signals. This physiologically guided strategy significantly enhances detection performance, achieving a coefficient of determination of 0.9753 and a root mean square error of prediction of 0.0287 mg/kg on the prediction set. The high accuracy is attributed to the cadmium-triggered physiological changes that directly influence spectral absorption and reflection characteristics within the selected bands. This study thus offers a novel strategy that integrates plant physiological mechanisms into spectral detection, providing a robust and intelligent solution for precise heavy metal monitoring in agriculture, which is crucial for timely risk assessment and effective management of ecological hazards.
Why it matches plant phenotyping methodsホウレンソウ葉のカドミウム状態をハイパースペクトル画像から定量推定する手法を開発・評価しており、植物フェノタイプ取得が中心である。
abstractthis study presents a hyperspectral imaging approach guided by the physiological response mechanisms of spinach to accurately quantify cadmium content in leaves
Porosity and permeability are critical physical parameters for accurately modelling macroscale heat and mass transfer processes during the cooling, thermal processing, and storage of leafy vegetables. However, existing estimation methods primarily rely on lumped semi-empirical approaches, which overlook the realistic 3D structural information, limiting insights into microscale water transport behaviour. This study utilised low- and high-resolution X-ray computed tomography (CT) combined with advanced cell segmentation techniques to determine the porosity-permeability correlation of spinach, a representative easily dehydrated leafy vegetable. Experiments demonstrated that the Cellpose, dilation, and erosion algorithms effectively segmented adhering cells and generated lamina and petiole slices with varying porosity gradients. Using 3D reconstruction and seepage simulation, the porosity and permeability of representative elementary volumes (REVs) in the lamina and petiole tissues were calculated, and the pressure and flow rate distributions within the intercellular spaces were visualised. The porosity-permeability relationship was fitted using the Kozeny-Carman (KC) formula as κₗ = 4.839 × 10⁻¹¹φ¹.⁷⁰/(1 - φ)⁰.⁷⁰ for lamina REVs and κₚ = 1.128 × 10⁻¹⁰φ².¹⁶/(1 - φ)¹.¹⁶ for petiole REVs. Grayscale-porosity and porosity-permeability correlations were further applied to characterise the heterogeneity of porosity (2.742%–53.30%) and permeability (4.925 × 10⁻¹⁴ - 2.829 × 10⁻¹¹ m²) of intact spinach. The study aims to provide technical and theoretical support for multiscale modelling in the quality control of leafy vegetables.
Why it matches plant phenotyping methodsX線CT、細胞セグメンテーション、3D再構成を中核として、ホウレンソウ組織の空隙率・透過率という構造的な植物形質を定量化する手法を開発・適用しているため。
abstractThis study utilised low- and high-resolution X-ray computed tomography (CT) combined with advanced cell segmentation techniques to determine the porosity-permeability correlation of spinach
Leafy vegetables present challenges for Raman-based carotenoid analysis due to strong fluorescence from chlorophyll and the coexistence of complex biomolecules. This study introduces a non-destructive approach combining Raman spectroscopy with Linear Discriminant Analysis (LDA) to classify carotenoid content levels. Arabidopsis thaliana mutants with controlled carotenoid levels were used to build and validate the model, which was then applied to cultivated Spinacia oleracea. Various spectral preprocessing methods and Raman shift subsets were tested to optimize model performance. The LDA model successfully distinguished lutein and β-carotene concentration levels, achieving up to 95.45 % accuracy in Arabidopsis and 90.91 % in spinach. This classification-based strategy offers practical advantages over continuous quantification, particularly in food quality monitoring and nutritional labeling. The findings demonstrate the potential of LDA-assisted Raman spectroscopy as a selective and reliable tool for carotenoid analysis in chlorophyll-rich vegetables, with strong applicability for non-destructive quality control across the food production and distribution chain.
Why it matches plant phenotyping methods葉のカロテノイド含量を非破壊的に推定するRaman＋LDA法を開発・検証しており、植物形質の取得手法が中心である。食品品質への応用を主眼とするが、植物材料で測定モデルを構築・検証している。
abstractThis study introduces a non-destructive approach combining Raman spectroscopy with Linear Discriminant Analysis (LDA) to classify carotenoid content levels.
This research presents an AI-powered automated hydroponic system designed to enhance the efficiency and sustainability of modern agriculture. The system integrates real-time environmental monitoring, automated nutrient management, and AI-based disease detection to optimize plant growth and minimize manual intervention. An ESP32 microcontroller collects data from specialized sensors measuring Total Dissolved Solids (TDS), pH, temperature, and light intensity. Data is wirelessly transmitted via MQTT to an EMQX broker, subsequently processed by an ExpressJS backend, and stored in a Firebase Realtime Database. A NextJS web application provides a user-friendly dashboard for visualization, alerts, and remote control. Automation is achieved using relay-controlled peristaltic and water pumps that adjust nutrient dosing and circulation based on sensor readings. A camera module captures plant images, which are analyzed by a CNN model running on a separate AI server to detect common spinach diseases like Anthracnose and Downy Mildew, enabling early intervention. This integrated system combines IoT, cloud data management, automation, and AI-based visual inspection to offer a comprehensive solution for precision hydroponic farming. Evaluation demonstrates high accuracy in disease detection, robust system performance, and significant potential for improving crop health, yield, and reducing manual labor in diverse agricultural settings. The system, along with its full codebase, has been made publicly available to promote reproducibility.• Automated Precision Hydroponics: Combines real-time environmental monitoring, automated nutrient management, and AI-powered disease detection for optimized spinach cultivation. • Reproducible and Scalable Method: Provides a detailed, step-by-step protocol for constructing and operating the system, adaptable to various hydroponic setups and crop types. • Sustainable and Efficient Agriculture: Minimizes resource consumption, reduces manual labour, and promotes environmentally friendly practices.
Why it matches plant phenotyping methods水耕栽培の統合システム全体に加え、植物画像をCNNで解析してホウレンソウ病害を検出する、再利用可能な画像ベース表現型取得機能が明示されており、方法・プラットフォームの中心的構成要素である。
abstractA camera module captures plant images, which are analyzed by a CNN model running on a separate AI server to detect common spinach diseases like Anthracnose and Downy Mildew, enabling early intervention.
Reproduction assets foundThe paper publicly releases its full codebase (ESP32 firmware, backend/frontend servers, AI model server) and uses a public Mendeley spinach disease image dataset as the phenotyping input for its CNN disease-detection analysis. All four assets are paper-specific, public, and actionable via author-provided URLs.Dataset · publicThe spinach disease dataset was obtained from the publicly available Mendeley Data repository: https://data.mendeley.com/datasets/n56pn9fncw/2.Open asset ↗Mendeley Data · n56pn9fncw/2html-lines:154-183Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
A country's economic growth heavily relies on agricultural productivity, specifically nutrition derived from vegetables and leafy greens. Spinach, abundant in iron, vitamins, and other essential nutrients, plays a vital role in maintaining the health of human tissues, cartilage, and hair. However, extreme summer heat and plant diseases can significantly reduce spinach yields, making it less nutritious and harder to obtain. Implementing improved detection and classification of bacterial and fungal diseases affecting spinach leaves is crucial for minimizing pesticide use and enhancing agricultural output. A cutting-edge approach was introduced for identifying diseases in spinach leaves through deep learning object detection. To tackle these issues, the DenseNet-121-DO model served as the basis for developing the Custom Monochromatic LeafAdaptNet (MLAN). Spinach leaves were classified as Half-Spinach, Curry Leaves, Drumstick Leaves, and Lettuce, with the aid of Google-Colaboratory. This model displayed impressive results, achieving an accuracy of 99.10% and a mean Average Precision (mAP) of 98.16%. Such outcomes promote higher agricultural productivity and reduced pesticide costs by showcasing the system's effectiveness in accurately identifying and classifying spinach leaf diseases.
Why it matches plant phenotyping methods深層学習による単色画像からの葉の病害検出・分類手法を開発し、精度とmAPで評価しており、植物の病害状態を推定する方法が中心である。
abstractA cutting-edge approach was introduced for identifying diseases in spinach leaves through deep learning object detection.
A country's economic growth heavily relies on agricultural productivity, specifically nutrition derived from vegetables and leafy greens. Spinach, abundant in iron, vitamins, and other essential nutrients, plays a vital role in maintaining the health of human tissues, cartilage, and hair. However, extreme summer heat and plant diseases can significantly reduce spinach yields, making it less nutritious and harder to obtain. Implementing improved detection and classification of bacterial and fungal diseases affecting spinach leaves is crucial for minimizing pesticide use and enhancing agricultural output. A cutting-edge approach was introduced for identifying diseases in spinach leaves through deep learning object detection. To tackle these issues, the DenseNet-121-DO model served as the basis for developing the Custom Monochromatic LeafAdaptNet (MLAN). Spinach leaves were classified as Half-Spinach, Curry Leaves, Drumstick Leaves, and Lettuce, with the aid of Google-Colaboratory. This model displayed impressive results, achieving an accuracy of 99.10% and a mean Average Precision (mAP) of 98.16%. Such outcomes promote higher agricultural productivity and reduced pesticide costs by showcasing the system’s effectiveness in accurately identifying and classifying spinach leaf diseases.
Why it matches plant phenotyping methods葉画像から病害を検出・分類する深層学習手法の開発が中心であり、植物の病徴状態を直接推定するため、植物フェノタイピング手法として採録する。
titlean adaptive approach to spinach leaf disease detection using monochromatic imaging
Accurate crop yield estimation is crucial for food security and effective crop management in precision agriculture. Previous studies have shown the correlation between remotely sensed data and crop yield, emphasizing the need for continuous time series of radiometric indices from satellite imagery. However, passive sensors are limited by cloud cover, restricting valid image acquisition. This study explored the integration of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data to enhance NDVI estimation and yield prediction of spinach. Random Forest Regression models were developed to predict NDVI from SAR data at two scales: (i) a general crop-scale model and (ii) specific plot-scale models. Both scales achieved R2 values above 0.9 for NDVI estimation, with better results at the plot scale. Integrating NDVI values derived from Sentinel-1 significantly improved yield estimation accuracy using NDVI time series compared to using NDVI from Sentinel-2 alone. The results indicated that plot-scale NDVI estimation had the lowest error rates (1.4%) and the highest R2 (0.89), outperforming the crop-scale model. The integration of SAR-based NDVI reduced data gaps caused by cloud cover and enabled earlier, more informed crop management decisions. These findings underscore the importance of SAR-based NDVI estimation for enhancing yield predictions in precision agriculture.
Why it matches plant phenotyping methodsSARと光学データを融合し、作物・圃場プロット単位のNDVIを推定する手法を開発・検証しており、植物状態の測定法が中心的です。
abstractRandom Forest Regression models were developed to predict NDVI from SAR data at two scales: (i) a general crop-scale model and (ii) specific plot-scale models.
The accurate evaluation of crop quality is vital for sustainable agriculture and optimized production. Raman spectroscopy, renowned for its insensitivity to water interference and its ability to deliver molecular-specific information, presents significant potential as a remote sensing technology. This study explores the feasibility of adapting advanced Raman spectroscopy as a remote crop quality sensor for the precise assessment of carotenoids. Carotenoids were chosen due to their dual role as key stress indicators in crops and their well-established antioxidant benefits for human health.To explore carotenoid variability, Arabidopsis thaliana and Spinacia oleracea were analyzed. Raman spectroscopy measurements were performed on two leaves per plant using a 785 nm laser. For the carotenoid quantification, Linear Discriminant Analysis (LDA) was adapted. The spectra were processed through smoothing, background removal, and normalization, followed by modification with an amplifying factor. This study evaluated the impact of these processing methods, particularly the application of the amplifying factor, on the accuracy of the model. High-Performance Liquid Chromatography (HPLC) was employed as the reference method for validation. Three-quarters of the samples were used to construct the model, while the remaining one-quarter was reserved for validation. As a result, the model utilizing spectra modified with the amplifying factor in most cases achieved higher validation accuracy compared to models based on unmodified spectra.This study introduces a novel Raman spectroscopy-based remote sensing approach for crop quality assessment, establishing an enhanced model for interpreting spectral data. By enabling precise detection of stress-induced changes in plant chemical composition, including carotenoids, this technique paves the way for scalable, real-time monitoring through Raman-equipped machinery or drones, advancing sustainable agriculture practices.
Why it matches plant phenotyping methods植物葉のカロテノイド量を推定するRaman分光センシング法を開発し、HPLCを基準に検証しており、植物フェノタイピング手法が中心である。
abstractThis study explores the feasibility of adapting advanced Raman spectroscopy as a remote crop quality sensor for the precise assessment of carotenoids.
The thylakoid membrane is the site of the light-dependent reactions of photosynthesis. It is a continuous membrane, folded into grana stacks and the interconnecting stroma lamellae. The CURVATURE THYLAKOID1 (CURT1) protein family is involved in the folding of the membrane into the grana stacks. The thylakoid membrane remodels its architecture in response to light conditions, but its 3D organisation and dynamics remain incompletely understood. To resolve these details, an imaging technique is needed that provides high-resolution 3D images in a high-throughput manner. Recently, we have used expansion microscopy, a technique that meets these criteria, to visualise the thylakoid membrane isolated from spinach. Here, we show that this protocol can also be used to visualise enveloped spinach chloroplasts. Additionally, we present an improved protocol for resolving the thylakoid structure of Arabidopsis thaliana. Using this protocol, we show the changes in thylakoid architecture in response to long-term far-red light acclimation and due to knocking out CURT1A. We show that far-red light acclimation results in higher grana stacks that are packed closer together. In addition, the distance between stroma lamellae, which are wrapped around the grana, decreases. In the curt1a mutant, grana have an increased diameter and height, and the distance between grana is increased. Interestingly, in this mutant, the stroma lamellae occasionally approach the grana stacks from the top. These observations show the potential of expansion microscopy to study the thylakoid membrane architecture.
Why it matches plant phenotyping methods植物のチラコイド膜構造を高解像度3D画像で取得する拡大顕微鏡法の改良・適用が中心であり、膜構造という植物形態形質を測定しているため。
abstractTo resolve these details, an imaging technique is needed that provides high-resolution 3D images in a high-throughput manner.
Reproduction assets foundThe article states that the data underlying the publication (expansion microscopy imaging/measurements of thylakoid architecture) are publicly available in the 4TU Research Data repository via the DOI 10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885. This is a paper-specific, publicly accessible data deposit with an authorDataset · publicUte Armbruster for providing the
seeds of the Ler0 curt1a-1 mutant. This work was supported by the Dutch
Organisation for Scientific Research (NWO) via a Vidi grant no. VI.Vidi
192.042 (E.W.) and by Wageningen Graduates Schools through a PhD
grant (J.B.).
Data availability
The data underlying this publication can be accessed at https://doi.org/10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885.References
[1] R.E. Blankenship, Molecular Mechanisms of Photosynthesis, John Wiley & Sons,
2021, https://doi.org/10.1002/9780470758472.
[2] H. Kirchhoff, Chloroplast ultrastructure in plants, New Phytol. 223 (2) (2019)
565–574, https://doi.org/10.1111/nph.15730.
[3] H. Kirchhoff, C. Hall, M. Wood, M. HerbstOpen asset ↗10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885pdf-raw-page:9 lines:1-68Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
SpinachGrowth chamberLeafGrowth / time-series analysisGrowth / development / phenologyLeaf traits
Global trends in health, climate, and population growth drive the demand for high-nutrient plants like spinach, which thrive under controlled conditions with minimal resources. Despite technological advances in agriculture, current systems often rely on traditional methods and need robust computational models for precise plant growth forecasting. Optimizing vegetable growth using advanced agricultural and computational techniques, addressing challenges in food security, and obtaining efficient resource utilization within urban agriculture systems are open problems for humanity. Considering the above, this paper presents an enclosed agriculture system for growth and modeling spinach of the Viroflay ( Spinacia oleracea L.) species. It encompasses a methodology combining data science, machine learning, and mathematical modeling. The growth system was built using LED lighting, automated irrigation, temperature control with fans, and sensors to monitor environmental variables. Data were collected over 60 days, recording temperature, humidity, substrate moisture, and light spectra information. The experimental results demonstrate the effectiveness of polynomial regression models in predicting spinach growth patterns. The best-fitting polynomial models for leaf length achieved a minimum Mean Squared Error (MSE) of 0.158, while the highest MSE observed was 1.2153, highlighting variability across different leaf pairs. Leaf width models exhibited improved predictability, with MSE values ranging from 0.0741 to 0.822. Similarly, leaf stem length models showed high accuracy, with the lowest MSE recorded at 0.0312 and the highest at 0.3907.
Why it matches plant phenotyping methods葉長・葉幅・葉柄長という植物形質の予測モデルを中心に構築・評価しており、単なる生育実験のルーチン測定ではなく、計算的な形質推定手法の応用に該当する。
abstractIt encompasses a methodology combining data science, machine learning, and mathematical modeling.
Leafy vegetables present challenges for Raman-based carotenoid analysis due to strong fluorescence from chlorophyll and the coexistence of complex biomolecules. This study introduces a non-destructive approach combining Raman spectroscopy with Linear Discriminant Analysis (LDA) to classify carotenoid content levels. Arabidopsis thaliana mutants with controlled carotenoid levels were used to build and validate the model, which was then applied to cultivated Spinacia oleracea. Various spectral preprocessing methods and Raman shift subsets were tested to optimize model performance. The LDA model successfully distinguished lutein and β-carotene concentration levels, achieving up to 95.45 % accuracy in Arabidopsis and 90.91 % in spinach. This classification-based strategy offers practical advantages over continuous quantification, particularly in food quality monitoring and nutritional labeling. The findings demonstrate the potential of LDA-assisted Raman spectroscopy as a selective and reliable tool for carotenoid analysis in chlorophyll-rich vegetables, with strong applicability for non-destructive quality control across the food production and distribution chain.
Why it matches plant phenotyping methodsラマン分光とLDAによる葉のカロテノイド濃度レベル推定・分類手法の開発と検証が研究の中心であり、植物器官の化学的形質を非破壊測定している。食品品質用途も示されるが、植物材料でモデル構築・検証を行っている。
abstractThis study introduces a non-destructive approach combining Raman spectroscopy with Linear Discriminant Analysis (LDA) to classify carotenoid content levels.
This research presents an AI-powered automated hydroponic system designed to enhance the efficiency and sustainability of modern agriculture. The system integrates real-time environmental monitoring, automated nutrient management, and AI-based disease detection to optimize plant growth and minimize manual intervention. An ESP32 microcontroller collects data from specialized sensors measuring Total Dissolved Solids (TDS), pH, temperature, and light intensity. Data is wirelessly transmitted via MQTT to an EMQX broker, subsequently processed by an ExpressJS backend, and stored in a Firebase Realtime Database. A NextJS web application provides a user-friendly dashboard for visualization, alerts, and remote control. Automation is achieved using relay-controlled peristaltic and water pumps that adjust nutrient dosing and circulation based on sensor readings. A camera module captures plant images, which are analyzed by a CNN model running on a separate AI server to detect common spinach diseases like Anthracnose and Downy Mildew, enabling early intervention. This integrated system combines IoT, cloud data management, automation, and AI-based visual inspection to offer a comprehensive solution for precision hydroponic farming. Evaluation demonstrates high accuracy in disease detection, robust system performance, and significant potential for improving crop health, yield, and reducing manual labor in diverse agricultural settings. The system, along with its full codebase, has been made publicly available to promote reproducibility.•Automated Precision Hydroponics:Combines real-time environmental monitoring, automated nutrient management, and AI-powered disease detection for optimized spinach cultivation.•Reproducible and Scalable Method:Provides a detailed, step-by-step protocol for constructing and operating the system, adaptable to various hydroponic setups and crop types.•Sustainable and Efficient Agriculture:Minimizes resource consumption, reduces manual labour, and promotes environmentally friendly practices.
Why it matches plant phenotyping methods植物画像をCNNで解析して病害状態を推定する手法がシステムの主要機能として記述・評価されており、植物フェノタイピングを含む統合プラットフォーム研究である。
abstractA camera module captures plant images, which are analyzed by a CNN model running on a separate AI server to detect common spinach diseases like Anthracnose and Downy Mildew, enabling early intervention.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 15 Sept 2026
Accurate estimation of leaf nitrogen concentration and shoot dry-weight biomass in leafy vegetables is crucial for crop yield management, stress assessment, and nutrient optimization in precision agriculture. However, obtaining this information often requires access to reliable plant physiological and biophysical data, which typically involves sophisticated equipment, such as high-resolution in-situ sensors and cameras. In contrast, smartphone-based sensing provides a cost-effective, manual alternative for gathering accurate plant data. In this study, we propose an innovative approach for estimating leaf nitrogen concentration and shoot biomass by integrating smartphone RGB imagery with Light Detection and Ranging (LiDAR) data, using Amaranthus dubius (Chinese spinach) as a case study. The influence of varying nitrogen dosages on individual spectral and structural features derived from smartphone RGB imagery and LiDAR data was modeled. Additionally, the spectral indices from RGB imagery and structural indices from LiDAR data were combined to model both leaf nitrogen concentration and shoot biomass. The performance of crop parameter modeling was evaluated using support vector regression, random forest regression, and lasso regression. Results demonstrate that the combined use of smartphone RGB imagery and LiDAR data can accurately estimate leaf total reduced nitrogen concentration, leaf nitrate concentration, and shoot dry-weight biomass, with average relative root mean square errors as low as 0.06, 0.16, and 0.05, respectively. Furthermore, the optimal nitrogen dosage for maximizing biomass yield in Chinese spinach was also estimated using the smartphone data. This study lays the groundwork for smartphone-based estimate leaf nitrogen concentration and shoot biomass, supporting accessible precision agriculture practices.
Why it matches plant phenotyping methodsスマートフォンRGB画像とLiDARから葉の窒素濃度およびシュート乾物バイオマスを推定する手法を開発・評価しており、植物形質の取得・抽出が研究の中心です。
abstractwe propose an innovative approach for estimating leaf nitrogen concentration and shoot biomass by integrating smartphone RGB imagery with Light Detection and Ranging (LiDAR) data
In the field of photosynthesis, only a limited number of approaches of super-resolution fluorescence microscopy can be used, as the functional architecture of the thylakoid membrane in chloroplasts is probed through the natural fluorescence of chlorophyll molecules. In this work, we have used a custom-built fluorescence microscopy method called Single Pixel Reconstruction Imaging (SPiRI) that yields a 1.4 gain in lateral and axial resolution relative to confocal fluorescence microscopy, to obtain 2D images and 3D-reconstucted volumes of isolated chloroplasts, obtained from pea (Pisum sativum), spinach (Spinacia oleracea) and Arabidopsis thaliana. In agreement with previous studies, SPiRI images exhibit larger thylakoid grana diameters when extracted from plants under low-light regimes. The three-dimensional thylakoid architecture, revealing the complete network of the thylakoid membrane in intact, non-chemically-fixed chloroplasts can be visualized from the volume reconstructions obtained at high resolution. From such reconstructions, the stromal connections between each granum can be determined and the fluorescence intensity in the stromal lamellae compared to those of neighboring grana.
Why it matches plant phenotyping methods植物葉緑体のチラコイド膜構造を高解像度・3D画像から抽出するカスタム蛍光顕微鏡法を開発・適用しており、植物形態・細胞内構造の表現型取得が中心である。
abstractwe have used a custom-built fluorescence microscopy method called Single Pixel Reconstruction Imaging (SPiRI) that yields a 1.4 gain in lateral and axial resolution relative to confocal fluorescence microscopy
Abstract: Because food is a basic requirement for all living beings on Earth, agriculture is especially important in our daily life. Agriculture is our main source of food. In addition to plant diseases that impede the growth and quality of food crops, agriculture works to generate food to feed the growing population. We'll look at five plants: bitter gourd, mango tree, spinach, tomato, and hibiscus. This study proposes a CNN-based technique for early plant disease diagnosis. The approach consisted of three steps: image segmentation, feature extraction, and picture pre-processing. The results of these three processes are combined to form a Convolutional Neural Network (CNN) classifier. To research and analyse a plant, an input image of the damaged portions is obtained and compared to the desired dataset. The disease is then anticipated, along with therapeutic treatments. Once the disease has been recognized, the quantity of pesticides, recommended application place, and chemicals themselves will be displayed. It will also identify the nearest location where pesticides are available.
Why it matches plant phenotyping methods植物病害の症状画像から病害状態・重症度を推定するCNNベースの画像解析法が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
titleA Deep Learning-based System for Automated Plant Disease Severity Detection and Fertilizer Optimization
The compact chlorophyll measurement system was developed to quantify chlorophyll a and chlorophyll b for agriculture applications. The developed system can identify spectral changes in light of wavelength 600 nm with a sensitivity of 0.01 nm or more. The proposed system was evaluated to confirm the chlorophyll content and chlorophyll a/b ratio using the chlorophyll a and chlorophyll b standard reagents of spinach. The system detected chlorophyll content with a sensitivity of 9.37 nA/µM. The sensor current ratio changed linearly to the transmitted light spectrum, which changed with the difference in the absorption wavelengths of chlorophyll a and chlorophyll b. As chlorophyll a/b increased by 1, a linear result was obtained in which the sensor current ratio decreased by -0.0129. It was demonstrated that chlorophyll can be quantitatively measured using the proposed compact chlorophyll measurement system. In the future, the proposed system is expected to be integrated with IoT technology to improve the productivity and quality of crops at agricultural sites.
Why it matches plant phenotyping methods植物のクロロフィル量およびa/b比を測定するセンサーシステムの開発と評価が研究の中心であり、植物生理形質の取得手法に該当する。
abstractThe compact chlorophyll measurement system was developed to quantify chlorophyll a and chlorophyll b for agriculture applications.
SpinachRootClassificationMorphology / geometry measurementGrowth / time-series analysisBiomass / plant weightGrowth / development / phenologyRoot system architecture
The root system is important for the growth and development of spinach. To reveal the temporal variability of the spinach root system, root traits of 40 spinach accessions were measured at three imaging times (20, 30, and 43 days after transplanting) in this study using a non-destructive and non-invasive root analysis system. Results showed that five root traits were reliably measured by this system (RootViz FS), and two of which were highly correlated with manually measured traits. Root traits had higher variations than shoot traits among spinach accessions, and the trait of mean growth rate of total root length had the largest coefficients of variation across the three imaging times. During the early stage, only tap root length was weakly correlated with shoot traits (plant height, leaf width, and object area (equivalent to plant surface area)), whereas in the third imaging, root fresh weight, total root length, and root area were strongly correlated with shoot biomass-related traits. Five root traits (total root length, tap root length, total root area, root tissue density, and maximal root width) showed high variations with coefficients of variation values (CV ≥ 0.3, except maximal root width) and high heritability (H 2 > 0.6) among the three stages. The 40 spinach accessions were classified into five subgroups with different growth dynamics of the primary and lateral roots by cluster analysis. Our results demonstrated the potential of in-situ phenotyping to assess dynamic root growth in spinach and provide new perspectives for biomass breeding based on root system ideotypes.
Why it matches plant phenotyping methodsRootViz FSによる非破壊・非侵襲的な根形質計測とその信頼性評価が研究の中心であり、動的な根系表現型の取得・解析を実施している。
abstractusing a non-destructive and non-invasive root analysis system
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Traditional methods for assessing plant health often lack the necessary attributes for continuous and non-destructive monitoring. In this pilot study, we present a novel technique utilizing a customized fiber optic probe based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) with a contact force control unit for non-invasive and continuous plant health monitoring. We also developed a normalized difference mid-infrared reflectance index through statistical analysis of spectral features, enabling differentiation of drought and age conditions in plants. Our research aims to characterize phytochemicals and plant endogenous status optically, addressing the need for improved analytical measurement methods for in situ plant health assessment. The probe configuration was optimized with a triple-loop tip and a 3 N contact force, allowing sensitive measurements while minimizing leaf damage. By combining polycrystalline and chalcogenide fiber probes, a comprehensive wavenumber range analysis (4000–900 cm −1 ) was achieved. Results revealed significant variations in phytochemical composition among plant species, for example, red spinach with the highest polyphenolic content and green kale with the highest lignin content. Petioles displayed higher lignin and cellulose absorbance values compared to veins. The technique effectively monitored drought stress on potted green bok choy plants in situ, facilitating the quantification of changes in water content, antioxidant activity, lignin, and cellulose levels. This research represents the first demonstration of the potential of fiber optic ATR-FTIR probes for non-invasive and rapid plant health measurements, providing insights into plant health and advancements in quantitative monitoring for indoor farming practices, bioanalytical chemistry, and environmental sciences.
Why it matches plant phenotyping methods植物の健康状態・乾燥ストレス・水分や生化学的状態を非侵襲的に定量する光ファイバーATR-FTIRプローブと指標を開発・適用しており、植物フェノタイピング手法が中心である。
abstractwe present a novel technique utilizing a customized fiber optic probe based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) with a contact force control unit for non-invasive and continuous plant health monitoring.
Farming and plants are crucial parts of the inward economy of a nation, which significantly boosts the economic growth of a country. Preserving plants from several disease infections at their early stage becomes cumbersome due to the absence of efficient diagnosis tools. Diverse difficulties lie in existing methods of plant disease recognition. As a result, developing a rapid and efficient multi-plant disease diagnosis system is a challenging task. At present, deep learning-based methods are frequently utilized for diagnosing plant diseases, which outperformed existing methods with higher efficiency. In order to investigate plant diseases more accurately, this article addresses an efficient hybrid approach using deep learning-based methods. Xception and ResNet50 models were applied for the classification of plant diseases, and these models were merged using the stacking ensemble learning technique to generate a hybrid model. A multi-plant dataset was created using leaf images of four plants: black gram, betel, Malabar spinach, and litchi, which contains nine classes and 44,972 images. Compared to existing individual convolutional neural networks (CNN) models, the proposed hybrid model is more feasible and effective, which acquired 99.20% accuracy. The outcomes and comparison with existing methods represent that the designed method can acquire competitive performance on the multi-plant disease diagnosis tasks.
Why it matches plant phenotyping methods植物の葉画像から病害状態を推定する深層学習診断手法の開発・比較が中心であり、植物フェノタイピング手法に該当する。
abstractdeveloping a rapid and efficient multi-plant disease diagnosis system is a challenging task
Demand for sustainable and safe raw agricultural commodities is growing rapidly worldwide. Reducing the risk of foodborne illnesses associated with fresh produce is a task which the industry and academic researchers have been struggling with for many years. There is an immediate need to devise a non‐invasive optical detection system to monitor the food‐borne pathogens on the leaf surface. The detection of foodborne pathogens on leafy produce is performed often too late because of the invasive techniques used to evaluate the pathogen colonization. Use of deep ultraviolet fluorescence (DUVF) sensing and visible–near infrared multispectral imaging (MSI) has previously been used to monitor plant interactions against both biotic and abiotic stress regimes. Using the patho‐system that we developed to monitor Salmonella sp. and Listeria sp. ingression in leafy greens such as lettuce/spinach, we show that plant response in terms of fluctuation of chlorophyll pigments post‐Salmonella/Listeria treatment is rapid. We also show that the mode of application of Salmonella/Listeria via foliar or root supplementation changes the ChlA response. Our data also reveals that the plant sentinel response in terms of early photosynthetic response may be critical to detect food‐borne pathogens on leafy greens. MSI demonstrated that plant stress was detectable and proportional to the bacterial inoculation rate on plants. Our research may lead to implementation of better strategies and technology to increase yield and reduce risks associated with contamination of foodborne bacterial pathogens.
Why it matches plant phenotyping methods深紫外蛍光センシングとマルチスペクトル画像を用いて、葉面病原体に対する植物のクロロフィル変動・ストレス応答を非侵襲的に検出する方法が研究の中心である。
abstractThere is an immediate need to devise a non‐invasive optical detection system to monitor the food‐borne pathogens on the leaf surface.
Our laboratory at MIT has been interested over the past few years in new techniques to facilitate the transfer of chemical information from living organisms, specifically plants, animals and humans, for applications ranging from precision agriculture to precision medicine. This presentation will discuss recent advances on this topic. As tool towards this end, fluorescent nanosensors hold the potential to revolutionize life sciences and medicine. However, their adaptation and translation into the in vivo environment is fundamentally hampered by unfavourable tissue scattering and intrinsic autofluorescence. Here we develop wavelength-induced frequency filtering (WIFF) whereby the fluorescence excitation wavelength is modulated across the absorption peak of a nanosensor, allowing the emission signal to be separated from the autofluorescence background, increasing the desired signal relative to noise, and internally referencing it to protect against artefacts. Using highly scattering phantom tissues, an SKH1-E mouse model and other complex tissue types, we show that WIFF improves the nanosensor signal-to-noise ratio across the visible and near-infrared spectra up to 52-fold. This improvement enables the ability to track fluorescent carbon nanotube sensor responses to riboflavin, ascorbic acid, hydrogen peroxide and a chemotherapeutic drug metabolite for depths up to 5.5 ± 0.1 cm when excited at 730 nm and emitting between 1,100 and 1,300 nm, even allowing the monitoring of riboflavin diffusion in thick tissue. As an application, nanosensors aided by WIFF detect the chemotherapeutic activity of temozolomide transcranially at 2.4 ± 0.1 cm through the porcine brain without the use of fibre optic or cranial window insertion. The ability of nanosensors to monitor previously inaccessible in vivo environments will be important for life-sciences research, therapeutics and medical diagnostics. Also towards this overall objective, our laboratory at MIT has been interested in exploring the relatively new interface between living plants and non-biological nanostructures to impart the former with new and enhanced functions, which we call Plant Nanobionics. We have developed a theory of subcellular uptake and kinetic trapping of a wide range of nanoparticles, validated in-vivo in living plants. Confocal visible and near infrared fluorescent microscopy and single particle tracking of Gold-Cystein-AF405 (GNP-Cys-AF405), Streptavidin-Quantum Dot (SA-QD), Dextran and Poly(acrylic acid) nanoceria, and various polymer-wrapped SWCNT, including lipid-PEG-SWCNT, chitosan-SWCNT and (AT)15-SWCNT, were used to demonstrate that particle size and the magnitude, but not the sign, of the zeta potential are key in determining whether a particle is spontaneously and kinetically trapped within chloroplasts or the cytosol. We develop a mathematical model of this Lipid Exchange Envelope Penetration (LEEP) mechanism, which agrees well with observations of this size and zeta potential dependence. As an application, we rationally designed a chitosan-complexed single-walled carbon nanotube (SWNT) as nanocarriers to selectively deliver plasmid DNA (pDNA) to chloroplasts of different plant species without external biolistic or chemical aid. We demonstrate chloroplast-targeted transgene delivery and expression in living mature arugula (Eruca sativa) and watercress (Nasturitium officinale) plants in planta and in isolated Arabidopsis thaliana mesophyll protoplasts. Another application of nanoparticles and nanotechnology to plant sciences is in the form of biochemical sensors that operate in planta and across diverse species. Using non-destructive optical nanosensors, we find that the spatial and temporal H2O2 concentration immediately post-wounding follows a simple logistic waveform for six dicot plant species: lettuce (Lactuca sativa), arugula (Eruca sativa), spinach (Spinacia oleracea), strawberry blite (Blitum capitatum), sorrel (Rumex acetosa), and Arabidopsis thaliana, ranked in order of wave speed from 0.44 to 3.10 cm/min. The H2O2 wave tracks the concomitant surface potential wave measured electrochemically for the series of plants. We show that the plant NADPH oxidase RbohD, glutamate receptor-like channels (GLR3.3 and GLR3.6) are all critical to the propagation of the H2O2 waveform upon wounding. Our findings highlight the utility of a new type of nanosensor probe that is species-independent and capable of real-time, spatial and temporal biochemical measurements in planta.
Why it matches plant phenotyping methods植物体内の化学状態を非破壊・リアルタイムに測定するナノセンサーと信号処理法を開発し、創傷後のH2O2波の空間・時間特性を植物で実証しており、植物フェノタイピング手法が中心である。
abstractUsing non-destructive optical nanosensors, we find that the spatial and temporal H2O2 concentration immediately post-wounding follows a simple logistic waveform for six dicot plant species
SpinachLeafRootClassificationMorphology / geometry measurementGrowth / time-series analysisBiomass / plant weightGrowth / development / phenologyRoot system architecture
The root system is important for the growth and development of spinach. To reveal the temporal variability of the spinach root system, root traits of 40 spinach accessions were measured at three continuous stages in this study using a non-destructive and non-invasive root analysis system. Results showed that root traits had higher variations than shoot traits among spinach accessions, and the trait of relative growth rate of total root length had the largest coefficients of variation across the three imaging times. Most of the root traits were correlated between the different stages, but the correlations decreased with increasing sampling intervals. At the early stage, only tap root length was weakly correlated with shoot traits (plant height, leaf width, and object area), whereas at the later stage, root fresh weight, total root length, and root area were strongly correlated with shoot biomass-related traits. Plants with halberd-shaped leaves tended to have stronger root systems than those with nearly orbicular-shaped leaves. The 40 spinach accessions were classified into five subgroups with different growth dynamics of the primary and lateral roots. Our results demonstrated the potential of in-situ phenotyping to assess dynamic root growth in spinach and provide new perspectives for biomass breeding based on root system ideotypes.
Why it matches plant phenotyping methods非破壊・非侵襲的な根系解析システムを用いたインサイチュ表現型計測が研究の中心で、根形質の動態評価と育種利用可能性を示している。
abstractroot traits of 40 spinach accessions were measured at three continuous stages in this study using a non-destructive and non-invasive root analysis system
Stereo matching is a depth perception method for plant phenotyping with high throughput. In recent years, the accuracy and real-time performance of the stereo matching models have been greatly improved. While the training process relies on specialized large-scale datasets, in this research, we aim to address the issue in building stereo matching datasets. A semi-automatic method was proposed to acquire the ground truth, including camera calibration, image registration, and disparity image generation. On the basis of this method, spinach, tomato, pepper, and pumpkin were considered for experiment, and a dataset named PlantStereo was built for reconstruction. Taking data size, disparity accuracy, disparity density, and data type into consideration, PlantStereo outperforms other representative stereo matching datasets. Experimental results showed that, compared with the disparity accuracy at pixel level, the disparity accuracy at sub-pixel level can remarkably improve the matching accuracy. More specifically, for PSMNet, the EPE and bad−3 error decreased 0.30 pixels and 2.13%, respectively. For GwcNet, the EPE and bad−3 error decreased 0.08 pixels and 0.42%, respectively. In addition, the proposed workflow based on stereo matching can achieve competitive results compared with other depth perception methods, such as Time-of-Flight (ToF) and structured light, when considering depth error (2.5 mm at 0.7 m), real-time performance (50 fps at 1046 × 606), and cost. The proposed method can be adopted to build stereo matching datasets, and the workflow can be used for depth perception in plant phenotyping.
Why it matches plant phenotyping methods植物フェノタイピング用のステレオマッチングデータセット構築法を開発し、精度・性能を検証した研究であり、表現型取得手法が中心である。
abstractStereo matching is a depth perception method for plant phenotyping with high throughput.
Accurate simultaneous semantic and instance segmentation of a plant 3D point cloud is critical for automatic plant phenotyping. Classically, each organ of the plant is detected based on the local geometry of the point cloud, but the consistency of the global structure of the plant is rarely assessed. We propose a two-level, graph-based approach for the automatic, fast and accurate segmentation of a plant into each of its organs with structural guarantees. We compute local geometric and spectral features on a neighbourhood graph of the points to distinguish between linear organs (main stem, branches, petioles) and two-dimensional ones (leaf blades) and even 3-dimensional ones (apices). Then a quotient graph connecting each detected macroscopic organ to its neighbors is used both to refine the labelling of the organs and to check the overall consistency of the segmentation. A refinement loop allows to correct segmentation defects. The method is assessed on both synthetic and real 3D point-cloud data sets of Chenopodium album (wild spinach) and Solanum lycopersicum (tomato plant).
Why it matches plant phenotyping methods植物3D点群から器官を自動分割・識別するグラフベース手法を開発し、合成および実データで評価しており、植物表現型取得の技術が中心である。
abstractAccurate simultaneous semantic and instance segmentation of a plant 3D point cloud is critical for automatic plant phenotyping.
Reproduction assets foundThe paper's Chenopodium 3D point cloud dataset (with ground truth annotations) is publicly deposited on Zenodo, and the reconstruction pipeline code is open source on GitHub (romi/plant-3d-vision).Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/record/6962994#.YuvYkS8itqs .Open asset ↗zenodo · 6962994lines:641-715Code · publicThe entire code is open source and available online ( https://github.com/romi/plant-3d-vision ).Open asset ↗github · romi/plant-3d-visionlines:428-438Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Crop productivity is largely dependent on canopy photosynthesis, which is difficult to measure at farming sites. Therefore, real-time estimation of the canopy photosynthetic rate (Ac) is expected to facilitate effective farm management. For the estimation of Ac, two types of mathematical models (i.e., process-based models and empirical models) have been used, although both types have their own weaknesses. Process-based models inevitably require many model parameters that are difficult to identify, while empirical models, including artificial neural network (ANN) models, have a low predictive ability outside of the range of training datasets. To overcome these weaknesses, we developed a hybrid canopy photosynthesis model that included components of both process-based models and ANN models. In this hybrid model, the single-leaf photosynthetic rate (AL) and leaf area index (LAI) were first estimated from information easily obtainable at farming sites: AL was estimated by the process-based model of AL (i.e., the biochemical photosynthesis model of Farquhar et al. (1980)) from environmental data (photosynthetic photon flux density (PPFD), air temperature (Tₐ), humidity, and atmospheric CO₂ concentration (Ca)), and the LAI was estimated by an analysis of crop canopy imagery. As highly explainable information for Ac, the estimated AL and LAI were input into the ANN model to estimate Ac. As such, the ANN model learned the logical relationships between the inputs (AL and LAI) and the output (Ac). Detailed validation analysis using nine spinach Ac datasets revealed that the hybrid ANN model can estimate Ac accurately throughout the whole growth period, even when training and test datasets were obtained in different seasons under different CO₂ concentrations and based on training datasets of only three days. This study highlights the high generalizability of the hybrid ANN model, which is a prerequisite for practical application in environmentally controlled crop production.
Why it matches plant phenotyping methods作物キャノピー光合成速度を推定するハイブリッドモデルを開発し、キャノピー画像からのLAI推定と光合成速度推定を詳細に検証しており、表現型取得・推定手法が中心である。
abstractwe developed a hybrid canopy photosynthesis model that included components of both process-based models and ANN models.
This paper proposes a 4D line-scan hyperspectral imager that combines 3D geometrical measurement and spectral detection with high spectral resolution and spatial accuracy. We investigated the geometrical optical model of a camera attaching with a spectrograph, theoretically explored the mathematical model for line-scan fringe projection profilometry, and established the 3D reconstruction and calibration methods under this proposed line-scan high-dimensional imaging system. The spectral resolution of the system is 2.8 nm, and the spatial root-mean-square-error is 0.0895 mm when measuring a standard sphere with a diameter of 40.234 mm. We measure a colored statue to showcase the intensity change along the dimension of wavelength. In addition, the quality and defect of the spinach leaves are inspected based on spectral data and depth data, which demonstrates the potential application of the system in the food industry.
Why it matches plant phenotyping methods植物葉の品質・欠陥を推定可能な4Dラインスキャンハイパースペクトル撮像法を開発し、3D再構成・校正と精度評価を行っているため、植物フェノタイピング手法が中心である。
abstractThis paper proposes a 4D line-scan hyperspectral imager that combines 3D geometrical measurement and spectral detection with high spectral resolution and spatial accuracy.
Nitrogen is an essential nutrient element required for optimum crop growth and yield. If a specific amount of nitrogen is not applied to crops, their yield is affected. Estimation of nitrogen level in crops is momentous to decide the nitrogen fertilization in crops. The amount of nitrogen in crops is measured through different techniques, including visual inspection of leaf color and texture and by laboratory analysis of plant leaves. Laboratory analysis-based techniques are more accurate than visual inspection, but they are costly, time-consuming, and require skilled laboratorian and precise equipment. Therefore, computer-based systems are required to estimate the amount of nitrogen in field crops. In this paper, a computer vision-based solution is introduced to solve this problem as well as to help farmers by providing an easier, cheaper, and faster approach for measuring nitrogen deficiency in crops. The system takes an image of the crop leaf as input and estimates the amount of nitrogen in it. The image is captured by placing the leaf on a specially designed slate that contains the reference green and yellow colors for that crop. The proposed algorithm automatically extracts the leaf from the image and computes its color similarity with the reference colors. In particular, we define a green color value (GCV) index from this analysis, which serves as a nitrogen indicator. We also present an evaluation of different color distance models to find a model able to accurately capture the color differences. The performance of the proposed system is evaluated on a Spinacia oleracea dataset. The results of the proposed system and laboratory analysis are highly correlated, which shows the effectiveness of the proposed system.
Why it matches plant phenotyping methods葉画像から窒素状態を推定するコンピュータビジョン手法の開発・評価が中心であり、植物生理状態の表現型取得法に該当する。
abstractIn this paper, a computer vision-based solution is introduced to solve this problem as well as to help farmers by providing an easier, cheaper, and faster approach for measuring nitrogen deficiency in crops.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicA software release of the proposed
vision-based framework for N-nutrient estimation in crops is made publicly available on
the project website: http://faculty.pucit.edu.pk/~farid/Research/GCV.html, accessed on
8 June 2021.Open asset ↗pdf-page:16 lines:1-55Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Synthetic auxins such as 1-naphthalene acetic acid (NAA) and 2,4-dichlorophenoxyacetic acid (2,4-D) have been extensively used in plant tissue cultures and as herbicides because they are chemically more stable and potent than most endogenous auxins. A tool for rapid in planta detection of these compounds will enhance our knowledge about hormone distribution and signaling and facilitate more efficient usage of synthetic auxins in agriculture. In this work, we show the development of real-time and nondestructive in planta NAA and 2,4-D nanosensors based on the concept of corona phase molecular recognition (CoPhMoRe), to replace the current state-of-the-art sensing methods that are destructive and laborious. By designing a library of cationic polymers wrapped around single-walled carbon nanotubes with general affinity for chemical moieties displayed on auxins and its derivatives, we developed selective sensors for these synthetic auxins, with a particularly large quenching response to NAA (46%) and a turn-on response to 2,4-D (51%). The NAA and 2,4-D nanosensors are demonstrated in planta across several plant species including spinach, Arabidopsis thaliana ( A. thaliana ), Brassica rapa subsp. chinensis (pak choi), and Oryza sativa (rice) grown in various media, including soil, hydroponic, and plant tissue culture media. After 5 h of 2,4-D supplementation to the hydroponic medium, 2,4-D is seen to accumulate in susceptible dicotyledon pak choi leaves, while no uptake is observed in tolerant monocotyledon rice leaves. As such, the 2,4-D nanosensor had demonstrated its capability for rapid testing of herbicide susceptibility and could help elucidate the mechanisms of 2,4-D transport and the basis for herbicide resistance in crops. The success of the CoPhMoRe technique for measuring these challenging plant hormones holds tremendous potential to advance the plant biology study.
Why it matches plant phenotyping methods植物体内の合成オーキシンをリアルタイム・非破壊で測定するナノセンサーを開発し、複数植物種で実証しており、植物状態の取得法が研究の中心である。
abstractIn this work, we show the development of real-time and nondestructive in planta NAA and 2,4-D nanosensors based on the concept of corona phase molecular recognition (CoPhMoRe)
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 8 Sept 2026
PurposeChlorophyll (Chl) content is a reliable indicator of leaf nitrogen content and plant health status. Currently available methods for image-based Chl estimation require complex mathematical derivations and high-throughput imaging set-up along with multiplex image-preprocessing steps. Further, the influence of carotenoid (CAR) content has been largely ignored in the process. The present study describes a smartphone-based leaf image analysis method for real-time estimation of Chl content and Chl/CAR ratio. MethodsColor features were obtained from RGB (red, green, blue) images of spinach leaves using a smartphone, and inverse R and G values were calculated. Correlation analysis of color indices and photosynthetic pigment (PP) contents was performed, followed by principal component analysis (PCA). Linear mathematical modeling was performed for describing regression equations for predicting PP contents. Results1/R and 1/G showed strong positive linear correlation (0.93 < r2 < 0.96) with Chl and CAR contents, respectively. Furthermore, 1/R+1/G and [1/R]/[1/G] presented strong positive linear correlation with Chl + CAR (r2 = 0.95) and Chl/CAR (r2 = 0.88), respectively. PCA confirmed the association of color indices with the respective PP features, which were subsequently estimated using the correlation models. A smartphone-based companion application was developed using the linear models for non-invasive, real-time estimation of Chl content and Chl/CAR ratio. ConclusionThe ratios 1/R and 1/G indicate the contents of Chl and CAR via linear models. The smartphone application developed using the linear models enables real-time estimation of Chl content and Chl/CAR ratio without complicated image preprocessing steps or mathematical derivations.
Why it matches plant phenotyping methodsスマートフォン画像から葉のクロロフィル・カロテノイド含量を推定する画像解析法とアプリを開発しており、植物形質取得が研究の中心である。
abstractThe present study describes a smartphone-based leaf image analysis method for real-time estimation of Chl content and Chl/CAR ratio.
Published15 Feb 2021Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biologyCited by 3 · OpenAlex ↗
This review describes image analyses for chromosome visible structures, focusing on the chromosome imaging system CHIAS (Chromosome Image Analyzing System). CHIAS is the first comprehensive imaging system for the analysis and characterization of plant chromosomes. A simulation method for human vision for capturing band positive regions was developed and used for the image analysis of large plant chromosomes with bands. Applying this method to C-banded Crepis chromosomes enabled recognition of band positive regions as seen by human vision. Furthermore, a new image parameter, condensation pattern was developed and successfully applied to identify small plant chromosomes such as rice and brassicas. Condensation profile (CP) derived from condensation pattern was also effective in developing quantitative chromosome maps. The result was quantitative chromosomal maps of several plants with small chromosomes, including Arabidopsis, diploid brassicas, rapeseed, rice, spinach, and sugarcane. In the final chapter, various applications of imaging techniques to the analysis of pachytene chromosomes, improved visibility of multicolor FISH images, 3D reconstruction of a human chromosome based on cross-section images obtained by a FIB/SEM, automatic extraction of chromosomal regions by machine learning, etc. are described.
Why it matches plant phenotyping methods植物染色体の画像解析システムと定量的画像パラメータを中心に扱う方法論レビューであり、植物染色体構造の画像ベース計測が中核です。
abstractThis review describes image analyses for chromosome visible structures, focusing on the chromosome imaging system CHIAS (Chromosome Image Analyzing System).
Knowing before harvesting how many plants have emerged and how they are growing is key in optimizing labour and efficient use of resources. Unmanned aerial vehicles (UAV) are a useful tool for fast and cost efficient data acquisition. However, imagery need to be converted into operational spatial products that can be further used by crop producers to have insight in the spatial distribution of the number of plants in the field. In this research, an automated method for counting plants from very high-resolution UAV imagery is addressed. The proposed method uses machine vision—Excess Green Index and Otsu’s method—and transfer learning using convolutional neural networks to identify and count plants. The integrated methods have been implemented to count 10 weeks old spinach plants in an experimental field with a surface area of 3.2 ha. Validation data of plant counts were available for 1/8 of the surface area. The results showed that the proposed methodology can count plants with an accuracy of 95% for a spatial resolution of 8 mm/pixel in an area up to 172 m². Moreover, when the spatial resolution decreases with 50%, the maximum additional counting error achieved is 0.7%. Finally, a total amount of 170 000 plants in an area of 3.5 ha with an error of 42.5% was computed. The study shows that it is feasible to count individual plants using UAV-based off-the-shelf products and that via machine vision/learning algorithms it is possible to translate image data in non-expert practical information.
Why it matches plant phenotyping methodsUAV画像から個体数という植物形態・個体状態を自動抽出する手法を開発し、精度検証まで行っており、フェノタイピング手法が中心である。
abstractIn this research, an automated method for counting plants from very high-resolution UAV imagery is addressed.
Cultural practices and harvesting in spinach plants should be based not only on subjective indexes such as freshness and green colour, which are both related with the visual appearance of the plants, but also on objective indexes that can be quantified non-destructively. The aim of this research was to develop a methodology based on the use of near infrared spectroscopy to monitor routinely the growth process of the spinach plants in the field. Using the MicroNIR™ OnSite-W spectrophotometer, which is ideally suited for in situ analysis, 261 spinach plants were analysed. Initially, calibration models for dry matter, soluble solid and nitrate contents were developed using 1 spectrum per plant for dry matter content, and nine spectra per plant for the other two parameters. These models were then validated using the same number of spectra per plant as for calibration purposes. After that, to establish a procedure more suitable to routine analysis in the field, the models were validated with only one spectrum per plant and the suitability of the predictions was measured considering the global and neighbourhood Mahalanobis distances, whose control limit values were defined as inferior to 4.0 and 1.0, respectively. The results showed that once the calibration models were developed, only one spectrum per plant was enough to predict dry matter and nitrate contents successfully. Therefore, the methodology developed will allow us to monitor in real time the complete growth process and to take decisions about spinach cultivation based on internal quality and safety indexes.
Why it matches plant phenotyping methodsホウレンソウ個体の内部品質・安全性指標をNIRSで非破壊推定する方法を開発し、スペクトル数を変えた検証も行っており、植物形質の取得法が中心である。
abstractThe aim of this research was to develop a methodology based on the use of near infrared spectroscopy to monitor routinely the growth process of the spinach plants in the field.
Background Field-grown leafy vegetables can be damaged by biotic and abiotic factors, or mechanically damaged by farming practices. Available methods to evaluate leaf tissue damage mainly rely on colour differentiation between healthy and damaged tissues. Alternatively, sophisticated equipment such as microscopy and hyperspectral cameras can be employed. Depending on the causal factor, colour change in the wounded area is not always induced and, by the time symptoms become visible, a plant can already be severely affected. To accurately detect and quantify damage on leaf scale, including microlesions, reliable differentiation between healthy and damaged tissue is essential. We stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantification of damage on leaf scale. Results We present a robust, fast and sensitive method for leaf-scale visualisation, accurate automated extraction and measurement of damaged area on leaves of leafy vegetables. The image analysis pipeline we developed automatically identifies leaf area and individual stained (lesion) areas down to cell level. As proof of principle, we tested the methodology for damage detection and quantification on two field-grown leafy vegetable species, spinach and Swiss chard. Conclusions Our novel lesion quantification method can be used for detection of large (macro) or single-cell (micro) lesions on leaf scale, enabling quantification of lesions at any stage and without requiring symptoms to be in the visible spectrum. Quantifying the wounded area on leaf scale is necessary for generating prediction models for economic losses and produce shelf-life. In addition, risk assessments are based on accurate prediction of the relationship between leaf damage and infection rates by opportunistic pathogens and our method helps determine the severity of leaf damage at fine resolution.
Why it matches plant phenotyping methods葉の病斑・損傷面積を染色と自動画像解析で定量する手法自体が中心的に開発・実証されており、植物病害・損傷状態の表現型計測に該当する。
abstractWe stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantification of damage on leaf scale.
Reproduction assets foundThe paper's LiMu image analysis pipeline (used for lesion quantification) is publicly available on PyPI. The leaf image datasets are only available from the corresponding author on request, and no public URL for the image data or supplements is present in the allowed list.Code · publicThe original LiMu code is made freely available in the Python Package Index (PyPI), and can be downloaded from https://pypi.org/project/limu/ .Open asset ↗limulines:184-227Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Certain engineered nanoparticles (NPs) have unique properties that have exhibited significant potential for promoting photosynthesis and enhancing crop productivity. Understanding the fundamental interactions between NPs and plants is crucial for the sustainable development of nanoenabled agriculture. Leaf mesophyll protoplasts, which maintain similar physiological response and cellular activity as intact plants, were selected as a model system to study the impact of NPs on photosynthesis. The mesophyll protoplasts isolated from spinach were cultivated with different NMs (Fe, Mn 3 O 4 , SiO 2 , Ag, and MoS 2 ) dosing at 50 mg/L for 2 h under illumination. The potential maximum quantum yield and adenosine triphosphate (ATP) production of mesophyll protoplasts were significantly increased by Mn 3 O 4 and Fe NPs (23% and 43%, respectively), and were decreased by Ag and MoS 2 NPs. The mechanism for the photosynthetic enhancement by Mn 3 O 4 and Fe is to increase the photocurrent and electron transfer rate, as revealed by photoelectrochemical measurement. GC-MS based single cell type metabolomics reveal that NPs (Fe and MoS 2 ) altered the metabolic profiles of mesophyll cells during 2 h of illumination period. Separately, the effect of NPs exposure on photosynthesis and biomass were also conducted at the whole plant level. A strong correlation was observed with protoplast data; plant biomass was significantly increased by Mn 3 O 4 exposure (57%) but was decreased (24%) by treatment of Ag NPs. The use of mesophyll protoplasts can be a fast and reliable tool for screening NPs to enhance photosynthesis for potential nanofertilizer use. Importantly, inclusion of a metabolic analysis can provide mechanistic toxicity data to ensure the development "safer-by-design" nanoenabled platforms.
Why it matches plant phenotyping methodsメソフィルプロトプラストを用いた光合成スクリーニング系を提案し、全植物レベルの光合成・バイオマスとの相関で有効性を検証しており、植物生理状態の取得法が実質的に中心です。
abstractThe use of mesophyll protoplasts can be a fast and reliable tool for screening NPs to enhance photosynthesis for potential nanofertilizer use.
Hyperspectral imaging for agricultural applications provides a solution for non-destructive, large-area crop monitoring. However, current products are bulky and expensive due to complicated optics and electronics. A linear variable filter was developed for implementation into a prototype hyperspectral imaging camera that demonstrates good spectral performance between 450 and 900 nm. Equipped with a feature extraction and classification algorithm, the proposed system can be used to determine potato plant health with ∼88 % accuracy. This algorithm was also capable of species identification and is demonstrated as being capable of differentiating between rocket, lettuce, and spinach. Results are promising for an entry-level, low-cost hyperspectral imaging solution for agriculture applications.
Why it matches plant phenotyping methods低コストのハイパースペクトル撮像カメラと特徴抽出・分類アルゴリズムを開発し、ジャガイモ植物の健全性を推定する方法を評価しており、植物フェノタイピング手法が中心である。
abstractA linear variable filter was developed for implementation into a prototype hyperspectral imaging camera that demonstrates good spectral performance between 450 and 900 nm.
CoffeePeaSpinachLeafClassificationPhysiological trait estimationWater status / transpiration
Abstract Background The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time–frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). Results The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves. Conclusion Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring.
Why it matches plant phenotyping methods植物葉の含水量という生理形質を、テラヘルツ計測と機械学習で非侵襲推定する手法の開発・評価が中心である。
abstractThis paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Core Ideas High‐throughput imaging and genomic information can be combined to optimize marker development. Genome‐wide association studies identified loci associated with plant growth traits. We identified candidate genes associated with plant growth and development. Despite advances in sequencing for genotyping, the lack of rapid, accurate, and reproducible phenotyping platforms has hampered efforts to use genetic analysis to predict traits of interest. Therefore, the use of high‐throughput systems to phenotype traits related to crop growth, yield, quality, and resistance to biotic and abiotic stresses has become a major asset for breeding. Here, we assessed the efficacy of unmanned aircraft system (UAS)‐based high‐throughput phenotyping to obtain data for molecular marker development for spinach ( Spinacia oleracea L.) improvement. We used a UAS equipped with a red–green–blue sensor to capture raw images of 284 spinach accessions throughout the crop cycle. Processed images generated orthomosaic and digital surface models for estimating canopy cover, canopy volume, and excess greenness index models. In addition, we manually recorded the number of days to bolting. Genome‐wide association studies against a single‐nucleotide polymorphism (SNP) panel obtained by ddRADseq identified 99 SNPs significantly associated with growth parameters. Some of these SNPs are in transcription factor and stress‐response genes with possible roles in plant growth and development. The results underscore the utility of combining aerial imaging and genomic data analysis to optimize marker development. This study lays the foundation for the use of UAS‐based high‐throughput phenotyping for the molecular breeding of spinach.
Why it matches plant phenotyping methodsUAS RGB画像からキャノピー形質を推定する高スループット表現型解析が研究の中心であり、育種・マーカー開発への実質的な適用を評価している。
abstractProcessed images generated orthomosaic and digital surface models for estimating canopy cover, canopy volume, and excess greenness index models.
Crystal structures of a rhodamine derivative in its closed and open spirolactam ring forms were developed, which allows selective and sensitive detection of Cu 2+ ions at a micromolar range in neutral medium. The chemosensing properties of the probe through a pentacoordinate Cu 2+ ions were proven by spectroscopic and theoretical analysis. The spirolactam ring opening as the Cu 2+ selective sensor was applied to spinach ( Spinacia oleracea ) to estimate the accumulation of copper as copper(II) in the plant.
Why it matches plant phenotyping methodsCu2+選択的な化学センサーを開発し、植物体内の銅蓄積という植物状態の推定に適用しており、測定法が中心的です。
abstractCrystal structures of a rhodamine derivative in its closed and open spirolactam ring forms were developed, which allows selective and sensitive detection of Cu 2+ ions at a micromolar range in neutral medium.
Green colour, texture and dry matter are important attributes to appreciate freshness and quality in spinach. However, there is currently no fast, economical and non-destructive method which allows producers to measure these parameters simultaneously in the plant, in a matter of seconds. However, Near-infrared (NIR) spectroscopy might bridge this gap. NIR spectra of intact spinach leaves and modified partial least square regression models were developed for colour (a* and b*), texture (maximum fracture force, toughness, stiffness and displacement) and dry matter. A calibration equation with a high prediction performance was devised for dry matter content (r2cv = 0.74), while calibration models for all the textural parameters analysed were considered suitable for screening purposes (r2cv > 0.6). For colour-related parameters, the models allowed test samples to be rough screened. We, therefore, suggest that the analysis of green colour, texture and dry matter of spinach leaves in situ, on the plant, using NIRS technology could prove to be a valuable tool for optimizing cultural practices such as fertilization and irrigation and to assess the quality of the spinach leaves when harvested.
Why it matches plant phenotyping methodsホウレンソウ葉の色、食感、乾物を非破壊NIR分光で推定する校正モデルを開発・評価しており、植物形質の取得手法が研究の中心である。
abstractNear-infrared (NIR) spectroscopy might bridge this gap.
Photosynthetic phenotyping requires quick characterization of dynamic traits when measuring large plant numbers in a fluctuating environment. Here, we evaluated the light-induced fluorescence transient (LIFT) method for its capacity to yield rapidly fluorometric parameters from 0.6 m distance. The close approximation of LIFT to conventional chlorophyll fluorescence (ChlF) parameters is shown under controlled conditions in spinach leaves and isolated thylakoids when electron transport was impaired by anoxic conditions or chemical inhibitors. The ChlF rise from minimum fluorescence (F o ) to maximum fluorescence induced by fast repetition rate (F m-FRR ) flashes was dominated by reduction of the primary electron acceptor in photosystem II (Q A ). The subsequent reoxidation of Q A - was quantified using the relaxation of ChlF in 0.65 ms (F r1 ) and 120 ms (F r2 ) phases. Reoxidation efficiency of Q A - (F r1 /F v , where F v = F m-FRR - F o ) decreased when electron transport was impaired, while quantum efficiency of photosystem II (F v /F m ) showed often no significant effect. ChlF relaxations of the LIFT were similar to an independent other method. Under increasing light intensities, F r2 '/F q ' (where F r2 ' and F q ' represent F r2 and F v in the light-adapted state, respectively) was hardly affected, whereas the operating efficiency of photosystem II (F q '/F m ') decreased due to non-photochemical quenching. F m-FRR was significantly lower than the ChlF maximum induced by multiple turnover (F m-MT ) flashes. However, the resulting F v /F m and F q '/F m ' from both flashes were highly correlated. The LIFT method complements F v /F m with information about efficiency of electron transport. Measurements in situ and from a distance facilitate application in high-throughput and automated phenotyping.
Why it matches plant phenotyping methodsLIFT法を植物の光合成生理形質測定に適用し、従来のクロロフィル蛍光法との一致性を評価・検証しているため、植物フェノタイピング手法が中心です。
abstractHere, we evaluated the light-induced fluorescence transient (LIFT) method for its capacity to yield rapidly fluorometric parameters from 0.6 m distance.
The study sought to perform a non-destructive and in-situ quality evaluation of spinach plants using near infrared (NIR) spectroscopy in order to establish its suitability for different uses once harvested. Modified partial least square (MPLS) regression models using NIR spectra of intact spinach leaves were developed for nitrate, ascorbic acid and soluble solid contents. The residual predictive deviation (RPD) values were 1.29, 1.21 and 2.54 for nitrate, ascorbic acid and soluble solid contents, respectively. Later, this predictive capacity increased for nitrate content (RPDcv = 1.63) when new models were developed, taking into account the influence on the robustness of the model exercised by the simultaneity between the NIR and laboratory analyses. Subsequently, using partial least squares discriminant analysis (PLS-DA), the ability of NIRS technology to classify spinach as a function of nitrate content was tested. PLS-DA yielded percentages of correctly classified samples ranging from 73.08-76.92% for the class 'spinach able to be used fresh' to 85.71-73.08% for the class 'preserved, deep-frozen or frozen spinach, both for unbalanced and balanced models respectively, based on NH signal associated with proteins. Overall, the data supports the capability of NIR spectroscopy to establish the final destination of the production of spinach analysed on the plant, as a screening tool for important safety and quality parameters.
Why it matches plant phenotyping methodsNIR分光を用いた生体 spinach の非破壊・現場品質評価法を開発し、回帰モデルの予測性能と分類性能を検証しているため、植物形質取得法が研究の中心である。
abstractThe study sought to perform a non-destructive and in-situ quality evaluation of spinach plants using near infrared (NIR) spectroscopy
Plant health and physiological status significantly influence chlorophyll content and photosynthetic capacity. Analysis of leaf reflectance information from digitized leaf images allows high-throughput, non-invasive and real-time estimation of chlorophyll content in a cost-effective manner. In the present study the application of multivariate data analysis tools, viz. principal component analysis (PCA) and agglomerative hierarchical clustering analysis (AHCA), has been discussed for distinguishing between spinach seedlings having high and low chlorophyll contents by simultaneously using the information provided by various image features. Further, leaf color information contained within different color spaces, viz. RGB (red, green and blue), rgb (normalized red, green and blue), HSI (hue, saturation and intensity), CIE (Commission Internationale de l’Eclairage) L∗a∗b∗, CIE-XYZ, and CIE-xyY color spaces, has been used to predict chlorophyll content in terms of SPAD (Soil Plant Analysis Development) chlorophyll meter values by multiple linear regression. It was observed that the color indices R, G, R + G, R−B, G−B, R + G−B, Y (luminance) and DGCI (dark-green color index) exhibited high correlation (R² > 0.8) with the SPAD values. Further, subjecting the leaf reflectance information provided by these color indices to PCA and AHCA enabled a clear segregation of seedlings with high and low chlorophyll contents. SPAD values predicted by the L∗a∗b∗ color space information yielded the lowest RMSE (root mean square error) and the highest R² (coefficient of determination) amongst the six color space features assessed. The findings of the present study indicate that concatenation of leaf reflectance information provided by different color indices may be more useful than individual color indices for assessing plant health status and predicting chlorophyll content using machine vision.
Why it matches plant phenotyping methods葉画像の色特徴量と多変量解析・回帰を用いて、SPAD値としてのクロロフィル量および苗の健康状態を非破壊推定する手法が研究の中心であり、複数の色空間を比較検証している。
abstractAnalysis of leaf reflectance information from digitized leaf images allows high-throughput, non-invasive and real-time estimation of chlorophyll content
Efficient management of irrigation water is fundamental in agriculture to reduce the environmental impacts and to increase the sustainability of crop production. The availability of adequate tools and methodologies to easily identify the crop water status in operating conditions is therefore crucial. This work aimed to assess the reliability of indices derived from imaging techniques—thermal indices (Ig (stomatal conductance index) and CWSI (Crop Water Stress Index)) and optical indices (NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index))—as operational tools to detect the crop water status, regardless the eventual presence of nitrogen stress. In particular, two separate experiments were carried out in a greenhouse, on two spinach varieties (Verdi F1 and SV2157VB), with different microclimatic conditions and under different levels of water and nitrogen application. Statistical analysis based on ANOVA test was carried out to assess the independence of thermal and optical indices from the crop nitrogen status. These imaging indices were successively compared through correlation analysis with reference destructive and non-destructive measurements of crop water status (stomatal conductance, chlorophyll a fluorescence, and leaf and soil water content), and linear regression models of thermal and optical indices versus reference measurements were calibrated. All models were significant (Fisher p-value lower than 0.05), and the highest R2 values (greater than 0.6) were found for the regression models between CWSI and the soil water content, NDVI and the leaf water content, and PRI and the stomatal conductance. Further analysis showed that imaging indices acquired by thermal cameras (especially CWSI) can be used as operational tools to detect the crop water status, since no dependence on plant nitrogen conditions was observed, even when the soil water depletion was very limited. Our results confirmed that imaging indices such as CWSI, NDVI and PRI can be used as operational tools to predict soil water status and to detect drought stress under different soil nitrogen conditions.
Why it matches plant phenotyping methods熱画像・光学画像から作物の水分状態を推定する指標を、異なる窒素条件下で検証・比較し、基準測定との回帰で性能評価しているため、植物表現型取得手法が中心である。
abstractThis work aimed to assess the reliability of indices derived from imaging techniques—thermal indices (Ig (stomatal conductance index) and CWSI (Crop Water Stress Index)) and optical indices (NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index))—as operational tools to detect the crop water status, regardless the eventual presence of nitrogen stress.
Background The growth and development of plants is deleteriously affected by various biotic and abiotic stress factors. Wounding in plants is caused by exposure to environmental stress, mechanical stress, and via herbivory. Typically, oxidative burst in response to wounding is associated with the formation of reactive oxygen species, such as the superoxide anion radical (O 2 •- ), hydrogen peroxide (H 2 O 2 ) and singlet oxygen; however, few experimental studies have provided direct evidence of their detection in plants. Detection of O 2 •- formation in plant tissues have been performed using various techniques including electron paramagnetic resonance spin-trap spectroscopy, epinephrine-adrenochrome acceptor methods, staining with dyes such as tetrazolium dye and nitro blue tetrazolium (NBT); however, kinetic measurements have not been performed. In the current study, we provide evidence of O 2 •- generation and its kinetics in the leaves of spinach ( Spinacia oleracea ) subjected to wounding. Methods Real-time monitoring of O 2 •- generation was performed using catalytic amperometry. Changes in oxidation current for O 2 •- was monitored using polymeric iron-porphyrin-based modified carbon electrodes ( φ = 1 mm) as working electrode with Ag/AgCl as the reference electrode. Result The results obtained show continuous generation of O 2 •- for minutes after wounding, followed by a decline. The exogenous addition of superoxide dismutase, which is known to dismutate O 2 •- to H 2 O 2 , significantly suppressed the oxidation current. Conclusion Catalytic amperometric measurements were performed using polymeric iron-porphyrin based modified carbon electrode. We claim it to be a useful tool and a direct method for real-time monitoring and precise detection of O 2 •- in biological samples, with the potential for wide application in plant research for specific and sensitive detection of O 2 •- .
Why it matches plant phenotyping methods植物の傷害応答におけるスーパーオキシド生成を、電気化学センサーでリアルタイム測定する手法の開発・検証が中心であり、植物生理状態の表現型測定に該当する。
abstractReal-time monitoring of O 2 •- generation was performed using catalytic amperometry.
This work had the goal to assess the capability of hyperspectral line scan imaging (400–1000 nm) to estimate crop variables in the greenhouse under combined water and nitrogen stress using multivariate data analysis and two data compression methods: canopy average spectra and hyperspectrogram extraction. Hyperspectral images contain far more information than do multispectral ones, which permits discrimination among minute pattern differences in canopy spectral reflectance.A pot greenhouse experiment of eight treatments, from the combination of four nitrogen supply levels and two water supply levels, was designed to test widely varied spinach canopies. Using partial least square regression models, the fresh and dry matter of aboveground biomasses and water and nitrogen contents were estimated from a 76-sample dataset. Both the canopy reflectance-based and hyperspectrogram-based models performed well in estimating variables strictly related to canopy leaf area index (LAI) and geometry, i.e., water content and fresh and dry matters, such that R2 in independent validation reached values of 0.87, 0.65, 0.65, and 0.86, 0.74, 0.72, respectively. Estimation of nitrogen concentration from single leaf spectra hyperspectral images produced a high cross-validation R2 (0.83), as opposed to the poor predictive results produced from canopy scans. This latter result arose from orientation effects due to canopy architecture. Finally, for estimation purposes, image hyperspectrogram compression without spatial information loss produced more encouraging results while considering canopy structure in crop variables than did average canopy spectra.
Why it matches plant phenotyping methods菠菜冠层高光谱成像与数据压缩、PLS建模是研究核心，并对生物量、水分和氮含量估计进行了独立验证，属于实质性的植物表型获取与计算估计方法研究。
abstractThis work had the goal to assess the capability of hyperspectral line scan imaging (400–1000 nm) to estimate crop variables