Plant phenotyping methods.

Visual-SLAM for the detection of hidden tomatoes in greenhouses by Hierarchical Localization and GLOMAP for robotized harvesting →

Advanced crop monitoring inside greenhouses is becoming one of the primary objectives of research centers. High-performance sensors, such as LiDAR or stereo cameras, have traditionally been employed for this purpose, though these often have a high cost. This work proposes a Visual-SLAM system using a monocular camera, which is significantly more cost-effective and specifically tailored for agricultural applications, such as mapping tomato crops in a greenhouse. Tests were carried out on a real tomato bunch, located in the Agroconnect experimental greenhouse. A ROS 2 Humble node was developed to run on the robot in order to capture images of these crops, which were then stored for offline processing. To generate a 3D mapped model for the crop in the greenhouse, the GLOMAP mapper, based on Structure-From-Motion, was integrated with the Hierarchical Localization toolbox. This initial mapping is a foundation for future, more advanced algorithms to analyze growth patterns, and optimize agricultural management. The system leverages a hierarchical localization paradigm based on a coarse-to-fine strategy: it first performs global retrieval to generate location hypotheses, then combines local features within the identified candidate regions. The results show a correct identification of the tomato cluster, correctly characterising the tomato that is occluded and inaccessible by classical vision technologies. The reconstructed 3D model was further validated against manual ground-truth measurements of fruit size, centroid position, and orientation, confirming the geometric accuracy of the proposed low-cost monocular pipeline.

Why it matches plant phenotyping methods単なる収穫対象の位置検出ではなく、単眼Visual-SLAMと3D再構成を開発し、果実サイズ・重心位置・向きを実測値で検証しているため、植物器官形質の取得手法が中心である。

abstractThis work proposes a Visual-SLAM system using a monocular camera, which is significantly more cost-effective and specifically tailored for agricultural applications, such as mapping tomato crops in a greenhouse.

AgriIDIA: Early Detection of Plant Diseases Using Transfer Learning on 24-Channel Multispectral Stacks with EfficientNet-B0 →

Plant diseases pose a serious threat to agriculture, causing yield losses of 20 to 40 percent each year, resulting in more than 220,000 million dollars in economic damage and significantly affecting the global food supply. Traditional plant health monitoring practices involve visual inspection of plant tissue and can only detect the presence of disease once visual symptoms are already evident. This article presents AgriIDIA, an early-detection plant disease recognition system trained on datasets of 24-channel multispectral images derived from six optical filters (BlueIR, Hotmirror, K590, K665, K720, and K850) and six vegetation indices (NDVI, GNDVI, NDRE, EVI, REI, and SAVI). First, an exploratory data analysis is conducted on the diagnostic capability of the described 24-channel data representation, using 1,266 image stacks labeled with six classes (diseased/healthy papaya, diseased/healthy potato, diseased/healthy tomato). Next, using the results of the exploratory data analysis, the manuscript describes the training and cross-validation performance of AgriIDIA, with a macro-F1 score of 83.91 ± 3.42% and an accuracy of 84.00 ± 3.17% on the validation set. Finally, the performance of the trained model is evaluated on the reserved test set (N=190), demonstrating an accuracy of 81.05%, a macro-F1 score of 0.7398, and a weighted ROC-AUC of 0.9383. The results of this study suggest that the 24-channel multispectral representation has significant diagnostic potential for the early detection of plant diseases and can be used to design accessible phytosanitary methods for small-scale farmers.

Why it matches plant phenotyping methods多チャネルマルチスペクトル画像から植物病害状態を推定する認識システムの開発・検証が研究の中心であり、植物表現型として病害状態を直接評価している。

abstractThis article presents AgriIDIA, an early-detection plant disease recognition system trained on datasets of 24-channel multispectral images

A gold nanoparticle-boosted disposable paper-based electrochemiluminescence biosensor for in situ detection of H 2 O 2 in tomato leaves. →

This work presents a gold nanoparticle (Au NP)-boosted disposable paper-based electrochemiluminescence (ECL) biosensor for minimally invasive on-leaf in situ detection of endogenous H 2 O 2 in tomato leaves. The inherent capillary action of filter paper (FP) was employed to simplify reagent delivery, while gold nanoparticles (Au NPs) catalytically activated H 2 O 2 to generate reactive oxygen species, thereby driving the ECL signal output. This simple, low-cost paper-based platform enabled time-resolved monitoring of H 2 O 2 in stressed plants, providing a reliable in situ strategy for evaluating tomato physiology and early disease/pest warning.

Why it matches plant phenotyping methodsトマト葉内のH2O2という植物生理状態をその場で測定する紙ベースECLバイオセンサーの開発が中心であり、植物フェノタイピング手法に該当する。

abstractThis work presents a gold nanoparticle (Au NP)-boosted disposable paper-based electrochemiluminescence (ECL) biosensor for minimally invasive on-leaf in situ detection of endogenous H 2 O 2 in tomato leaves.

In Situ Monitoring of Stress-Induced Hydrogen Peroxide in Plants Using NIR-II Fluorescent Microneedles. →

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.

PlantC2USeg: Cross-Scale Consistent Pre-Training for Few-Shot Unified Plant Point Cloud Segmentation →

Modern crop breeding demands precise organ-level analysis for trait quantification, making plant point cloud segmentation (PPCS) increasingly important. However, conventional deep learning approaches rely heavily on densely annotated datasets that are labor-intensive to acquire. Unified PPCS adaptation from distribution-shifted examples with minimal additional training remains challenging. To address this, we propose PlantC2USeg, a deep transfer learning framework featuring cross-scale consistency learning to explicitly align features across spatial scales and an information-restricted decoding strategy that prevents reconstruction shortcuts and promotes robust adaptation. The resulting pre-training enables stable few-shot generalization across species and sensing conditions, while unified fine-tuning with inherited thresholds further reduces adaptation overhead. Under full supervision on Soybean3D, PlantC2USeg achieves the highest semantic IoU and instance mWCov among compared methods, at 91.91% and 94.62%. With 20 labeled samples, it leads both metrics at 89.78% and 90.27%; with only 10 samples, it retains the highest mWCov of 83.23% while achieving 83.19% IoU. Across HR3D, 10-shot transfer to tobacco, tomato, and sorghum averages 78.41% IoU and 79.42% mWCov, while 22-shot transfer to SYAU-Maize achieves the highest IoU and mRec at 92.75% and 93.51%. Furthermore, a leading category-averaged mIoU of 85.0% on ShapeNet Part demonstrates the framework's capability to handle diverse shape variations beyond agricultural domains. These results demonstrate that PlantC2USeg reduces overall adaptation effort under distribution shifts, enabling scalable plant phenotyping and transferable 3D representation learning beyond agriculture.

Why it matches plant phenotyping methods植物点群の器官レベル形質定量を目的とするセグメンテーション手法を開発し、複数データセット・作物・ショット条件で性能評価しているため、植物フェノタイピング手法が中心である。

abstractwe propose PlantC2USeg, a deep transfer learning framework featuring cross-scale consistency learning

Plant disease identification through Explainable AI: A deep learning architecture using fine-tuned EfficientNet for sustainable agriculture →

Problem: Agriculture plays a pivotal role in the Indian economy, where crop production quality and quantity directly impact the livelihoods of millions. Climate variability, emerging plant diseases, and improper pesticide application contribute significantly to agricultural losses. Early and accurate disease detection is crucial for mitigating crop damage and ensuring food security. Methodology: This study presents a novel deep learning framework based on EfficientNet architecture, enhanced with Progressive Fine-Tuning Strategy for automated plant disease detection. The proposed methodology was evaluated on two benchmark datasets: the Plant Village dataset comprising 20,639 images of tomato, potato, and bell pepper with 15 disease varieties; and a drone-captured rice plant dataset containing 4432 samples from public repositories. The model’s performance was assessed using multiple metrics, including classification accuracy, precision, recall, and F1-score. To strengthen the validation of high accuracy results, additional statistical analyses like class imbalance ratio, Entropy, Chi-Square test, convergence curve, ANOVA test, Tukey’s post hoc HSD test, confidence interval, Cohen’s Kappa result, fold-wise dispersion analysis, mean, std deviation are included in the manuscript. Result: Experimental results demonstrate that the fine-tuned EfficientNetV2-B1 architecture achieved exceptional performance with 99.7% classification accuracy on the PlantVillage dataset and 99.03% accuracy on the drone-based rice disease dataset, significantly outperforming existing state-of-the-art transfer learning models. Model explainability techniques further validated the reliability and interpretability of the predictions, confirming the model’s focus on disease-relevant features.

Why it matches plant phenotyping methods植物画像から病害状態を推定する深層学習手法を開発し、複数データセットで性能検証しており、植物フェノタイピング手法が中心である。

abstractThis study presents a novel deep learning framework based on EfficientNet architecture, enhanced with Progressive Fine-Tuning Strategy for automated plant disease detection.

Leaf Curl Disease Resistance Landscaping in Homozygous Ty-Gene Donor Tomato Genotypes Using a Robust Disease Scoring System and Indexing of Begomoviruses Under Natural Epiphytotic Conditions. →

Whitefly-transmitted begomoviruses cause tomato leaf curl disease (ToLCD). In India, at least 15 begomoviruses are known to cause ToLCD, posing a major challenge to resistance breeding. Although several Ty resistance loci have been introgressed from wild tomato relatives, variable resistance responses are frequently observed, likely due to mixed infections and the absence of a standardized disease scoring system. Moreover, limited knowledge of the infecting begomoviruses in resistant genotypes has hindered the effective use of donor lines in breeding programs. This study evaluated 17 homozygous Ty-gene donor tomato genotypes under natural epiphytotic conditions and identified the associated begomoviruses. To quantify disease severity, a robust disease scoring system was developed using coefficient of infection (CI) by integrating symptom parameters- leaf curling, leaf smalling, stunting, and fruiting, with population-level disease incidence. Field evaluations for two years revealed that genotypes carrying both Ty-2 and Ty-3 loci showed higher resistance, though variability existed among them. Genotypes with Ty-3 alone or Ty-5 + Ty-6 combinations also displayed substantial tolerance, and five genotypes were identified as highly resistant. Molecular indexing revealed frequent mixed infections and identified multiple begomoviruses, including a newly characterized species, tomato leaf curl Ty Pusa virus, alongside tomato leaf curl New Delhi virus, tomato leaf curl Palampur virus, tomato leaf curl Gujarat virus, and tomato leaf curl Joydebpur virus. These findings highlight a shift in begomovirus predominance and possible recombination-driven emergence of new variants. This study provides an integrated framework for evaluating ToLCD resistance and emphasizes the need for continuous reassessment of resistance sources to ensure durable tomato cultivar development.

Why it matches plant phenotyping methods植物の病徴と発病率を統合した病害重症度スコアリング法を開発し、抵抗性評価に適用しており、表現型取得法が研究の中心である。

abstractTo quantify disease severity, a robust disease scoring system was developed using coefficient of infection (CI) by integrating symptom parameters- leaf curling, leaf smalling, stunting, and fruiting, with population-level disease incidence.

Channel-spatial dual-attention for plant disease detection: CBAM-ECA integrated CNN models with visual explainability →

Early detection of plant leaf diseases is critical for minimizing crop losses and supporting precision agriculture. While Convolutional Neural Networks (CNNs) have demonstrated high accuracy in image-based diagnosis, conventional architectures may not optimally balance spatial localization and channel-wise feature refinement, particularly in multi-crop classification settings. This study proposes a redundancy-aware dual-attention architecture, termed ATSA-DenseNet, which integrates the spatial branch of the Convolutional Block Attention Module (CBAM-Spatial) with Efficient Channel Attention (ECA) within a DenseNet121 backbone. Unlike prior dual-attention frameworks that retain full CBAM and introduce channel-level redundancy, the proposed design isolates complementary spatial and channel mechanisms to improve representational efficiency without increasing computational complexity. The framework is evaluated on controlled multi-crop PlantVillage-derived datasets comprising tomato, potato, pepper, and maize. Across both 3-crop and 4-crop configuration, ATSA-DenseNet consistently outperforms baseline DenseNet121 and single-attention variants, achieving 99.94% accuracy and 0.9994 macro-F1 on the 4-crop setting while maintaining a lightweight footprint (6.96M parameters, 2.87G FLOPs). Grad-CAM visualizations indicate improved localization of disease-relevant regions compared to the baseline. While results are obtained under controlled imaging conditions, the findings demonstrate that redundancy-aware dual-attention enhances feature discrimination efficiency in multi-class agricultural classification tasks. Future work will extend validation to real-field datasets with natural variability.

Why it matches plant phenotyping methods植物葉の病害状態を画像から推定するCNN手法を提案・評価しており、病害表現型の取得・分類手法が研究の中心である。

abstractThis study proposes a redundancy-aware dual-attention architecture, termed ATSA-DenseNet, which integrates the spatial branch of the Convolutional Block Attention Module (CBAM-Spatial) with Efficient Channel Attention (ECA) within a DenseNet121 backbone.

Correlation of Plant Bioelectrical Signals with Potential Ionic Energy Flow under Different Stress →

All living organisms rely on the movement of ions across cell membranes as the fundamental physical basis of their internal energy and signaling, and plants are no exception. Plants perceive, integrate, and respond to environmental stimuli through electrical signals, classified as action, variation, and system potentials, that are coupled with calcium waves, reactive oxygen species, and hydraulic and hormonal changes to coordinate whole-organism responses despite the absence of a nervous system. Yet most studies characterize these signals using a single feature, such as amplitude or spike duration, in a single tissue, an approach that cannot establish how such signals correspond to the underlying ionic activity, mobility, and structural complexity of the signaling environment, or how this correspondence varies across organs. Here, we correlate plant bioelectrical signals with potential ionic energy flow using a multi-domain framework, combining discrete spike events, continuous waveform properties, spectral composition, and signal complexity applied to leaf, stem, and root recordings from tomato ( Solanum lycopersicum ) exposed to different stimulus. Electrical activity with increased stimulus strength, likely reflecting increased ionic flow, with the root showing the largest response. This suggests plant electrical signaling works as a distributed, ion-based information system, useful for stress monitoring and bio-inspired sensor design.

Why it matches plant phenotyping methods植物の電気生理シグナルを多面的に取得・解析する枠組みを中心に扱い、ストレスモニタリングへの応用可能性を示しているため、植物状態の測定方法として含める。

abstractHere, we correlate plant bioelectrical signals with potential ionic energy flow using a multi-domain framework, combining discrete spike events, continuous waveform properties, spectral composition, and signal complexity applied to leaf, stem, and root recordings from tomato

Systematic evaluation of hyperspectral imaging workflows for predicting pigment and nutrient traits in tomato leaves under varying nitrogen supply levels →

Abstract Purpose Rapid and non-destructive detection of pigment and nutrient traits in tomato ( Solanum lycopersicum L.) leaves is essential for precision fertilization and greenhouse management. However, most existing studies focus on individual traits (e.g., chlorophyll or nitrogen) with isolated models, limiting the establishment of robust analytical workflows across growth stages and cultivation conditions. This study systematically evaluated hyperspectral imaging workflows for estimating pigment and nutrient traits in tomato leaves. Methods Hyperspectral images were collected at the stages of flowering-fruiting, ripening and harvest from tomato plants supplied with nitrogen at 0, 210, 300 and 390 kg N ha⁻¹. Total contents of chlorophyll, total nitrogen and nitrate were measured by standard biochemical assays for model calibration and validation. Result After sample partition with four strategies, seven spectral preprocessing methods were evaluated, namely moving average (MA), Savitzky-Golay smoothing (SG), Gaussian filtering (GF), median filtering (MF), normalization, baseline correction and standard normal variate (SNV), with normalization, MA and SNV yielded the best predictive performance for chlorophyll, total nitrogen and nitrate, respectively. For feature wavelength selection, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used with CARS yielding the best performance for total chlorophyll and nitrate prediction, while SPA was optimal for total nitrogen prediction. By application of the above optimal methods, random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost) and convolutional neural network (CNN) models were developed to predict pigment and nutrient indicators. Conclusion The SVM performed best for chlorophyll ( R c ²=0.823, R p ²=0.431) prediction, while the CNN achieved higher accuracy for total nitrogen ( R c ²=0.826, R p ²=0.780) and nitrate ( R c ²=0.851, R p ²=0.753) prediction. Overall, leaf nitrogen-related traits were predicted more reliably than total chlorophyll, for which validation performance remained limited. Impact These findings demonstrate that sample partitioning, spectral preprocessing, wavelength selection and model selection should be optimized for each target trait rather than applied uniformly. This study provides a methodological basis for non-destructive assessment of tomato leaf N status and precision fertilization management.

Why it matches plant phenotyping methodsトマト葉の色素・養分形質を対象に、ハイパースペクトル画像処理、波長選択、機械学習モデルを体系的に比較・検証しており、植物形質取得手法が研究の中心である。

abstractThis study systematically evaluated hyperspectral imaging workflows for estimating pigment and nutrient traits in tomato leaves.

A Novel Lightweight AlexNet Convolutional Neural Network for Tomato Leaf Disease Classification →

Tomato leaf diseases must be identified early and accurately in order to reduce output loss and advance sustainable agriculture. Deep learning models have shown encouraging results in the identification of plant diseases, but their high processing requirements and inability to adjust to field-specific limitations sometimes make it difficult to implement them in real-world applications. For the purpose of accurately and efficiently classifying tomato leaf diseases, we present a convolutional neural network based on AlexNet that is lightweight and field-aware. Our proposed model is designed with less complexity than traditional architectures and is able to perform inference faster without sacrificing accuracy, making it suitable for real-time implementation in low resource agricultural environments. The algorithm was trained and tested on a dataset of 7704 augmented images of tomato leaves from 7 different disease categories. The Lightweight AlexNet achieved accuracy comparable to VGG variants and outperformed deeper models such as ResNet (96.81%) with lower parameter overhead. It was trained from scratch using TensorFlow & Keras on 100×100 pixel inputs, achieving a training accuracy of 99.15% and a validation accuracy of 99.74%. Moreover, an effective structure of the model enables the installation on edge devices, which offers a scalable precision farming solution. Our work helps to bridge the gap between deep learning research and real-world application in agriculture, allowing the development of real-field, resource-efficient disease detection systems.

Why it matches plant phenotyping methodsトマト葉の病害状態を画像から分類する軽量CNNを開発し、精度・計算量・エッジ実装性を評価しており、植物フェノタイピング手法が研究の中心である。

abstractFor the purpose of accurately and efficiently classifying tomato leaf diseases, we present a convolutional neural network based on AlexNet that is lightweight and field-aware.

SolanAPP: An Offline-First Mobile Framework for Segmentation-Based Diagnosis of Solanaceae Crop Diseases →

Abstract Purpose : The system for diagnosing diseases in Solanaceae crops (SolanAPP), including tomatoes, potatoes, peppers, and eggplants, represents a promising tool for supporting decision-making in agricultural fields using AI. This system reconciles the computational intensity of multitasking models with the infrastructural limitations of rural environments, thereby increasing digital literacy. Its architecture is based on two fundamental pillars: (i) autonomous, offline operation for the detection and classification of diseases in Solanaceae crops; (ii) a georeferenced epidemiological surveillance network with agricultural recommendations for crop monitoring. Methods : The core diagnostic process combines crop-specific semantic segmentation and disease classification models exported to TensorFlow Lite, enabling on-device visual inference and pixel-level severity estimation. An optional online layer integrates Groq’s large language model (LLM)-based reasoning and Firebase services to generate structured agronomic explanations and facilitate the creation of georeferenced community reports when a connection is available. Preprocessing and management of the dataset were performed using the Roboflow platform. The mobile app was developed natively in Kotlin. Model performance was rigorously evaluated using accuracy, recall, F1 score, mean IoU, mPA, inference latency, model size, and decision matrix. The optimal model for the crop was selected using Simple Additive Weighting (SAW). Finally, the overall framework quality and usability were evaluated in Cuba through a user validation survey using a 5-point Likert scale and aligned with the ISO/IEC 25010 software quality model. Results : The model that yielded the best results for most crops was DeepLabV3+ with MobileNetV2, which achieved a classification accuracy of over 97\% while operating with lower inference latency. Beyond individual diagnoses, the system incorporates a collaborative georeferencing feature that allows users to share observations and precise geographic coordinates of detected pathologies to facilitate regional epidemiological monitoring. The user satisfaction survey yielded a satisfaction rating of 4.5/5, with users highlighting the importance of offline diagnosis. Conclusion : Plant disease diagnosis using computer vision can support earlier intervention in resource-constrained agricultural settings, but practical deployment requires models that are accurate, lightweight, interpretable, and usable under limited connectivity. SolanAPP, an offline-first Android framework for detecting foliar pathologies in Solanaceae crops, not only establishes a framework for disease identification in complex natural environments but also provides a theoretical and practical foundation for automated agronomic treatment recommendations and community-based crop surveillance. Impact SolanAPP is a free framework that supports the synergy between multitask deep learning for offline disease diagnosis and LLM-driven reasoning for decision-making in the field. Beyond the quantitative metrics obtained from the selected models, the deployment of SolanAPP in rural contexts serves a fundamental socio-technical purpose: it acts as a catalyst for open access, digital literacy, and agronomic decision-making under unfavorable development conditions. It also represents a strong effort to foster a collaborative epidemiological surveillance network in the agricultural sector. Although it faces challenges, such as the use of field images for model training, this framework marks a promising step in the deployment of edge AI, balancing technical accuracy with practical utility.

Why it matches plant phenotyping methods植物病害を画像からセグメンテーションし、病害のピクセル単位の重症度を推定する手法と、オフライン実装・性能評価を中心とした研究であり、植物状態の計測方法が中核です。

abstractThe core diagnostic process combines crop-specific semantic segmentation and disease classification models exported to TensorFlow Lite, enabling on-device visual inference and pixel-level severity estimation.

Efficient Ripeness Monitoring in Open-Facility Environments Using a Quadruped Robot and Panoramic AI Recognition →

Efficient facility-scale tomato ripeness monitoring remains difficult in greenhouses where uneven terrain limits conventional wheeled and rail-guided platforms and planar cameras provide restricted coverage. This study developed a wheel-legged quadruped monitoring system integrating LiDAR, a depth camera, and a panoramic camera. An adaptive gait-switching strategy supported navigation across heterogeneous terrain. Panoramic images were projected into six perspective views, and the left and right views were processed using a YOLOv8-based ripeness recognition model. Time-synchronized detections and robot poses were fused to map ripeness observations into three-dimensional greenhouse coordinates. Five field experiments in a commercial tomato facility demonstrated autonomous row traversal, inter-row transition, and avoidance of pedestrians, obstacles, and cultivation boundaries. The recognition pipeline continuously identified multiple ripeness stages under variable illumination, foliage occlusion, and robot motion, while the spatial fusion procedure produced a facility-scale three-dimensional ripeness distribution. The integration of terrain-adaptive quadruped mobility, panoramic perception, and spatial mapping provides a practical framework for continuous ripeness monitoring and can support targeted harvesting, yield forecasting, and crop management.

Why it matches plant phenotyping methodsトマト果実の成熟度を画像認識で取得するロボット型フェノタイピングシステムを開発し、実環境で評価しており、表現型取得法が研究の中心である。

abstractThis study developed a wheel-legged quadruped monitoring system integrating LiDAR, a depth camera, and a panoramic camera.

Development and Validation of a MobileNetV2 Convolutional Neural Network for Automated Diagnosis of Tomato Fungal Diseases in Northern Nigeria →

Accurate, field-deployable diagnostic tools are needed to close the diagnostic gap that limits fungicide targeting among smallholder tomato farmers in Northern Nigeria. This study developed and validated a lightweight convolutional neural network (CNN) for automated diagnosis of five major tomato fungal diseases plus three additional common conditions, trained on field-collected leaf images from Kano and Kaduna States. A MobileNetV2 architecture pre-trained on ImageNet was fine-tuned via transfer learning on more than 10,000 images across ten disease and health classes, using farm-level dataset splitting to prevent data leakage and five-fold cross-validation for model selection.

Why it matches plant phenotyping methodsトマト葉画像から病害・健全状態を直接推定するCNNを開発し、データ分割と交差検証で技術検証しており、植物状態の取得・推定手法が中心である。

abstractThis study developed and validated a lightweight convolutional neural network (CNN) for automated diagnosis of five major tomato fungal diseases plus three additional common conditions

Effect of a Smartphone-Based Diagnostic Application on Tomato Farmer Disease Identification Accuracy: A Controlled Field Evaluation in Northern Nigeria →

Smallholder tomato farmers in Northern Nigeria correctly identify major fungal diseases only about 41% of the time using unaided visual inspection, contributing to fungicide misapplication and avoidable yield loss. This study evaluated the field impact of SmartfarmerApp, a smartphone-based diagnostic application built on a validated convolutional neural network, on farmer disease identification accuracy. A pre-test/post-test controlled design allocated 240 tomato farmers across eight Local Government Areas in Kano and Kaduna States to an intervention group (n = 120, received the application) or a control group (n = 120, continued with conventional information sources), using computer-generated random allocation stratified by location and gender.

Why it matches plant phenotyping methodsトマトの病害状態を画像・視覚観察から判定するスマートフォン診断アプリを対象に、現場での識別精度を対照評価しており、植物病害フェノタイピング手法の応用・検証が中心である。

abstractThis study evaluated the field impact of SmartfarmerApp, a smartphone-based diagnostic application built on a validated convolutional neural network, on farmer disease identification accuracy.

A deep learning-based VMSNet for high-precision point cloud segmentation and ripe tomato diameter phenotyping in greenhouses →

Ripe tomato fruit display diverse 3D morphologies driven by genetics, environment, and management, yet these differences remain hard to quantify in the absence of precise point-cloud segmentation tools. This paper proposes the VMSNet to accurately segment tomato fruits and extract phenotypic traits based on the segmentation results, including horizontal and vertical diameters. Point clouds are obtained through depth cameras. After preprocessing and labeling the fruits, a dataset is established using global enhancement and local enhancement. On the base framework of PointNet++, the downsampling method was replaced, a multi-scale attention module (MS_A) was integrated, the combination scheduling strategy was optimized, and VMSNet was constructed. Following segmentation, the fruit growth direction is estimated by density-weighted method, and principal component analysis (PCA) is used to establish a rotation plane. By rotating according to the slicing angle, the fruit point cloud is completed and fitted into an ellipsoid. Random Sample Consensus (RANSAC) is used to smooth the outliers. The OBB is applied to extract the horizontal and vertical diameters, which are compared with measurement to verify the algorithm’s accuracy. The results indicate that the accuracy of VMSNet in segmenting ripe tomato fruits is 97.96%. The correlation coefficients R 2 between the calculated and measured values of the horizontal and vertical diameters reached 0.89 and 0.86, respectively. This proposed proposal provides robust point cloud segmentation and completion for phenotypic analysis for other same species greenhouse crop.

Why it matches plant phenotyping methods深度学習による点群分割とトマト果実径の抽出手法を開発し、実測値との比較で精度検証しており、植物表現型取得が研究の中心である。

abstractThis paper proposes the VMSNet to accurately segment tomato fruits and extract phenotypic traits based on the segmentation results, including horizontal and vertical diameters.

A comparative study of baseline convolutional neural network and ResNet50 for image-based tomato leaf disease classification →

Image-based techniques are widely used in plant disease classification to support agricultural productivity and facilitate early detection. This study presents a comparative analysis between a baseline convolutional neural network (CNN) and a ResNet50-based transfer learning model for tomato leaf disease classification. A publicly available dataset containing five categories—tomato bacterial spot, tomato late blight, tomato septoria leaf spot, tomato yellow leaf curl virus, and healthy leaves—was used in the experiments. Model performance was evaluated using several standard classification metrics, with emphasis on overall accuracy. The baseline CNN achieved an accuracy of 97.0%, whereas the ResNet50 model reached 99.6%. The results demonstrate that the ResNet50 model produces more stable and reliable predictions, particularly when distinguishing between visually similar disease classes. These findings confirm that transfer learning can effectively improve classification performance in plant disease recognition tasks.

Why it matches plant phenotyping methodsトマト葉の病徴を画像から分類するCNN/ResNet50手法を比較評価しており、植物病害状態の取得・推定が研究の中心である。

abstractThis study presents a comparative analysis between a baseline convolutional neural network (CNN) and a ResNet50-based transfer learning model for tomato leaf disease classification.

Android-based tomato leaf disease classification using a lightweight MobileNetV2 convolutional neural network →

Early screening of tomato leaf diseases is important because foliar symptoms can reduce plant vigor and delay appropriate crop management. This study develops an Android-based tomato leaf disease classification system using MobileNetV2 as a lightweight convolutional neural network (CNN) architecture. The contribution of this work is the integration of model training, independent testing, and on-device Android deployment that supports camera and gallery inputs without relying on server-side computation. The dataset consisted of 1,200 balanced tomato leaf images from five classes: bacterial spot, late blight, target spot, tomato yellow leaf curl virus, and healthy leaf. Images were resized, normalized, augmented for training, and divided into training, validation, and independent testing subsets. The model obtained 94.12% training accuracy, 93.00% validation accuracy, and 89.00% independent test accuracy. The confusion matrix showed that tomato yellow leaf curl virus was classified without error, whereas bacterial spot, late blight, target spot, and healthy leaves produced several misclassifications because of similar lesion and discoloration patterns. The results show that MobileNetV2 is suitable for lightweight mobile disease screening, although larger field datasets, cross-validation, model comparison, and explainability analysis are still needed for broader deployment.

Why it matches plant phenotyping methodsトマト葉の病徴画像から病害状態を分類するCNN手法を開発し、独立テストとモバイル実装まで評価しており、植物表現型取得・抽出が中心である。

abstractThis study develops an Android-based tomato leaf disease classification system using MobileNetV2 as a lightweight convolutional neural network (CNN) architecture.

Detect Plant Disease and Recommend Fertilizer and Supplement Using CNN & Mobile Net Algorithm →

Agriculture plays a crucial role in the Indian economy. Early detection of plant diseases is very much essential to prevent crop loss and further spread of diseases. Most plants such as apple, tomato, cherry, grapes show visible symptoms of the disease on the leaf. These visible patterns can be identified to correctly predict the disease and take early actions to prevent it. This can be overcome by the use of machine learning and deep learning algorithms. Hence, we are proposing a method that which is detecting the disease of a tomato plant from their leaf images. Here the process is performed with the deep learning algorithms Convolutional Neural Network (CNN), and MobileNet which is a one of the transfer learning method of CNN. Once after training the dataset with the algorithms, the accuracy of algorithms is compared and the images are classified. And the precautions are also provided for the classified plant.

Why it matches plant phenotyping methodsトマト葉画像から病害状態をCNN/MobileNetで推定・分類する手法が研究の中心であり、植物病害フェノタイピングに該当する。

abstractwe are proposing a method that which is detecting the disease of a tomato plant from their leaf images

From point clouds to plant traits: investigation of a pipeline for phenotyping of tomato plants using skeletonisation →

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

Why it matches plant phenotyping methodsトマトの点群を骨格化して植物形質へ変換するフェノタイピング・パイプラインの調査であり、形質取得・抽出手法が中心です。

titleFrom point clouds to plant traits: investigation of a pipeline for phenotyping of tomato plants using skeletonisation

Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies →

Accurate and quick detection of plant leaf diseases is essential for precision agriculture to intervene promptly and boost crop yields. A new deep learning model called ResVNet has been introduced in this study. It combines the powerful local feature detection of ResNet152 with the global attention capabilities of Vision Transformer (ViT) and utilises Low-Rank Adaptation (LoRA) to accelerate fine-tuning. The PlantVillage dataset, which contains both healthy and diseased tomato samples, was used to train and test ResVNet. Experimental evaluation on the PlantVillage tomato dataset using stratified 5-fold cross-validation demonstrates that the proposed ResVNet model achieves a mean classification accuracy of 97.45%, along with superior macro-precision, macro-recall, and macro-F1 scores compared to existing deep learning architectures. The results of the confusion matrix and the ROC analysis validate its discriminatory power. The results highlight the potential of architectures strengthened with transformers in agricultural diagnostics. For real-time disease detection in the field, ResVNet is perfect for edge device deployment on drones and smartphones thanks to its high accuracy and adaptability. The application of Explainable AI (XAI) technologies for interpretability, integration with the Internet of Things (IoT), and multi-crop classification will all be explored in future studies. We will also look into model compression approaches so we can deploy efficiently in low-resource settings without sacrificing performance.

Why it matches plant phenotyping methods植物葉の病害状態を画像から分類する深層学習手法を開発し、PlantVillageで交差検証して性能評価しているため、植物フェノタイピング手法が中心である。

abstractA new deep learning model called ResVNet has been introduced in this study.

UAV-based monitoring of fruit-manifested abiotic stress in crops under label scarcity: a case study of blossom-end rot in processing tomatoes. →

Introduction Safeguarding the yield and quality of field crops against abiotic stresses is critical for large-scale agricultural production and precision agronomy. This study addresses the challenges of detecting concealed fruit stress and overcoming label scarcity in unmanned aerial vehicle (UAV) multispectral monitoring, using blossom-end rot (BER) in processing tomatoes as a case study. Methods We developed a leaf-fruit synergistic Roll and Color Disease Index (RCDI), integrating fruit incidence with visible canopy phenotypic features to characterize the combined canopy-fruit stress status associated with BER. A three-level screening strategy was used to identify the optimal spectral feature set. A Multi-model Collaborative Cyclic Self-Training (MCC-ST) framework was subsequently developed to address the limited availability of severity-labeled samples. Results The combination of GRVI, NDVI, and SAVI was identified as the optimal spectral feature set, achieving stable within-dataset binary classification performance of approximately 97% in repeated cross-validation. Under 30 random-seed repeated stratified three-fold cross-validations, MCC-ST + DT and MCC-ST + RF achieved RCDI-based BER severity-grading accuracies of 85.00% ± 0.31% and 85.07% ± 0.42%, respectively. Compared with the corresponding original DT and RF models, MCC-ST improved repeated-validation accuracy by 16.19-4.09 percentage points. Discussion The RCDI helps bridge the observational gap between canopy signals and concealed fruit stress, while MCC-ST alleviates the bottleneck associated with label scarcity. The proposed approach provides a promising framework for crop abiotic-stress monitoring under limited-label conditions.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からトマトの果実ストレス状態・BER重症度を推定する指標と、ラベル不足に対応する機械学習フレームワークを開発・検証しており、植物表現型取得・推定手法が研究の中心である。

abstractThis study addresses the challenges of detecting concealed fruit stress and overcoming label scarcity in unmanned aerial vehicle (UAV) multispectral monitoring, using blossom-end rot (BER) in processing tomatoes as a case study.

FolioClip: Comprehensive Plant Health Monitoring and Early Stress Detection with Real-Time Multimodal Wearable Sensing and Online Machine Learning →

Wearable plant sensing systems for simultaneous biochemical and physiological monitoring with real-time multimodal data analysis remain limited. Here, we present FolioClip, a multimodal wearable patch that continuously monitors leaf temperature, humidity, light, CO 2 , and three volatile organic compounds (VOCs) with high selectivity. Its bookmark-inspired design enables secure attachment to plant leaves of diverse morphologies and it integrates a flexible printed circuit board for wireless data transmission. We also develop FolioOmni, an open-source machine learning (ML) framework for sensor importance ranking, multi-stress classification, and early stress detection. The integrated FolioClip–FolioOmni platform detects and classifies nine stresses, including light, water, CO 2 , mechanical cut, P. infestans , and A. alternata , in tomato plants with 92% accuracy. Notably, P. infestans on tomato was detected within 15.5 h post-inoculation, earlier than quantitative polymerase chain reaction (qPCR) (~4 days) and visual phenotyping (~7 days), highlighting the potential of integrating multimodal wearable sensing and online ML for precision agriculture.

Why it matches plant phenotyping methods葉の生理・健康状態とストレスを測定するウェアラブルセンシング装置および機械学習解析基盤を開発し、複数ストレスで性能評価しているため、植物フェノタイピング手法が中心である。

abstractWe also develop FolioOmni, an open-source machine learning (ML) framework for sensor importance ranking, multi-stress classification, and early stress detection.

Improving the prediction of water stress-related traits in open-field tomato using multivariate models and variable importance-based indices →

The assessment of water stress levels in plants should be essential part of precise irrigation management, and a good and quick method is useful in plant phenotyping. There are many options for this task, but the effectiveness varies between crop species and environments. Apparently open field applications face the most difficulties. This study aimed to test a large number of vegetation indices (VIs) and multivariate models based on hyperspectral reflectance data (325-1075 nm) regarding their correlation and prediction abilities to leaf stomatal conductance, relative water content (RWC), and detailed chlorophyll, and carotenoid components. Data of the abovementioned variables was collected during three consecutive growing seasons in processing tomato cultivated under different water supply regimes to provide data with varying water stress levels. Then the relation of the measured variables to 226 VIs was tested created according to the formulas collected in the Index DataBase (IDB Project, indexdatabas.de ). New VIs were also developed derived from the most important variables of the ML algorithms, customised to tomato water stress assessment. Standard normal variate and its combination with Savitzky-Golay first derivative were used for pre-processing the spectra and principal component regression (PCR), partial least squares regression (PLSR), elastic net (ENET), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGB) algorithms were tested. The newly developed indices outperformed the existing formulas, except in the case of β-carotene. The most reliable index was developed for RWC estimation; that was the difference of the reflectance on the 986 and 701 nm wavelengths. The ENET and SVR algorithms produced the best models depending on the pre-processing method. The blue, near-infrared (NIR) and green regions, respectively, were the most important regarding all models according to the variable importance analysis. The model with the best metrics was developed for chlorophyll-a (R 2 =0.82, nRMSE=11%, RPIQ=2.41), followed by RWC (R 2 =0.72, nRMSE=14%, RPIQ=2.53).

Why it matches plant phenotyping methodsハイパースペクトル反射データと多変量・機械学習モデルを用いて、トマトの水ストレス関連生理形質を推定し、新規指標も開発・評価しているため、表現型取得・推定手法が中心である。

abstractThis study aimed to test a large number of vegetation indices (VIs) and multivariate models based on hyperspectral reflectance data (325-1075 nm) regarding their correlation and prediction abilities to leaf stomatal conductance, relative water content (RWC), and detailed chlorophyll, and carotenoid components.

A Robust Method for Real-Field Tomato Disease Classification Using FRCNN-Based Leaf Isolation and FCM-Guided Variability Estimation →

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

Why it matches plant phenotyping methodsトマト葉の分離と疾患分類・変動推定を中心とする画像解析手法であり、植物の病害状態を抽出する方法開発に該当する。

titleA Robust Method for Real-Field Tomato Disease Classification Using FRCNN-Based Leaf Isolation and FCM-Guided Variability Estimation

Computer Vision and Field Validation of an Artificial Intelligence-Based Tomato Grader for Large-Scale Production in Northeastern Thailand →

Tomato (Solanum lycopersicum L.) quality grading based on visual inspection often yields inconsistent results and reduces market value. The 'Perfect Gold 111' variety presents distinct morphological traits, including a characteristic green-to-red color transition, specific calyx structure, and defect patterns such as greenback distribution and suberization. These characteristics differ substantially from internationally studied cultivars, rendering generic pre-trained models insufficient for accurate grading under the Thai TACFS 1503–2007 standard. This study developed an automated grading model for 'Perfect Gold 111' tomatoes using a deep learning model based on a flow-based (node-based) architecture integrated with the Robot Operating System (ROS) framework and implemented on the CiRA CORE platform. A total of 220 samples were collected and graded according to the TACFS 1503–2007 standard. Top and side-view images were used to create a dataset comprising 165 tomatoes for training and 55 for testing. Model performance was evaluated using Precision, Recall, F1-Score, and Accuracy, and was compared with manual grading performed by farmers. The AI model achieved 80.00% of accuracy, outperforming farmer grading, which achieved 52.72% accuracy. In addition, the model reduced misclassification among visually similar grades and provided consistent, quantitative assessments of color, shape, and defects. These findings highlight the potential of AI-based grading systems to improve quality consistency, reduce labor, and support automated postharvest sorting for both smallholder and industrial tomato production.

Why it matches plant phenotyping methodsトマトの色・形状・欠陥という観察可能な器官形質を画像から抽出し、深層学習による自動等級判定法を開発・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。

abstractThis study developed an automated grading model for 'Perfect Gold 111' tomatoes using a deep learning model based on a flow-based (node-based) architecture integrated with the Robot Operating System (ROS) framework and implemented on the CiRA CORE platform.

From Benchmark Accuracy to Field Performance: Hybrid Deep Learning-Based Plant Disease Classification with IoT-Enabled Environmental Monitoring →

Accurate detection of plant leaf diseases is essential for enhancing crop productivity and supporting global food security. In addition to disease classification, understanding how environmental and soil conditions affect model performance is important for developing robust real-world agricultural monitoring systems. Although deep learning (DL) models achieve high accuracy on benchmark datasets, their performance in real-world settings is often limited by variations in illumination, background complexity, and environmental conditions. This study proposes a smart DL framework for detecting and classifying multiple leaf diseases in tomato, potato, and pepper plants. The framework combines U2-Net-based leaf segmentation with a Convolutional Neural Network–Bidirectional Gated Recurrent Unit (CNN–Bi-GRU) architecture. MobileNetV2 is employed as the feature extraction backbone to capture spatial characteristics, while Bi-GRU layers model sequential feature dependencies, forming a spatio-temporal network whose architectural design prioritizes parameter efficiency through depthwise separable convolutions and reduced gating complexity. The model was trained and validated using the PlantVillage benchmark dataset and achieved a classification accuracy of 99.8% with a macro-averaged F1-score of 94%, outperforming several state-of-the-art architectures. To assess robustness under real-world conditions, the trained model was further tested on leaf images collected from open-field environments near Eluru, South India. The field evaluation revealed a reduction in classification accuracy to 61.97%, indicating the impact of domain shift and environmental variability. To investigate potential contributing factors, soil parameters, including pH, temperature, moisture, and NPK levels, were monitored using an IoT-based Arduino sensing system over ten consecutive days. Rather than serving as direct inputs to the disease classification model, these environmental measurements were analyzed to assess their potential influence on disease symptom expression and the observed reduction in model performance under field conditions. The results suggest that environmental conditions may influence disease symptom expression and model transferability. This study highlights the importance of integrating DL-based disease recognition with environmental monitoring for reliable field-level agricultural applications. Nevertheless, computational complexity metrics, including inference latency and memory footprint, were not evaluated in the present work and are identified as a priority for future edge deployment studies.

Why it matches plant phenotyping methods植物葉の病徴を画像から分類するセグメンテーション・深層学習手法を開発し、ベンチマークと圃場画像で性能を検証しているため、植物フェノタイピング手法が中心である。

abstractThis study proposes a smart DL framework for detecting and classifying multiple leaf diseases in tomato, potato, and pepper plants.

A sugar flow model predicts cell dynamics, weight and quality of tomato at varying sink-source ratios and temperatures. →

A new tomato fruit model predicts cell numbers, cell sizes, sugar contents, and fresh weight. Transport of water and saccharides from plant stem to fruit cells is computed following biophysical rules. Saccharide fruit sink is based on sugar metabolism, rates of cell division and expansion, and starch and cell wall dynamics. Osmotic and hydraulic potentials in cells and their vacuoles drive water import at given cell-wall extensibility. The interaction of demand and transport determines saccharide flow and biomass. We incorporated physiological responses to temperature, pruning, and plant shading. Existing and new parameters were calibrated with data from fruit heating and fruit pruning experiments of contrasting tomato cultivars. Model validation for different strategies of fruit heating and pruning, and plant shading was successful. Increased fruit temperature was shown to reduce fruit weight, as expected. Growth response to fruit pruning or shading were fully explained by changes in phloem sucrose concentration. Hydraulic conductivity of vascular tissue as well as sucrose and hexose carrier capacities were crucial fruit properties determining sugar flux. Model scenarios on knockdown of sucrose synthase and active hexose uptake affected sugar composition. The model creates an important step towards predicting fruit quality and taste under diverse growth conditions.

Why it matches plant phenotyping methodsトマト果実の細胞動態、糖含量、重量、品質を予測する新規モデルを開発し、複数条件・品種で較正および検証しているため、植物形質推定手法が中心です。

abstractA new tomato fruit model predicts cell numbers, cell sizes, sugar contents, and fresh weight.

Integrating Kolmogorov-Arnold networks and sparse attention for robust visual plant disease symptom identification across diverse agricultural crops →

Introduction Accurate and rapid diagnosis of plant leaf disease symptoms is critical for sustainable agricultural crop production, yet traditional methods often lack efficiency and robustness under field conditions. Methods Here, we propose a deep learning framework based on an improved Vision Transformer architecture that integrates a dynamic sparse attention mechanism, termed KBTNet, for targeted feature extraction in symptom-affected regions of leaf images. The model incorporates a learnable nonlinear enhancement module to capture subtle visual disease symptom variations such as lesions, discoloration patterns, and spot distributions, and a lightweight Transformer design to reduce computational cost. Results Evaluated on a multisource dataset containing soybean and tomato leaf images representing diverse disease symptom patterns, our approach achieved 93.19% classification accuracy, outperforming current state-of-the-art models. Additional evaluations on public plant disease datasets from multiple crops further demonstrate the model's ability to recognize disease symptom patterns across diverse crop species. Discussion The proposed framework achieves stable performance across diverse crop and disease symptom categories, maintains high efficiency under reduced parameter complexity, and exhibits strong potential for realtime field diagnostics on edge devices. This work provides a scalable and efficient tool for plant disease symptom detection and classification and supports the integration of visionbased intelligence into crop disease monitoring and management systems.

Why it matches plant phenotyping methods植物葉画像から病徴（病斑、変色、斑点分布）を抽出・分類する深層学習手法を開発しており、植物病害状態の表現型取得が研究の中心である。

abstractwe propose a deep learning framework based on an improved Vision Transformer architecture that integrates a dynamic sparse attention mechanism, termed KBTNet, for targeted feature extraction in symptom-affected regions of leaf images.

Photoelectrochemical aptasensor based on biomass-derived carbon/BiOI nanoflowers for in-situ monitoring of abscisic acid. →

A photoelectrochemical (PEC) aptasensor based on bismuth oxyiodide (BiOI) nanoflower/biomass carbon (BiOI@BC) was fabricated for in-situ detecting abscisic acid (ABA) in tomato leaves under salt stress. Shrimp shells-derived biomass carbon acted as an enhanced carrier, and the biomass carbon improves the PEC performance of BiOI by extending the visible light absorption range and promoting the charge transfer of pure BiOI nanoflower. The BiOI@BC exhibited high photocurrent, which was about 19 times in contrast to pristine BiOI, attributing to the synergistic effects of biomass carbon self-doped with N, P, and S atoms. Furthermore, a PEC aptasensing platform was developed for the sensitive and selective determination of ABA, with a wide linear range from 0.1 to 1000 pM and a remarkably low detection limit of 0.03 pM. The practical applicability of the device was further validated by on-site monitoring of ABA levels in tomato leaves under salt stress, demonstrating good stability and accuracy. This work provides a robust strategy for real-time phytohormone detection, facilitating precise crop regulation in plant biology and agriculture.

Why it matches plant phenotyping methods植物葉内のABAをその場で測定するPECアプタセンサー自体の開発と実用検証が中心であり、塩ストレス下の植物生理状態を抽出するセンサー型表現型計測に該当する。

abstractA photoelectrochemical (PEC) aptasensor based on bismuth oxyiodide (BiOI) nanoflower/biomass carbon (BiOI@BC) was fabricated for in-situ detecting abscisic acid (ABA) in tomato leaves under salt stress.

AI-Driven Crop Detection and Plant Disease Prediction →

Crop disease identification is still a big problem in agriculture, which results in large yield losses and food lacks, especially in regions dependent on manual monitoring. Traditional methods of identifying plant diseases are often labor intensive, error-prone, and ineffective in early-stage diagnosis. To overcome these limitations, this study proposes a hybrid machine learning model for accurate crop type identification, disease classification, and severity prediction using image data. The methodology utilizes the PlantVillage dataset, encompassing over 50,000 annotated leaf images across 14 crops. After rigorous preprocessing involving image resizing, normalization, and cleaning, Improved Deep Joint Segmentation is applied to localize disease-affected regions. Feature extraction incorporates color, texture (GLCM, LBP), and shape attributes to enhance classification accuracy. A hybrid approach integrating XGBoost for feature selection and Support Vector Machine (SVM) for classification is proposed to capture both overarching trends and intricate details. Experimental results across four major crops—potato, tomato, corn, and grape—demonstrate superior performance, with the hybrid model achieving 98.6% accuracy, 98.3% precision, 99.0% recall, and 99.1% F1-score. The model outperforms existing approaches, offering a robust, scalable, and accurate solution for early crop disease detection in precision agriculture.

Why it matches plant phenotyping methods画像から病変領域を抽出し、植物病害の分類と重症度を推定する機械学習ワークフローが研究の中心であり、植物状態の表現型計測に該当する。

abstractthis study proposes a hybrid machine learning model for accurate crop type identification, disease classification, and severity prediction using image data.

ResNet-18 Convolutional Neural Network Framework for Tomato Disease Detection with Agrochemical Dosage Recommendation →

This study presents a deep learning-based framework for automated tomato leaf disease detection using a transfer learning approach built on ResNet18 architecture. The system is designed to classify 10 disease categories using image inputs resized to 224×224 pixels, leveraging ImageNet pre-trained weights to enhance feature extraction. The dataset consists of approximately 18,345 training samples with a batch size of 16 and trained over 10 epochs using the Adam optimizer and CrossEntropy loss function. Experimental results demonstrate strong classification performance, achieving a peak validation accuracy of 98.2% at epoch 9, with validation loss reduced to 0.64 from an initial value of approximately 1.8. The model shows high confidence predictions, with over 90% of test samples falling within the 90–100% confidence range. Per-class F1-scores range between 0.82 and 0.95, indicating consistent performance across multiple disease categories. The confusion matrix reveals strong diagonal dominance, confirming correct classification for most classes; however, specific misclassification patterns were observed. For instance, Bacterial_spot and Early_blight exhibit mutual confusion, while Septoria_leaf_spot shows complete misclassification into Tomato_mold, indicating limitations in distinguishing visually similar disease patterns. A balanced test dataset with class distribution ranging between 9% and 11% ensures unbiased evaluation. Additionally, Grad-CAM visualization confirms that the model focuses on biologically relevant regions such as lesion areas and leaf textures, improving interpretability. Despite achieving high accuracy under controlled conditions, the model’s generalization to real-world environments remains a challenge. The study highlights the need for improved robustness against variations in lighting, background, and disease severity. Overall, the proposed system demonstrates strong potential for precision agriculture applications, particularly in mobile-based disease diagnosis systems for farmers.

Why it matches plant phenotyping methodsトマト葉の病徴を画像から分類する深層学習手法を開発・評価しており、植物の病害状態を直接推定する方法が研究の中心です。

abstractThis study presents a deep learning-based framework for automated tomato leaf disease detection using a transfer learning approach built on ResNet18 architecture.

SPATIAL MULTI-SCALE FRACTAL ANALYSIS OF TOMATO LEAF STRUCTURAL DEGRADATION UNDER DISEASE PROGRESSION →

Tomato (Solanum lycopersicum) is an important horticultural crop worldwide, and its productivity is significantly limited by the widespread occurrence of foliar diseases that gradually modify leaf morphology and physiological function. Disease progression in the leaves entails intricate spatial transformations like lesion development, vein proliferation and disruption in venation, tissue necrosis, and disruption of normal laminar architecture. Often, these structural alterations may be challenging to express in a conventional visual or categorical scoring system, which is often subjective and lacks granularity. In the present study, a spatial multi-scale fractal analysis framework has been proposed to quantitatively capture structural degeneration in tomato leaves during disease progression. Leaf images from sources with known disease severity levels were analyzed through fractal geometry-based metrics, focusing on two key descriptors: fractal dimension (FD) and lacunarity.to express structural complexity and heterogeneity. Fractal dimension serves as a description of overall morphological complexity in this context, whereas lacunarity is widely used to quantify spatial irregularity and gap distributions within leaf tissues. Multi-scale analysis was employed to examine structural changes at different spatial resolutions. This helped us better understand both global shape changes and localized tissue damage. The findings demonstrate a steady reduction in fractal dimension as disease severity escalates, indicating a gradual deterioration of structural complexity. On the other hand, lacunarity values increase as the disease gets worse, which means that the leaf architecture becomes more varied and broken up. The results show that fractal-based spatial analysis is a strong, non-destructive, and objective way to measure how disease damages the structure of plant leaves. This technique holds considerable promise for utilization in automated plant disease diagnostic systems, precision agriculture, and digital plant phenotyping platforms.

Why it matches plant phenotyping methods病害進行に伴う葉の構造劣化を、画像からフラクタル次元とラacunarityで定量化する解析手法が研究の中心であり、植物表現型の取得・抽出に該当する。

abstracta spatial multi-scale fractal analysis framework has been proposed to quantitatively capture structural degeneration in tomato leaves during disease progression.

Klasifikasi Penyakit Daun Tomat Menggunakan Convolutional Neural Network (CNN) Berbasis EfficientNetB0 →

Penelitian ini dirancang untuk mengembangkan sistem klasifikasi penyakit pada daun tomat menggunakan metode Deep Learning dengan arsitektur Convolutional Neural Network (CNN) berbasis arsitektur model EfficientNetB0 dengan pendekatan Transfer Learning. Dataset yang digunakan untuk penelitian diambil dari Kaggle (ashishmotwani/tomat) dan terdiri dari lebih 20.000 gambar daun tomat yang terbagi menjadi 11 kelas yaitu 10 kelas daun sakit dan 1 kelas daun sehat. Penelitian dilakukan menggunakan platform Google Colab dengan GPU T4. Tahapan penelitian meliputi preprocessing dataset, augmentasi dataset, pembagian data train-validation-test, pembangunan model CNN berbasis EfficientNetB0, pelatihan model dengan mekanisme fine-tuning, serta evaluasi performa menggunakan confusion matrix, precision, recall, dan F1-score. Konfigurasi menggunakan input size 224 × 224 pixel, batch size 32, learning rate awal 0,001 dan maksimal 50 epoch. Hasil menunjukkan bahwa seluruh penyelesaian 50 epoch dengan training accuracy terbaik adalah 99,63% dan validation accuracy terbaik adalah 87,27% yang dicapai pada epoch ke-49. Model terbaik di-restore dari epoch ke-49 berdasarkan nilai val accuracy tertinggi. Kelas Powdery Mildew, Target Spot, dan Tomato Yellow Leaf Curl Virus memperoleh nilai recall tertinggi. Implementasi EfficientNetB0 terbukti efektif dalam melakukan klasifikasi penyakit daun tomat sehingga dapat untuk diterapkan pada sistem pertanian cerdas berbasis mobile maupun web.

Why it matches plant phenotyping methodsトマト葉の画像から病害状態を推定するCNN分類手法の開発・性能評価が研究の中心であり、植物表現型（病害状態）の画像ベース推定に該当する。

abstractmengembangkan sistem klasifikasi penyakit pada daun tomat menggunakan metode Deep Learning dengan arsitektur Convolutional Neural Network (CNN) berbasis arsitektur model EfficientNetB0

Adaptive spectral-thermal illumination management for protected tomato cultivation: a fused deep learning and pareto-based decision framework. →

This study presents an intelligent greenhouse lighting control framework that integrates a CNN-ELM photosynthesis prediction model with MOEA/D-based multi-objective optimization to improve tomato production while reducing the carbon impact of supplemental LED lighting. The CNN-ELM model was trained using key environmental variables, including photosynthetic photon flux density (PPFD), red-to-blue light ratio, canopy temperature, CO 2 concentration, and relative humidity. Within the experimental conditions, the model achieved high predictive accuracy, with an R² of 0.976 and an RMSE of 0.712 µmol m -2 s -1 . Using these predictions, the MOEA/D algorithm generated Pareto-optimal lighting strategies, which were ranked through entropy-weighted TOPSIS and implemented via cloud-based control connected to a LoRa wireless sensor network and pulse-width-modulated LED drivers. The system was evaluated during a 110-day tomato cultivation trial and compared with single-parameter control and ambient-condition treatments. Results showed a 38.4% reduction in LED-related carbon emissions, a 22.6% increase in net photosynthetic rate, and a 31.7% improvement in harvestable yield relative to ambient conditions. Physiological analyses further indicated enhanced photosynthetic performance, radiation-use efficiency, and light utilization. Overall, the findings demonstrate that data-driven, closed-loop lighting management can simultaneously enhance productivity and reduce greenhouse gas emissions in controlled-environment agriculture when applied within the validated operational domain.

Why it matches plant phenotyping methods光合成という植物生理形質を予測するCNN-ELMモデルを中核に、センサーネットワークと閉ループ制御を統合・評価しており、単なる栽培試験ではなく形質推定手法の応用が主要内容である。

abstractThis study presents an intelligent greenhouse lighting control framework that integrates a CNN-ELM photosynthesis prediction model with MOEA/D-based multi-objective optimization

DAPR-AM-Net: an end-to-end smart farming system powered by dual-attention progressive refinement and adaptive MixUp for explainable tomato leaf disease classification and forecasting. →

Vision-based crop disease diagnosis plays a pivotal role in smart agriculture, yet challenges such as complex field backgrounds, high intra-class similarity of lesion morphology, and severe data imbalance continue to impede model stability and interpretability. To address these issues, this study proposes DAPR-AM-Net, an intelligent diagnostic framework for tomato leaf diseases that integrates dual-attention progressive refinement with adaptive MixUp. The method introduces four key innovations: (1) a Dual Attention Fusion Mechanism (DAFM) that jointly leverages channel-wise and spatial attention to enhance lesion-related texture, color, and structural cues while suppressing background noise via the CBAM module, thereby directing the network's focus toward pathogenic regions; (2) an Adaptive MixUp with Attention-Aware Sampling (AMAAS) module that dynamically adjusts sample mixing ratios according to attention maps, effectively improving discrimination in complex boundary areas; (3) a Progressive Feature Refinement with Dual Attention (PFR-DA) module that incrementally optimizes deep feature representations through cross-hierarchical information flows; and (4) an Imbalance-Aware Multi-Objective Optimization (IAMOO) strategy that adaptively modulates loss weights based on category distribution to strengthen recognition of minority disease classes. On our self-constructed Tomato-DD dataset, DAPR-AM-Net achieves superior performance across all major metrics-including an accuracy of 99.73%, precision of 99.73%, recall of 99.74%, and an F1-score of 99.73%-outperforming current state-of-the-art approaches. On the full Plant-Village dataset, the model achieves 99.85% accuracy, 99.78% precision, 99.84% recall, and a 99.81% F1-score, while maintaining a compact model size of only 4.72 M parameters. Multi-level interpretability analyses corroborate the transparency and reliability of the model's inference process. Additionally, we developed an end-to-end smart agriculture platform powered by DAPR-AM-Net. Overall, DAPR-AM-Net provides a forward-looking yet practical solution for high-accuracy and strongly interpretable disease diagnosis in smart agriculture scenarios, demonstrating both methodological innovation and real-world applicability.

Why it matches plant phenotyping methodsトマト葉の病害状態を画像から分類する新規深層学習手法を開発し、複数データセットで性能評価するとともに、エンドツーエンドの診断プラットフォームを構築しているため、植物表現型取得・抽出法が中心である。

abstractthis study proposes DAPR-AM-Net, an intelligent diagnostic framework for tomato leaf diseases

SLIF-Tomato: Inverted Residual Convolutional Block Attention Module for In-field Tomato Leaf Disease Recognition →

Abstract Tomato cultivation represents a critical component of nutrition, economic development, and public health, yet it is increasingly compromised by foliar diseases that diminish yield and intensify dependency on hazardous agrochemicals. Although deep learning models have demonstrated strong capabilities for automated disease recognition, existing benchmark datasets exhibit limited real-world utility, primarily due to the absence of in-field imagery and precise annotations of diseased regions. A novel attention mechanism, I nverted R esidual C onvolutional B lock A ttention M odule ( IR-CBAM ), is proposed, combining inverted residual blocks with the CBAM Module, and is specifically tailored to address challenges posed by in-field image variability, such as complex backgrounds and inconsistent lighting. Furthermore, this study introduces SLIF-Tomato , the S ri L ankan I n- F ield Tomato leaf disease dataset, which is the first complete in-field dataset comprising class labels and bounding box annotations collected under diverse real-world conditions. The proposed approach achieved 99.66% and 99.91% accuracy rates on two curated versions of the SLIF-Tomato dataset. Subsequently, the YOLOv12-large model is employed to detect diseased regions, which obtained an average precision score of 88.5%. These contributions advance the development of accurate, efficient and field-adaptable diagnostic systems for tomato leaf disease management in precision agriculture.

Why it matches plant phenotyping methodsトマト葉の病害領域を画像から検出・認識する手法を開発し、実圃場データセットも構築・評価しており、植物病害状態の表現型取得が中心的です。

abstractA novel attention mechanism, I nverted R esidual C onvolutional B lock A ttention M odule ( IR-CBAM ), is proposed, combining inverted residual blocks with the CBAM Module, and is specifically tailored to address challenges posed by in-field image variability, such as complex backgrounds and inconsistent lighting.

PhytoClip: Multimodal Wearable Sensing and Online Machine Learning for Real-Time Plant Health Monitoring and Early Stress Detection →

Wearable plant sensing systems for simultaneous biochemical and physical monitoring with real-time multimodal data analysis remain limited. Here, we present PhytoClip, a multimodal wearable patch that continuously monitors leaf temperature, humidity, three volatile organic compounds (VOCs) with high selectivity, and microenvironmental light intensity and CO2 concentration. PhytoClip features a bookmark-inspired design for secure attachment to leaves of diverse morphologies, supported by a flexible printed circuit board for data acquisition, wireless communication, and cloud-based monitoring. We develop PhytoSense, an open-source machine learning (ML) framework for sensor importance ranking, multi-stress classification, and early stress detection. The integrated PhytoClip-PhytoSense platform detects and classifies nine biotic and abiotic stresses in tomato plants with 92% accuracy. Notably, P. infestans on tomato was detected within 15.5 h post-inoculation, earlier than quantitative polymerase chain reaction (qPCR) (~4 days) and visual phenotyping (~7 days), highlighting the potential of integrating multimodal wearable sensing and online ML for precision agriculture.

Why it matches plant phenotyping methods植物の葉に装着するマルチモーダルセンサーとオンラインMLによるストレス・病害状態の取得および分類が研究の中心であり、植物フェノタイピング手法として明確に該当する。

abstractWe develop PhytoSense, an open-source machine learning (ML) framework for sensor importance ranking, multi-stress classification, and early stress detection.

Non-destructive assessment of soluble solids content and firmness in tomatoes using dual-mode hyperspectral imaging technology. →

Background Non-destructive assessment of tomato internal quality, including soluble solids content (SSC) and firmness, is important for grading and postharvest management. However, the varying capabilities of reflectance and transmittance hyperspectral imaging for predicting biochemical and mechanical quality attributes have not been sufficiently compared. Results In this study, a dual-mode hyperspectral imaging system covering 500-950 nm was developed to evaluate SSC and firmness in 160 'Yuan Wei No. 1' tomatoes. Four preprocessing methods, including Savitzky-Golay smoothing (SG), standard normal variate (SNV), multiplicative scatter correction (MSC), and orthogonal signal correction (OSC), and three feature-wavelength selection strategies, including uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and UVE-CARS, were compared using partial least squares regression. Transmittance spectra outperformed reflectance spectra for SSC prediction. The CARS filtered transmittance model achieved the best performance, with R p = 0.9256 and residual predictive deviation (RPD) = 2.4208. Firmness prediction was less accurate; the best model was obtained using reflectance spectra combined with SG-SNV preprocessing and UVE-CARS feature selection, yielding R p = 0.8008 and RPD = 1.6696. Conclusion Dual-mode hyperspectral imaging is effective for non-destructive SSC prediction in tomatoes, whereas firmness prediction remains limited because mechanical quality attributes are less directly represented by visible-near-infrared spectral information. The results provide a basis for tomato quality assessment and suggest that future firmness prediction may benefit from multi-modal data fusion. © 2026 Society of Chemical Industry.

Why it matches plant phenotyping methodsトマトのSSCと硬度という植物器官形質を、デュアルモード・ハイパースペクトル画像で非破壊推定するシステムを開発・比較評価しており、形質取得手法が研究の中心である。

abstracta dual-mode hyperspectral imaging system covering 500-950 nm was developed to evaluate SSC and firmness in 160 'Yuan Wei No. 1' tomatoes.

Machine learning-assisted non-destructive prediction of root architecture in early-stage tomato rootstocks. →

The architecture of the root system is a primary factor in determining rootstock performance, affecting water and nutrient uptake, biomass accumulation, and overall vigor. However, direct root phenotyping is destructive, labor-intensive, and difficult to do routinely in breeding programs. The present study investigated early root morphological variation among developed interspecific tomato rootstock candidates (Solanum lycopersicum x S. habrochaites). The ability of linear regression and machine learning models to predict root traits from easily measured plant growth parameters was assessed. Nineteen interspecific hybrid rootstock candidates, two commercial rootstocks, and one scion were grown under optimal greenhouse conditions and evaluated at 0, 10, 20, and 30 days after planting. Root length, root surface area, root diameter, and root volume were determined by digital image analysis. In contrast, genotype, plant length, and stem diameter were used as input variables. Significant genotype x sampling date effects were observed for most morphological and biomass traits, indicating dynamic changes in root and shoot development during the first 30 days of growth. The rootstock candidates RSH-17 and RSH-6 generally showed relatively higher root length, surface area, root volume, and biomass accumulation than the commercial rootstocks and scion. XGBoost and OLR were the best predictive models, with R 2 values as high as 0.95 for root length, surface area, and volume. Root diameter was predicted less accurately than root length, surface area, and volume, suggesting that it might be a more independent or less variable root trait during early development. Overall, results suggest that vigor-related traits can serve as useful proxies for estimating major root architectural traits in early-stage tomato rootstock selection. Both XGBoost and OLR performed well, suggesting that root and shoot development were highly coordinated under optimal (non-stress) conditions. Hence, predictive modeling may help prioritize promising rootstock candidates before destructive root analysis. However, more validation under stress conditions and for longer periods of development is needed to determine the greater applicability of these models.

Why it matches plant phenotyping methods根系形態形質をデジタル画像解析で取得し、線形回帰・機械学習による非破壊予測モデルを評価・比較しており、植物フェノタイピング手法が研究の中心である。

abstractThe ability of linear regression and machine learning models to predict root traits from easily measured plant growth parameters was assessed.

Lightweight Real-Time Detection Transformer for Tomato Leaf Disease Recognition in Complex Agricultural Scenarios →

Abstract Accurate detection of tomato leaf diseases is essential for sustainable tomato production. To overcome the limitations of existing detection models, such as large parameter sizes, insufficient accuracy, weak robustness, and poor small-target detection performance, this study proposes a lightweight tomato leaf disease detection algorithm based on an improved Real-Time Detection Transformer (RT-DETR), named Tomato Leaf Diseases RT-DETR (TLD-RTDETR). Specifically, a partial convolution-based PConvBlock is introduced into the backbone to enhance feature extraction while reducing model complexity. In addition, a Coordinate Attention-based hierarchical feature pyramid module (CA_HSFPN) is designed to suppress background interference and strengthen small-target feature representation. Furthermore, a learnable positional encoding strategy is integrated into the feature encoding stage to improve the extraction of critical disease features in complex environments. Experimental results show that TLD-RTDETR achieves an mAP of 92.9%, precision of 94.2%, and recall of 87.5% on the tomato leaf disease dataset, outperforming the RT-DETR-R18 baseline by 2.4%, 0.5%, and 3.3%, respectively. Meanwhile, the model size, parameter count, and computational cost are reduced by 38.1%, 38.1%, and 31.2%. Compared with mainstream methods, the proposed model achieves better detection performance with a more lightweight architecture. Additional visualization, anti-interference, and generalization experiments further verify its robustness and cross-scene adaptability, demonstrating its potential for practical deployment in tomato leaf disease detection.

Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出・認識する軽量Transformer手法を開発し、精度・頑健性・汎化性能を検証しており、植物病害状態のフェノタイピング手法が中心である。

abstractthis study proposes a lightweight tomato leaf disease detection algorithm based on an improved Real-Time Detection Transformer (RT-DETR), named Tomato Leaf Diseases RT-DETR (TLD-RTDETR).

Transfer Learning-based Multi-Class Plant Disease Detection Using MobileNetV2 and EfficientNet-B0 →

An early and precise identification of plant diseases helps to increase the efficiency of farming operations and minimize the economic losses associated with plant diseases. Nevertheless, applying deep learning models for plant disease identification in an agricultural setting poses certain difficulties due to high computational costs and insufficient edge device computing power. This paper presents a transfer learning-based framework for multi-class plant disease detection using MobileNetV2 and EfficientNet-B0 models. For the purpose of research, the PlantVillage dataset including potato, bell pepper, and tomato leaves was used. Images from this source underwent pre-processing that included resizing, normalizing, and augmenting images. Both transfer learning approach and fine-tuning helped to modify pre-trained CNNs for multi-class classification of different diseases affecting plants' leaves. Experiments have shown that EfficientNet-B0 model performed much better with accuracy of 95.7% and AUC of 0.98. Moreover, the proposed algorithm was exported as a TensorFlow Lite model and implemented in the Streamlit application for efficient edge deployment.

Why it matches plant phenotyping methods植物葉画像から病害を推定する深層学習フレームワークの開発・性能評価が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採録する。

abstractThis paper presents a transfer learning-based framework for multi-class plant disease detection using MobileNetV2 and EfficientNet-B0 models.

Editorial: Plant phenotyping for agriculture →

Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-

Why it matches plant phenotyping methods植物フェノタイピングの技術動向を扱うEditorialであり、画像解析、UAVセンシング、深層学習、形質抽出などの方法が中心的に整理されている。

titleEditorial: Plant phenotyping for agriculture

Fusionnet: Multi-Backbone Feature Fusion Deep Neural Network for Tomato Leaf Disease Classification →

Plant diseases cause a substantial reduction in crop yield, thus affecting food security in the farming industry. The timely discovery and precise diagnosis of the diseases on tomato leaves is very important to take corrective measures. This paper proposes FusionNet, that combines several backbones for identifying diseases on the leaves of tomatoes. The proposed model combines the complementary feature learning capabilities of MobileNetV3-Small, SE-ResNet50, and ECAResNet50d through a concatenation-based fusion approach to comprehensively learn fine-grained texture details, global contextual semantics, and channel-attentive information. The proposed framework is evaluated on a curated dataset of$\mathbf{1 8, 1 6 0}$images from ten classes. To guarantee statistical significance, 5fold cross-validation with two separate runs per fold is conducted, achieving a mean validation accuracy of$99.30 \% \pm \mathbf{0. 1 9 \%}$, indicating sTable generalization. To validate the fusion strategy, t-SNE visualizations show enhanced inter-class separation as well as intra-class compactness in the fused feature space compared to the individual backbones. Cosine similarity analysis also confirms a decrease in inter-class correlation and an improvement in the discriminative structure. The experimental results show that FusionNet achieves robust, reliable, and highly discriminative performance for automated plant disease diagnosis in precision agriculture applications.

Why it matches plant phenotyping methodsトマト葉の画像から病害状態を推定する深層学習手法を開発・評価しており、植物病害フェノタイピングが中心である。

abstractThis paper proposes FusionNet, that combines several backbones for identifying diseases on the leaves of tomatoes.

RootNet: A deep learning framework for automated tomato radicle segmentation and length measurement →

Automated phenotyping of crops is a vital component of precision agriculture. As a representative economic crop, the radicle length of tomato seeds is a key phenotypic indicator for assessing seed vigor and seedling health. To reduce labor costs and improve measurement efficiency, we developed an integrated seed germination phenotype acquisition system that combines cultivation and imaging, enabling continuous image acquisition throughout the tomato seed germination process. To achieve automatic measurement of radicle length, we propose a deep learning-based segmentation framework, termed RootNet. The framework incorporates a custom-designed module, MambaNextBlock (MNB), within skip connections to enhance long-range feature modelling, and integrates an Atrous Spatial Pyramid Pooling (ASPP) module at the bottleneck to capture multi-scale contextual information. Post-segmentation, Canny edge detection is employed to extract radicle contours, from which actual lengths are computed based on contour arc length. Experimental results show that RootNet achieved 77.75% Intersection over Union (IOU), 86.03% Precision, 88.72% Recall, and 87.46% F1-Score on the root class. Compared with manual measurements conducted using ImageJ, our method showed high agreement across 1170 radicle measurements, with an R² of 0.9785, an MAE of 0.274 mm, an RMSE of 0.344 mm, and a Bias of −0.008 mm. Bland–Altman analysis further confirmed the absence of systematic bias, with 95% limits of agreement ranging from −0.682 mm to +0.666 mm. Meanwhile, measurement efficiency was improved by approximately 680-fold. Furthermore, the method was applied to evaluate the effects of drought, salinity stress, and different concentrations of Streptomyces albidoflavus (HL4) and Streptomyces virginiae (GZ2) on radicle growth. The results indicated that drought stress, salinity stress, and undiluted HL4 inhibited radicle elongation, whereas diluted HL4, as well as both undiluted and diluted GZ2, significantly promoted radicle growth. This study provides an efficient and cost-effective solution for non-destructive crop phenotyping and intelligent agricultural management in precision farming.

Why it matches plant phenotyping methodsトマト幼根長を画像から自動抽出・測定する深層学習フレームワークを開発し、手動測定との定量的検証も行っており、植物フェノタイピング手法が研究の中心である。

abstractwe developed an integrated seed germination phenotype acquisition system that combines cultivation and imaging, enabling continuous image acquisition throughout the tomato seed germination process.

Software and reproducibility package →

First stable release of the RGB and NPQ pixel-wise phenotyping pipeline associated with the manuscript "Image-based biomarkers effectively predict salt and drought stress in dwarf tomatoes (Solanum lycopersicum L.)". This repository includes a Python-based image analysis pipeline for high-throughput plant phenotyping using RGB and chlorophyll fluorescence (NPQ) imaging data. The workflow is designed for pixel-wise extraction and analysis of image-derived traits, with a specific focus on preserving full spatial distributions rather than relying on image-level summary statistics. The pipeline processes RGB images to compute vegetation indices derived from color channel combinations, and NPQ fluorescence images to extract pixel-level chlorophyll fluorescence metrics. Both data types are integrated with experimental metadata through structured indexing files. The analysis framework is organised into two main stages: (i) data pre-processing and structuring into long-format pixel-wise datasets, and (ii) distribution-based statistical analysis of trait variability across treatments and conditions. The latter includes normalised histograms, Jensen–Shannon and Wasserstein distance metrics, and cluster-based permutation testing to identify statistically significant differences between distributions. A minimal example dataset is provided to enable end-to-end testing of the workflow, including image processing, metadata integration, and statistical analysis. An additional archive containing representative example outputs generated from the example dataset is included to illustrate the structure and format of intermediate and final pipeline outputs. To facilitate computational reproducibility, the repository also includes complete derived outputs generated from the full study dataset, including distribution-comparison results (Jensen–Shannon and Wasserstein distances) and cluster analysis outputs for all evaluated RGB and chlorophyll fluorescence traits. These files are provided as supplementary computational products of the workflow and can be used to verify, inspect, and reproduce the analyses described in the associated manuscript.

Why it matches plant phenotyping methodsRGBおよびNPQ画像から植物形質を画素単位で抽出・解析する再利用可能なパイプラインと再現性用データを提供しており、フェノタイピング手法が中心である。

abstractFirst stable release of the RGB and NPQ pixel-wise phenotyping pipeline

Software and reproducibility package →

First stable release of the RGB and NPQ pixel-wise phenotyping pipeline associated with the manuscript "Image-based biomarkers effectively predict salt and drought stress in dwarf tomatoes (Solanum lycopersicum L.)". This repository includes a Python-based image analysis pipeline for high-throughput plant phenotyping using RGB and chlorophyll fluorescence (NPQ) imaging data. The workflow is designed for pixel-wise extraction and analysis of image-derived traits, with a specific focus on preserving full spatial distributions rather than relying on image-level summary statistics. The pipeline processes RGB images to compute vegetation indices derived from color channel combinations, and NPQ fluorescence images to extract pixel-level chlorophyll fluorescence metrics. Both data types are integrated with experimental metadata through structured indexing files. The analysis framework is organised into two main stages: (i) data pre-processing and structuring into long-format pixel-wise datasets, and (ii) distribution-based statistical analysis of trait variability across treatments and conditions. The latter includes normalised histograms, Jensen–Shannon and Wasserstein distance metrics, and cluster-based permutation testing to identify statistically significant differences between distributions. A minimal example dataset is provided to enable end-to-end testing of the workflow, including image processing, metadata integration, and statistical analysis. An additional archive containing representative example outputs generated from the example dataset is included to illustrate the structure and format of intermediate and final pipeline outputs. To facilitate computational reproducibility, the repository also includes complete derived outputs generated from the full study dataset, including distribution-comparison results (Jensen–Shannon and Wasserstein distances) and cluster analysis outputs for all evaluated RGB and chlorophyll fluorescence traits. These files are provided as supplementary computational products of the workflow and can be used to verify, inspect, and reproduce the analyses described in the associated manuscript.

Why it matches plant phenotyping methodsRGBおよびNPQ画像から植物形質を画素単位で抽出・解析する再利用可能なパイプラインと再現性資料が中心であり、植物フェノタイピング手法に該当する。

abstractPython-based image analysis pipeline for high-throughput plant phenotyping using RGB and chlorophyll fluorescence (NPQ) imaging data.

Construction of a Virtual Sensor-Driven Digital Twin System for Plant Growth Monitoring on Rooftop Farms →

Rooftop farms are urban green infrastructure integrating food production, ecological regulation, and public services, and their management increasingly relies on data-driven approaches. However, open built environments, microclimatic heterogeneity, and limited sensor deployment challenge continuous monitoring and short-term prediction of rooftop plant growth. This study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China. The system adopts a five-layer architecture comprising data acquisition, transmission, modeling, processing, and application service layers. By coupling a Long Short-Term Memory (LSTM) weather prediction model with the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth model, a predictive virtual sensor module was developed to forecast leaf area index (LAI), aboveground biomass, phenology, and yield for seven days. Results show that the system links environmental data acquisition, LSTM–DSSAT prediction, database storage, and three-dimensional visualization, transforming rooftop plant growth into an updatable, predictable, and visualized digital twin object. The coupled model showed high predictive accuracy, with R2 values of 0.9814 for LAI and 0.9966 for aboveground biomass, while supporting phenology and yield prediction. The system supports irrigation optimization, landscape management, and activity planning in sensor-constrained rooftop farms.

Why it matches plant phenotyping methods植物成長のLAI、地上部バイオマス、フェノロジー、収量を予測する仮想センサー・デジタルツインを開発し、精度検証しており、表現型推定手法が研究の中心である。

abstractThis study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China.

Rapid preliminary screening of Tomato brown rugose fruit virus based on surface-enhanced Raman spectroscopy and machine learning. →

Introduction Tomato brown rugose fruit virus (ToBRFV) represents a growing threat to global tomato production, causing severe losses in crop yield and fruit quality. Although the standard RT-qPCR assay is highly accurate, its reliance on laboratory processing, specialized equipment, and trained personnel limits its applicability for rapid on-site diagnostics. To address this limitation, this study evaluated a biosensing method that does not require labels and combines surface-enhanced Raman scattering (SERS) with machine learning to distinguish tomato leaves infected with ToBRFV from healthy leaves. Methods Following health status confirmation via RT-qPCR, leaf extracts were directly deposited onto silver nanorod arrays for SERS spectral acquisition. Three classification models were evaluated: PCA-LDA, PLS-DA, and SVM. Results The results showed that all models were able to discriminate infected samples from healthy samples in the present dataset. Notably, the SVM model exhibited the best performance, achieving an accuracy of 91.67%, a sensitivity of 100.00%, a specificity of 81.48%, and an area under the ROC curve (AUC) of 0.993. Discussion This result suggests that SERS spectra may contain biochemical information associated with ToBRFV infection and that such information can be used for sample classification using machine learning models. In its present form, this approach is intended as a rapid, low-cost, field-deployable preliminary screening tool - not a replacement for RT-qPCR or other confirmatory molecular assays. The reported accuracy was obtained on mechanically inoculated plants of a single cultivar under controlled greenhouse conditions and should therefore be interpreted as a proof-of-concept upper bound; field-scale validation is the focus of ongoing work.

Why it matches plant phenotyping methodsSERSと機械学習を用いて感染トマト葉と健全葉を識別する植物病害状態の取得・分類法が研究の中心であり、複数モデルの性能評価も行っている。

abstractthis study evaluated a biosensing method that does not require labels and combines surface-enhanced Raman scattering (SERS) with machine learning to distinguish tomato leaves infected with ToBRFV from healthy leaves.

Robust multi-target multi-scale tomato leaf disease detection for precision agriculture applications. →

The tomato is one of the most important economic crops worldwide; frequent occurrences of foliar diseases can severely affect its quality and yield, resulting in substantial economic losses. However, state-of-the-art methods still struggle with multi-target, multi-scale disease detection in complex scenarios, lacking accuracy and speed for tomato leaf diagnosis. A novel improved YOLO v8s model is proposed in this study to achieve high-precision and fast identification of multi-target and multi-scale tomato leaf diseases. First, a multi-target, multi-scale image dataset encompassing seven typical tomato diseases was developed to effectively enhance the model's robustness under complex practical scenario by integrating multiple public datasets and employing diverse data augmentation techniques. Second, a transfer learning strategy was employed to transfer high-quality features from a pretrained model to the disease detection task, thereby improving convergence speed and generalization ability. Finally, the CBAM (Convolutional Block Attention Module) channel-spatial attention mechanism was introduced into the YOLO v8s network, enabling the model to adaptively focus on critical regions and significantly enhance feature extraction and target localization performance. Experimental results demonstrate that the improved YOLOv8s-CBAM model achieves superior performance in complex scenarios, with a precision of 96.9%, recall of 97.3%, F1 score of 97.0%, and mAP@0.5 of 99.1%, representing improvements of 2.5%, 2.0%, 2.2%, and 1.8%, respectively, over the original YOLO v8s model. Moreover, the model size was reduced to 24.8 MB, a decrease of 11.7 MB compared to the original, achieving an effective balance between accuracy and lightweight design. These results indicate that the proposed method exhibits enhanced feature extraction and localization stability in multi-target, multi-scale disease identification tasks, providing an effective technical solution for automated detection in complex agricultural disease scenarios.

Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出・識別するYOLOv8s-CBAM手法を開発し、データセット構築と性能比較検証を行っており、植物病害状態の画像ベース表現型計測が中心である。

abstractA novel improved YOLO v8s model is proposed in this study to achieve high-precision and fast identification of multi-target and multi-scale tomato leaf diseases.

Large language model assisted decision support framework for uncertainty aware detection and management of tomato lateral shoots. →

Accurate identification of tomato lateral shoots is essential for automated pruning and plant monitoring in greenhouse production. However, complex illumination, leaf occlusion, and morphological variability often reduce detection reliability in optical vision systems. This study proposes an optical vision-based framework that integrates deep learning perception with large language model assisted pruning decision support. A tomato lateral Shoot image dataset was constructed using RGB imaging in greenhouse environments. A lightweight YOLOv8n instance segmentation model with the Convolutional Block Attention Module (CBAM) was developed to enhance feature representation. Data augmentation strategies were applied to simulate illumination variations and improve model robustness. Model interpretability was analyzed using Principal Component Analysis (PCA) and Gradient weighted Class Activation Mapping (Grad CAM). Experimental results show that the proposed YOLOv8n-seg+CBAM model achieves a mAP 0.5 of 98.1% with only 3.28M parameters and an average inference time of 8.0 ms per image. Monte Carlo Dropout was further introduced to estimate the spatial uncertainty of cutting points. These structured perception features were provided to a large language model (LLM), enabling context aware pruning decision assistance. The proposed framework integrates vision-based shoot detection, uncertainty estimation, and LLM-assisted reasoning into a unified pipeline, enabling more reliable pruning decisions and improving safety and robustness compared with vision-only approaches in greenhouse environments.

Why it matches plant phenotyping methodsトマト側枝をRGB画像から検出・セグメンテーションし、不確実性推定まで行う画像ベースの植物形態計測手法を開発しており、方法論が中心である。

abstractThis study proposes an optical vision-based framework that integrates deep learning perception with large language model assisted pruning decision support.

An Efficient Dynamic Deep Learning Methodology for Identification of Plant Disease and it’s classification →

Abstract Nowadays, estimation & identification of plant diseases (PD) pointedly shows the high impact on agricultural & food productivity. In this paper, develop the Dynamic Deep Learning Methodology-(DDLM) for Plant Disease Classification-(PDC) using the Residual Neural Network (ResNet) Architecture, enhanced with an intelligent supplement recommendation module. The procedure of present Deep Learning Methodology (ResNet) is gathering the images from the number of input sensors, create large amount of dataset that contains number of sample leaf images (both diseased & healthy) finally applying ResNet model to dataset. The model is trained (80 %) on a large dataset of plant leaf images, including healthy and diseased samples across various species. Pre-processing steps such as (R_N_A) Resizing (R), Normalization (N), and Augmentation (A) are working on development of the model to improve model generalization. Once a disease is detected, Methodology generates output including the disease name (e.g., "Tomato Late Blight") and a Recommended Supplement (e.g., "Apply Copper-Based Fungicide, Ensure Proper Drainage"). The ResNet50 model, fine-tuned using Transfer Learning (TL), achieves a classification accuracy of 97.4%, outperforming traditional CNN models. Early estimation & identification of plant diseases (PD) gives the high increases the yield of the crop. Evaluation metrics such as Confusion Matrix, Precision, Recall & F1-score validate the reliability of the model across multiple classes. By integrating accurate disease detection with actionable supplement guidance, the proposed solution empowers farmers to take immediate and informed actions, enhancing crop health and yield with supplement recommendation. When comparing with resnet50 the other methods had a less accuracy. KEYWORDS— Hybrid Machine Learning Methodology (Dynamic Deep Learning Methodology-(DDL) for Plant Disease Classification-(PDC), Transfer Learning (TL), Residual Neural Network (ResNet), Image Classification, Accuracy, Disease Detection, Precision Agriculture, Smart Farming, Transfer Learning.

Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習手法の開発・評価が中心であり、植物フェノタイピング手法に該当する。

abstractdevelop the Dynamic Deep Learning Methodology-(DDLM) for Plant Disease Classification-(PDC) using the Residual Neural Network (ResNet) Architecture

An Ai-Powered Based Solution for Automated Plant Disease Detection →

Reducing agricultural yield and food security all over the world are major impacts of diseases on crops. Particularly in developing regions this is becoming a severe issue. Early disease identification followed by necessary steps is the way to control further infection. But, identification of crop diseases on the spot can be quite tough because of the scarcity of skilled agronomists who can recognize different plant diseases. A web application based framework is introduced in this paper for real time, automatic recognition of leaf diseases using an AI application. With this framework the plants which have got infected with 38 categories of diseases over 14 plants of apple, corn, tomato, grape, peach, strawberry, citrus, etc can be detected automatically. Size of the image can be anything but here, 160 x 160 pixel leaf image is used as input to the algorithm. It would helps to identify the category of the plant, disease, probability of detection and reason of the infection along with suggesting the appropriate solutions. It is being developed in Python language with the support of TensorFlow, Keras and flask web application for instant response via an interactive web application with Drag and Drop facility. In this, experimental results show good accuracy to classify and recognize the leaf diseases making this system a smart application for farmers, researcher and the expert.

Why it matches plant phenotyping methods植物葉の画像から病害状態を自動推定するAI手法とWebアプリケーションが中心であり、植物の病徴・病害状態を直接評価するため、植物フェノタイピング手法として収録する。

abstractA web application based framework is introduced in this paper for real time, automatic recognition of leaf diseases using an AI application.

A Lightweight Real-Time Tomato Leaf Disease Detection System for Edge-Based Smart Agriculture. →

Tomato leaf diseases substantially reduce tomato yields and quality and remain a persistent challenge for efficient crop management. Although deep learning-based detectors have achieved strong accuracy in controlled benchmarks, many existing solutions are still difficult to transfer to resource-constrained agricultural systems because they rely on high-end GPUs, consume considerable power, and often lose performance after deployment on embedded devices. To address this practical gap, this study proposes HGS-YOLO, a system-oriented deployable lightweight adaptation of YOLOv11 for leaf-level tomato disease detection, together with an end-to-end edge sensing pipeline for low-power agricultural deployment. The main contribution lies in the coordinated system-level co-design of model structure, optimization, and deployment rather than in a novel detector architecture. Specifically, YOLOv11 is adapted through three coordinated modifications: an HGNetV2 backbone for efficient feature extraction, an HS-FPN neck with channel attention for lightweight multi-scale fusion, and an MPDIoU loss function for more stable localization optimization. Beyond the model architecture, the study establishes a complete engineering pipeline that includes training, optimization, post-training quantization, and hardware deployment with BPU acceleration on a D-Robotics RDK X5 handheld platform. Comprehensive benchmark experiments indicate that HGS-YOLO achieves 93.6% mAP50 and 72.1% mAP@[0.5:0.95] with 86.5% recall, only 1.3 M parameters, and a 3.1 MB model size, substantially reducing the model complexity and storage cost relative to the YOLOv11 baseline. A three-seed retraining comparison shows that HGS-YOLO trades roughly 0.5 mAP50 points for this compactness (a statistically significant but small concession) and recovers the cost on the deployment side: on the RDK X5 chip, HGS-YOLO is the fastest, most memory-efficient, and lowest-power model among all compared detectors. Indoor deployment tests using separately collected tomato leaf samples further achieve 90.3% mAP50, 82.3% recall, 89.0% precision, 25.0 ± 0.4 ms end-to-end latency, 40.0 ± 0.6 FPS, and 9.8 ± 0.4 W average system power. After PTQ, the mAP50 drops from 93.6% to 93.0% on the same benchmark; because this figure was measured under controlled imaging conditions, it is presented as an in-distribution reference point rather than as evidence of robustness in the open field. We also took the handheld system into a working tomato greenhouse for a small outdoor field round, where it ran end-to-end and produced on-device disease detections under natural sunlight, specular highlights, partial occlusion, background clutter, and handheld motion blur. These results show that HGS-YOLO reaches a good balance of accuracy, efficiency, and deployability and that it works in the field on an independent small-scale test; validating it more widely across sites, seasons, and weather is left to future work.

Why it matches plant phenotyping methodsトマト葉の病害状態を画像から推定する軽量検出モデルとエッジセンシング・展開パイプラインを開発し、ベンチマーク、量子化、ハードウェア性能、屋内外試験で検証しており、植物表現型取得が中心である。

abstractthis study proposes HGS-YOLO, a system-oriented deployable lightweight adaptation of YOLOv11 for leaf-level tomato disease detection, together with an end-to-end edge sensing pipeline for low-power agricultural deployment.

Intelligent Crop Disease Detection System using Leaf image Analysis with Computer Vision →

Burkina Faso's agriculture sector faces major challenges, with annual crop losses reaching 40% due to plant diseases, affecting 2.7 million people in a situation of food insecurity. This research presents an innovative automatic plant disease detection system using computer vision, specifically developed for West African constraints. The system is based on the YOLOv11 (You Only Look Once) unified detection architecture, recognised for its optimal balance between speed and accuracy in real time, essential for mobile deployment to detect diseases in maize, tomatoes and chillies with an overall accuracy of ~99%. The image dataset from Kaggle has been validated by local agronomic expertise from INERA, ensuring the relevance of disease classes specific to the Sahelian context. The proposed architecture demonstrates superior performance to existing approaches while being optimised for mobile deployment. This solution contributes to the development of decision support tools for precision agriculture in West Africa.

Why it matches plant phenotyping methods葉画像から植物病害を自動検出するコンピュータビジョン手法の開発が中心で、植物の病害状態を直接推定しているため、植物フェノタイピング手法として採用。

abstractThis research presents an innovative automatic plant disease detection system using computer vision

Implementation for Plant Disease Classification via Telegram →

This study aims to develop an automated system for classifying vegetable plant diseases using the MobileNet algorithm integrated with a Telegram Bot. The system is designed to assist users, especially farmers, in identifying plant diseases quickly and efficiently through leaf images. The research method applies a Convolutional Neural Network with the MobileNet architecture due to its lightweight and efficient computational performance. The dataset used in this study consists of tomato leaf images obtained from a public dataset on Kaggle, which includes several disease categories and healthy leaves. The system is implemented using Python and integrated with the Telegram Bot API to enable real-time interaction. The process begins when users upload leaf images, followed by image preprocessing and classification using the trained model. The results show that the system is capable of providing accurate classification with good performance and can handle various input conditions. In addition, the integration with Telegram makes the system easily accessible without requiring additional applications. Therefore, this study offers a practical and efficient solution for early detection of plant diseases using deep learning technology.

Why it matches plant phenotyping methods葉画像から植物病害を分類する深層学習システムの開発が研究の中心であり、植物の病害状態を直接推定する画像ベースのフェノタイピング手法に該当する。

abstractdevelop an automated system for classifying vegetable plant diseases using the MobileNet algorithm integrated with a Telegram Bot

Resource-Efficient MobileNetV2 Model for Multiclass Plant Disease Prediction Using Real-Time Data in Smart Farming →

Agriculture remains a cornerstone of Namibias economy, yet small-scale crop farmers continue to face significant productivity losses due to late or inaccurate diagnosis of plant diseases. Tomato, a major crop in the countrys semi-arid regions, is highly susceptible to fungal and bacterial infections that spread rapidly under local climatic conditions. Manual inspection is labour-intensive, subjective, and ineffective for large-scale monitoring. In the literature, many studies have used high-quality datasetsto train deep learning models. However, these datasets are not real-time and rarely reflect Namibias specific atmospheric and climatic conditions. To address this challenge, this study uses a blended dataset combining the Plant Village Tomato Leaf Dataset from Kaggle with real-time images collected from small farms in Namibia. The study further investigates a resource efficient and reliable deep learning model, namely MobileNetV2, for multiclass classification of plant diseases. The proposed framework using the MobileNetV2 model is benchmarked against the VGG16 and ResNet50 models, both trained and fine-tuned on the blended dataset. The models are compared in terms of the overall prediction accuracy from the multiclass confusion matrix and their computational cost. The results indicate that the proposed multiclass classification model based on the MobileNetV2 architecture has achieved the best performance near to 90 percent accuracy, compared to VGG16 (88.33 percent) and ResNet50 (58.02 percent), while incurring minimal computational cost. The model achieved fast predictions with reasonable accuracy, enabling mobile deployment to monitor crop health in the field. The results show that MobileNetV2 offers a low-cost way to assess tomato crop health and support farmers in Namibia using digital technologies.

Why it matches plant phenotyping methods植物葉画像から病害状態を推定するMobileNetV2モデルを開発し、他モデルと精度・計算コストを比較しており、植物フェノタイピング手法が中心です。

abstractThe study further investigates a resource efficient and reliable deep learning model, namely MobileNetV2, for multiclass classification of plant diseases.

A Stomata-Infiltratable SERS Nanosensor for Real-Time Monitoring of Hydrogen Sulfide Dynamics in Plants. →

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.

A real-time ripeness detection model for tomatoes in complex greenhouse environments. →

Timely harvesting of fresh tomatoes is urgently needed. To address this issue, this study proposes DDC-YOLOv11n, a model suitable for real-time detection of tomato ripeness in complex greenhouse environments. A Zero-DCE adaptive enhancement module is first deployed at the input stage to restore and enhance the true color and texture details of the images. An improved Deep Residual Shrinkage Network (DRSN) is then added to YOLOv11n to perform adaptive soft-threshold filtering on feature maps, reducing the interference of image noise on the detection targets. Finally, the CBAM spatial attention is enhanced through dilated convolution and channel grouping to form the LKCBAM module, which expands the equivalent receptive field while controlling the increase in parameters, thereby improving tomato detection accuracy in occluded and dense scenes. Experimental results show that the DDC-YOLOv11n model achieves the best recognition performance: compared with the original YOLOv11n, its mAP@0.5, precision, recall, and F1 score are increased by 16.8%, 24.6%, 8.3%, and 18.1%, respectively. These findings facilitate real-time tomato ripeness detection in complex greenhouse environments and provide perceptual information for subsequent management tasks such as harvesting.

Why it matches plant phenotyping methodsトマト果実の成熟度という植物器官の状態を画像から推定するモデルを開発・評価しており、フェノタイピング手法が研究の中心である。

titleA real-time ripeness detection model for tomatoes in complex greenhouse environments.

FNE-RTDETR: A lightweight end-to-end model for small-area tomato leaf disease detection and fine-grained classification. →

Tomatoes, widely cultivated around the world, are not only an important source of daily nutrition but also a crop with economic significance. However, the yield and quality of tomatoes are highly susceptible to environmental and climatic factors. In view of this, timely detection and accurate identification of leaf diseases in tomato plants play a crucial role in maintaining optimal production and quality. Current detection methods rely on the subjective expertise of agricultural specialists, leading to inefficiencies and inconsistent outcomes that fail to meet the requirement for precise and timely disease management. In addition, it remains a persistent challenge to trade off between lightweight modeling and recognition accuracy in natural environments. In this study, FNE-RTDETR model for detecting tomato leaf diseases was introduced, where FasterNet network was used in place of the original backbone network of RT-DETR to integrate lightweight PConv so as to reduce network parameters and computational complexity while retaining detection performance, thereby improving feature extraction efficiency. Additionally, the combination of Deformable attention and AIFI modules enhances the model's fine-grained classification capability for various disease types. Also, the cross-attention mechanism in the decoder was replaced by an efficient multi-channel attention mechanism, which strengthens the model's ability to fuse multi-scale semantic features across spatial dimensions, thereby improving the detection performance of small lesion areas and addressing effectively the challenges of disease detection. Experimental results demonstrate that FNE-RTDETR achieves an mAP50 of 91.5%, outperforming RT-DETR by 4.1%, while reducing parameters by 14.7% and GFLOPs by 9%. Ablation and robustness experiments further validate the model's superior convergence speed and generalization ability. Compared with YOLOv3, YOLOv5, YOLOv6, YOLOv8, YOLOv9, YOLOv10, and various DETR variants, FNE-RTDETR consistently achieves higher accuracy and better lightweight performance across multiple datasets, demonstrating strong potential for practical application in tomato leaf disease detection.

Why it matches plant phenotyping methodsトマト葉の病斑・病害を画像から検出・分類するFNE-RTDETRモデルを開発し、複数モデル・データセットとの比較、アブレーションおよび頑健性実験で技術検証しているため、植物病害状態のフェノタイピング手法が中心である。

abstractIn this study, FNE-RTDETR model for detecting tomato leaf diseases was introduced

Minimally invasive microneedle sensor for in vivo monitoring of indole-3-acetic acid in plant leaves. →

Indole-3-acetic acid (IAA), one of the most important phytohormones, plays critical roles in plant growth, development, and stress response. However, minimally invasive and in-situ monitoring of IAA in living plants remains challenging due to the complex biological matrix and the lack of suitable in vivo sensing platforms. Herein, we report a minimally invasive microneedle electrochemical sensor for in vivo monitoring of IAA in plant leaves. A polymer microneedle array was fabricated and coated with a conductive Au layer, followed by electropolymerization of a molecularly imprinted poly(o-phenylenediamine) (poly-OPD) recognition film using IAA as the template molecule. The resulting microneedle sensor exhibited a selective electrochemical response toward IAA with good anti-interference capability against common electroactive plant metabolites. The sensor showed a linear response toward IAA over a wide concentration range with a low detection limit (0.44 μM). The microscale structure of the microneedles enabled minimally invasive insertion into plant tissues while maintaining structural integrity and stable electrochemical performance of the sensing platform. The developed sensor was further applied for in vivo monitoring of IAA fluctuations in tomato leaves under different physiological conditions. Distinct dynamic electrochemical response patterns were observed between normal and drought-stressed leaves, demonstrating the potential of the microneedle platform for plant physiological analysis and precision agriculture applications. This work provides a promising strategy for minimally invasive phytohormone sensing in living plants and expands the application of microneedle electrochemical devices in plant bioanalysis.

Why it matches plant phenotyping methods植物葉内のIAAを非侵襲的に測定するマイクロニードル電気化学センサーを開発・性能評価し、乾燥ストレス下の生理状態を実植物で検証しており、表現型取得法が中心である。

abstractwe report a minimally invasive microneedle electrochemical sensor for in vivo monitoring of IAA in plant leaves.

Deep Learning for Tomato Disease Detection and Severity Assessment: A Systematic Analytical Review of Methods, Datasets, and Challenges →

Abstract Tomato diseases significantly affect crop productivity and food security, necessitating accurate and timely detection methods. This paper presents a systematic and analytical review of deep learning approaches for tomato disease detection and severity assessment, based on 76 research studies. Existing methods are categorized into classification, detection, segmentation, and emerging multi-task frameworks. The analysis shows that convolutional neural networks achieve high accuracy on controlled datasets but exhibit limited generalization in real-world conditions. Advanced architectures, including transformer-based and hybrid models, improve performance but increase computational complexity. A key finding is the limited focus on disease severity assessment, which remains underexplored despite its importance for precision agriculture. The review identifies major challenges, including dataset limitations, lack of standardized benchmarks, and deployment constraints. Future directions emphasize multi-task learning, real-world dataset development, lightweight models, and explainable AI. This study provides a foundation for developing robust and practical tomato disease detection systems.

Why it matches plant phenotyping methodsトマト病害の検出・重症度評価という植物状態の画像推定手法を、研究・データセット・課題の観点から体系的にレビューしており、フェノタイピング手法のレビューが中心である。

abstractThis paper presents a systematic and analytical review of deep learning approaches for tomato disease detection and severity assessment, based on 76 research studies.

LeafDet: A lightweight and interpretable deep learning framework for tomato leaf disease detection. →

Ensuring global food security depends on timely and reliable plant disease identification. Traditional disease detection methods often prove inefficient because of the lack of necessary precision. Furthermore, public datasets typically suffer from the class imbalance issue, which can obstruct reliable model testing and lead to biased performance evaluations. This paper introduces LeafDet, an object detection model based on the YOLOv8 architecture, specifically designed for the effective detection of tomato leaf diseases. Moreover, a revised, balanced dataset, named PlantTom, is developed by combining images from various public sources to reduce the existing dataset limitations. PlantTom has 7836 images with 8 distinct classes, each representing a tomato leaf disease. The proposed LeafDet model includes CBM, C2f, SPPF, and ECA attention modules in the backbone section; BiFPN, GSConv, VoVGSCSP, and Shuffle Attention in the neck section. Efficient attention methods like ECA and Shuffle Attention are used to improve both accuracy and speed. LeafDet model achieves 91.6% mAP@0.5 on the PlantTom dataset, which is a 2.2% improvement over the original YOLOv8n with 2.69M parameters and an inference time of 2.4ms. The proposed model also outperforms several other state-of-the-art object detection models, including the latest YOLOv11n and YOLOv12n. Ablation studies show that each part of the model helps to improve its performance, and the PIoUv2 loss function is found to be the optimal choice for this use. The model predictions are then validated using Eigen-CAM, which provides a visualization of the decision-making process. These results demonstrate that LeafDet provides a deployable and interpretable framework for plant disease detection in smart agriculture.

Why it matches plant phenotyping methodsトマト葉の病害状態を画像から検出する深層学習手法と評価用データセットを開発・検証しており、植物の表現型状態の取得が中心である。

abstractThis paper introduces LeafDet, an object detection model based on the YOLOv8 architecture, specifically designed for the effective detection of tomato leaf diseases.

Early Detection of Tomato Leaf Diseases Using a Deep Learning Approach. A Field-Based Implementation in Ghana →

Tomato farming plays a major role in Ghana’s agricultural sector by contributing to food supply and serving as a source of income for many smallholder farmers. However, tomato plants are easily affected by leaf diseases that can spread quickly and reduce crop yield if not detected early. In many farming communities, disease detection is still done manually, which is often slow, inconsistent, and affected by human error. This study developed a deep learning-based system for the early detection of tomato leaf diseases using a Convolutional Neural Network (CNN) based on the VGG16 architecture. A total of 16,211 tomato leaf images, comprising healthy leaves and nine disease classes, were used for the study. The images were resized, normalized, and enhanced through data augmentation techniques such as rotation, flipping, shifting, and zooming. The model achieved its highest validation accuracy of 96.8% at epoch 16, while the final validation accuracy at epoch 20 was 94.30%, demonstrating strong performance in tomato disease classification.

Why it matches plant phenotyping methodsトマト葉の病徴を画像からCNNで分類する手法を開発・評価しており、植物の病害状態を直接推定する方法が研究の中心である。

abstractThis study developed a deep learning-based system for the early detection of tomato leaf diseases using a Convolutional Neural Network (CNN) based on the VGG16 architecture.

An Intelligent CNN-Based System for Automated Crop Disease Diagnosis and Farmer Assistance →

Agriculture constitutes a foundational pillar of the Indian economy, yet crop diseases remain one of the most persistent threats to agricultural productivity, particularly for smallholder farmers who lack immediate access to plant pathology expertise. To bridge this critical gap, the present work proposes Smart Crop Doctor, an intelligent web-based platform that leverages Artificial Intelligence to perform automated detection of crop diseases. Within this framework, users submit photographs of plant foliage, which are subsequently analyzed by a trained Convolutional Neural Network (CNN) capable of recognizing pathological conditions across 15 distinct disease categories spanning tomato, potato, and bell pepper cultivars. A dedicated input validation mechanism is incorporated to ascertain whether a submitted photograph genuinely depicts leaf tissue, thereby filtering out extraneous objects such as rocks or paper-based documents. Upon successful identification, the platform furnishes comprehensive output including disease characterization, recommended treatment protocols, and guidance on both organic and chemical fertilizer application, in addition to broader agronomic advisory content. Beyond disease diagnosis, the system integrates a suite of ancillary services: a%, confirming that the system delivers dependable performance suited to practical deployment in agricultural settings.

Why it matches plant phenotyping methods植物葉の画像から病害状態をCNNで推定する診断システムが研究の中心であり、植物病害フェノタイピング手法・プラットフォームに該当する。

abstractthe present work proposes Smart Crop Doctor, an intelligent web-based platform that leverages Artificial Intelligence to perform automated detection of crop diseases.

Hard Attention Mechanism Integrated with VGG16 for High-Precision Tomato Leaf Disease Classification →

Abstract Tomato diseases represent a major threat to agricultural productivity and global food security. Early and accurate identification of these diseases is essential for effective crop management and yield preservation. In this study, we propose a hard attention–enhanced VGG16 model for automatic tomato leaf disease identification. By integrating a hard attention mechanism into the VGG16 architecture, the proposed model is able to focus on the most informative regions of the input images, thereby improving its capability to discriminate between healthy and diseased tomato leaves. The proposed approach is evaluated using a publicly available dataset of tomato leaf images. Experimental results demonstrate that the hard attention–based VGG16 model significantly improves classification performance compared to the standard VGG16 architecture and several existing deep learning models. In particular, the proposed model achieves an accuracy of 96.0%, outperforming the baseline VGG16 model, which achieves 92.3% accuracy on the same dataset. Furthermore, the integration of the attention mechanism enhances the interpretability of the model by highlighting the image regions that contribute most to the prediction process. This explainability provides valuable insights into the model’s decision-making process, making it a reliable and transparent tool for intelligent plant disease identification systems.

Why it matches plant phenotyping methodsトマト葉の病徴を画像から識別する注意機構付き分類手法を開発し、既存モデルとの性能比較・検証を行っており、植物病害状態のフェノタイピング手法が中心である。

abstractwe propose a hard attention–enhanced VGG16 model for automatic tomato leaf disease identification.

Deep Learning-Based Plant Disease Detection and Pesticide Recommendation System for Smart Agriculture →

Agriculture is an essential part of the worldwide economy, and initial detection of crop disease is essential to avoid substantial yield reduction. Conventional approaches of disease detection are primarily completed manually by specialists, which is expensive and frequently requires human errors. This survey aims to introduce an intelligent deep learning model to identify crop disease and suggest pesticides. This model is established on Convolutional Neural Networks (CNN) and uses the idea of transfer learning to sort the disease from the leaves of crops such as tomato and pomegranate. The proposed model works on the rule of image classification and is accomplished through image preprocessing, feature extraction, and classification employing the pre-trained model MobileNetV2. Once the disease is detected, it is mapped to the dataset.

Why it matches plant phenotyping methods作物葉の画像から病害を分類する深層学習手法が中心で、植物の病害状態を直接推定しているため収載。農薬推薦も含むが、病害検出モデル自体が主要な技術的貢献である。

abstractThis survey aims to introduce an intelligent deep learning model to identify crop disease and suggest pesticides.

EpiReasoner: An Integrated Artificial Intelligence Framework for Phenotype-to-Genotype Reasoning in Plant Epidermal Development →

Achieving high-throughput and precise phenotypic quantification and imaging modalities of stomatal and epidermal cells across diverse species remains a primary bottleneck in elucidating the mechanisms of stomatal dynamics, epidermal patterning, and environmental adaptation of plants. Here, we developed EpiReasoner, an artificial intelligence framework comprising a vision module, EpiVision, and a knowledge-based reasoning module, EpiBrain, for the quantitative phenotypic analysis and domain-specific knowledge reasoning of stomatal complexes and pavement cells in plants. Operating across bright-field, scanning electron microscopy, and differential interference contrast modalities, EpiVision achieves precise instance segmentation in various monocotyledonous, dicotyledonous, and fern species. Its performance significantly surpasses current state-of-the-art models. Moreover, we defined 23 quantitative indices describing stomatal cell morphology and spatial distribution. For domain-specific tasks such as phenotype prediction, genotype deduction, and molecular mechanism reasoning, EpiBrain demonstrates a human preference rate significantly higher than that of general-purpose large language models, including GPT-5 and Claude Sonnet 4. The application of EpiReasoner to phenotypic data of stomatal density derived from a tomato natural population of 170 accessions successfully identified a major quantitative trait locus on chromosome 8. The candidate gene, SKP1-interaction partner 19L ( SKIP19L ), encoding an F-box family protein, exhibited severe allele frequency drift during tomato domestication, which is highly consistent with the adaptive trend of reduced stomatal density under artificial selection. EpiReasoner provides a novel paradigm that unifies visual phenomics and knowledge-driven reasoning for the biology of stomata and pavement cells, thereby significantly accelerating scientific discovery in plant science.

Why it matches plant phenotyping methods植物の気孔・表皮細胞を対象に、画像解析と知識推論を統合したフェノタイピング手法を開発しており、形態・空間分布の定量化が中心的な貢献である。

abstractwe developed EpiReasoner, an artificial intelligence framework comprising a vision module, EpiVision, and a knowledge-based reasoning module, EpiBrain, for the quantitative phenotypic analysis and domain-specific knowledge reasoning of stomatal complexes and pavement cells in plants.

Tomato Ripeness Detection and Localization Based on the Intelligent Inspection Robot Platform. →

The field inspection and ripeness detection of tomatoes in China remain heavily dependent on manual labor, while existing robotic solutions often exhibit limited functionality, poor environmental adaptability, prohibitive hardware costs, and unstable positioning accuracy. To address these limitations, this study proposes an intelligent tomato inspection robot that seamlessly integrates real-time ripeness recognition with precise spatial localization. Built upon a Raspberry Pi 5 core controller, the robot employs a lightweight, layered modular architecture designed to flexibly navigate complex agricultural environments. A comprehensive, multi-dimensional image dataset of tomato ripeness was constructed to train a three-category detection model based on the YOLOv8n architecture. Following 413 training epochs, the model demonstrated exceptional performance, achieving an overall mAP@0.5 of 87.8% and an mAP@0.5:0.95 of 72.7% on the held-out test dataset. In field inspections, the system achieved detection precisions of 82.22% for immature tomatoes, 92.66% for half-ripened tomatoes, and 100% for fully ripe tomatoes, successfully identifying all ripe tomatoes and satisfying the practical demands of field inspection. Furthermore, the integration of an Ultra-Wideband positioning system yielded an overall Root Mean Square Error of 0.231 m, successfully confining positioning errors to within 0.24 m to fully satisfy the stringent localization demands of crop-level inspection. Field evaluations confirmed that under optimal configurations, the robot can efficiently inspect a 50-m planting row in 10 min (±1 min) and maintains a continuous operational battery life of 2 h (±10 min). The core contribution of this work is the system-level integration and optimization of technologies for greenhouse agriculture. This integrated design achieves low hardware cost and high deployment flexibility, addressing longstanding challenges of labor-intensive inspection and delayed harvesting, and delivering a practical solution for intelligent tomato plantation management.

Why it matches plant phenotyping methodsトマト果実の成熟状態を画像から推定するモデルと、データセット・ロボット検査プラットフォームを開発・評価しており、植物状態の取得方法が中心的です。

abstractThe core contribution of this work is the system-level integration and optimization of technologies for greenhouse agriculture.

Deep convolutional models for robust multi-crop disease recognition in real-world conditions. →

Crop diseases significantly reduce agricultural output and are a serious problem, especially in the parts of the world where diagnostic experts are not readily available. Deep learning has recently shown us that it is possible for a computer to identify plant diseases directly from images of the leaves. Nevertheless, to make such solutions available on the web or mobile devices one has to really think about how heavy the calculations will be, how easy the user interface should be, and also the limit on the data used. Here is a paper on a web-based applied deep learning system for disease detection in multiple crops. The system detects disease in eight crops Apple, Banana, Grape, Mango, Cauliflower, Tomato, Potato, and Corn with each crop having several disease classes and healthy samples. Three transfer-learning-based CNN architectures MobileNetV3, EfficientNetB4, and ResNet50 were compared for classification performance on the public datasets collected from PlantVillage, Kaggle, and Mendeley. Considering class-wise accuracy, prediction time, and deployment scenarios, MobileNetV3 was picked as the main model to be integrated into the system. To compensate for the differences in image quality often found in pictures taken by users, an optional super-resolution preprocessing step with Real-ESRGAN is added and quantitatively assessed. Disease prediction with spectral activation maps (Grad-CAM) enhances the model's interpretability by highlighting image areas where the disease is detected. The resulting model is embedded in a multilingual Progressive Web Application (PWA). The platform enables users to submit their crop images and receive predicted disease names and treatment options, which are generated by a Large Language Model (LLM) using structured disease metadata. The research acknowledges dataset bias and limitations in extrapolating from curated datasets to the general real-world setting although it reports very good performance of the method on the test sets. In summary, the system proposed here is intended as a practical digital agriculture decision-support tool that demonstrates deployment feasibility and raises a few issues for future validation at the field level and improvement.

Why it matches plant phenotyping methods葉画像から植物病害を推定するCNN手法の比較・前処理評価・実装を中心とした研究であり、植物の病害状態を直接評価するため、植物フェノタイピング手法として中心的です。

abstractDeep learning has recently shown us that it is possible for a computer to identify plant diseases directly from images of the leaves.

Multi-scale thermal homeostasis: Plants achieve temperature control through hierarchical regulation →

Abstract Temperature fundamentally impacts plants growth and physiology. However, the mechanisms by which plants sense and response to environmental changes remain unclear due to the lack of effective methods for measuring internal plant temperatures. Here, by combining lab-made nanothermometric probes with time-gated imaging technique, we accurately detected the change in internal plant temperature in response to environmental temperature variations. We discovered a multilevel temperature regulation mechanism during the process by which plants establish thermal homeostasis. In Nicotiana benthamiana leaves, when environment temperature changes from approximately 24°C to 45°C, the maximum of internal plant temperature change is only approximately 10°C near cell wall, and less than 7°C in cytoplasm, while remaining nearly constant in chloroplasts (ΔTchl ≈ 1°C). Similar compartment-specific thermal regulation was observed in Arabidopsis thaliana and tomato, indicating that hierarchical regulation represents a conserved strategy for maintaining internal temperature stability in plants. Together, these findings provide direct evidence for multiscale thermal homeostasis in plants and establish a framework for understanding how cellular and subcellular organization contributes to temperature regulation.

Why it matches plant phenotyping methodsナノ温度計プローブと時間ゲート imaging により植物内部温度を測定する手法が研究の中心であり、植物の生理状態を直接定量している。

abstractby combining lab-made nanothermometric probes with time-gated imaging technique, we accurately detected the change in internal plant temperature

Macronutrient deficiency reduces growth and influences vegetation indices of greenhouse grown ornamental and vegetable plants as measured by the TraitFinder digital phenotyping system. →

Introduction: Recent technological advances in high resolution image capture and analysis have led to increased adoption of high-throughput digital phenotyping in plant science research. High-throughput digital phenotyping provides a nondestructive method to quantify changes in plant growth and health in response to environmental factors or developmental cues. Moreover, it allows researchers to conduct large experiments in a time- and cost-efficient manner. The TraitFinder is a digital phenotyping system developed by Phenospex (Heerlen, Netherlands) that measures plant morphological (e.g., digital biomass) and spectral (leaf light reflectance) information. Leaf reflectance is presented as five vegetation indices (e.g., normalized difference vegetation index). Methods: This project evaluated nitrogen (N), phosphorus (P), and potassium (K) deficiency in greenhouse grown ornamental and vegetable plants using the TraitFinder. Plant species included celosia, coleus, marigold, petunia, and tomato. Plants were fertilized with a complete Hoagland's solution (control), and three modified solutions: Hoagland's solution without nitrogen (-N), phosphorus (-P), or potassium (-K). Each plant species was evaluated separately with eight replicate plants per treatment, organized as a randomized complete block design. Results: Treatment with -N, -P, and -K solutions resulted in reduced vegetative growth and decreased concentration of the corresponding macronutrient in leaf tissue for all species evaluated. We observed that the presence of flowers would negatively affect calculations of the vegetation indices due to their distinct spectral properties; therefore, flowers must be excluded to accurately quantify plant health parameters. In general, we observed a common trend where GLI (green leaf index) and NDVI (normalized difference vegetation index) decreased, and NPCI (normalized pigment chlorophyll index) and PSRI (plant senescence reflectance index) increased in response to macronutrient deficiency. The measure of GLI, NDVI, NPCI, and PSRI were different from the control plants, but these observations were dependent on the nutrient deficiency and species tested. Discussion: Our results underscore the importance of accounting for species-specific spectral signatures when assessing plant responses to nutrient deficiencies. This project also provides reference values for interpreting vegetation indices, offering valuable guidance for scientists implementing digital phenotyping in their experimental protocols. Digital phenotyping can significantly improve experimental throughput and provide quantitative insights into plant health.

Why it matches plant phenotyping methodsTraitFinderによる形態・スペクトル形質の取得と、花の除外や種特異的スペクトルへの対応を含むデジタルフェノタイピングの実質的な適用・評価が中心である。

abstractThe TraitFinder is a digital phenotyping system developed by Phenospex (Heerlen, Netherlands) that measures plant morphological (e.g., digital biomass) and spectral (leaf light reflectance) information.

Proactive Irrigation Timing Decision-Making for Greenhouse Tomatoes via STL-LSTM Deep Learning and Plant–Soil Dual-Threshold Sensing →

Traditional irrigation management for tomatoes in solar greenhouses relies heavily on empirical manual experience and single soil moisture indicators, often leading to irrigation scheduling that lacks crop-specific physiological evidence and results in suboptimal water-use efficiency. To address these challenges, this study developed an intelligent, plant-centric irrigation decision-making framework for greenhouse tomatoes in the arid region of Xinjiang. Central to this framework is the precise identification of irrigation timing—the most critical first step and a fundamental prerequisite for achieving true on-demand irrigation. By monitoring the high-frequency dynamics of stem diameter (SD) and integrating soil moisture data, the physiological responsiveness of tomatoes to water stress was systematically analyzed. A hybrid predictive model, STL-LSTM, was constructed by coupling Seasonal-Trend decomposition using Loess (STL) with Long Short-Term Memory (LSTM) networks to forecast 24-h SD trends. Furthermore, an innovative dual-threshold irrigation mechanism was established, utilizing a physiological trigger (Maximum Daily Shrinkage, MDS > 70 μm) and a soil moisture constraint (Volumetric Water Content, VWC ≤ 17%). Results demonstrated that tomato SD exhibited distinct diurnal rhythms, with MDS and Daily Increment (DI) identified as highly sensitive indicators of plant water status. The proposed STL-LSTM model achieved superior predictive performance during the peak fruiting stage, with a coefficient of determination (R2) of 0.9184, representing an improvement of 14.8% and 27.56% over standalone LSTM and ARIMA models, respectively. The validation of the dual-threshold mechanism confirms its ability to balance real-time crop water demand with conservation requirements, effectively mitigating the risks of premature or delayed irrigation inherent in traditional methods. This research provides scientific rationale and technical support for the transition of greenhouse agriculture in arid regions towards precision irrigation and optimised water resource management.

Why it matches plant phenotyping methodsトマト茎径を植物の水分状態指標として高頻度センシングし、STL-LSTMによる予測と二重閾値の検証を行うことが中心であり、単なる灌漑実験ではない。

abstractCentral to this framework is the precise identification of irrigation timing—the most critical first step and a fundamental prerequisite for achieving true on-demand irrigation.

Image-Based Analysis for Identification of Plant Leaf Pathologics Using Deep Learning →

This project introduces a Convolutional Neural Network (CNN) as the proposed system for plant disease prediction, with a comparative analysis conducted against two existing models: Recurrent Neural Networks (RNN_GRU) and Artificial Neural Networks (ANN_MLP). The proposed CNN model is specifically designed to address the limitations of traditional approaches, such as lower accuracy and slower prediction times, particularly when handling complex image data. The system allows users to upload images of plant leaves, select the type of plant (e.g., potato, tomato, grape), and choose between RNN_GRU, ANN_MLP, or the newly developed CNN model for disease prediction. Additionally, users can run all three models simultaneously to compare their outputs, enabling a comprehensive evaluation of performance. Predictions are securely stored in a SQLite database, along with metadata such as confidence scores, prediction times, timestamps, and a unique group ID for efficient retrieval and management. Built using Flask, the application provides a professional-grade user interface with features like secure authentication, prediction history tracking, and deletion of past predictions. Comparative analysis demonstrates that the proposed CNN model significantly outperforms RNN_GRU and ANN_MLP in terms of accuracy, prediction speed, and overall reliability, making it a more effective tool for real-time agricultural applications. This advancement highlights the potential of CNNs in transforming agricultural practices by providing faster, more accurate, and reliable disease predictions, thereby contributing to improved crop health, reduced losses, and increased agricultural productivity.

Why it matches plant phenotyping methods植物葉画像から病害状態を推定するCNN手法を開発し、既存モデルとの精度・速度比較を行っているため、植物フェノタイピング手法が中心である。

abstractThis project introduces a Convolutional Neural Network (CNN) as the proposed system for plant disease prediction

Enhancing Crop Health and Early Detection of Tomato Leaf Diseases Using Deep Learning Techniques →

Background/Objectives: One of the most widely produced and consumed crops in the world, tomatoes are often threatened by various leaf diseases, including early blight, late blight, and leaf mold, which can result in significant yield losses if not detected and managed promptly. Due to reliance on manual inspection, tomato leaf diseases are often detected late, resulting in significant crop loss. The goal of this research is to develop a deep learning (DL) model that accurately classifies tomato leaf diseases. The model is trained using supervised learning on the PlantVillage dataset, which includes labelled images of tomato leaves under different conditions. Method: Gaussian Blurring-Gaussian Mixture Models (GB-GMM) are a preprocessing method used to enhance the image quality. While EfficientNet and VGGNet architectures are utilised for accurate classification, data augmentation is used to boost model robustness. A standard train-validation-test split is used to assess the model. Findings: The results demonstrate that the proposed method, implemented in Python, performs better than the others in terms of accuracy (94.3%), precision (93.7%), recall (92.5%), and F1-score (93.1%) in VGGNet architectures. These results indicate that the proposed model is very effective overall and produces balanced predictions. In the future, the system may be integrated with drone and IoT technologies for automatic disease warnings and real-time field surveillance. Novelty: The Multivariable Grey Prediction Evolution Algorithm (MGPEA) is included for illness trend forecasting to enhance predictive power further. This technology facilitates large-scale, sustainable agricultural management, minimizes human inspection, and enables prompt disease response. Keywords: Tomato Leaf Diseases, Crop Health, Gaussian Mixture Models, Multivariable Grey Prediction Evolution Algorithm, EfficientNet, VGGNet

Why it matches plant phenotyping methodsトマト葉画像から病害状態を分類する深層学習手法の開発・評価が研究の中心であり、植物の病害表現型を直接推定している。

abstractThe goal of this research is to develop a deep learning (DL) model that accurately classifies tomato leaf diseases.

Research on the Application of Agricultural Big Data in Plant Growth Prediction →

The intelligent transformation of agriculture places plant growth prediction as a critical component for ensuring food security, optimizing resource allocation, and enhancing sustainable productivity. Traditional methods reliant on empirical or simplified mechanistic models struggle with the nonlinearity, high dimensionality, and spatiotemporal heterogeneity inherent in agro-ecological systems. This study investigates the paradigm shift enabled by agricultural big data integrating multi-source, real-time streams from IoT sensors, satellites, UAVs, and farm management systems. We propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically couples process-based crop models with ensemble machine learning algorithms---including Transformer-based architectures and Physics-Informed Neural Networks---within a holistic pipeline encompassing multi-modal data fusion, hybrid modeling, and scalable deployment. Empirical validation across major crops (rice, wheat, maize, tomato) in diverse eco-regions of China (2023--2025) demonstrates significant improvements: the MDA-HI model achieved average RMSE reductions of 42.7% for yield prediction and 38.1% for key phenological stage prediction relative to best-in-class standalone models. A large-scale case study on rice-wheat rotation systems showed that data-driven prescriptions reduced nitrogen fertilizer use by 22.5% and irrigation water by 18.3% while increasing yield by 5.1%. The study further establishes a five-dimensional evaluation system covering accuracy, robustness, interpretability, scalability, and economic benefit. Remaining challenges include edge computing for real-time inference, federated learning for privacy-preserving collaboration, and explainability of complex ``black-box'' models. This research concludes that agricultural big data constitutes a foundational catalyst for predictive, precise, and proactive cognitive agriculture, with profound implications for global food system resilience.

Why it matches plant phenotyping methods農業ビッグデータを用いて生育・収量・フェノロジーを推定するMDA-HI手法を提案し、複数作物・地域で性能検証しており、植物形質推定手法が研究の中心である。

abstractWe propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically couples process-based crop models with ensemble machine learning algorithms---including Transformer-based architectures and Physics-Informed Neural Networks---within a holistic pipeline encompassing multi-modal data fusion, hybrid modeling, and scalable deployment.

Dual-Path Deep Learning and Hierarchical Spectral Clustering: A Versatile Framework for Semantic and Instance Segmentation of Plant Point Clouds →

Accurate plant organ segmentation is essential for high-throughput phenotyping and ideotype selection. However, current methods struggle with plants of complex morphology, particularly small organ categories with sparse point distributions. In addition, severe leaf adhesion in dense canopies often hinders reliable leaf instance segmentation using conventional clustering methods. To address these challenges, we propose a dual-path fusion network (DPFuseNet) for semantic segmentation and a hierarchical multi-scale spectral clustering algorithm (HMSC) for instance segmentation of plant point clouds. DPFuseNet introduces three innovations: a high-frequency information embedding strategy, a dual-path feature extraction module integrating CNN and Transformer branches, and a cross-attention–based dual-granularity feature fusion block. Evaluated on tomato, cabbage, and soybean datasets, DPFuseNet achieved superior performance over state-of-the-art baselines such as Stratified Transformer and Point Transformer v3, reaching average precision, recall, F1-score, and IoU of 96.51%, 96.27%, 96.38%, and 93.32%, respectively. Compared with the current leading single-branch model Point Transformer v3, DPFuseNet improves these metrics by 1.19%, 1.20%, 1.21%, and 2.05%, and by 0.89%, 1.14%, 1.02%, and 1.70% over the dual-branch model PVDST. For instance segmentation, the proposed HMSC algorithm, combined with region growing, achieved mPrec 89.65%, mRec 78.70%, mCov 76.88%, and mWCov 85.11% on multi-stage tomato, cabbage, and soybean datasets, consistently outperforming conventional spectral clustering. Overall, the proposed framework demonstrates robustness and efficiency in both semantic and instance segmentation, offering a novel pathway for advancing plant point cloud analysis and smart agriculture.

Why it matches plant phenotyping methods植物点群から器官の意味・個体分割を行う深層学習およびクラスタリング手法の開発と評価が研究の中心であり、植物表現型解析への直接的な応用を示している。

abstractAccurate plant organ segmentation is essential for high-throughput phenotyping and ideotype selection.

Fungal infections: Classification performance and detectability with machine learning models. →

This study explores the use of AI-driven machine learning algorithms for the early detection of fungal diseases in tomato plants, a method that enhances diagnostic accuracy and enables more effective crop protection. The research was conducted in 2023 in Shandong Province, China, using two tomato cultivars - Dongfeng-1 and Gypsy. Ten greenhouse plots (2,000 m² each) and ten open-field plots (5,000 m² each) were studied, with image data collected via high-resolution cameras, multispectral sensors, and microclimate sensors, yielding approximately 20,000 annotated images. Five machine learning models were tested: convolutional neural networks (CNNs), random forests, gradient boosting, support vector machines (SVMs), and k-nearest neighbors (KNN). CNNs demonstrated superior accuracy in both greenhouse (95.2% ± 0.3) and open-field plots (92.5% ± 0.5), with corresponding AUC-ROC values of 0.96 and 0.93 (p = 0.001). The false positive rate for CNNs was 4.1% in greenhouses and 5.3% in open-field plots, while diagnostic time was shorter in greenhouses (8.3 s vs. 10.5 s). Compared to visual inspection, CNNs significantly improved diagnostic accuracy and reduced fungicide use. To ensure robustness, the models were evaluated under varying lighting and microclimate conditions. Assessments on both GPU and CPU platforms demonstrated the model's feasibility for deployment on edge devices and cloud-based systems.

Why it matches plant phenotyping methodsトマト植物の真菌病を画像・センサー観測から推定する機械学習手法を開発・比較し、精度、頑健性、計算環境で評価しており、病害状態の表現型取得が中心である。

abstractThis study explores the use of AI-driven machine learning algorithms for the early detection of fungal diseases in tomato plants

Parameters calibration and experimentation of a discrete element model for tomato stems →

Abstract The effective optimization of tomato pruning robots was hindered by the lack of accurate simulation models for the shearing process of tomato stems and precise calibration and optimization methods for bonding parameters to predict shearing force. This paper proposed a simulation model along with a bonding parameter calibration method. Taking shearing force as the evaluation metric, a two-level factorial experiment was conducted to screen for significant parameters. A steepest ascent experiment was employed to determine the optimal range of these significant parameters. Then a Box-Behnken design was implemented, and the optimal combination of bonding parameters was derived based on the established regression model. Finally, comparative experiments were conducted to validate the simulation's shearing performance under this optimal parameter set. The results show that the optimal combination for the tomato stem model bonding parameters was a normal stiffness x 3 = 2.05×10⁸ N·m⁻³, tangential stiffness x₈=1.62×10⁸ N·m⁻³, and a bonding radius x₂₁=3.26×10⁻⁴ m. The optimized model reduced the shearing force simulation error by 75.8 and 43.7 percentage points compared to the traditional and pre-optimization models. These results demonstrate that the calibrated parameters of the simulation model are accurate and reliable. It can provide valuable parameters for optimizing the design of a tomato pruning robot.

Why it matches plant phenotyping methodsトマト茎のせん断力を推定する離散要素シミュレーションモデルと結合パラメータ校正法を開発し、実験で性能検証しており、植物器官の測定・推定手法が中心である。

abstractThis paper proposed a simulation model along with a bonding parameter calibration method.

Boundary-Refined DeepLabV3+ for Crop Disease Detection in Greenhouse Vegetable Images →

Accurate pixel-level delineation of crop disease in greenhouse images is challenging due to weak lesion margins, scale variations, leaf-vein interference, specular highlights and partial occlusion. This paper introduces a boundary-refined DeepLabV3+ model that keeps the encoder-decoder efficiency of the original framework and adds a boundary supervision branch, uncertainty-aware cross-scale fusion, and an adaptive refinement gate. The network is tested on a set of 6,840 curated greenhouse images that include leaves of tomatoes, cucumbers, peppers and eggplants, as well as 8 disease categories and healthy tissue. Under a fixed split by greenhouse compartment, the proposed model has achieved a mean intersection over union of 89.7%, a mean F1 score of 94.8%, and a boundary F1 of 86.9% at a two-pixel tolerance. The corresponding values are 3.8, 2.4 and 7.6 percentage points higher than those of the standard DeepLabV3+. The mean Intersection over Union (IoU) under low-illumination and condensation-blur conditions are 3.1 and 2.7, respectively. Ablation studies show that boundary supervision is responsible for most of the contour improvement, and uncertainty-aware fusion reduces false lesion expansion along veins. The model has 31.6 million parameters and, after mixed-precision optimisation, runs at 18.7 frames per second on an embedded graphics chip. Based on the above results, explicit boundary reasoning can improve the precision of disease-area estimation without sacrificing the efficiency required in practice; it is thus suitable for greenhouse scouting, targeted spraying and longitudinal severity assessment.

Why it matches plant phenotyping methods温室画像から作物病斑の画素レベル境界と病害面積を推定する画像解析手法を開発・検証しており、植物病害状態の表現型取得が中心である。

abstractThis paper introduces a boundary-refined DeepLabV3+ model that keeps the encoder-decoder efficiency of the original framework and adds a boundary supervision branch, uncertainty-aware cross-scale fusion, and an adaptive refinement gate.

Embedding of ripening topology into one-stage detection for tomato cluster phenotyping. →

The automated assessment of tomato ripeness is vital for modern greenhouse operations, yet challenges remain due to variable environmental conditions. To provide a solution, we propose rank-aware You Only Look Once (YOLO), a novel detection framework that incorporates the biological prior of top-to-bottom ripening within fruit clusters. This is achieved through two key innovations: an efficient position-aware head for regressing relative height for fruits and a dynamic margin-aware ranking loss (DM-RankLoss) that enforces the correct spatial sequence. Evaluated on a 3500-image dataset from a solar greenhouse, our plug-and-play module could boost the mean average precision (mAP) at intersection over union (IoU) threshold of 0.50 (mAP 50 ) of multiple YOLO architectures by up to 5.66 pecentage points. The model effectively learns the cluster topology, achieving a height-mean absolute error (H-MAE) of 0.107 (normalized) and a pairwise ranking accuracy (PRA) of 84.59%, while it reduces the parameter count by over 10% compared to the baseline for efficient deployment. Visualizations confirm that the model leverages spatial context to resolve color ambiguities. Our work offers a sensor-free, accurate, and efficient solution for in situ phenotyping in agricultural robotics.

Why it matches plant phenotyping methodsトマト果実の熟度・クラスター内位置関係を推定するYOLOベースの画像解析法を開発し、データセットで性能評価しており、フェノタイピング手法が中心である。

abstractwe propose rank-aware You Only Look Once (YOLO), a novel detection framework that incorporates the biological prior of top-to-bottom ripening within fruit clusters.

Hybrid Optimized AgriShieldNet Framework for an Interpretable Multi‐Crop Leaf Disease Classification →

ABSTRACT Early detection of plant leaf diseases is essential for improving crop productivity, reducing financial losses, and enhancing sustainability in modern agricultural practices. However, most automated systems struggle to accurately capture diverse morphological variations of plant leaf diseases across multiple crop types, while manual inspection methods are time‐consuming, labor‐intensive, and prone to human error. To address these challenges, this paper proposes Hy‐OptiASNet, a hybrid optimized deep learning framework for interpretable multi‐crop leaf disease classification. The proposed framework utilizes EfficientNet‐B0 for robust spatial feature extraction; a Long Short‐Term Memory (LSTM) network is employed to model sequential feature dependencies and improve structured feature learning, followed by a Morphology‐Aware Vision Embedding (MAVE) module to enhance representation of fine‐grained morphological disease patterns. In addition, a novel Sandpiper Optimization Algorithm (SPOA) is incorporated for feature refinement and hyperparameter optimization, thereby improving generalization capability and classification stability. To enhance interpretability, visualization techniques are used to highlight biologically relevant disease regions, enabling improved understanding of model decisions. Extensive experiments were conducted on multi‐crop datasets consisting of apple, tomato, and grape leaf images. The experimental results demonstrate that the proposed Hy‐OptiASNet framework achieves classification accuracies of 98.98%, 98.21%, and 98.35% for apple, tomato, and grape datasets, respectively, outperforming several existing state‐of‐the‐art methods. These findings indicate that the proposed framework provides an effective and reliable solution for precision agriculture and real‐world plant disease monitoring applications.

Why it matches plant phenotyping methods植物葉の病徴を画像から分類する新規深層学習フレームワークの開発・評価が中心であり、植物の疾病状態を直接推定するため、植物フェノタイピング手法に該当します。

abstractthis paper proposes Hy‐OptiASNet, a hybrid optimized deep learning framework for interpretable multi‐crop leaf disease classification

An integrated framework to elucidate mechanisms underlying host-branched broomrape infection. →

Branched broomrape (Phelipanche ramosa) is an obligate root parasitic weed that threatens tomato production in many regions. Progress in understanding host resistance mechanisms has been hindered by the parasite's subterranean life cycle and the technical limitations of traditional soil-based assays. Here, we introduce an integrated experimental framework that enables molecular, genetic, and cellular analysis of broomrape parasitism in tomato under controlled conditions. We implemented a transparent, soil-less co-cultivation system for non-destructive, real-time monitoring of broomrape development on tomato roots, and a dual-compartment in vitro co-culture system supporting parasite infection of transgenic hairy roots. This methodology enabled rapid functional testing of candidate host resistance genes, exemplified by CRISPR-edited mutants of the tomato transcription factor SCHIZORIZA (SlSCZ), which displayed localized lignin accumulation at the parasite entry site in the root. The observed lignification suggests a role for this gene in regulating inducible cell wall lignification against broomrape. Together, these tomato-focused integrated methods enable reproducible imaging, genetic perturbation, and high-resolution analysis of host-parasite interfaces. These provide a scalable platform for dissecting broomrape resistance and accelerating resistance gene discovery in tomato and a critical tool for combating the devastating consequences of this parasite on agriculture.

Why it matches plant phenotyping methodsトマト根上の寄生進展を非破壊・リアルタイムに観察する共培養系と再現可能なイメージングを開発し、植物の感染状態を取得する基盤が研究の中心である。

abstractWe implemented a transparent, soil-less co-cultivation system for non-destructive, real-time monitoring of broomrape development on tomato roots

PHENOTYPIC CHARACTER EXTRACTION OF TOMATO PLANT BASED ON 3D POINT CLOUD DATA →

To address the issue of 3D reconstruction information loss caused by occlusion during single-view camera acquisition of crop phenotypic parameters, this study proposes a detection method for tomato plant phenotypic parameters based on multi-view 3D point cloud reconstruction. The Kinect 2.0 sensor was employed to acquire point cloud data of tomato plants from three different viewpoints. Background noise was effectively removed using a combination of Conditional Filtering and Statistical Outlier Removal methods. By extracting surface normal features and calculating Fast Point Feature Histograms (FPFH), the Sample Consensus Initial Alignment (SAC-IA) and Iterative Closest Point (ICP) algorithms were utilized to accomplish coarse and accurate registration of the point clouds, respectively, ultimately achieving 3D reconstruction. Experimental results demonstrated that the reconstructed 3D model of the tomato plant was clear in outline and complete in structure. For the phenotypic parameters of plant height, canopy width, and leaf angle, the coefficients of determination (R²) between the calculated and manually measured values were 0.98, 0.94, and 0.89, respectively, with Root Mean Square Errors (RMSE) of 0.75 cm, 1.10 cm, and 4.43 °. Compared to single-view measurements, the accuracy of plant height and maximum canopy width derived from multi-view reconstruction increased by 15.31% and 13.12%, respectively. This method provides technical support for the rapid and accurate extraction of phenotypic parameters in tomato plants.

Why it matches plant phenotyping methodsトマトの草丈、キャノピー幅、葉角を抽出するマルチビュー3D点群再構成法を開発し、手動測定との精度検証も行っており、植物表現型取得が研究の中心である。

abstractthis study proposes a detection method for tomato plant phenotypic parameters based on multi-view 3D point cloud reconstruction.

Machine learning-enabled implantable plant biomarker sensor for early detection and classification of acid and salt stress. →

Abiotic stresses, particularly acid and salt stress, severely limit plant productivity. Conventional detection is often hindered by physiological lags and phenotypic latency. Here, we develop a machine learning-enabled implantable plant biomarker sensor (MLIPBS) for early stress diagnosis. Featuring a foldable design, MLIPBS enables conformal integration into plant tissues for continuous monitoring of H 2 O 2 , K + , and pH. We confirm the robust sensing capabilities and favorable biocompatibility of MLIPBS through cross-species validation in lettuce, tomato, and Aloe vera. Additionally, leveraging the LightGBM architecture, we demonstrate that MLIPBS successfully classifies combined stress conditions and varying intensity levels of acid and salt stress, achieving an average accuracy of 90.5%. We further show that the system identifies stress types and intensities within 8 hours of onset, providing an early-warning window at least 48 hours before symptom manifestation. Our study provides reliable wearable tools for stress-resistant crop screening and precision management in smart agriculture.

Why it matches plant phenotyping methods植物組織内の生体指標を連続測定し、ストレスの種類・強度を分類するセンサーと機械学習システムの開発・検証が中心であり、植物ストレス状態のフェノタイピング手法に該当する。

abstractHere, we develop a machine learning-enabled implantable plant biomarker sensor (MLIPBS) for early stress diagnosis.

Predicting water status, growth and yield of tomato under different irrigation regimes using the RGB image indices and artificial neural network model. →

Water stress is a global challenge that severely impacts crop production by hindering essential physiological processes. To address this issue, proximal sensing has emerged as a promising technique for the early identification of stress in vegetables, enabling timely management interventions and optimizing yield. This study aimed to use RGB image indices and an artificial neural network (ANN) model to quantify the responses of various plant traits, such as fresh biomass (FB) weight, dry biomass (DB) weight, canopy water content (CWC), relative chlorophyll content (SPAD), soil moisture content (SMC), and tomato yield across different irrigation levels. Field experiments were conducted during the 2022 and 2023 growing seasons, capturing digital RGB images and measuring plant traits at the flowering and fruit-ripening stages. The results revealed that a reduced irrigation level led to a decrease in various plant traits. The study also revealed significant differences in RGB image indices between different irrigation levels, with strong positive relationships identified for the majority of RGB image indices incorporating green components (G) and R2 reaching 0.99 for various plant traits. However, the red-blue simple ratio (RB) index, which does not consider the G, did not significantly correlate with any of the plant traits. The ANN models achieved high prediction accuracy, with high R2 values reaching 0.99 for various plant traits and yields. These findings underscore the practicality and reliability of employing RGB imaging indices in conjunction with ANN models for effectively managing tomato crop growth and production, particularly under limited water conditions.

Why it matches plant phenotyping methodsRGB画像指標とANNによる植物形質・収量の定量推定が研究の中心であり、予測精度も評価しているため、画像ベース形質推定の方法適用・検証に該当する。

abstractThis study aimed to use RGB image indices and an artificial neural network (ANN) model to quantify the responses of various plant traits

Quantifying the dynamic recovery of plants through stress memory and physiological attractors. →

Climate change-induced weather variability poses a growing threat to global food security, yet plant resilience is still interpreted through static and reductionist models that treat stress as independent and transient. Here, we introduce a unified quantitative framework grounded in dynamical systems theory. We formalize three novel metrics: (1) the Phenological Weather Memory Index (PWMI), that quantifies exponentially decaying stress memory across developmental stages (with a decay constant α = 0.10 determined by cross‑validation); (2) the Treatment-Weather Resonance Coefficient (TWRC), which measures the alignment of agronomic interventions with favorable weather conditions; and (3) the Physiological State-Space Trajectory (PSST), which maps multi-trait plant physiology into low dimensional attractor basins. Analyzing 288 tomato plants across 24 cultivars under hot, sub-tropical conditions (mean VPD: 2.53 kPa, 65 heat days > 35 °C), we discovered that stress memory is strongly phase-dependent, remaining minimal during vegetative growth (PWMI = 0.009) but increasing sharply during reproductive phase (PWMI = 0.574). Despite the prolonged thermal stress, 97.9% of plants converged into a stable high-yield attractor basin, revealing a fundamental nonlinearity in plant performance. This convergence was driven by dynamic recovery, defined as the capacity of certain cultivars to rapidly forget the stress memory while maintaining internal physiological flexibility. Cultivars such as 'Pony Express', combined low PWMI with effective treatment-weather synchronization, enabling stable productivity under extreme conditions. Together, these results demonstrate that resilience is not a static trait of endurance, but an emergent property arising from temporal synchronization, rapid stress recovery and stable physiological organization. By quantifying stress "forgetting curves" and attractor dynamics, this framework provides a predictive, systems-based foundation for breeding and management strategies that prioritize dynamic recovery over stress tolerance alone.

Why it matches plant phenotyping methods植物のストレス記憶・回復・生理状態を定量化する新規指標と状態空間フレームワークを中心に提示しており、単なる生理測定ではなく表現型抽出・解析手法の開発に該当する。

abstractHere, we introduce a unified quantitative framework grounded in dynamical systems theory.

ICAG ‐Net: An Interactive CNN –Transformer Architecture With Attention‐Guided Gated Fusion for Crop Disease Detection →

ABSTRACT Smart agriculture based on the use of Artificial Intelligence for crop disease detection to ensure food security. Fungal disease is one of the major causes that affects the quality of the vegetables. Convolutional neural networks (CNNs) and vision transformers (ViTs) enable the detection of crop diseases at an early stage, allowing farmers to take preventive measures and minimize further losses. The proposed method introduces an ensemble of Custom CNN to understand multilevel local features and Pretrained ViT to capture global dependencies as well as contextual information from the dataset. An interactive cross attention module (ICAM) facilitates bidirectional information exchange between CNN and transformer token representations, while an attention guided gated fusion (AGGF) mechanism adaptively combines complementary features. The Indian Crop Visual Disease Dataset (ICVDD‐5) has been developed in a real field for the proposed work with the help of domain experts. The dataset contains 880 diverse images depicting both healthy and diseased specimens of five vegetable crops. The crops selected for this research initiative include Brinjal, Cabbage, Chili, Okra, and Tomato. These five crops are examined for about 21 distinct disease classes. Comprehensive ablation studies are conducted to prove the contributions of each architectural component, including CNN‐only, ICAM‐disabled, and AGGF‐disabled configurations. Experimental results demonstrate that the proposed ICAG‐Net achieves a test accuracy of approximately 70%–73% with improved macro‐F1 score compared to baseline CNN models under identical training settings. The novelty of this work lies in an extensible solution for real world crop disease diagnosis systems and offers insights into hybrid CNN–transformer architectures for small scale agricultural datasets.

Why it matches plant phenotyping methodsCNN・Transformerによる植物病害症状の画像検出手法を開発し、実圃場画像データセットの構築、アブレーション、ベースライン比較で性能検証しているため、植物フェノタイピング手法が中心である。

abstractThe proposed method introduces an ensemble of Custom CNN to understand multilevel local features and Pretrained ViT to capture global dependencies as well as contextual information from the dataset.

YieldNet: A Near-Zero-Cost YOLOv8n Enhancement for UAV-Based Real-Time Green Tomato Detection to Support Pre-Harvest Yield Forecasting →

Abstract Accurate pre-harvest yield forecasting of greenhouse tomatoes is essential for reducing post-harvest losses, with reliable detection of immature green tomatoes being the core challenge. However, these fruits are small, heavily occluded, and chromatically highly similar to foliage, making real-time detection from low-altitude UAV imagery extremely difficult, while onboard edge processors impose stringent power and weight constraints. To address this, we propose YieldNet, an ultra-lightweight framework that introduces near-zero-overhead enhancements to vanilla YOLOv8n: the backbone is replaced with ShuffleNetV2 to strengthen small-object representation; Efficient Channel Attention (ECA) modules are embedded after the P3–P5 layers in the neck to suppress leaf-background interference; and PIoU v2 loss is adopted to refine bounding-box regression for densely overlapped fruits via size-adaptive and non-monotonic focusing mechanisms. The model is rigorously validated on both a self-collected real-world UAV dataset comprising 600 low-altitude green-tomato images and a public multi-ripeness benchmark. Compared with the YOLOv8n baseline, YieldNet achieves relative improvements in mAP@50-95, Recall, and F1-score by 18.9%, 6.1%, and 5.8%, respectively, on the large dataset, and enhances Recall, F1-score, and Precision by relative gains of 4.3%, 4.0%, and 3.8%, respectively, on the small dataset, while increasing parameters only from 3.0\,M to 3.3\,M and reducing FLOPs from 8.1\,G to 8.0\,G. This work provides an efficient, readily deployable solution for high-precision real-time detection of immature green tomatoes on UAV platforms, enabling reliable pre-harvest yield estimation.

Why it matches plant phenotyping methodsUAV画像から未成熟トマト果実を検出し、収量予測に用いるYOLOベースの画像解析手法を開発・複数データセットで検証しており、植物器官の表現型取得が中心です。

abstractwe propose YieldNet, an ultra-lightweight framework that introduces near-zero-overhead enhancements to vanilla YOLOv8n

Chlorophyll Fluorescence-Based High-Throughput Phenotyping Reveals Mechanisms and Enables Rapid Screening of Desiccation-Tolerant Wild Tomato Species. →

Desiccation tolerance is a critical adaptive trait that enables plants to survive extreme water loss, yet its physiological basis in tomato and its wild relatives remains poorly understood. In this study, chlorophyll a fluorescence imaging was used as a reliable tool to evaluate photosystem II (PSII) response to progressive desiccation. The analysis was conducted in cultivated tomato (Solanum lycopersicum) and five wild relatives (Solanum chilense, Solanum habrochaites, Solanum peruvianum, Solanum pimpinellifolium, and Solanum pennellii). Detached leaves were subjected to controlled desiccation for up to 50 h. During this period, tissue moisture content (TMC), relative water content (RWC), PSII photochemical efficiency [Fv/Fm; maximum quantum yield (QY_max)], minimal fluorescence (F0), maximal fluorescence (Fm), and variable fluorescence (Fv) were monitored to assess changes in photosynthetic performance. Desiccation caused a significant, moisture-dependent decline in PSII efficiency across all species, with QY_max showing a strong linear relationship with RWC (R2 = 0.80–0.90). Interspecific variation was evident as S. chilense, S. habrochaites, S. peruvianum, and S. pimpinellifolium exhibited rapid PSII impairment, while S. lycopersicum showed moderate tolerance. In contrast, S. pennellii maintained higher PSII stability, with 50% loss of efficiency occurring only at lower RWC (30–35%). Overall, chlorophyll fluorescence imaging effectively captured functional diversity in desiccation tolerance, highlighting S. pennellii as a valuable genetic resource for improving drought resilience in tomato.

Why it matches plant phenotyping methodsクロロフィル蛍光イメージングを用いた高スループット表現型解析と乾燥耐性スクリーニングが研究の中心であり、PSII効率などの植物生理形質を定量化している。

titleChlorophyll Fluorescence-Based High-Throughput Phenotyping Reveals Mechanisms and Enables Rapid Screening of Desiccation-Tolerant Wild Tomato Species.

Dynamics and Crosstalks of H 2 S and H 2 O 2 Signaling in Plant Abiotic Stress Response Deciphered by a Disposable SERS Sensing Patch. →

Abiotic stresses caused by climate change pose a serious threat to global crop productivity, making the early detection of plant stress responses crucial. Hydrogen sulfide (H 2 S) and hydrogen peroxide (H 2 O 2 ), as key signaling molecules, their dynamic synergistic effects are central to understanding the mechanisms of plant stress adaptation. However, real-time tracking of the dynamic changes of these molecules remains challenging. This study developed a wearable plasmonic nanoarray sensor integrated with metal-organic frameworks (MOFs), which cleverly combines the high sensitivity of surface-enhanced Raman scattering (SERS) with the gas enrichment capacity of the MOF, incorporates 2D plasmonic membrane assembly technology and 4-mercaptophenylboronic acid (4-MPBA) conjugation strategy, and successfully achieves real-time and synchronous detection of H 2 S and H 2 O 2 in plants. The 24 h dynamic monitoring results showed that under different stress conditions, H 2 S and H 2 O 2 in tomatoes and rice both had specific dynamic change rules, and there was a complex cross-regulation mechanism between them. By combining sensor data with partial least squares discriminant analysis (PLS-DA), the classification accuracy of stress types exceeds 95%. This non-destructive and highly sensitive detection system can provide real-time dynamic data of stress signals, bringing a breakthrough to the in-situ monitoring of plant physiological states.

Why it matches plant phenotyping methods植物内のストレスシグナルをリアルタイム測定するウェアラブルSERSセンサーの開発が研究の中心であり、植物の生理状態の表現型取得に直接結び付いている。

abstractThis study developed a wearable plasmonic nanoarray sensor integrated with metal-organic frameworks (MOFs)

Hybrid CNN-transformer architecture for multi-class crop disease detection and severity assessment: CropHybrid-Net with benchmark evaluation on CropDisease-12 →

Grain diseases lead to losses of 20-40% of the harvests each year, representing a threat to the food security of the world. Accurate and automated diagnosis of disease from remote picture taking would be key to prompt and directed interventions. Most current deep-learning approaches, however, are based on controlled lab images, on a single crop and ignore the assessment of disease severity. CropHybrid-Net is a three-branch ensemble architecture with ResNet-50, EfficientNet-B4 and Swin Transformer (Swin-T) that combines them using Efficient Channel Attention (ECA) fusion layer for simultaneous disease detection and severity estimation. CropDisease-12 is a benchmark dataset of 43200 images belonging to 12 classes representing four major crops (tomato, wheat, rice, and cotton) from PlantVillage and its own disease dataset collected in Yavatmal, Maharashtra, India. When evaluated on the CropDisease-12 test split, CropHybrid-Net outperforms all baselines tested such as standalone Swin-T (94.5%), ViT-B/16 (93.9%) and EfficientNet-B4 (93.7%), with the highest accuracy of 97.8%, and macro f1 score of 97.3%. The average value of AUC for the 12 classes is 0.995. In addition, a comprehensive literature review has been conducted, comprising of 62 papers (2015-2024), and grouped into five research streams: conventional machine learning, CNN-based methods, transfer learning, transformer-based methods, and multi-task severity approaches. The Grad-CAM visualizations are in line with the locations of biologically meaningful lesions. The framework proposed is deployed on common precision agriculture-edge of-use devices and achieves the goal of 39.3 ms per image, being relevant to smart precision agriculture applications.

Why it matches plant phenotyping methods植物病害の画像から病害状態と重症度を推定するCNN・Transformer手法を開発し、ベンチマークデータセットで評価しているため、植物フェノタイピング手法が中心である。

abstractCropHybrid-Net is a three-branch ensemble architecture with ResNet-50, EfficientNet-B4 and Swin Transformer (Swin-T) that combines them using Efficient Channel Attention (ECA) fusion layer for simultaneous disease detection and severity estimation.

Boosting innovative microbial solutions by understanding the functional benefits of endophytic rhizobacteria on tomato growth and protection using plant phenomics →

Biological control represents a valuable tool for the sustainable management of soil-borne diseases in tomato cultivation and relies on the availability of effective microbial solutions. Digital technologies can support to the ecodesign stage by accelerating the screening and selection of high-performing microbial biocontrol agents. In this study, a collection of eleven endophytic bacteria strains recruited from the tomato root endosphere and proved to be compatible with Trichoderma spp. (non-target effect), was characterized for antagonistic and biofertilization/biostimulant traits, and evaluated in planta against two major tomato pathogens: Fusarium oxysporum f. sp. lycopersici and Sclerotium rolfsii. Plant phenomics realized using the PlantEye 500 multispectral dual scanner, was used to screen the effectiveness of microbial agents determining plant performances under both infection and healthy conditions. Multivariate analysis of 20 digitally computed phenotypic traits helped the detection of Peribacillus sp. C5NA and Neobacillus sp. TR12 as highly effective against wilt, and capable of counteracting the reduction in leaf angle surface and chlorophyll: typical tracheofusariosis symptoms. On the other hand, Peribacillus strains TR2 and C6 treatments caused partial phenotypic recovery in plants affected by Sclerotium rot. Interestingly, Microbacterium sp. TR9, appeared to be multifaceted. It showed mild multisuppressivity against both pathogens coherently with the exhibited N-acetyl-b-glucosaminidase, polysaccharide breaking and in vitro antifungal activities. In addition, it also acted as a putative biostimulant in the absence of pathogens, increasing digital biomass, plant height, and NDVI, in line with its proven strong ability to produce ammonia, fix nitrogen, solubilize phosphates, and release indoleacetic acid. Overall, the integration of phenomics supported the high-resolution detection of plant responses and supported the identification of multifunctional microbial strains with biocontrol and biofertilization potential for sustainable tomato production.

Why it matches plant phenotyping methodsPlantEye 500によるマルチスペクトル表現型取得と20形質のデジタル解析が、微生物資材のスクリーニングおよび植物応答評価の中心的手法として用いられているため。

abstractPlant phenomics realized using the PlantEye 500 multispectral dual scanner, was used to screen the effectiveness of microbial agents determining plant performances under both infection and healthy conditions.

Multi-class classification of plant leaf diseases using a hybrid deep neural transformer system and explainable AI techniques. →

An effective framework based on deep learning (DL) is developed in this study for reliable and accurate performance. The multi-class detection of crops such as corn, tomato and potato is accurate and reliable. The aim is to improve early disease detection which guarantees good classification accuracy, strong generalization across datasets, enhanced interpretability through XAI methods and enabling realistic agricultural applications. : The study uses two large publicly available datasets of plant leaf disease images of corn, tomato and potato. The first dataset designated as D-I consists of 39,203 images belonging to 18 classes of disease and the second dataset designated as D-II consists of 65,565 images also belonging to 18 classes of disease. The two datasets contain images showing different visual scenarios and variations of disease which will help form a multi-class classifier. In this study, five DL architectures were used; InceptionNetV3, ResNet152V2, ViT, BERT, and the proposed hybrid model (ResViT-152) that combined convolutional feature extraction with transformer-based global attention. Every model was trained, validated, and tested under the same experimental setup. Cross validation and multi-phase testing assessed their performances in their capacity to learn discriminative parameters in corn, tomato and potato disease classes. The hybrid model exhibited a better performance in all test conditions. In IntraTest1, the accuracies were 99.12%, 98.94% and 99.06% for corn, tomato and potato respectively. In IntraTest2, the model achieves accuracy of 99.23% for corn, 98.97% for tomato, and 98.98% for potato on D-II. The precise percentages for the cross-tests were 96.27% (corn), 95.14% (tomato), 95.06% (potato) for CrossTest1 and 95.77% (corn), 96.22% (tomato), 96.15% (potato) for CrossTest2. The performance across datasets for all three crops is good and generalization is robust. A study is conducted on an efficient, high-performing, and interpretable DL framework for plant leaf disease detection. The experimental results verify that the proposed work provides superior performance. Generalization outperforms standard architectures in effectiveness. In addition, there is also stable performance with varying datasets. With Explainability included, the model becomes more transparent and a strong candidate for further validation toward deployment in precision agriculture, pending evaluation on real-world field datasets.

Why it matches plant phenotyping methods植物葉の画像から病害状態を分類・検出する深層学習手法の開発と、複数データセットでの交差検証・比較評価が中心であり、植物フェノタイピング手法に該当する。

abstractA study is conducted on an efficient, high-performing, and interpretable DL framework for plant leaf disease detection.

DSH_YOLO enables factory farming production: a method for detecting and grading the cumulative internode length of tomato seedlings. →

Introduction The industrial cultivation of tomato seedlings requires a high degree ofuniformity and consistency in grading. However, traditional grading methods based on phenotypictraits such as leaf area and canopy width are susceptible to environmental conditions, therebylimiting the accuracy and efficiency of grading. Since cumulative internode length is relativelystable and closely correlated with seedling vigor, this study aims to develop an accurate methodfor measuring and grading the cumulative internode length of tomato seedlings. Methods A tomato seedling cumulative internode length detection method, termedDSH_YOLO (Deformable Convolution-SIoU-Haar Wavelet Downsampling YOLO), wasproposed based on the YOLOv8-seg framework. First, deformable convolution was introducedinto the backbone to enhance feature extraction for curved stems and occluded regions. Second,the original loss function was replaced with SIoU to improve the alignment between predictedregions and the actual stem structure. Third, a Haar wavelet downsampling module was embeddedinto the backbone to preserve high-frequency detail information and reduce information loss underocclusion conditions. An intelligent grading system was further developed to verify the practicalapplicability of the proposed method. Results Experimental results showed that DSH_YOLO achieved a Precision of 96.1%, aRecall of 94.3%, and an mAP@0.5 of 92.1%. Compared with the baseline YOLOv8 model, theproposed method substantially improved segmentation performance for cumulative internoderegions in tomato seedlings. In prototype validation, the intelligent grading system achieved anaverage grading success rate of 87.50%, with an average cumulative internode length error of 8.0mm. Discussion The results indicate that DSH_YOLO and the grading system can meet therequirements for large-scale grading and detection of tomato seedlings, demonstrating highdetection accuracy and success rates. This approach can provide insights for grading other types ofseedlings during their growth stages and offer support for seedling production and thedevelopment of intelligent agricultural equipment.

Why it matches plant phenotyping methodsトマト苗の累積節間長という植物形態形質を画像分割・検出し、実用的な等級判定まで検証する手法開発が研究の中心であるため。

abstractA tomato seedling cumulative internode length detection method, termedDSH_YOLO (Deformable Convolution-SIoU-Haar Wavelet Downsampling YOLO), wasproposed based on the YOLOv8-seg framework.

Multi-Class Tuber Plant Leaf Disease Detection Using Hybrid Deep Learning Framework for Real Time Application →

The productivity and sustainability of agriculture depend on the early diagnosis of plant diseases, particularly for root crops such as potatoes, tomatoes, and carrots. The hybrid deep model proposed in this study employs a Convolutional Neural Network (CNN) architecture to provide precise and realtime multi-categorization of several leafy tuber crop diseases. A complete dataset of 20,657 labeled photos from 16 diseases was used to train the model, and regular classes were employed. Our goal is to build a scalable deep learning model that can diagnose multiple tuber crop diseases in real time, reduce the need for manual surveys through a cost-effective web tool, and support farmers with early detection for smarter and more sustainable crop management. The proposed CNN architecture, which uses convolutional, pooling, and fully connected layers that are modified by the Adam optimizer, was developed using TensorFlow and Keras. The constructed model was highly successful in detecting widespread illnesses such as early blight, late blight, and other tomato and carrot leaf diseases, as demonstrated by its 92.2% total accuracy rate. Being developed as a web application later on, the system gave farmers and agri-parties an efficient and economical diagnosis tool. Based on knowledge, early detection of disease, lower reliance on manual surveys, and crop management decisions, this work encourages precision agriculture.

Why it matches plant phenotyping methods植物葉の画像から病害状態を推定する深層学習手法の開発・評価が中心であり、植物フェノタイピング手法に該当する。

abstractThe hybrid deep model proposed in this study employs a Convolutional Neural Network (CNN) architecture to provide precise and realtime multi-categorization of several leafy tuber crop diseases.

AG-Vision: a dual-module approach for tomato leaf disease diagnosis. →

Accurate and timely identification of tomato leaf diseases is critical for precision agriculture. Although convolutional neural networks (CNNs) perform well in extracting local visual features, they often lack the ability to model global contextual relationships, limiting robustness in real-world field conditions. To overcome this challenge, we propose a hybrid architecture that jointly learns local and global representations. We present AG-Vision, a dual-module framework that integrates an EfficientNet-B4 CNN backbone (DeepFolia) for fine-grained local feature extraction with a Transformer encoder (VisiLeaf) to capture long-range global dependencies through self-attention. The architecture incorporates positional encoding and optimized attention heads to enhance spatial awareness. AG-Vision was evaluated on the controlled PlantVillage dataset and the real-world PlantDoc dataset. Ablation studies assessed the contribution of individual components, and Grad-CAM visualizations were used to analyze model interpretability. AG-Vision achieved state-of-the-art performance on both datasets, obtaining 99.97% accuracy and an F1-score of 99.53% on PlantVillage, and 96.97% accuracy with an F1-score of 94.47% on PlantDoc. Despite its high accuracy, the model maintained real-time efficiency with an average inference time of approximately 25 ms per image. Ablation experiments confirmed the importance of combining CNN and Transformer modules, positional encoding, and optimized attention mechanisms. Grad-CAM results demonstrated that the model consistently focuses on disease-relevant regions. The findings confirm that fusing local and global feature learning significantly enhances classification accuracy and robustness under diverse conditions. AG-Vision offers an efficient and scalable solution suitable for edge deployment in precision agriculture.

Why it matches plant phenotyping methodsトマト葉の病害状態を画像から推定するCNN・Transformer統合手法を開発し、複数データセットとアブレーションで検証しており、植物フェノタイピング手法が中心である。

abstractWe present AG-Vision, a dual-module framework that integrates an EfficientNet-B4 CNN backbone (DeepFolia) for fine-grained local feature extraction with a Transformer encoder (VisiLeaf) to capture long-range global dependencies through self-attention.

VGDS-PointNet++ for organ segmentation and phenotypic trait estimation in greenhouse tomato seedlings. →

Introduction To accurately segment point clouds and quickly calculate leaf length and stem diameter, thereby enabling phenotypic analysis and variety selection of greenhouse tomato plants, this paper proposes a voxel grid downsampling (VGDS)-PointNet++-based model for point cloud segmentation and trait calculation. Methods The point clouds of the tomato canopy were acquired using a depth camera. After labeling, point cloud augmentation was performed, and the tomato point cloud dataset (TPCD) containing 1,552 sets of data was rebuilt. Voxel grid downsampling was applied to replace the original sampling strategy of PointNet++. Models of PointNet, PointNet++, VGDS-PointNet++, and Point Transformer were trained with the TPCD and compared on segmentation quality with accuracy and mean Intersection over Union (mIoU). After segmentation, skeletal morphology was fitted for non-occluded leaves by applying a series of surface fitting techniques. The leaf lengths and stem diameters were automatically calculated and compared with the manually measured values. Results The validation results showed that the average runtime of voxel grid downsampling was 0.132 s, which was lower than under the same number of sampled points. Compared to the other three models, the proposed model had higher accuracy and mIoU, reaching up to 96.80% and 88.95%, respectively. The proposal's accuracy and mIoU increased by 3.9% and 4.45% over PointNet++, respectively. The determination coefficient R 2 between the automatic calculation and manual measurement values of leaf length and stem diameter was 0.93 and 0.87, respectively. Discussion This can help extract phenotypic traits of tomatoes using depth cameras.

Why it matches plant phenotyping methods深度カメラ点群のセグメンテーションと葉長・茎径の自動推定手法を開発し、手測定および複数モデルと比較検証しており、表現型取得が研究の中心である。

abstractthis paper proposes a voxel grid downsampling (VGDS)-PointNet++-based model for point cloud segmentation and trait calculation

Precise identification of tomato leaf diseases based on the real background of DPEN. →

As an important economic crop, tomato is vulnerable to various diseases, and these diseases often have high visual similarity, making identification difficult. A delay in diagnosis can have a significant effect on tomato yields. Traditional manual visual inspection methods have poor accuracy, while laboratory diagnostic methods are inefficient, making them unsuitable for large-scale agricultural scenarios. To address this challenge, this study involved the collection and construction of a tomato leaf disease dataset in a real planting environment, and AutoAugment was used to achieve sample diversity and balance the number of training samples in different categories. Additionally, a dual-path ensemble network (DPEN) was proposed, which combines the multiscale feature extraction advantages of GoogLeNet with the dense connection mechanism of DenseNet121. The experimental results show that, compared with the comparison models, the DPEN achieves an identification precision of 98.80% on the self-built dataset, which is an improvement of 2.33% to 9.24%, and a reduction in the number of parameters by 7.09 M compared with GoogLeNet and 2.07 M compared with DenseNet121. The experimental results on public datasets further demonstrated the accuracy of the proposed DPEN model in identifying tomato leaf diseases in complex backgrounds. These results prove that the DPEN model can achieve precise, rapid, and efficient identification of tomato leaf diseases in complex backgrounds, providing technical support for smart agriculture applications.

Why it matches plant phenotyping methodsトマト葉の病徴を画像から識別する深層学習手法を開発し、実環境データセットと公開データセットで精度検証しているため、植物病害状態のフェノタイピング手法が中心である。

abstracta dual-path ensemble network (DPEN) was proposed

Comparative Assessment of Hyperspectral Image Segmentation Algorithms for Fruit Defect Detection Under Different Illumination Conditions. →

This study presents a comparative analysis of hyperspectral image segmentation algorithms for fruit defect detection under different illumination conditions. The research evaluates the performance of four segmentation methods (Spectral Angle Mapper, Random Forest, Support Vector Machine, and Neural Network) using three distinct illumination modes (local, simultaneous and sequential). The experimental setup employed hyperspectral imaging to assess tomato fruit samples, with data acquisition performed across the 450-850 nm spectral range. Quantitative metrics, including accuracy, error rate, precision, recall, F1-score, and Intersection over Union (IoU), were used to evaluate algorithm performance. Key findings indicate that Random Forest demonstrated superior performance across most metrics, particularly under simultaneous illumination conditions. The highest accuracy was achieved by Random Forest under sequential illumination (0.9971), while the best combination of segmentation metrics was obtained under simultaneous illumination, with an F1-score of 0.8996 and an IoU of 0.8176. The Neural Network showed competitive results. The Spectral Angle Mapper proved sensitive to illumination variations but excelled in specific scenarios requiring minimal memory usage. By demonstrating that acquisition protocol optimization can substantially improve segmentation performance, our results support the development of accurate, non-contact, high-throughput inspection systems and contribute to reducing postharvest losses and improving supply chain quality control.

Why it matches plant phenotyping methodsトマト果実の欠陥という植物状態を対象に、ハイパースペクトル画像セグメンテーション手法を比較・評価し、照明条件と取得プロトコルの最適化を検証しているため、方法が中心的である。

abstractThis study presents a comparative analysis of hyperspectral image segmentation algorithms for fruit defect detection under different illumination conditions.

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

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

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

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

Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep Convolutional Neural Network and Segmentation Techniques →

ABSTRACT A multi-stage approach for plant leaf disease classification using Convolutional Neural Networks (CNN) and image segmentation is presented. The system identifies plant types and classifies diseases from leaf images. Preprocessing includes noise removal and image enhancement, followed by K-means clustering for segmentation. A hybrid CNN model performs accurate classification of plant species and disease type, and suitable fertilizer recommendations are provided. The method achieves accuracies of 99.8%, 96.5%, and 98.3% on apple, tomato, and grape datasets. The results show improved accuracy and robustness, making the system effective for practical agricultural applications. INDEX TERMS Convolutional Neural Network (CNN), Image Segmentation, K-means clustering, Plant Leaf Diseases.

Why it matches plant phenotyping methods葉画像から病害状態を直接推定するCNN・画像セグメンテーション手法が研究の中心であり、植物病害フェノタイピング手法として該当する。

abstractA multi-stage approach for plant leaf disease classification using Convolutional Neural Networks (CNN) and image segmentation is presented.

SCEA-YOLO: A General-Purpose Maturity Grading Model of Multi-Crop Greenhouse Robots. →

Accurate classification of fruit maturity is essential for automated grading and robotic manipulation in modern greenhouse cultivation. Most existing methods rely on crop-specific models, severely restricting their scalability in multi-crop scenarios. To overcome this limitation, this study presents SCEA-YOLO, a unified and efficient instance segmentation framework built on YOLOv11s-seg, for simultaneous maturity classification of tomatoes and sweet peppers. To boost feature discrimination, reduce computational redundancy, and alleviate class imbalance, SCEA-YOLO integrates spatial-channel reconstruction convolution and an efficient multi-scale attention mechanism, while replacing the original detection head with the proposed EA-Head. The model is evaluated on a hybrid dataset captured under diverse greenhouse conditions, including varying illumination, fruit occlusion, and overlapping canopies. Its robustness to different viewing angles and camera distances is further validated via deployment on an automated grading robot. Compared with the baseline, SCEA-YOLO enhances classification precision and mAP50–95 by 5.3% and 2.3% for tomatoes, and 1.2% and 1.4% for sweet peppers, respectively. With only 33.2 GFLOPs, the model satisfies real-time inference demands. Benefiting from its lightweight structure and real-time performance, SCEA-YOLO can be readily deployed on embedded systems and robotic platforms. It offers a practical, unified, and scalable solution for intelligent fruit maturity evaluation in multi-crop greenhouse production.

Why it matches plant phenotyping methodsトマトとピーマン果実の成熟度を画像から分類・評価するモデルを開発し、データセットおよびロボット上で性能検証しており、植物表現型取得手法が中心である。

abstractthis study presents SCEA-YOLO, a unified and efficient instance segmentation framework built on YOLOv11s-seg, for simultaneous maturity classification of tomatoes and sweet peppers.

AI - Driven Drone System Using CNN For Detecting Manganese Toxicity & Bacterial Diseases In Tomato Crop →

in machine learning ml and deep learning dl have revolutionized plant disease detection significantly enhancing agricultural productivity and improving food security this paper presents smart-crop defender an innovative drone-based system that integrates internet of things iot capabilities with deep learning algorithms for autonomous plant disease detection and targeted pesticide application the system leverages convolutional neural networks cnn trained on comprehensive datasets such as plant village to achieve accurate real-time disease identification

Why it matches plant phenotyping methodsCNNを用いたドローン画像によるトマトの病害・マンガン毒性検出が研究の中心であり、植物の状態を直接推定するフェノタイピング手法である。

abstractthis paper presents smart-crop defender an innovative drone-based system that integrates internet of things iot capabilities with deep learning algorithms for autonomous plant disease detection and targeted pesticide application

Image-Based Crop Disease Detection Using Machine Learning →

Crop disease detection is critical for agricultural productivity and global food security. Traditional methods rely on labour-intensive field surveys prone to human error. This paper presents an image-based crop disease detection system using a hybrid Convolutional Neural Network (CNN) augmented with pre-trained AlexNet weights. The system captures leaf images, applies preprocessing (resizing, normalization, augmentation), and classifies diseases with high accuracy. A Flask-based web application enables real-time prediction accessible to farmers via smartphone. Trained on 2,000 field-collected images across potato, pepper, and tomato crops, the proposed model achieves approximately 97% classification accuracy, outperforming standalone classifiers including SVM, Logistic Regression, Decision Tree, and Naïve Bayes

Why it matches plant phenotyping methods葉画像から植物病害を推定する画像ベース手法の開発・比較検証が研究の中心であり、植物の病害状態を直接評価しているため。

abstractThis paper presents an image-based crop disease detection system using a hybrid Convolutional Neural Network (CNN) augmented with pre-trained AlexNet weights.

Digital Platform for Crop Health and Agricultural Services →

Modern precision agriculture requires the incorporation of high-accuracy diagnostic instruments to guarantee food security for inexperienced practitioners. This paper introduces an AI-driven agricultural web architecture that connects deep learning-based diagnostics with real-world farm management. The main contribution is a Convolutional Neural Network (CNN) framework that can automatically find diseases in five common crops: Capsicum annuum, Vitis vinifera, Zea mays, Solanum tuberosum, and Solanum lycopersicum. The proposed model reached a final training accuracy of 98.30% and a validation accuracy of 90.12% over 10 epochs by using a sequential architecture with optimized convolutional layers and data augmentation. The platform has a localized marketplace, a government scheme eligibility engine, and a Crop Journal for long-term record-keeping to make it useful in the real world. Results demonstrate that this unified ecosystem provides a transparent and accessible framework for data-informed agricultural management, effectively lowering the technical barrier for new farmers.

Why it matches plant phenotyping methodsCNNによる作物病害の自動検出が中心的な技術貢献であり、植物の病害状態を画像ベースで推定するため、農業サービス部分を含んでも植物フェノタイピング手法として採用する。

abstractThis paper introduces an AI-driven agricultural web architecture that connects deep learning-based diagnostics with real-world farm management.

A Bioluminescent Reporter System for Real-Time Monitoring of the Unfolded Protein Response in Plants. →

The unfolded protein response (UPR) is a critical mechanism for maintaining endoplasmic reticulum (ER) homeostasis under stress. Here, we developed a bioluminescent reporter system, AtbZIP60-LUC, in Arabidopsis to dynamically monitor ER stress by coupling IRE1-mediated splicing of bZIP60 mRNA to firefly luciferase (LUC) expression. Under ER stress, IRE1 removes a 23-bp sequence from bZIP60u, producing a spliced bZIP60s transcript in-frame with LUC, enabling luciferin-dependent luminescence. Transgenic AtbZIP60-LUC lines exhibited specificity for canonical ER stressors (heat, DTT, tunicamycin) but not osmotic stressors (NaCl, mannitol), confirmed by bioluminescence, qPCR, and immunoblotting. Time-course assays revealed rapid LUC induction by DTT (peak at 1 h) and delayed activation by tunicamycin (peak at 1-2 h), followed by signal decline, reflecting adaptive UPR dynamics. Heat stress optimization identified 38°C as optimal, inducing robust LUC expression after 2-3 h without compromising viability, while 42°C caused irreversible damage. Genetic validation in ire1a ire1b mutants abolished LUC induction, confirming IRE1 dependency, whereas constitutive UPR activation via maize 16-kDa γ-zein (16γz) overexpression triggered LUC expression without stress. Extending this system to tobacco and tomato, we engineered NbbZIP60-LUC and SlbZIP60-LUC, which similarly responded to heat (38°C), DTT, tunicamycin, and ER-localized protein aggregation (16γz, zeolin) in transient and stable assays. This work establishes bZIP60-LUC as versatile, non-invasive tools for real-time UPR monitoring in plants, offering insights into ER stress dynamics and enabling cross-species studies of stress adaptation mechanisms.

Why it matches plant phenotyping methods植物のERストレス状態を非侵襲的・リアルタイムに測定するルシフェラーゼレポーター法を開発し、ストレス特異性、時間応答、遺伝的依存性、複数種での性能を検証しており、表現型取得法が研究の中心である。

abstractHere, we developed a bioluminescent reporter system, AtbZIP60-LUC, in Arabidopsis to dynamically monitor ER stress by coupling IRE1-mediated splicing of bZIP60 mRNA to firefly luciferase (LUC) expression.

Enhancing plant disease detection through multi-modal integration of visual and textual data. →

Plant diseases pose a significant threat to global agriculture, impacting crop yields and quality. Early and accurate detection is essential for effective health management but remains challenging due to visual similarity among diseases and complex field backgrounds. This study introduces AgriMM, a novel multi-modal detection framework that integrates visual images with expert-validated textual descriptions to improve diagnostic precision. The framework features three key innovations: a Hybrid Convolutional-Attention Collaborative Backbone (HCACB) to capture both fine-grained lesions and global context; a Context-enhanced Visual-Language Path Aggregation Network (CVL-PAN) for multi-scale feature fusion; and an Adaptive Region-Text Contrastive Learning (AR-TCL) module to enforce precise semantic alignment. We constructed a comprehensive dataset comprising 30,000 images and detailed symptom descriptions across five major crops (tomato, cucumber, pepper, eggplant, and squash). Experimental results demonstrate that AgriMM achieves a mean Average Precision (mAP) of 95.2%, significantly outperforming state-of-the-art unimodal baselines by 11.6%. These findings confirm that integrating linguistic semantic priors effectively resolves visual ambiguity, providing a robust tool for precision agriculture and sustainable crop protection.

Why it matches plant phenotyping methods植物病害の症状を画像から検出・診断するマルチモーダル手法を開発し、データセットと性能比較で検証しているため、植物フェノタイピング手法が中心である。

abstractThis study introduces AgriMM, a novel multi-modal detection framework that integrates visual images with expert-validated textual descriptions to improve diagnostic precision.

Cross-Domain Tomato Disease Classification via Flexible Contrastive Clustering in Vision-Language Models →

Abstract Plant disease detection systems face significant challenges in cross-domain generalization, particularly when transitioning from controlled laboratory settings to diverse field conditions. Traditional deep learning approaches exhibit severe performance degradation across different imaging environments, limiting practical deployment in real-world agricultural scenarios. This paper introduces a novel Flexible Contrastive Clustering (FCC) framework for zero-shot tomato disease classification that addresses fundamental generalization limitations through vision-language learning. Unlike standard CLIP’s one-to-one image-text pairing, our method leverages one-to-many relationships where each disease image is associated with multiple diverse textual descriptions, enabling robust representation learning across linguistic variations. The FCC framework optimizes class-based clustering in joint embedding space through a specialized loss function that treats all same-class descriptions as positives, facilitating effective handling of both seen and unseen disease categories during zero-shot evaluation. We evaluate our approach on PlantDoc training data (740 images) and test across four diverse tomato disease datasets totaling 17,313 images, spanning laboratory and field conditions. Experimental results demonstrate substantial improvements over state-of-the-art vision-language models, achieving an average of 30.15% accuracy and 28.05% weighted F1-score on average across all test datasets. Our method shows particularly strong performance on field datasets, achieving 59.70% accuracy on FieldPlant and 26.52% on Tomato Village, significantly outperforming existing approaches. Attention visualization analysis reveals effective disease localization capabilities for both seen and unseen categories, validating the practical applicability of our approach for real-world agricultural monitoring systems.

Why it matches plant phenotyping methods植物画像から病害状態を推定する新規視覚言語分類法を開発し、複数データセットで性能評価しており、病害フェノタイピング手法が研究の中心である。

abstractThis paper introduces a novel Flexible Contrastive Clustering (FCC) framework for zero-shot tomato disease classification

A Three-Year Multimodal Holistic Dataset For Horticultural Tomato Cultivation. →

Existing tomato datasets often focus on short-term experiments or lack integrated environmental and agronomic data. We present Horti-M3-Tomato, a comprehensive three-year dataset collected in Northeast China's greenhouse, including high-resolution RGB images, environmental sensor data (recorded every 30 minutes), soil conditions, and detailed agronomic records such as yield data and management practices. Spanning three growing seasons (2023-2025), the dataset integrates temporal imaging, environmental monitoring, soil data, and manual phenotypic and yield records. Horti-M3-Tomato supports research on growth dynamics, genotype-environment interactions, and provides a benchmark for AI-based phenotyping and precision horticulture. The dataset is openly available for further research in controlled-environment agriculture.

Why it matches plant phenotyping methodsトマトの画像・環境センサーデータ・手動表現型記録を統合したデータセットであり、AIベースの表現型解析のベンチマークとして明示されているため、表現型データ基盤が中心です。

abstractincluding high-resolution RGB images, environmental sensor data (recorded every 30 minutes), soil conditions, and detailed agronomic records such as yield data and management practices.

Hybrid Deep Learning Framework for Intelligent Plant Disease Detection using Leaf Image Analysis →

Plant diseases have a major impact on food security and agricultural productivity because they lower crop quality and output. For crop management to be effective, plant diseases must be identified early and accurately. Using leaf image analysis, this study suggests an intelligent deep learning-based approach for automatically identifying illnesses in tomato plants (Solanum Lycopersicon). Plants that produce tomatoes are particularly vulnerable to a number of diseases, including leaf Mold, early blight, and late blight, which can negatively affect crop yield. In order to increase feature visibility, a dataset of tomato leaf photos that includes both healthy and diseased samples is gathered and preprocessed utilizing image enhancement and normalization techniques. In order to precisely classify various disease categories and extract deep information from leaf photos, the suggested method uses a Convolutional Neural Network (CNN) in conjunction with transfer learning utilizing the Mobile Net architecture. Significant visual characteristics like Colour shifts, textural alterations, and lesion shapes on infected leaves are immediately picked up by the CNN model. The suggested model's efficacy is shown by experimental study. Strong classification capabilities are demonstrated by the system's 97.8% training accuracy and 96.4% testing accuracy. The algorithm's reliability is further supported by performance assessment metrics, which show an F1-score of 96.0%, recall of 96.1%, and precision of 95%. Stable model convergence is also shown by the training and validation loss values, which drop from 0.45 and 0.50 at the first epoch to 0.11 and 0.15, correspondingly. The suggested MobileNet-CNN models works better than current designs like VGG16 (92.3%), ResNet50 (94.1%), and InceptionV3 (95.2%), according to comparison studies. By offering a quick and accurate tool for early disease identification, the proposed system can assist cultivators and farming specialists, facilitate prompt medical care and enhance total crop production and methods that are environmentally friendly.

Why it matches plant phenotyping methodsトマト葉画像から病害症状を抽出・分類する深層学習手法が研究の中心であり、植物の病害状態を直接推定しているため、植物フェノタイピング手法として適格です。

abstractUsing leaf image analysis, this study suggests an intelligent deep learning-based approach for automatically identifying illnesses in tomato plants (Solanum Lycopersicon).

Deep learning-based seed germination prediction using morphological traits and RGB images. →

Seed selection constitutes the initial and one of the most critical steps in agricultural productivity. The identification of high-quality seeds is a labor-intensive and costly process that requires considerable expertise. Within the scope of smart farming applications, this study proposes a deep learning–based model designed to automate the seed selection process by accurately predicting seed germination capacity from seed images. The proposed model determines whether a seed will germinate using RGB images and morphological traits automatically extracted from these images. The dataset used in this study comprises a total of 3,645 images belonging to three different seed types. For each seed type (okra, eggplant, and tomato), 405 seed images were acquired from three distinct imaging sources (digital microscope, camera, and scanner), labeled, and subsequently sown in seed trays. The germination status of each sown seed was systematically monitored and matched with its corresponding image data. The dataset was partitioned into 80% training and 20% testing subsets. Following 5-fold stratified cross-validation, the proposed model achieved an average weighted F1-score of 0.95 on the training set and 0.93 on the testing set for germination capacity prediction. The performance of the proposed model was further compared with widely used deep convolutional neural network architectures, including VGG19, ResNet50, and EfficientNetB5. Comparative results demonstrate that the proposed model provides competitive and robust performance for seed germination prediction. Overall, the findings indicate that the proposed approach can effectively be utilized for automated seed germination prediction. Future research should evaluate the generalizability of the model by conducting performance assessments on additional seed types.

Why it matches plant phenotyping methodsRGB画像から種子の形態形質を自動抽出し、発芽状態を予測する深層学習手法が研究の中心であるため、植物フェノタイピング手法として含める。

abstractthis study proposes a deep learning–based model designed to automate the seed selection process by accurately predicting seed germination capacity from seed images.

Simulation and prediction of post-harvest ripening processes for tomatoes with different ripeness levels based on electrical characteristics. →

Detecting postharvest tomato ripeness is essential for quality control. To reveal the evolution of complex conductivity σ ∗ and complex permittivityε ∗ during tomato ripening, this study integrates bioimpedance spectroscopy (BIS) and finite element method (FEM) to predict postharvest tomato maturity. Based on the Maxwell-Wagner equation, σ ∗ and ε ∗ were derived from the measured impedance and conductance data. BIS measurements were conducted on whole tomatoes at four ripening periods and their components (pericarp, chamber, core, cavity). A finite element model was implemented in COMSOL to simulate electrical field distribution and quantify tissue-specific differences. Continuous monitoring of white ripening period tomatoes was used to validate the model, yielding an average accuracy of 85.16%, peaking at 92.86% in red ripening period and dipping to 80.30% in color change period, elucidate the dynamic changes in electrical properties during tomato ripening and provide a basis for nondestructive maturity assessment.

Why it matches plant phenotyping methodsトマトの成熟状態を電気特性から非破壊推定するBIS・FEM手法を開発・検証しており、植物状態の取得・推定が研究の中心である。

abstractthis study integrates bioimpedance spectroscopy (BIS) and finite element method (FEM) to predict postharvest tomato maturity.

Mobile-Optimised Deep Learning Architecture for Multi-Crop Disease Detection Using CNN and SVM →

In this paper, we propose a deep learning pipeline for real-time crop disease classification on mobile devices. Our system employs a custom Convolutional Neural Network (CNN) trained on publicly available crop disease datasets (Maize, Tomato, Potato, Rice). In addition, two transfer-learning models; ResNet-50 and MobileNet are used as fixed feature extractors, with their output features classified by a multi-class Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. We compare the models’ performance across all crop datasets and evaluate inference latency and model size. Experimental results show that the ResNet50-SVM hybrid attains near-perfect accuracy (≈100% for Maize, Tomato, Potato; 99.96% for Rice) on plant disease classification, far exceeding both the custom CNN and MobileNet-SVM approaches. The MobileNet-SVM pipeline is notably faster (≈23–66 ms per image) and compact (~8.7 MB) than ResNet50+SVM (≈108–192 ms, ~90 MB), making it well-suited for on-device deployment. The final model is converted to TensorFlow Lite for mobile inference; on a typical smartphone CPU it processes an input image in ~0.15–0.19s on average, enabling practical field use. These results demonstrate an efficient mobile AI solution for crop disease detection that balances accuracy with resource constraints. The proposed system can empower farmers with timely, in-field disease diagnosis, helping to mitigate yield losses and improve crop management through accessible AI-driven tools.

Why it matches plant phenotyping methods植物画像から病害状態を分類する深層学習パイプラインを開発・比較し、精度、推論遅延、モデルサイズ、モバイル実装を評価しており、植物病害表現型の取得・抽出が中心です。

abstractwe propose a deep learning pipeline for real-time crop disease classification on mobile devices

Full-Spectrum Hyperspectral Modeling of Leaf Dry Matter Content Using a Stacked Ensemble Framework. →

The objective of this study was to assess the predictability of leaf dry matter content across a diverse range of plant species using hyperspectral reflectance data. The dataset encompassed leaves from multiple crops, including potatoes, beans, wheat, maize, peas, tomatoes, basil, and cucumbers, collected under varying growth conditions, cultivation systems, seasonal contexts, and developmental stages. As an initial benchmark, commonly used narrow-band spectral indices and their combinations were evaluated, but they exhibited limited predictive performance for dry matter content. Consequently, several full-spectrum machine learning models were trained and compared to assess their individual predictive ability. Given their complementary strengths, these models were integrated into a stacked ensemble framework to enhance overall accuracy. The resulting ensemble, combining the outputs of multiple base learners through a meta-learner, achieved a coefficient of determination of R2=0.896 on an independent test set, outperforming all individual models. The findings highlight the potential of a multi-model stacking approach to improve the accuracy and robustness of leaf biochemical property estimation from hyperspectral data.

Why it matches plant phenotyping methodsハイパースペクトル反射データから葉乾物含量を推定する機械学習手法を開発・比較・検証しており、植物形質の取得方法が研究の中心である。

abstractassess the predictability of leaf dry matter content across a diverse range of plant species using hyperspectral reflectance data

A high-throughput fluorescence-based microplate reader assay to quantify total flavonol levels in plant tissues. →

Background Flavonols are plant specialized metabolites that regulate plant growth, development, and stress responses. Due to their antioxidant activity, they also confer nutritional benefits to human health. Quantification of flavonols in plant tissues typically relies on chromatographic methods such as HPLC or LC-MS, or on microscopy-based approaches using diphenylboric acid 2-aminoethyl ester (DPBA) staining to visualize flavonols in plant tissues. These methods are time-consuming and resource intensive. Here, we present a rapid, high-throughput, fluorescence-based microplate reader assay for flavonol quantification, in which the flavonol-specific dye DPBA is added to plant extracts to form fluorescent complexes. Results The assay was optimized for extraction efficiency and validated for sensitivity, accuracy, and reproducibility. It also shows consistency with HPLC measurements. We demonstrate its utility by quantifying flavonol levels in different tomato tissues, across different cultivars, and even between plant species. Our assay showed that reproductive tissue in tomato plants has higher flavonol levels than vegetative tissue. Also, we found variation in flavonol levels in tomato fruit skin across different laboratory and commercial cultivars, suggesting that our approach shows promise for use in genome-wide association studies to identify genetic factors underlying variation in flavonol levels. Lastly, we measured flavonol levels in dry leaves of different plants used for brewed beverages. Conclusion In conclusion, the assay represents a simple, robust, and scalable flavonol screening tool for studies in plant metabolism, environmental physiology, breeding, and metabolic engineering.

Why it matches plant phenotyping methods植物組織中のフラボノール量を定量する高スループット測定法を開発し、感度・精度・再現性およびHPLCとの一致を検証している。単なる代謝測定ではなく、植物育種・代謝研究向けのスクリーニングツールとして方法自体が中心である。

abstractHere, we present a rapid, high-throughput, fluorescence-based microplate reader assay for flavonol quantification

ExoHeat: A continuous heating solution for bi-directional sap flow in small-diameter plant organs demonstrated in tomato peduncles →

Accurate sap flow measurements in small-diameter plant organs are essential for understanding water transport and source-sink dynamics, yet existing methods are limited by their temporal resolution, reduced sensitivity to low or reverse flow, and incompatibility with small organ dimensions. In this study, the ExoHeat sensor, a continuous-heating solution was developed for bidirectional sap flow measurements in small-diameter plant organs. Its performance was validated on tomato truss peduncles (Solanum lycopersicum L.). Zero-flow corrections accounting for ambient temperature and peduncle diameter ensured robust baseline adjustment, while gravimetric calibrations revealed a strong linear relationship between the sensor-measured temperature difference and sap flow rate up to 2 g h⁻¹, corresponding to a sap flux density of 5.8 10⁻³ cm³ cm⁻² s⁻¹. Whole-plant validation further demonstrated close agreement between ExoHeat-derived sap flow and gravimetric transpiration data. Anatomical imaging showed an asymmetrical distribution of xylem vessels in the tomato truss peduncle, underscoring the importance of correct sensor orientation. High-resolution measurements on ripening trusses successfully captured dynamic bidirectional flow patterns. The ExoHeat sensor thus provides a novel, high-temporal-resolution tool for accurate monitoring of sap flow in small-diameter organs, with promising applications in plant physiology, irrigation optimisation and stress detection.

Why it matches plant phenotyping methods小径植物器官の双方向樹液流を測定するセンサーを開発し、重力法による校正・検証を行った、植物生理状態の取得手法が中心の研究。

abstractIn this study, the ExoHeat sensor, a continuous-heating solution was developed for bidirectional sap flow measurements in small-diameter plant organs.

DRP-Net and clustering algorithm for Stem-Leaf segmentation and phenotypic trait extraction from tomato point clouds →

Tomatoes are a globally important horticultural crop, and their high-yield, high-quality breeding relies on high-throughput, precise phenotyping. While 3D point cloud technology offers a new avenue for non-destructive plant phenotyping, the inherent complexity of tomato plant organ morphology and growth dynamics poses a significant challenge to existing segmentation methods. To address this, this study employed multi-view RGB image reconstruction to cost-effectively acquire high-quality point cloud data from four growth cycles. Based on the characteristics of our data, we adapted and proposed a hybrid dual-path downsampling method (HDPD) for dataset augmentation, and constructed a dynamic reference point propagation network (DRP-Net) for semantic segmentation. The DRP-Net architecture addresses geometric feature mismatches between organs through a dynamic kernel edge convolution module (DKEC). Furthermore, it utilizes a global–local semantic feature fusion upsampling module (GL-SFFU) to overcome boundary blurring caused by plant growth and enhance detail discrimination. Based on the semantic segmentation results, a clustering algorithm was used to achieve leaf instance segmentation and extract key phenotypic parameters. Experimental results demonstrate that DRP-Net achieves significant performance in the tomato stem and leaf segmentation task, with mean precision, recall, F1 score, and mIoU reaching 94.97%, 93.93%, 94.43%, and 89.34%, respectively. The extracted phenotypic parameters, such as leaf length, leaf width, and leaf area, exhibit strong correlations with manual measurements (R² greater than 0.92 and 0.88, respectively). This study provides an effective technical solution for the precise segmentation of complex plant organs and high-throughput phenotyping analysis for breeding.

Why it matches plant phenotyping methodsトマトの3D点群から茎葉をセグメンテーションし、葉形質を抽出する手法を開発・検証しており、植物フェノタイピングが研究の中心である。

abstractconstructed a dynamic reference point propagation network (DRP-Net) for semantic segmentation

Innovative photosynthesis model twinning after intelligent interpretation of complex sensor analytics →

Accurate canopy photosynthesis modeling is essential for understanding and optimizing crop growth and yield in greenhouse agriculture. Current models have limited predictive capability due to inadequate responsiveness to dynamic environments and delays in parameter acquisition, making accurate predictions challenging under the complex conditions of solar greenhouses. This study aimed to develop a dynamic canopy photosynthesis model for greenhouse tomatoes, leveraging an IoT sensor network for real-time biological feedback and parameterization. By integrating real-time monitoring with dynamic feedback, the model facilitates precision management of greenhouse tomato cultivation, thereby optimizing plant growth, resource use efficiency, and yield predictability. To achieve this, a non-destructive inversion method based on a dual weighing system was developed, enabling accurate dynamic monitoring of tomato canopy leaf area index (LAI, R² ≥ 0.94) and the photosynthetic leaf area index (LAIₚ, R² ≥ 0.91), continuously providing parameters for updating modelling (validated against destructive sampling and actual measurements for trait specifics). Based on accurate parameter acquisition, a dynamic canopy photosynthesis model was developed using LAIₚ as the core variable, integrating above-canopy radiation. A newly developed parameter, which integrates the radiation component of transpiration, serves as a key factor for estimating photosynthesis. This innovative approach allows for accurate daily prediction and assessment of assimilated biomass. Experimental results from 2022 and 2023 showed that the LAIₚ model performed better than the comparison model, showing higher accuracy and adaptability (R² = 0.87 and 0.89, NRMSE = 0.17 and 0.12 vs. R² = 0.70 and 0.80, NRMSE = 0.26 and 0.15). These results confirmed the reliability of the integrated modeling framework, which forms a closed-loop system connecting real-time plant monitoring, statistical parameter inversion, online model adaptation, and biomass feedback verification. This modeling approach provides a solid foundation for precise growth simulation, sustainably improving yield and quality in solar greenhouse tomatoes, and advancing digital twin-enabled intelligent production.

Why it matches plant phenotyping methods植物キャノピーのLAIおよび光合成LAIを非破壊・連続推定するセンサー／逆解析法を開発し、破壊サンプリング等で検証している。植物形質取得とモデル連携が研究の中心である。

abstracta non-destructive inversion method based on a dual weighing system was developed, enabling accurate dynamic monitoring of tomato canopy leaf area index (LAI, R² ≥ 0.94) and the photosynthetic leaf area index (LAIₚ, R² ≥ 0.91)

Image processing–assisted evaluation of yeast-encapsulated clove formulations against tomato brown rugose fruit virus →

Tomato brown rugose fruit virus is an emerging threat to tomato and other solanaceous crops, demanding eco-friendly antiviral approaches. Plant-derived clove products, including essential oil and ethanolic extract, possess antiviral potential but are limited by instability and delivery challenges. In this study, clove essential oil and extract were encapsulated in plasmolysed and non-plasmolysed yeast cells (Saccharomyces cerevisiae) through an environmentally friendly process to enhance their stability and effectiveness. The encapsules were analysed by gas chromatography–mass spectrometry, Fourier-transform infrared spectroscopy, and scanning electron microscopy. Their antiviral activity against tomato brown rugose fruit virus infection was evaluated under greenhouse conditions, and virus presence was confirmed by reverse transcription–polymerase chain reaction. Treatment efficacy was assessed via image-based lesion quantification, including area, count, and color parameters. Image-derived results were consistent with manual measurements, validating the reliability of digital evaluation. Treated plants exhibited reduced lesion formation and color alterations associated with symptom suppression. Yeast encapsulation thus improved the antiviral performance of clove-based products, while image processing provided a non-destructive and straightforward tool for early detection and monitoring of plant virus infections.

Why it matches plant phenotyping methods画像処理による植物ウイルス症状（病斑）の定量化と手動測定との信頼性検証が研究の中心的要素であり、植物病害状態の表現型測定に該当する。

abstractTreatment efficacy was assessed via image-based lesion quantification, including area, count, and color parameters.

Tomato Maturity Classification and Yield Estimation for RGB and Multispectral Images →

With the increasing cost of labor, smart agriculture has emerged as a key trend for the future of agricultural development. This paper presents an integrated approach for tomato maturity clas-sification and yield estimation using both RGB and multispectral images. The proposed approach consists of three main components: tomato detection, tomato tracking and counting, and maturity classification of tomatoes. YOLOv8 combined with OSNet is first employed to detect tomatoes, while StrongSORT is then adopted to track consistent identities across image sequences. For maturity classification, multiple vegetation indices, including NDVI, GNDVI, and GRRI, are first transformed using principal component analysis, followed by classification using support vector machines, k-nearest neighbors, and neural networks. Tomatoes are categorized into three ma-turity levels: immature, almost mature, and mature. Results demonstrate that the proposed ap-proach can effectively estimate yield of tomatoes at each maturity stage. This capability provides practical support for harvest planning and labor allocation in precision agriculture.

Why it matches plant phenotyping methodsRGB・マルチスペクトル画像からトマトの成熟度と収量を推定する画像解析ワークフローが中心で、果実状態および収量という植物形質を直接評価している。

abstractThe proposed approach consists of three main components: tomato detection, tomato tracking and counting, and maturity classification of tomatoes.

MSP-Net: An Effective Multi-Scale Feature-Aware Detection Network for the Detection of Tomato Leaf Diseases. →

To advance automatic tomato leaf disease detection in precision agriculture, this study addresses critical challenges in complex field environments, such as variable lesion scales, background interference, and deployment constraints. We propose MSP-Net, a task-driven detection framework with targeted architectural refinements integrating three specific optimizations. First, a Multi-Scale Perception Convolution Module (MSPCM) is introduced to capture diverse disease features across early-to-late infection stages. Second, SimAM-enhanced C3k2 layers are utilized to suppress background noise and focus on fine-grained lesion cues. Third, a Multi-Scale Feature Enhancement Module (MSFEM) bridges the semantic gap between shallow and deep features to improve fusion efficacy. Furthermore, we construct a lightweight variant, L-MSP-Net, using architectural migration and structured pruning for edge efficiency. Experimental results on the real-world Tomato-Village dataset show that MSP-Net achieves 92.0% mAP@0.5, outperforming the YOLOv11s baseline by 2.0%. L-MSP-Net attains 86.1% mAP@0.5, improving by 3.6% over the lightweight YOLOv11n baseline while reducing parameters by 10.5%, and is successfully deployed on the RK3588 edge platform. Additional cross-dataset experiments on PASCAL VOC and MS COCO evaluate the transferability of the proposed architectural refinements to generic object detection tasks.

Why it matches plant phenotyping methodsトマト葉の病変・病害状態を画像から検出するネットワークを開発し、データセット間評価とエッジ実装まで行っており、植物病害フェノタイピング手法が中心である。

abstractExperimental results on the real-world Tomato-Village dataset show that MSP-Net achieves 92.0% mAP@0.5

Enhancing Generalization of Tomato Leaf Disease Classification via TDR Model and Field-Conditioned Data Augmentation →

Tomato leaf diseases significantly affect agricultural productivity, particularly when detection systems are deployed under real-field conditions characterized by illumination variation, background clutter, and image noise. Although deep learning-based models have achieved high accuracy on laboratory datasets such as PlantVillage, their generalization performance often degrades when applied to real-world environments. This study proposes a lightweight CNN-based tomato leaf disease recognition model, referred to as the TDR-Model, combined with field-conditioned data augmentation strategies. The proposed model integrates MobileNetV3 with Convolutional Block Attention Module (CBAM) and Omni-Dimensional Dynamic Convolution (ODC) to enhance feature representation while maintaining computational efficiency. Field-conditioned augmentation using the Albumentations library to simulate real-world visual variations during training. The model is evaluated on the real-world tomato set consisting of 10 classes and 885 leaf images. Experimental results show that the proposed model achieves an overall test accuracy of 82.94%, with precision, recall, and F1-score of 85.06%, 83.04%, and 83.03%, respectively. Furthermore, the model requires only 3.47 million parameters, 0.23 GFLOPs, and an average inference time of 5.15 ms, making it suitable for real-time and resource-constrained agricultural applications. These results indicate that the proposed approach effectively balances accuracy and efficiency for practical tomato leaf disease detection.

Why it matches plant phenotyping methodsトマト葉の病害状態を画像から分類するCNNモデルと現場条件向けデータ拡張を開発し、精度・計算量・推論時間を評価しているため、植物表現型取得手法が中心である。

abstractThis study proposes a lightweight CNN-based tomato leaf disease recognition model, referred to as the TDR-Model, combined with field-conditioned data augmentation strategies.

Comprehensive Review of Machine Learning and Deep Learning Methods for Plant Disease Detection via PlantVillage Dataset →

Plant diseases continue to pose a serious challenge to agriculture, leading to substantial yield losses and posing a threat to global food security. Accurate and early identification of plant diseases is crucial for effective crop management. However, traditional manual inspection methods are time-consuming, subjective, and heavily dependent on expert knowledge. With recent advances in artificial intelligence and computer vision, automated plant disease detection systems have emerged as a reliable alternative. These systems depend strongly on large, well-annotated image datasets for training and evaluation. Among publicly available resources, the PlantVillage dataset is one of the most widely used benchmarks for plant disease research. It contains over 50,000 high-resolution RGB leaf images captured under controlled conditions and covers 14 crop species, including tomato, potato, and bell pepper, along with multiple other plants. The dataset represents 38 distinct classes encompassing both healthy and diseased leaf categories. All images are collected against uniform backgrounds, providing visual consistency and making the dataset suitable for benchmarking machine learning and deep learning–based plant disease classification models. This survey provides a comprehensive review of the PlantVillage dataset and its contribution to the advancement of automated plant disease detection. It traces the progression from traditional handcrafted feature–based classifiers to convolutional neural networks, transfer learning approaches, and recent transformer-based architectures. Various methods are compared in terms of classification accuracy, generalization ability, computational efficiency, and robustness. In the survey we have identified that Transfer Learning model showed 99.75% accuracy. The survey also discusses key limitations of the dataset, particularly its controlled imaging conditions and challenges related to real-field deployment. Finally, future research directions are highlighted, including domain adaptation, explainable artificial intelligence, multi-disease recognition, and real-world agricultural applications. This work aims to offer researchers a structured understanding of the PlantVillage dataset and support the development of next-generation intelligent crop disease diagnostic systems.

Why it matches plant phenotyping methodsPlantVillage画像を用いた植物病害状態の画像ベース推定手法とデータセットを中心に、複数の機械学習手法を比較・レビューしているため、植物フェノタイピング方法論のレビューとして含める。

abstractThis survey provides a comprehensive review of the PlantVillage dataset and its contribution to the advancement of automated plant disease detection.

Efficient Plant Disease Detection Using Ceta‐Coyote Calibrated Deep Convolutional Neural Network Classifier →

Early plant disease detection is crucial to mitigate the disease progression and avoid the negative impacts on crops. Consequently, this research proposes the Ceta‐Coyote calibrated deep convolutional neural network (CtCODCN) for early plant leaf disease detection. Specifically, the proposed approach exploits the inherent feature extraction capability of CtCODCN and effectively learns the complex relationship between features, leading to improved disease detection. Besides, the Ceta‐Coyote optimization (CtCO) adaptively fine‐tunes the hyperparameters of the CtCODCN and improves the overall detection accuracy. In addition, the Local Binary and Ternary Residual Wavelet (LBTRW) approach extracts the textural and color data via capturing the smaller intensity variations by utilizing the wavelet features, local binary pattern (LBP), local ternary pattern (LTP), and Residual network‐101 (ResNet‐101). Moreover, the proposed approach utilizes advanced mechanisms to minimize the computational complexity and the error rate of the plant disease detection. The extensive experiments demonstrate the proposed CtCODCN model's effectiveness, evaluated in terms of metrics, achieving 98.270% of accuracy, 98.680% of sensitivity, and 97.04% of specificity, respectively, while using real‐time tomato images. Further, the proposed approach obtains the metric values of 96.39%, 99.64%, and 96.14% while using real‐time soya bean images, demonstrating the optimized performance of the proposed model.

Why it matches plant phenotyping methods植物葉の病害状態を画像から推定する深層学習手法を提案・評価しており、病害フェノタイピング手法が研究の中心です。

abstractthis research proposes the Ceta‐Coyote calibrated deep convolutional neural network (CtCODCN) for early plant leaf disease detection.

Lightweight deep learning for tomato disease detection: trends, challenges, and edge AI perspectives. →

Tomato ( Solanum lycopersicum ) is a globally cultivated horticultural crop, yet its productivity is severely constrained by foliar and insect-vectored diseases that reduce its quality and production. Early and accurate diagnosis of these diseases, along with sustainable biocontrol strategies, is essential for improving crop health and reducing economic losses. This review synthesizes and evaluates the recent progress in lightweight deep learning models and edge AI for tomato disease detection, highlighting their potential for practical deployment in precision agriculture. A comprehensive survey of recent literature was conducted, which covers convolutional neural networks, transformer-based models, optimization techniques including pruning, quantization, and knowledge distillation, and use of explainable AI tools to enhance transparency and trust. In addition, experimental validation was performed by utilizing MobileNetV2 and EfficientNetB0 on a subset of tomato diseases that are most common and prevalent in Tamil Nadu. The test performance of both the models resulted in an overall accuracy of 99.9% and macro-F1 nearly 0.99. Further, a unique framework that combines AI-powered diagnosis with microbial biocontrol recommendations is proposed offering a solution to manage diseases in both eco-friendly and region-specific way. Overall, this work provides a roadmap for combining sustainable methods with AI-driven diagnosis, promoting resilient, scalable, and farmer-friendly agricultural systems.

Why it matches plant phenotyping methodsトマト葉の病害状態を画像から推定する深層学習手法をレビューし、モデル性能も実験検証しているため、植物フェノタイピング手法が中心です。

abstractThis review synthesizes and evaluates the recent progress in lightweight deep learning models and edge AI for tomato disease detection

Machine learning model provides stress biomarkers for the classification of abiotic stress in Micro-Tom. →

Water deficit, salinity, and cadmium (Cd) contamination have generated an environmental problem worldwide, leading to damages to plant growth due to alteration in their metabolism. This study aimed to classify enzymatic and non-enzymatic antioxidant systems in Micro-Tom (MT) plants when subjected to two intensities (moderate and severe) of water deficit, salinity, and Cd exposure. The experimental design was a completely randomized 3 × 2 factorial, with the first factor representing the stress agents (water deficit, salinity, and Cd) and the second factor indicating stress intensities (moderate and severe), along with a control group. After an acclimation period, plants were exposed to 10 days of stress. Water deficit treatments were imposed using solutions adjusted to osmotic potentials of − 0.40 MPa and − 1.00 MPa; salinity stress was established with nutrient solutions containing 40 mM or 120 mM NaCl; and Cd stress was induced using nutrient solutions with 0.25 mM or 0.5 mM CdCl₂. Laboratory analyses included lipid peroxidation, hydrogen peroxide content, proline accumulation, protein quantification, and enzyme extraction. Descriptive analyses, and a Spearman’s correlation, identified the behavior of enzymatic and non-enzymatic systems for each stress agents and intensities, enabling the selection of key influencing factors. A factorial analysis of variance was performed to assess the mean differences among the treatments (α = 0.05) for enzymatic, non-enzymatic systems, MDA and, H₂O₂. Using this data, a decision tree model classified the stresses into four levels: low, low-medium, medium-high, and high. Variations in antioxidant response and stress biomarkers were detailed, with proline and superoxide dismutase identified as the primary variables of significance across stress indicators. Furthermore, the model achieved robust classification performance with Matthew’s correlation coefficients exceeding 0.80 in the extreme classes; however, it encountered limitations in distinguishing between classes with closely proximate values. The findings indicate the capability of the decision tree to classify stress levels in plants.

Why it matches plant phenotyping methods植物の抗酸化・生理指標から非生物的ストレス強度を推定・分類する決定木モデルが中心で、性能評価も行っているため、植物状態の計算的フェノタイピングに該当します。

abstractUsing this data, a decision tree model classified the stresses into four levels: low, low-medium, medium-high, and high.

From pixels to points: An AI framework with weaker-and-fewer-labels for lightweight 3D phenotyping using 2D-3D coordinate mapping and VLMs →

3D phenotyping of seedlings is crucial to tomato cultivation in greenhouse facilities. Current studies focus on high-quality point cloud reconstruction and artificial intelligence (AI) 3D segmentation to derive phenotypic traits like plant height and crown width, which heavily rely on manual annotation and possess high complexity in deployment. This study proposes a novel AI framework from pixels to points, for efficient 3D plant phenotyping of tomato seedlings. Through the integration of 2D-3D coordinate mapping and AI vision language models, the proposed method enables accurate reconstruction and analysis of 3D phenotypic traits from single-view data. Top-down RGB images and corresponding point clouds with spatial alignment are captured using a binocular camera. Vision language models are employed with the text prompt “plant” to automatically generate bounding boxes and masks, thereby minimizing manual annotation. These outputs are further transferred to a lightweight YOLO11-segment model. The core innovation is established in our 2D-3D mapping strategy, through which plant-specific 3D points are efficiently extracted using only 2D masks. Non-plant points within initial masks are repurposed to determine ground height for improved plant height estimation, while masks are refined using the Excess Green Index to enhance crown width measurement. An mAP₅₀ of 96.0% is achieved by the YOLO11-segment model. Concerning sparse canopy, highly accurate results are yielded by our phenotyping approach, with RMSE values of 1.7 cm for plant height and 1.0 cm for crown width, and R 2 values of 0.93 and 0.95 against manual measurements. For dense canopy, the usage of a reference chessboard improves the performance (RMSE was reduced from 9.57 cm to 2.07 cm). Annotation dependency is significantly reduced, computational complexity is decreased, edge deployment is supported, and efficient technology transfer is enabled by the presented method. Considerable potential is offered for high-throughput screening of elite tomato varieties with desirable agronomic traits. • Real-time low-cost 3D phenotyping of tomato plants is proposed. • Weak labels simplify the 3D plant segmentation. • Segment the 3D point cloud using 2D pixel-masks with spatial alignment. • Vision language models and knowledge transfer further simplify the AI application.

Why it matches plant phenotyping methodsトマト苗の3D表現型を抽出する画像・点群・AI統合手法を開発し、手動測定との精度検証も行っており、表現型取得法が研究の中心である。

abstractThis study proposes a novel AI framework from pixels to points, for efficient 3D plant phenotyping of tomato seedlings.

Novel estimation of tomato soluble solids content using linearly transformed reflectance-based spectral indices. →

Rapid and non-destructive estimation of soluble solids content (SSC) is essential for tomato quality evaluation, yet the generalization ability of many existing spectral models remains limited when applied across multiple cultivars. In this study, hyperspectral reflectance was combined with a genetic algorithm (GA)-based optimization strategy to develop a robust SSC prediction framework applicable to diverse tomato types. Spectral reflectance and SSC (°Brix) were measured for 152 fruits representing 13 cultivars, including large red, medium red, red cherry, and yellow cherry types. To overcome the structural rigidity of conventional fixed-form spectral indices, reflectance spectra were linearly transformed to construct three novel indices: the linearly transformed difference spectral index (ltDSI), linearly transformed normalized difference spectral index (ltNDSI), and linearly transformed ratio spectral index (ltRSI). For each index, GA was employed to simultaneously optimize wavelength combinations and transformation coefficients. Under identical calibration and validation datasets, the GA-optimized indices consistently outperformed conventional two-band spectral indices as well as full-spectrum partial least squares models, while exhibiting markedly reduced sensitivity to tomato type. Across all validation datasets, the proposed models achieved coefficients of determination of approximately 0.80, with root mean square errors around 0.6°Brix and mean relative errors close to 10%. These results demonstrate that joint optimization of spectral index structure and parameters is an effective strategy for improving model robustness and transferability. The proposed framework provides a scalable solution for non-destructive SSC assessment and offers practical guidance for the development of low-cost, field-deployable spectral sensing tools for fruit quality phenotyping across cultivars and growing conditions.

Why it matches plant phenotyping methodsトマト果実のSSCという植物形質を対象に、ハイパースペクトル反射とGA最適化による新規推定指標を開発・検証しており、形質取得手法が研究の中心である。

abstracthyperspectral reflectance was combined with a genetic algorithm (GA)-based optimization strategy to develop a robust SSC prediction framework applicable to diverse tomato types.

Ultra-flexible PINE arrays for month-long, continuous intracellular ion flux monitoring in plants with nanomolar accuracy →

High-precision in vivo monitoring of ion fluxes is essential yet challenging studying plant electrophysiology such as growth regulation, signal transduction and stress responses. Existing methods for probing ion dynamics are limited by low sensitivity, high invasiveness that interferes physiological processes, and the inability to accurately resolve ion homeostasis with required spatial and temporal resolution. Here, we introduce ultraflexible, plant implantable nanoelectrode (PINE) arrays manufactured on 1.2-μm-thick polymer substrates, which enable ultrasensitive and selective measurement of ionic current for month-long via scalable nanofabrication techniques. The fabricated PINE arrays have a smaller dimension than typical plant cells as well as less stiffness, facilitating minimally invasive integration with living plant cells. This subcellular-scale plant-electronic interface allows for reliable, selective detection of K + flux with a detection limit of ∼10⁻⁸ M, and thus allows continuous, stable monitoring of tomato stem cells over six weeks, capturing dynamic potassium fluctuations during all key growth stages. More importantly, the method permits long-term, real-time tracking of ion-specific dynamics without disrupting plant cellular structure or altering endogenous ion concentrations. Therefore, PINE provides unprecedented access to ion homeostasis and signaling networks, making it an excellent platform for precision agriculture and a foundational tool for future digital plant engineering.

Why it matches plant phenotyping methods植物細胞内のK+フラックスを長期間・リアルタイムに測定する超柔軟ナノ電極アレイを開発し、感度・選択性・長期安定性を実証した研究であり、植物生理状態の取得手法が中心である。

abstractHere, we introduce ultraflexible, plant implantable nanoelectrode (PINE) arrays

Feature extraction in sensor plant disease datasets using reformed membership functions independent of class variables. →

Sensor-based datasets often have limited features because continuous sensor deployment is expensive and complex. This study aims to develop a Membership Function-based Feature Extraction (MFFE) technique that operates without dependency on class variables to enhance small-sized sensor-based plant datasets. The research utilizes two sensor-based tomato disease datasets - TomEBD and TPMD, which have been collected in real-time. To address the dataset imbalance, the KMeans-SMOTE technique is applied. Feature extraction is performed using reformed triangular and gaussian membership functions, where all parameters are computed solely from the training data to prevent information leakage and biased evaluation. The enhanced datasets are classified using two optimized models: Optimized Kernel Extreme Learning Machine (OKELM) and Optimized Radial Basis Function Neural Network (ORBFNN), both tuned using the Optuna framework. The proposed technique is further validated on eight benchmarking non-plant-based datasets. Among all models, the TMF-ORBFNN achieved the highest accuracy across both plant-disease and benchmark datasets. Further, statistical analysis using the Friedman test and post-hoc Bonferroni-Dunn test showed that TMF-ORBFNN performed significantly differently from its counterparts. The time complexity of the proposed approach has also been analysed. The proposed MFFE technique provides effective feature extraction in small, sensor-based datasets without class-variable dependency. Enhancing and classifying plant-disease datasets using the proposed TMF-ORBFNN model will help farmers take timely actions to prevent crop diseases and reduce pesticide use.

Why it matches plant phenotyping methods植物病害データから病害状態を抽出・分類する特徴抽出法と分類ワークフローが研究の中心であり、センサベースの植物病害フェノタイピング手法として適格です。

abstractThis study aims to develop a Membership Function-based Feature Extraction (MFFE) technique that operates without dependency on class variables to enhance small-sized sensor-based plant datasets.

Edge-Ready Lightweight CNN Architectures for Tomato Leaf Disease Detection Using Transfer Learning →

Tomato ( Solanum lycopersicum ) is a globally important horticultural crop whose productivity is severely constrained by foliar diseases caused by fungal, bacterial, and viral pathogens. Early, accurate detection is essential for minimising yield losses and supporting precision agriculture, yet traditional diagnosis remains time-consuming, subjective, and heavily dependent on expert knowledge. With the growth of deep learning techniques, transfer learning based convolutional neural networks (CNNs) have emerged as one of the powerful tools for automation of plant disease classifications. But the main problem is that comparative analyses of multiple architectures trained under ideal conditions remain limited. This study evaluates the performances of five widely used CNN models, Inception V3, EfficientNet-B0, ResNet50, VGG16 and AlexNet. These models were fine-tuned using a curated PlantVillage tomato leaf dataset consolidated into four major classes named as: Fungal, Bacterial, Viral and Healthy. Standard preprocessing techniques, augmentation and hyperparameter settings were applied across all networks to ensure fair comparison. Experimental results have shown that Inception V3 achieved the highest accuracy (97%), followed by ResNet (95%) and EfficientNet-B0 (91%), while VGG16 and AlexNet showed low performance due to limited depth and representation capacities. Analysis of the confusion matrix indicated a consistent distinction between healthy leaves, while the primary cause of misclassification was the visual overlap between fungal and bacterial lesions. These results suggest that Inception V3 is a strong candidate for practical use in automated disease monitoring systems. Subsequent research should focus on validation in real-world settings, interpretable AI techniques, and lightweight architectures that are suitable for mobile and edge-based smart farming solutions.

Why it matches plant phenotyping methodsトマト葉の病徴を画像から分類するCNN手法を複数モデルで比較評価しており、植物の病害状態推定と手法検証が研究の中心です。

abstractThis study evaluates the performances of five widely used CNN models, Inception V3, EfficientNet-B0, ResNet50, VGG16 and AlexNet.

TRD-Net: an efficient tomato ripeness detection network based on improved YOLO v8 for selective harvesting. →

Fruit recognition and ripeness detection are crucial steps in selective harvesting. To better address the difficulties of fruit recognition and ripeness detection techniques in complex facility environments, a novel lightweight tomato ripeness detection network model based on an improved YOLO v8s is proposed (called TRD-Net). Here, a tomato dataset including 3,330 images from real scenarios was constructed, and an accurate lightweight tomato ripeness detection model trained on the captured images was developed. The TRD-Net model achieves efficient detection of tomatoes affected by overlapping occlusions, lighting variations, and capture angles, offering swifter detection speeds and lower computational demands. Specifically, the feature extraction module of YOLO v8s was refactored by employing spatial and channel reconstruction convolution (SCRConv) and adding the SimAM attention mechanism. The CIoU loss function was replaced by the MPDIoU loss function. The performance of the novel TRD-Net was comprehensively investigated. The proposed TRD-Net achieved an mAP@0.5 of 0.9581 with an improvement of 4.32 percentage points, and the model size decreased from 22.5 M to 17.6 M with an inference time of 8.7 ms per image. The number of model parameters and floating-point operations per second (FLOPs) decreased by 19.69% and 22.03%, respectively. Compared with state-of-the-art models, the proposed TRD-Net is notably promising for real-time tomato recognition and maturity detection. The study contributes to the establishment of a machine vision sensing system for a selective harvesting robot in a complex gardening environment.

Why it matches plant phenotyping methodsトマト果実の成熟状態を画像から推定する軽量検出ネットワークを開発し、データセット上で性能評価しているため、植物状態の取得・抽出手法が中心である。

abstracta novel lightweight tomato ripeness detection network model based on an improved YOLO v8s is proposed

Software application in early blight detection in tomatoes using modified MobileNet architecture. →

This study presents an automated framework for early blight detection in tomato plants using a modified MobileNet architecture. Addressing the limitations of traditional labor-intensive methods, this study proposes a two-stage pipeline combining (1) transfer learning with depthwise separable convolutions for efficient feature extraction and (2) a meta-learned ensemble of Random Forest, SVM, and Gradient Boosting classifiers to handle real-world variability in lighting and environmental conditions. The approach introduces two custom convolutional layers (Custom_Feature_Extraction_Block) that improve F1-score by + 3.8 points over the MobileNet baseline, with the ensemble contributing an additional + 2.1 points. Evaluated on a balanced PlantVillage dataset (1,982 images) with extensive augmentation to simulate variable lighting and orientations, the system achieved up to 100% accuracy with selected classifiers on a held-out validation subset of 30 images under controlled conditions. To assess generalization, we further validated the framework on an independent dataset (tomato_dataset_v2, 30, 609 images, 10 classes) containing field-acquired tomato leaf images, where the model attained 94.5% accuracy, confirming robustness beyond control environments. Comparative analysis with 10 recent methods demonstrates superior accuracy-efficiency trade-offs, offering practical on-device decision support for smallholder farmers. The framework’s lightweight design (4.2 M parameters, 23 ms/image on Raspberry Pi 4) and validated scalability underscore its potential for mobile and drone-based agricultural deployment. This addresses critical needs in global food security through accessible plant disease detection.

Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出する深層学習パイプラインを開発し、独立データセットで性能検証しており、植物病害状態の表現型取得が中心である。

abstractThis study presents an automated framework for early blight detection in tomato plants using a modified MobileNet architecture.

Development of a Novel Method for Disease Severity Driven Variable Rate Chemical Application Based on Plant Morphological Indicators →

Early blight, caused by Alternaria alternata, poses a critical challenge to tomato (Solanum lycopersicum L.) production, causing significant yield losses worldwide. Accurate quantification of plant disease severity is essential for the development of intelligent, site-specific crop protection systems. This study investigates the morphological responses of tomato plants following incidence of early blight disease across different stages of disease progression, with the objective of establishing biologically meaningful indicators for imaging-based disease severity classification. Key plant morphological parameters, including plant height, total leaf area, and diseased leaf area, were monitored over time and compared with healthy plants. Analysis of variance revealed a statistically significant difference in plant height between healthy and diseased plants after inoculation of disease, with average plant height after 90 days of growth were 94.86 and 81.81 cm respectively, indicating the impact of disease on overall plant growth. Temporal analysis of leaf area and diseased area exhibited distinct disease progression patterns, comprising an initial latent phase, a rapid symptom expansion phase, and a terminal phase characterized by tissue degradation. Disease severity was quantified using an area-based severity percentage derived from the ratio of diseased area to total leaf area, providing a normalized and scalable metric of infection intensity. The observed morphological and spatial disease characteristics closely correspond to features that can be extracted using machine vision techniques, such as changes in canopy geometry and lesion extent. The findings highlight the potential and the importance of severity based assessment of disease for variable-rate site-specific spraying systems, demonstrating clear advantages over conventional target-specific approaches in reducing chemical application, improving disease control efficiency, and supporting sustainable crop protection practices.

Why it matches plant phenotyping methods植物病害の重症度を、葉面積・病斑面積などの形態指標から画像ベースで定量化する方法の開発が中心であり、単なる病害実験のルーチン測定ではない。

abstractwith the objective of establishing biologically meaningful indicators for imaging-based disease severity classification

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

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

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

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

Water status diagnosis in greenhouse drip-irrigated tomato and celery using leaf turgor dynamics and machine learning. →

Introduction Accurate crop water status monitoring is crucial for optimized irrigation in controlled environments, but traditional approaches relying on damaging measurements or sporadic sampling frequently restrict real-time evaluation. Methods This study explored the non-invasive leaf patch clamp pressure (LPCP) probe to evaluate the water status of drip-irrigated tomato and celery. Leaf turgor dynamics analysis enabled the characterization of the LPCP probe's output parameter (P p ) and its environmental drivers, and the development of predictive machine learning models. Results The results indicated that diurnal patterns of P p in drip-irrigated tomato and celery exhibited two distinct states: State I (unimodal) and State II (troughed), corresponding to moisture conditions with no or mild stress, and severe stress, respectively. The soil water content (SWC) thresholds for State I were set at SWC > 20% (tomato) and SWC > 19% (celery), whereas those for State II were set at SWC p was positively associated with solar radiation but negatively associated with SWC (in tomato) and wind speed (in celery). For State II, the associations between P p and environmental parameters were less than those in State I. Interestingly, compared to full irrigation, non-full irrigation treatments not only showed a higher proportion of State II but also resulted in an increase in both P p,max and P p,min by 15.39%-138.39% in tomato and 3.44%-94.02% in celery. These analytical results yielded four model parameter combinations based on the inclusion of SWC and the management of distinct P p states. The prediction model that integrated Combination 4 (substate P p prediction based on meteorological factors and SWC) with the random forest approach exhibited the highest accuracy (R 2 = 0.995, MSE = 2.419, RMSE = 1.540, and MAE = 0.531), with SWC identified as its key feature parameter. Discussion These findings provide a scientific foundation for optimizing the precision irrigation of greenhouse vegetables in drip systems.

Why it matches plant phenotyping methodsLPCPプローブによる葉の膨圧動態（水分状態）の非破壊測定と、機械学習による予測モデル開発が研究の中心であり、植物生理状態を抽出するフェノタイピング手法に該当する。

abstractThis study explored the non-invasive leaf patch clamp pressure (LPCP) probe to evaluate the water status of drip-irrigated tomato and celery.

High-Performance Deep Learning Techniques for Plant Disease Detection: Performance Analysis, Validation, and Applications →

The early detection of plant diseases is an indispensable task to improve crop yields and production quality. Crop disease observations by experienced pathologists are difficult and might take a long time. Therefore, deep learning (DL) techniques have been utilized to present an automated detection technique that could accurately and timely detect plant diseases. Several DL models in the literature were proposed, but no paper conducted a comparative study between those models to determine which of them was the best alternative for this task. Therefore, twenty-one DL models are compared in this review paper to show which of them could achieve better classification accuracy when applied to detect plant diseases. Three publicly available datasets, namely PlantVillage, Tomato Leaves, and Groundnut Plant Leaf, are used to assess the performance of those models under five different performance metrics, such as accuracy, precision, recall, F1-score, and area under curve (AUC). The extensive experiments conducted in the same environments under the same number of epochs and batch size for all models show that EfficientNetB0 is the best for both PlantVillage and Tomato Leaves datasets, with a classification accuracy of around 99% and 98%, respectively, and ResNet152 is the best for the Groundnut Plant Leaf dataset, with a classification accuracy of 99.7%.

Why it matches plant phenotyping methods植物葉の病徴を画像から分類する深層学習手法を21モデルで比較・評価しており、植物病害状態の表現型推定と技術ベンチマークが中心である。

abstractTherefore, twenty-one DL models are compared in this review paper to show which of them could achieve better classification accuracy when applied to detect plant diseases.

Lightweight Vision-Transformer Network for Early Insect Pest Identification in Greenhouse Agricultural Environments. →

This study addresses the challenges of early recognition of fruit and vegetable diseases and pests in facility horticultural greenhouses and the difficulty of real-time deployment on edge devices, and proposes a lightweight cross-scale intelligent recognition network, Light-HortiNet, designed to achieve a balance between high accuracy and high efficiency for automated greenhouse pest and disease detection. The method is built upon a lightweight Mobile-Transformer backbone and integrates a cross-scale lightweight attention mechanism, a small-object enhancement branch, and an alternative block distillation strategy, thereby effectively improving robustness and stability under complex illumination, high-humidity environments, and small-scale target scenarios. Systematic experimental evaluations were conducted on a greenhouse pest and disease dataset covering crops such as tomato, cucumber, strawberry, and pepper. The results demonstrate significant advantages in detection performance, with mAP@50 reaching 0.872, mAP@50:95 reaching 0.561, classification accuracy reaching 0.894, precision reaching 0.886, recall reaching 0.879, and F1-score reaching 0.882, substantially outperforming mainstream lightweight models such as YOLOv8n, YOLOv11n, MobileNetV3, and Tiny-DETR. In terms of small-object recognition capability, the model achieved an mAP-small of 0.536 and a recall-small of 0.589, markedly enhancing detection stability for micro pests such as whiteflies and thrips as well as early-stage disease lesions. In addition, real-time inference performance exceeding 20 FPS was achieved on edge platforms such as Jetson Nano, demonstrating favorable deployment adaptability.

Why it matches plant phenotyping methods植物の病変・病害状態を画像から検出する軽量モデルの開発と性能評価が中心であり、単なる生物学的実験の routine 測定ではない。害虫検出も含むが、早期病変検出という植物状態の推定を技術的に評価しているため含める。

abstractproposes a lightweight cross-scale intelligent recognition network, Light-HortiNet, designed to achieve a balance between high accuracy and high efficiency for automated greenhouse pest and disease detection.

Agricultural robot plant automatic detection integrating visual navigation and phenotype recognition →

With the continuous improvement of the intelligence level of facility agriculture, agricultural robots are undertaking more and more autonomous tasks in greenhouse environments, and the multifunctional integration of visual perception systems has become a key technological bottleneck. A perception system for agricultural robots that integrates visual navigation and phenotype recognition is developed to address issues such as path recognition being susceptible to environmental interference and poor real-time plant detection. The system consists of a path navigation module and a plant detection module. The former introduces an image segmentation method based on visual transformation structure to extract agricultural path information. The latter adopts a lightweight instance segmentation structure to achieve precise segmentation and structural localization of crop phenotype regions. In the navigation model test, in the rain and fog disturbance scene, the average delay is 51.0 ms, the frame rate is 44.2 FPS, and the control jitter amplitude is 1.36°. The test results of the detection module show that its boundary F1 values for Tomato, Cucumber, Pepper, and Lettuce crops are 90.2%, 87.6%, 88.8%, and 86.3%, respectively. The experimental results show that the proposed scheme reduces inference delay while ensuring accuracy, has good environmental adaptability and edge deployment potential, and demonstrates good robustness and practicality in complex greenhouse environments.

Why it matches plant phenotyping methods植物の表現型領域を画像分割・認識する手法を開発し、複数作物で精度と実時間性能を評価しており、表現型取得が中心的な技術貢献です。

abstractA perception system for agricultural robots that integrates visual navigation and phenotype recognition is developed

Computer vision-based non-invasive biomass estimation and water stress monitoring in tomato plants →

Precision horticulture demands intelligent monitoring systems for automated crop management. While traditional biomass estimation relies on destructive sampling, modern ICT-driven approaches offer transformative solutions. This study presents an AI-powered methodology integrating YOLOv11 for autonomous biomass estimation and stress monitoring in tomato crops. High-resolution RGB imagery was captured across multiple phenological stages under two irrigation regimes. YOLOv11’s computer vision capabilities enabled automated canopy detection, segmentation, and digital biomass quantification, eliminating destructive sampling. Novel AI-driven metrics were introduced: convex hull area for stress-induced canopy alterations and compactness (digital biomass to convex hull ratio) for automated canopy assessment. This integrated approach achieved robust accuracy in stress detection and biomass estimation (R² =0.821), enabling real-time monitoring for precision horticulture. The model demonstrated exceptional performance with segmentation precision of 0.950, recall of 0.979, and mean average precision of 0.975 at IoU 0.5, with mAP50-95 of 0.826. The rapid inference time of 2.3ms per image enables high-throughput phenotyping and decision support for site-specific management. YOLOv11-derived digital biomass correlated strongly with fresh biomass (R² = 0.821). Image-derived features effectively differentiated control and stress conditions. Genotype analysis revealed variation in biomass accumulation: Arka Abhed and Arka Rakshak performed better under optimal irrigation, while Arka Vikas showed greater stress resilience. These results validate YOLOv11 as a scalable solution for intelligent crop monitoring and precision input application in next-generation digital horticulture.

Why it matches plant phenotyping methods植物の画像からバイオマスと水ストレスを推定するコンピュータビジョン手法を開発・検証しており、表現型取得が研究の中心である。

abstractThis study presents an AI-powered methodology integrating YOLOv11 for autonomous biomass estimation and stress monitoring in tomato crops.

Smart Agriculture in Morocco: An Intelligent Deep Learning Framework for Crop Disease Diagnosis →

The Moroccan agricultural sector is currently navigating a pivotal transformation driven by the “Generation Green 2020–2030” national strategy, which places a high priority on the digitalization of farming practices to bolster resilience against climate volatility and phytopathological risks. This study proposes a robust Smart Agriculture Framework engineered to automate crop disease diagnosis within mobile environments with limited resources. Unlike generic standard Deep Learning models often unsuited for local specificities, the methodology presented here is specifically tailored to Morocco’s agroecological context, targeting three strategic crops: Tomato (Souss-Massa region), Potato (Gharb plains), and Wheat (Chaouia region). A hybrid intelligent architecture is introduced that integrates a lightweight Convolutional Neural Network (CNN) with Particle Swarm Optimization (PSO-CNN) for autonomous hyperparameter tuning. The proposed framework was validated using a curated dataset of 15,000 images, rigorously augmented to reflect local field conditions, yielding a classification accuracy of 94.7%. This work effectively bridges the gap between theoretical AI architectures and practical Precision Farming, providing a rapid decision support system to minimize yield losses and align with the national objective of establishing a digitally empowered agricultural ecosystem.

Why it matches plant phenotyping methods植物画像から病害状態を推定するCNNベースの診断手法を開発し、画像データセットで検証しており、表現型取得・推定が中心である。

abstractThis study proposes a robust Smart Agriculture Framework engineered to automate crop disease diagnosis within mobile environments with limited resources.

EPDNet: A method for identifying tomato leaf diseases with uneven samples →

Tomato industry is one of the important parts of agriculture, and the timely diagnosis of leaf diseases plays a key role in ensuring its production safety. At present, most of the mainstream recognition technologies are based on laboratory standard images to construct recognition models. Although they can achieve accurate analysis of isolated leaves, it is difficult to cope with the practical challenges such as complex climate and environmental factors of plant growth in open environments. The recognition is difficult due to the influence factors of open environment, such as the distortion of disease spots caused by light, the occlusion of branches and leaves, and the confusion of soil attachment and real disease spots. To solve the problem of unbalanced distribution of tomato leaf disease samples in open environment and the big difference of similar diseases, this study proposes a single-modal recognition architecture for tomato leaf diseases based on Efficient localization and Physical information and Dynamic adaptive optimization Network (EPDNet). Firstly, an efficient positioning feature enhancement module is designed to effectively enhance the network’s attention to important regions by calculating and fusing the horizontal and vertical attention weights. Then, a physical information neural network-cross entropy hybrid loss function was designed to ensure the accuracy of prediction, while restricting the smoothness and continuity of the feature map to improve the robustness of the model. Finally, a dynamic adaptive optimization algorithm was designed to iteratively update the learning rate to improve the feature discrimination ability, so as to reduce the identification differences of diseases within the class. Experimental results show that the accuracy of EPDNet on the tomato leaf dataset reaches 93.62%, and the F1 score reaches 93.31 %, which is significantly better than the existing methods. This study provides an effective solution for the application of deep learning methods in crop diseases.

Why it matches plant phenotyping methodsトマト葉の病斑・病害状態を画像から認識する深層学習手法を開発し、データセットで性能評価しているため、植物病害フェノタイピング手法が中心である。

abstractthis study proposes a single-modal recognition architecture for tomato leaf diseases based on Efficient localization and Physical information and Dynamic adaptive optimization Network (EPDNet).

Edge Device-Oriented Tomato Fruit Thinning and Harvesting Model Under Adverse Weather Conditions. →

Accurate detection of tomato ripeness and size is critical for robotic thinning and harvesting but remains challenged by performance degradation in adverse weather, imprecise size estimation, and computational constraints on edge devices. To bridge this gap, we introduced (1) the TIDAW dataset (Tomato Images in Diverse Adverse Weather), synthetically generated via a physically-grounded atmospheric scattering model to simulate realistic rain and fog; and (2) Edge-YOLO-Tomato, a novel YOLOv8-based architecture, featuring four key innovations: a physics-aware scattering module that unifies multi-particle light transport theory with dual-attention mechanisms to explicitly model wavelength-dependent scattering for robust feature disentanglement; dilated convolutions enhancing receptive fields; a prior-embedded Wise-IoU loss incorporating botanical size distribution priors to rectify bounding box bias; and a compression framework that combines magnitude pruning and layer-wise pruning using neural architecture search. Extensive evaluations demonstrate leading performance: Edge-YOLO-Tomato achieves 93.3% mAP 50 and 74.3% mAP 50:95 on TIDAW, surpassing YOLOv8, YOLOv11, Faster R-CNN, and RT-DETR etc. by 1.1%-26.3% and 0.2%-2.2%, respectively. The compressed model attains a 4.7373 MB footprint (20.58% size reduction) with ≦ 0.5% accuracy loss and delivers 50% latency reduction on CPU. This work establishes a new paradigm for vision-based precision agriculture by unifying physical data synthesis, physics-aware modeling, and compression framework, enabling real-time robust fruit detection in uncontrolled environments. The codes are available at https://github.com/YLu567/Edge-YOLO-Tomato.

Why it matches plant phenotyping methodsトマト果実の成熟度・サイズを画像から推定するデータセットとエッジ向けモデルを開発・評価しており、果実形質の取得手法が中心的な貢献である。

abstractAccurate detection of tomato ripeness and size is critical for robotic thinning and harvesting

A multi-scale linear cross-modal fusion architecture for tomato leaf disease segmentation →

Tomato, as a globally important economic crop, requires precise and timely disease management to secure yield and quality. Yet segmentation robustness is often limited by weak semantic understanding from single-modality images, narrow receptive fields of convolutional structures, and discontinuous boundary predictions. To address these issues, we propose the Multi-scale Linear Cross-modal Fusion Architecture for Tomato Leaf Disease Segmentation (MS-LCFNet). We construct a real-world field dataset covering five major tomato leaf diseases, annotated by experts with detailed textual descriptions to enable multimodal learning. MS-LCFNet strengthens semantic representation via cross-modal fusion, captures local and global context through an Adaptive Long-short Distance Perception module, and improves boundary continuity with a Physics-informed Smoothness-constrained Loss. Experiments show that MS-LCFNet achieves 87.13 % mIoU on our dataset and 90.78 % on PlantVillage, improving over previous state-of-the-art methods by + 4.62 % and + 4.48 %, respectively, and demonstrating superior accuracy and robustness in complex agricultural scenarios.

Why it matches plant phenotyping methodsトマト葉の病害状態を画像からセグメンテーションする手法を開発し、独自データセットで性能評価しており、植物病害表現型の取得・抽出が中心である。

titleA multi-scale linear cross-modal fusion architecture for tomato leaf disease segmentation

Multi-model ensembles for object detection in multispectral images: A case study for precision agriculture →

Every year, 20%–40% of the global harvest is lost to pests and diseases, underlining the need for rapid and accurate diagnosis. Precision agriculture exploits intelligent devices, such as robots and drones, to enable early detection of pathogens through non-destructive imaging techniques and AI processing. In this study, we exploit Deep Learning techniques for handling multispectral images in agriculture field. In particular, we introduce an adaptive Multi-Model Ensemble framework that processes multispectral data without dimensionality reduction, fully exploiting spectral information to improve early disease detection. Furthermore, several comparisons with dimensionality reduction and data combinations were conducted, exploring different image stack configurations to find the optimal solution in disease detection. We validated our approach on a dataset of tomato plants affected by Tuta Absoluta and Leveillula Taurica, where it improves the ability of disease identification and classification even at early developmental stages, offering promising perspectives for phytosanitary monitoring and sustainable resource management.

Why it matches plant phenotyping methodsトマトの病徴・病害状態をマルチスペクトル画像から識別する深層学習アンサンブル手法を開発し、病害データセットで検証しており、植物フェノタイピング手法が研究の中心である。

abstractwe introduce an adaptive Multi-Model Ensemble framework that processes multispectral data without dimensionality reduction

High-throughput Morphological Phenotyping of Tomato Seedlings Using Computer Vision and Machine Learning →

Monitoring the early-stage vegetative growth is particularly critical for determining the plant's vigour, stress tolerance, and yield potential. Accurate and timely phenotyping of these traits is essential for informed decision-making in cultivation. However, traditional phenotyping methods are labour-intensive and time-consuming since most of them rely on manual inspection and subjective criteria. This study presents an automated, image-based approach integrated with artificial intelligence to monitor morphological traits of early growth stages of tomato plants. In this study, images of the seedlings of the tomato variety called “Thilina” were used, and an Excess Green (ExG) based segmentation method was employed for image preprocessing. The K-nearest neighbour machine learning model has gained the highest accuracy of 89% out of all machine learning models, and the convolutional neural network was utilised in identifying the plant timelines with prominent growth. The results of seedling evaluation through image-based phenotyping reveal that early vegetative parameters are reliable predictors of transplant readiness and vigour.

Why it matches plant phenotyping methodsトマト苗の形態形質を画像解析と機械学習で自動抽出・評価する手法が研究の中心であり、植物フェノタイピング手法の開発に該当する。

abstractThis study presents an automated, image-based approach integrated with artificial intelligence to monitor morphological traits of early growth stages of tomato plants.

DRP-Net and clustering algorithm for Stem-Leaf segmentation and phenotypic trait extraction from tomato point clouds →

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

Why it matches plant phenotyping methodsトマト点群から茎・葉を分割し、表現型形質を抽出する手法の開発がタイトルで明示されており、植物フェノタイピング手法が中心です。

titleDRP-Net and clustering algorithm for Stem-Leaf segmentation and phenotypic trait extraction from tomato point clouds

Plant Disease Detection Using CNN →

Abstract: Agriculture forms a cornerstone of the Indian economy, with food and cash crops playing a critical role in sustaining both the environment and human livelihoods. However, crop yields are significantly impacted each year by various plant diseases. The lack of efficient diagnostic methods, combined with limited awareness of disease symptoms and treatment options, often leads to widespread crop losses. This study explores the application of machine learning for plant disease detection, focusing on Convolutional Neural Networks (CNNs) to identify and classify diseases. The proposed approach employs advanced image processing techniques to analyze infected leaf regions, examining metrics such as time complexity and lesion area. The model was trained and tested on a curated dataset comprising 15 cases, including 12 disease categories such as Bell Pepper Bacterial Spot, Potato Early Blight, and Tomato Leaf Mold, alongside 3 categories of healthy leaves. The system achieved a test accuracy of 88.8%, demonstrating its potential for accurate plant disease detection. Performance evaluation was conducted using standard metrics to validate the model's reliability.

Why it matches plant phenotyping methods感染葉の画像から病害を分類し、病斑面積を解析するCNN手法の開発・評価が中心であり、植物の病害状態を直接推定するため含める。

abstractThis study explores the application of machine learning for plant disease detection, focusing on Convolutional Neural Networks (CNNs) to identify and classify diseases.

Estimating Chlorophyll Content by Optical Density of Plant Leaves Using Machine Learning →

Introduction. Chlorophyll plays a crucial role in absorbing and transforming light energy into a chemical form that provides organic matter production in plants. Monitoring of chlorophyll content helps to assess plant-environment interactions and the degree of influence of stress factors that are essential for yield management. Traditional laboratory methods of analyzing are time-consuming, destroying samples and unsuitable for rapid field evaluations. A more reasonable solution is to use lowcost, portable devices. Aim of the Study. The study is aimed at developing and training an ANN architecture to predict the chlorophyll content in plant leaves based on their optical density within specific visible spectrum ranges. Materials and Methods. The artificial neural network dataset was compiled from experi- mental measurements using the DP-1M densitometer and the CCM-200 chlorophyll meter. Data were collected from lettuce, pepper, tomato and zucchini leaves of different ages, which were grown in different light environments. The artificial neural network training was carried out in the Google Colab environment with subsequent adaptation of the model for using in a microcontroller device – a photocolorimeter for leaves. Results. The dataset with 1,000 entries showed that the leaf optical density range isfrom 0.57 to 2.54 relative units (red), from 0.9 to 1.66 relative units (green), and from 1.09 to 3.53 relative units (blue). According to these data, the chlorophyll content variations are from 3.1 to 156.5 relative units. In the study, there were compared six artificial neural network architectures that differed by hidden-layer neurons. The structure “32:32” had the highest accuracy (MAE = 6.64 rel. units, MAPE = 16.34%, R² = 0.8886). A simplified structure “4:4” was selected to simplify the model and improve the microcontroller efficiency. This structure maintained the performance (MAE = 6.83 rel. units, MAPE = 16.86%, R² = 0.8808) with much smaller amount of resources used – 41 weight parameters and 164 bytes of memory. A comparative evaluation with classical machine learning algorithms demonstrated the superiority of the developed model across all metrics. Discussion and Conclusion. The trained artificial neural network was implemented on a microcontroller-based photocolorimeter for leaves that enabled the non-destroying optical density measurements. The developed model allows implementing non-destroying and operational monitoring of the condition of plants, which is especially important in precision farming systems. This approach has significant potential for ecological monitoring and precision agriculture. The study results demonstrate the viability of machine learning for improving plant status assessment and developing digital agrotechnology solutions.

Why it matches plant phenotyping methods葉の光学密度からクロロフィル含量を推定するANNとマイコン実装型フォトカラリメータを開発・比較評価しており、植物形質取得が研究の中心です。

abstractThe study is aimed at developing and training an ANN architecture to predict the chlorophyll content in plant leaves based on their optical density within specific visible spectrum ranges.

DexFruit: Dexterous Manipulation and Gaussian Splatting Inspection of Fruit →

Dexfruit is a robotic manipulation framework that enables gentle, autonomous handling of fragile fruit and precise evaluation of damage. Soft fruits have long faced an issue of produce loss in both the harvesting and post-harvesting processes due to their extreme fragility and susceptibility to bruising, making them one of the hardest produce type to manipulate with automation. In this work, we demonstrate by using optical tactile sensing, autonomous manipulation of fruit with minimal damage can be achieved. We show that our tactile informed diffusion policies outperform baselines in both reduced bruising and pickand- place success rate across three fruits: strawberries, tomatoes, and blackberries. In addition, we introduce FruitSplat, a novel technique to represent and quantify visual damage in a highresolution 3D representation via 3D Gaussian Splatting (3DGS). Existing metrics for measuring damage lack quantitative rigor or require expensive equipment. With FruitSplat, we distill a 2D fruit mask as well as a 2D bruise segmentation mask into the 3DGS representation from just a web-cam video. Furthermore, this representation is modular and general, compatible with any relevant 2D model. Overall, we demonstrate a 92% grasping policy success rate, up to a 15% reduction in visual bruising, and up to a 31% improvement in grasp success rate on challenging fruit compared to our baselines across our three tested fruits. We rigorously evaluate this result with over 630 trials. Please checkout our website, which contains our code and datasets athttps://dex-fruit.github.io/.

Why it matches plant phenotyping methodsFruitSplatは、果実の損傷・打撲を3D表現として定量化する画像ベースの植物状態計測手法であり、開発と厳密な評価が研究の中心です。

abstractwe introduce FruitSplat, a novel technique to represent and quantify visual damage in a highresolution 3D representation via 3D Gaussian Splatting (3DGS).

An efficient and low-cost 3D phenotyping framework for tomato seedlings via neural radiance fields and PointNet++ →

Accurate and efficient plant phenotyping is essential for modern precision agriculture. as it provides reliable information for seedling quality evaluation, early detection of plant stress, and data support for crop breeding and yield prediction. Traditional three-dimensional (3D) reconstruction and analysis methods are often costly and time-consuming, because they usually depend on expensive laser scanning devices or require many input images. Even with these resources, they often fail to capture fine plant structures such as leaves and branches, which limits their application in seedling monitoring. To address these challenges, we propose an integrated framework that combines neural radiance fields (NeRFs) for high-fidelity 3D reconstruction, PointNet++ for robust semantic segmentation, and a customized algorithm for extracting key morphological parameters of tomato seedlings. The proposed framework can be used to reconstruct detailed 3D models at a low computational cost using only ordinary cameras and a limited number of 2D images. We validate the framework on the basis of a tomato seedling dataset and show that our approach outperforms traditional multiview stereo scanners and simple commercial 3D scanners in terms of both detail and efficiency. The accuracy of plant part segmentation reaches 90 %, and the extracted parameters (e.g., leaf area, stem height, branch angle, and internode distance) are highly correlated with the manual measurements (e.g., R 2 = 0.875 for the leaf area). This study provides a low-cost and scalable solution for 3D plant analysis, with direct benefits for automated monitoring of seedling quality in nursery production. Moreover, the proposed framework can be extended to other crops with complex structures, thus supporting wider applications in smart agriculture.

Why it matches plant phenotyping methodsトマト苗の3D再構成、植物部位分割、形態形質抽出を統合した低コスト画像ベース手法を開発・検証しており、植物フェノタイピングが研究の中心である。

abstractwe propose an integrated framework that combines neural radiance fields (NeRFs) for high-fidelity 3D reconstruction, PointNet++ for robust semantic segmentation, and a customized algorithm for extracting key morphological parameters of tomato seedlings.

Tomato Plant Leaf Disease Classification using Deep Learning: A Review →

Tomato plants frequently be afflicted by diseases that may damage plants and decrease farmers’ earnings. finding these illnesse s early could be very vital for coping with them that affect tomato plants, like Yellow Leaf Curl Virus, Leaf mildew , late Blight, Early Blight, Septorial Spot, Bacterial Spot, goal Spot, Mosaic Virus, healthful, spotted Spider Mite, Powdery mould .We use deep getting to know strategies, especially a combination of EfficientNet-B0 with VGG-16, EfficientNet-B0 with CNN and VGG-16 with CNN, to investigate images of tomato leaves and decide if they may be healthful or diseased. The model learns to spot the specific signs of each disease, ensuring accurate detection. The system also suggests the best pesticides for treatment. By providing both disease identification and pesticide recommendations, this system helps farmers make better decisions to protect their crops, improve plant health and increase yield. This helps farmers grow healthier crops and increase food production in a sustainable way.

Why it matches plant phenotyping methodsトマト葉の画像から健全・罹病状態や病徴を分類する手法が中心であり、植物病害状態の画像ベース表現型計測に該当する。タイトル上はレビューで、手法の整理・評価を主題としている。

titleTomato Plant Leaf Disease Classification using Deep Learning: A Review

A machine learning based model for the precise regulation of tomato seedling growth for automatic grafting. →

Introduction The morphological characteristics of grafting seedlings affect the quality of automatic grafting. Because of the non-uniform and unstable lighting conditions in greenhouses, it is difficult to implement targeted control over seedlings. In contrast, plant factories are able to cultivate grafted seedlings in a more optimal environment by adjusting environmental factors like light. This research aims to propose an intelligent control method for seedling growth, in order to precisely cultivate seedlings that meet the requirements of different grafting machines. Methods This research established an evaluation method for tomato seedlings (suitable for automatic grafting) and scored seedlings that underwent light recipe transitions at different time points. Based on the comprehensive weighting of tomato seedlings suitable for automatic grafting, combined with the growth data of seedlings under different light environments, six machine learning algorithms were used to establish growth prediction models. Results The results indicate that the length of the hypocotyl and the diameter of the stem are crucial factors influencing whether the seedling can be mechanically grafted. And the transition of light recipes during cultivation can regulate seedling quality. XGBoost achieved the best accuracy for predicting rootstock and scion growth, with R 2 values of 0.9253 and 0.9334, respectively. A smart light control system was established and grafting experiments were conducted. The results showed that the automatic grafting success rate and post-grafting survival rate of light- regulated seedlings were 8.3% and 1.4% higher than those of commercially available seedlings, respectively. Discussion This demonstrates the feasibility of the model and highlights the practical application of the system in precision agriculture.

Why it matches plant phenotyping methodsトマト苗の接ぎ木適性を評価する方法と、胚軸長・茎径などの形質を予測する機械学習モデルを開発し、光制御へ適用しているため、表現型取得・推定手法が中心である。

abstractThis research established an evaluation method for tomato seedlings (suitable for automatic grafting)

Electrophysiological monitoring of plants: an exploratory study on drought stress →

Climate change is increasing environmental stress, particularly rising temperatures and water scarcity, in both natural and human-managed systems such as agroecosystems and urban environments. Traditional methods for monitoring plant health in human-managed systems remain limited, underscoring the need for novel approaches. This study explores the potential of plant electrophysiological signals (EPS) and derived statistical features for the early detection of drought stress. The two main objectives of this research are: i) to identify EPS features that are both ecologically relevant and statistically robust for detecting drought stress, and ii) to develop statistical models that integrate these features. EPS data was collected from two drought-stress experiments, one on tomato plants and one on apricot trees. Sixteen features from both time and frequency domains were selected and evaluated. Two models, a logistic and a machine learning classifier, were developed and compared using accuracy, precision, and recall metrics. In apricots, ten time-domain features (Frequency Center, Generalized Hurst Exponent, Hjorth Complexity, Hjorth Mobility, Kurtosis, Root Mean Squared Frequency, Root Variance Frequency, Shape Factor, Skewness and Standard Deviation) showed significant differences between stressed and control groups. In tomatoes, four frequency-domain features (Frequency Centre, Root Variance Frequency, Root Mean Squared Frequency, and Power Law Distribution Exponent) were significantly different. Model accuracy was approximately 50% for apricots and 66% for tomatoes, insufficient for practical deployment but indicative of potential. This study illustrates the potential value of plant EPS data, its derived statistical features, and models for developing early drought stress detection systems in both agricultural and urban plant management contexts.

Why it matches plant phenotyping methods植物の電気生理信号から干ばつストレス状態を検出する特徴量と分類モデルを開発・評価しており、表現型取得・抽出手法が研究の中心です。

abstractThis study explores the potential of plant electrophysiological signals (EPS) and derived statistical features for the early detection of drought stress.

DPDB-YOLO: A lightweight YOLOv13 cherry tomato ripeness detection method with adaptive extraction module and multi-scale feature fusion architecture →

In this paper, we propose a lightweight and efficient cherry tomato ripeness detection model, named DPDB-YOLO, based on YOLOv13n, for fast and accurate detection in natural environments. The improvements are as follows: first, the DPC3k2 (DWConv+PConv+C3k2) module replaces the DSC3k2 module in the backbone and neck as well as the A2C2f module, constructing a compact feature extraction unit that improves accuracy and reduces parameter overhead. Secondly, structural DLAE (Depth Light-weight Adaptive Extraction) is introduced in the backbone instead of ordinary convolution to enhance adaptive learning in key regions and reduce computation. In addition, structural BSMFM (Bounded Sigmoid Modulation Fusion Module) is used in the neck instead of FullPaD to strengthen spatial perception and semantic discrimination. Experiments show the model improves accuracy by 4.88 %, recall by 4.84 %, F1 score by 4.86 %, mAP50 by 3.13 %, mAP50–95 by 8.13 %, with parameters reduced by 40 %, model size by 38 %, and GFLOPS by 20 % compared with the original. Compared to the SSD model, the EfficientDet model, and other YOLO series models, it achieves superior detection with fewer parameters, validating its effectiveness for embedded devices and providing accurate support for automated harvesting.

Why it matches plant phenotyping methodsチェリートマト果実の成熟度を画像から推定するYOLOベース手法の開発と比較検証が中心であり、単なる収穫対象の位置検出を超える植物状態のフェノタイピングに該当する。

titleA lightweight YOLOv13 cherry tomato ripeness detection method with adaptive extraction module and multi-scale feature fusion architecture

Plant-specific crop evapotranspiration estimation system for greenhouse tomatoes using convolutional neural network and rail-based monitoring device →

Accurately estimating individual plant evapotranspiration is essential for precise management and sustainable resource use in greenhouse cultivation. Integrating evapotranspiration models with crop-monitoring devices capable of acquiring images and solar radiation data may enable plant-level estimation of crop evapotranspiration. In this study, a plant-specific crop evapotranspiration estimation system was developed for hydroponic tomato cultivation in greenhouses during the harvest season. The evapotranspiration was estimated using a simplified Penman–Monteith model based on the leaf area index (LAI), solar radiation, air temperature, and relative humidity. The model was subsequently generalized through z-score normalization. To acquire side-view RGB images of individual tomato plants and measure the solar radiation distribution, a rail-based crop-monitoring device was employed. A ResNet-based convolutional neural network model was developed to estimate the LAI from the acquired images. The images were augmented via permutations with repetition to enhance the model’s accuracy. An image-merging method and a You Only Look Once version 8 Nano-based object detection model were used for rapid and automated image acquisition. The system calculated the crop evapotranspiration for each plant, and its performance was evaluated in a tomato cultivation greenhouse. Validation tests revealed strong correlations between the estimated and measured LAI (R² = 0.89, RMSE = 0.06) and between the predicted and actual evapotranspiration values (R² = 0.88, RMSE = 26.43 g h⁻¹ plant⁻¹). Distribution maps for the LAI and evapotranspiration were generated using the developed system. The system can accurately assess plant-specific evapotranspiration, thereby supporting precision crop management and helping improve productivity in greenhouse cultivation.

Why it matches plant phenotyping methods個体別のLAI画像推定と蒸発散量推定を中核とする監視システムを開発し、実測値との相関で検証しているため、植物表現型取得・推定手法として対象に含める。

abstracta plant-specific crop evapotranspiration estimation system was developed for hydroponic tomato cultivation in greenhouses

Data-driven localization of the TOMGRO model: Cultivar-specific parameter optimization for Shanghai greenhouse tomato production →

Crop models are an integral component in greenhouse control systems, enabling the simulation of plant responses to environmental conditions and facilitating optimal operational decisions for high productivity with low energy use. However, existing crop models often lack transferability beyond their original development conditions. Additionally, cultivar-specific parameterization remains challenging, as some parameters can be empirically determined while others require complex calibration. This study adapted the reduced TOMGRO model to simulate growth and yield for four local tomato cultivars under Shanghai greenhouse conditions. Through Sobol’s global sensitivity analysis and Bayesian optimization, four highly influential parameters were identified and optimized, including growth efficiency (E), maintenance respiration coefficient (rₘ), extinction light coefficient (K), and leaf quantum efficiency (Qₑ). This combined approach provides an effective framework for model calibration, with the calibrated model achieving an average R² > 0.94 for node number, plant dry weight, fruit dry weight, and leaf area index predictions in all cultivars. Model validation using 2023–2024 greenhouse data confirmed model effectiveness for the target variables (average R² > 0.92 for cultivar QX and > 0.88 for LZ), whereas the model showed limitations in simulating mature fruit growth. This calibrated model offers reliable predictions of key growth variables, informing both plant breeding and greenhouse management.

Why it matches plant phenotyping methods作物モデルの感度分析・ベイズ最適化によるパラメータ校正と、植物成長形質予測の検証が研究の中心であり、再利用可能な計算フェノタイピング手法に該当する。

abstractThrough Sobol’s global sensitivity analysis and Bayesian optimization, four highly influential parameters were identified and optimized

A hybrid deep learning paradigm integrating segmentation architectures for precise plant disease identification and classification →

Addressing the challenging issue of complex background interference in plant disease identification, recent research employs diverse deep learning (DL) methodologies on both publicly available and customized datasets. This study introduces a two-step DL approach for plant disease classification. Initially, an enhanced convolutional neural network (CNN) is developed through a comparative analysis of prominent CNN architectures, including customized and cascaded versions of select DL models, achieving an accuracy of 93.3%. To further enhance accuracy, segmentation architectures such as DeepLabV3+, UNet, Iterative UNet, and UNet with Atrous Spatial Pyramid Pooling (ASPP) are integrated before customized CNN architectures. These segmentation algorithms effectively isolate the diseased portions of leaf images. Notably, the UNet with ASPP architecture demonstrates reduced time complexity, minimizing the number of features to be trained, and significantly improves accuracy to 99.8%, outperforming other predefined architectures. The models are trained on a plant village dataset, detecting 10 different diseases across various plant species, including tomato, corn, and potato.

Why it matches plant phenotyping methods葉画像から病変部位を分離し、植物の病害状態を分類する画像解析手法が研究の中心であるため、植物フェノタイピング手法として含める。

abstractThis study introduces a two-step DL approach for plant disease classification.

Plant Disease Detection Using Deep Learning →

Agriculture contributes enormously to global food security, but crop diseases result in huge yield losses every year, compromising food production globally. Conventional disease identification practices depend mostly on visual inspection by agricultural specialists, which is time-consuming, subjective, and not accessible to small farmers. Deep Learning (DL) has come forward as a revolutionary technology to implement automated plant disease detection with the promise of quick, precise, and scalable applications. This work introduces an end-to-end deep learning-based plant disease detection and classification system using Convolutional Neural Networks (CNNs). The system applies transfer learning using pre-trained networks like VGG16, ResNet50, and MobileNetV2 with a high accuracy and efficiency in computations. The system takes leaf images using smartphones or Internet of Things (IoT)-based cameras, performs processing using a trained CNN model, and makes real-time diagnosis along with treatment suggestions. Experimental outcomes show classification accuracy of over 95% for various crop species like tomato, potato, apple, and corn. Combining this technology with IoT devices and mobile applications facilitates farmers to make prompt decisions based on accurate information, saving losses on crops and ensuring eco-friendly farming. This work contributes to precision agriculture by narrowing the gap between cutting-edge AI technologies and effective farming requirements

Why it matches plant phenotyping methods植物の葉画像から病害状態をCNNで検出・分類する手法が研究の中心であり、植物病害フェノタイピングに該当する。

abstractThis work introduces an end-to-end deep learning-based plant disease detection and classification system using Convolutional Neural Networks (CNNs).

Utilizing an Efficient Deep Convolutional Neural Network for the Automatic Classification of Plant Leaf Diseases →

Globally, India is second in terms of tomato and apple production. On an average of nearly, 18.399 tons of tomatoes and 30.500 tons of apples are produced in India. Likewise, after rice and wheat, maize scores third most significant food in India and it is one of the top crop producers in the world. The growth and health of these plants are typically afflicted by the diseases. Variety of leaf diseases available under apple, tomato and maize leaves which affect the production. This paper proposes an efficient deep convolutional neural network for identifying and detecting diseases in plant leaves through automatic image classification. The proposed model’s primary goal is to pinpoint a fix for the issue with leaf diseases that affect maize, tomatoes, and apples. The proposed efficient model comprises of residual layers, modified residual layers and global average pooling layers that yield better efficiency in terms of feature extraction and high throughput. The performance of the proposed model is evaluated through training and testing using the Plant Village dataset, which was sourced from a GitHub repository. In addition, the proposed model is evaluated through standard classification metrics and achieved better accuracy in the range 97% to 99%.

Why it matches plant phenotyping methods植物葉の病害状態を画像から自動分類する深層学習手法が研究の中心であり、病害表現型の取得・推定に該当する。

abstractThis paper proposes an efficient deep convolutional neural network for identifying and detecting diseases in plant leaves through automatic image classification.

Explainable AI-driven interpretation of environmental drivers of tomato fruit expansion in smart greenhouses using IoT sensing. →

Tomato fruit expansion is a key physiological process that determines fruit size, marketability, and yield, yet its quantitative and threshold-based response to microclimatic factors in smart greenhouses has been insufficiently studied. This study develops an IoT-driven sensing framework combined with explainable artificial intelligence (XAI) to interpret the environmental drivers of fruit expansion. A robust environmental monitoring system continuously captured key factors including air and soil temperature, humidity, light intensity, CO 2 concentration, soil moisture, and soil electrical conductivity. These variables were fed into a Random Forest regression model enhanced with SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) for interpretability. Results revealed that soil temperature (~ 21.8 °C), light intensity, and soil electrical conductivity were the most influential drivers of fruit expansion, each exhibiting distinct threshold behaviors, and the proposed IoT-XAI framework achieved R 2 = 0.82 with an MSE of 0.0046, confirming both predictive accuracy and interpretability. Our approach transforms raw sensor data into actionable insights for precision climate and fertigation management, supporting sustainable smart agriculture through interpretable machine learning.

Why it matches plant phenotyping methodsトマト果実の膨張という植物形質を対象に、IoTセンシングと機械学習・XAIによる推定および環境要因解析を中心的に行っているため、フェノタイピング手法の応用として含める。

abstractThis study develops an IoT-driven sensing framework combined with explainable artificial intelligence (XAI) to interpret the environmental drivers of fruit expansion.

AI Driven Crop Disease Detection and Management System →

Crop diseases cause large yield losses worldwide and represent a serious threat to food security. Traditional detection methods rely on manual inspection, which is time-consuming and error-prone. The AI-driven Crop Disease Detection and Management System presented in this paper combines environmental data analytics utilizing Random Forest regression for disease risk predictions with Convolutional Neural Networks (CNNs) for image-based disease identification. A carefully selected portion of the PlantVillage dataset, with an emphasis on the crops maize, tomato, and potato, is used to train the model. The hybrid approach leverages temperature, humidity, and rainfall data to increase prediction reliability. When compared to traditional CNN-only methods, experimental evaluation shows an accuracy of 94.33% and enhanced early disease prediction skills. The system, which offers real-time disease monitoring, is implemented as a mobile application and web platform. detection, forecasting, and treatment suggestions. This hybrid approach promotes sustainable agriculture through proactive disease management and optimized resource use.

Why it matches plant phenotyping methods植物画像から病害状態を識別するAI手法と、その評価・実装が研究の中心であり、植物病害フェノタイピングに該当する。

titleAI Driven Crop Disease Detection and Management System

High-throughput phenotyping of sweetness and sourness components in tomato fruits by near-infrared spectroscopy and chemometrics methods →

Tomato ( S. lycopersicum ) is a precious fruit crop, and flavor quality is one of the most important commodity traits and directly affects the commodity value and economic returns. The composition and content of sugars and acids in tomato fruits, as well as their balance, are closely related to tomato quality, especially soluble sugars and organic acids, and so on. However, the lack of an efficient approach for quality evaluation of tomato significantly hinders progress in flavor quality breeding. Near infrared spectroscopy technology (NIRS) utilizes the absorption characteristics of near-infrared light by molecular vibrations of substances, and establishes a quantitative relationship model between spectra and component content through chemometric methods. Therefore, this study aimed to establish an NIRS assay for high-throughput analysis of tomato fruit quality, including fructose, sucrose, glucose, malic acid, and citric acid content. A total of 190 representative samples were utilized, and a dual-optimized strategy (optimization of sample subset partitioning and variable selection) was applied to NIRS modeling. Partial least squares regression (PLSR) model were developed with an excellent coefficient of determination for the coefficient of determination of calibration (R C 2 ) and coefficient of determination of validation (R v 2 ) of this model, with 0.962 and 0.942, respectively. what's more, the root mean square error of calibration (RMSEc) and root mean square error of prediction (RMSEP) were 0.36 mg/g and 0.44 mg/g,respectively.This model can effectively compress useless variables and interference information in near-infrared spectra. Overall, these NIRS models provide a feasible approach for high-throughput analysis of fruit quality and permit large-scale screening of elite germplasm in future tomato breeding.

Why it matches plant phenotyping methodsトマト果実の糖・酸含量という植物器官形質を対象に、NIRSとケモメトリクスによるハイスループット測定法を開発・検証しており、表現型取得法が研究の中心である。

abstractthis study aimed to establish an NIRS assay for high-throughput analysis of tomato fruit quality, including fructose, sucrose, glucose, malic acid, and citric acid content.

Tomato Growth Monitoring and Phenological Analysis Using Deep Learning-Based Instance Segmentation and 3D Point Cloud Reconstruction →

Accurate and nondestructive monitoring of tomato growth is essential for large-scale greenhouse production; however, it remains challenging for small-fruited cultivars such as cherry tomatoes. Traditional 2D image analysis often fails to capture precise morphological traits, limiting its usefulness in growth modeling and yield estimation. This study proposes an automated phenotyping framework that integrates deep learning-based instance segmentation with high-resolution 3D point cloud reconstruction and ellipsoid fitting to estimate fruit size and ripeness from daily video recordings. These techniques enable accurate camera pose estimation and dense geometric reconstruction (via SfM and MVS), while Nerfacto enhances surface continuity and photorealistic fidelity, resulting in highly precise and visually consistent 3D representations. The reconstructed models are followed by CIELAB color analysis and logistic curve fitting to characterize the growth dynamics. When applied to real greenhouse conditions, the method achieved an average size estimation error of 8.01% compared to manual caliper measurements. During summer, the maximum growth rate (gmax) of size and ripeness were 24.14%, and 95.24% higher than in winter, respectively. Seasonal analysis revealed that winter-grown tomatoes matured approximately 10 days later than summer-grown fruits, highlighting environmental influences on phenological development. By enabling precise, noninvasive tracking of size and ripeness progression, this approach is a novel tool for smart and sustainable agriculture.

Why it matches plant phenotyping methods深層学習によるインスタンスセグメンテーション、3D再構成、色解析を統合し、トマト果実のサイズと成熟度を推定するフェノタイピング手法の開発・評価が中心である。

abstractThis study proposes an automated phenotyping framework that integrates deep learning-based instance segmentation with high-resolution 3D point cloud reconstruction and ellipsoid fitting to estimate fruit size and ripeness from daily video recordings.

RSA-YOLO: tomato leaf disease and pest detection model based on improved YOLOv8 →

Abstract Currently, the detection of diseases and pests on tomato leaves presents several critical challenges, including ambiguous multi-scale target recognition, significant background interference, and limited adaptability to varying lighting conditions. To address these issues, this study proposed an efficient detection model for tomato leaf diseases and pests based on YOLOv8n, named RSA-YOLO. First, a Receptive-Field Attention (RFA) mechanism was integrated into the backbone network to overcome the limitations of conventional convolutional kernel parameter sharing by dynamically adjusting the spatial features within the convolutional kernel’s receptive-field. This enhancement effectively improves the model’s capability to extract features from complex patterns. Second, to enhance both efficiency and accuracy in multi-scale feature fusion, the original Spatial Pyramid Pooling Fast (SPPF) module in YOLOv8n was replaced with the Spatial Pyramid Pooling with Efficient Layer Aggregation Network (SPPELAN) module. Finally, an Adaptive Spatial Feature Fusion (ASFF) mechanism was incorporated into the detection head to strengthen the scale invariance of features, thereby better addressing variations in target size. Experimental results demonstrated that RSA-YOLO significantly outperformed the original YOLOv8n, with Precision (P) increasing by 1.6%, mean Average Precision (mAP@0.5) improving by 2.6%, and F1-score rising by 2.4%. Furthermore, compared to YOLOv3-tiny, YOLOv5, YOLOv6, YOLOv9, YOLOv11, YOLOV12, and YOLOv13, RSA-YOLO achieved mAP@0.5 improvements of 6.8%, 4.1%, 4.5%, 2.4%, 2.6%,3.5% and 1.9%, respectively. The results indicate that RSA-YOLO significantly improves detection accuracy while maintaining a moderate and controllable computational cost and model size. This demonstrates its feasibility for deployment on portable or resource-constrained devices in agricultural scenarios and provides technical support for monitoring and controlling tomato leaf diseases and pests.

Why it matches plant phenotyping methodsトマト葉の病害・害虫状態を画像から検出するYOLOベース手法を開発・比較評価しており、植物状態の取得・推定が研究の中心である。

abstractthe detection of diseases and pests on tomato leaves presents several critical challenges

Image-Based Detection of Plant Leaf Diseases Using Convolutional Neural Networks →

This study employs CNN models, specifically pretrained VGG-16 and VGG-19, to classify plant leaf diseases using transfer learning. For this, first the set of training plant leaf images used for the classification purpose has been preprocessed. Next, pre-trained VGG-16 and VGG-19 models are applied to the training plant leaf images using the steps of transfer learning. After training, the models are validated. Then, the models are used to classify the plant leaf diseases. Also, in the transfer learning process, fine-tuning (FT) is applied to retrain selected layers of the pre-trained CNN models (i.e., VGG 16 and VGG 19) to enhance performance. Thus, four models (Model 1: VGG 16 (without FT), Model 2: VGG 19 (without FT), Model 3: VGG 16 (with FT), and Model 4: VGG 19 (with FT)) have been created. In this work, the tomato plant leaves dataset has been considered. All the models have been used to classify the leaves into five categories: bacterial spot, early blight, late blight, leaf mold, and healthy. The models have been analyzed by comparing the classification accuracies produced by them.

Why it matches plant phenotyping methods植物葉の画像から病害状態を分類するCNN手法の開発・比較・検証が中心であり、植物表現型（病徴・病害状態）の画像ベース推定に該当する。

abstractThis study employs CNN models, specifically pretrained VGG-16 and VGG-19, to classify plant leaf diseases using transfer learning.

YOLO-ALDS: an instance segmentation framework for tomato defect segmentation and grading based on active learning and improved YOLO11 →

Tomato defect detection and grading based on machine vision are crucial in post-harvest operations, significantly enhancing agricultural product value and market competitiveness. However, accurate segmentation and grading of tomato surface defects remain challenging due to significant intra-class variations, imbalanced defect categories, and especially high manual annotation costs. Therefore, an instance segmentation framework (YOLO-ALDS) was proposed for tomato defect segmentation and grading automatically. The proposed framework included fast dataset preparation based on Active Learning (AL) and segmentation based on improved YOLO-DS. For the dataset preparation part, an uncertainty and diversity-driven active learning (UDAL) strategy was proposed for selecting the most informative defect samples to alleviate the annotation cost and enhance labeling efficiency. For defect segmentation, an improved YOLO11-DS segmentation model is developed by introducing Dynamic Convolution modules in the backbone network, adaptively capturing subtle variations and indistinct boundaries of tomato defects. Moreover, to specifically improve the learning capability for challenging samples with complex and ambiguous morphology selected by the UDAL, a novel SlideLoss function is integrated into the YOLO-DS model, dynamically emphasizing optimization on hard-to-segment instances. Experimental results demonstrate that the proposed YOLO-ALDS reduces manual annotation workload by over 40%, and achieves an mAP@0.5 of 84.1%, surpassing traditional YOLO11 by 0.8%, with notable performance improvements of 1.2%, 1.7%, and 3.7% for white defects, hyperplasia, and cracks, respectively. Compared to mainstream segmentation networks, our approach exhibits significant performance advantages. Furthermore, the developed intelligent tomato grading system based on our model attains practical classification accuracy exceeding 96%, highlighting its promising potential for cost-effective and efficient agricultural automation.

Why it matches plant phenotyping methodsトマト表面欠陥を画像からセグメンテーションし、欠陥状態と等級を推定する手法の開発・評価が中心であり、植物状態の観測手法に該当する。

abstractTherefore, an instance segmentation framework (YOLO-ALDS) was proposed for tomato defect segmentation and grading automatically.

Wearable electrochemical immunosensor based on ultra-thin flexible stainless steel sheets for detection of methyl jasmonate in tomato leaves →

Methyl jasmonate (MeJA), a key plant hormone, plays essential roles in plant growth, development, biotic stress responses, and wound-induced defense. Monitoring dynamic changes in MeJA in situ is vital for botanical research. Herein, coupling with paper-based analytical devices, the ultra-thin flexible stainless steel sheets with the excellent flexibility and conductivity were used to develop wearable electrochemical immunosensor for in situ and continuous detection of MeJA in plants. The ultra-thin flexible stainless steel sheets were modified with conducting carbon cement, ferrocene - graphene oxide - multi-walled carbon nanotubes composites, and MeJA antibodies to construct the wearable electrochemical immunosensor, which can detect the MeJA in the range of 10 pM–100 μM, and with a limit of detection of 5.4 pM. Using this wearable electrochemical immunosensor, the MeJA content in tomato leaves under wound stimulation was detected in situ and continuously. The results showed that MeJA levels in tomato leaves increased significantly with mechanical damage. A significant difference was observed between the untreated control group (0 cm) and the mechanically damaged group (2.0 cm), confirming the sensor's capability to monitor dynamic changes in MeJA in response to stress in real-time. In all, this study not only suggested that the ultra-thin flexible stainless steel sheets with the excellent flexibility and conductivity can be used to fabricated the wearable electrochemical sensors, but also provided a novel method for continuous in situ MeJA detection, which contributed to the understanding of MeJA regulatory mechanisms in plants and advancing precision agriculture technologies.

Why it matches plant phenotyping methods植物葉内ホルモンの動態をリアルタイム測定するウェアラブル電気化学センサーの開発と実植物での検証が中心であり、植物の生理状態を取得するフェノタイピング手法に該当する。

abstractdevelop wearable electrochemical immunosensor for in situ and continuous detection of MeJA in plants

Designing optimal Vision Transformer architecture using differential evolution for tomato leaf disease classification →

Vision Transformers (ViTs) have recently demonstrated promising achievements in various computer vision tasks. However, designing a ViT model architecture requires high-level domain expertise, which can be challenging for new researchers to solve real-life problems, such as those in agricultural imaging. Agricultural datasets often include region-specific patterns influenced by factors such as soil metrics, weather conditions, crop imagery, and spectral signatures, making the design of the manual ViT model a time-consuming and expertise-driven process. To address these limitations, this study proposes a Neural Architecture Search (NAS) leveraging Differential Evolution (DE) algorithm which automates the process of fine-tuning hyperparameters, reducing the reliance on manual intervention and enabling the creation of highly optimized ViT models. The proposed approach is trained and tested on agricultural images dataset of tomato leaves, consisting of ten classes. The experimental results demonstrate the effectiveness of DE-based optimized ViT models by showcasing their ability to handle the unique complexities of agricultural datasets while achieving superior accuracy and reliability in classification tasks.

Why it matches plant phenotyping methodsトマト葉の病害状態を画像から分類するVision Transformerのアーキテクチャ探索手法を開発・評価しており、植物病害表現型の取得・推定が中心である。

abstractthis study proposes a Neural Architecture Search (NAS) leveraging Differential Evolution (DE) algorithm which automates the process of fine-tuning hyperparameters

Integration of temperature control and imaging-based prediction for grafting-related morphological regulation in tomato seedlings in a closed transplant production system →

• ADT and DIF precisely regulated elongation and thickening in tomato seedlings. • Canopy traits imaging predicted grafting traits with high accuracy (R 2 > 0.9). • CTPS-imaging integration offers real-time monitoring for grafting suitability. • First trial on tailored production with imaging for an automated grafting. Tomato seedling growth and quality are crucial determinants of the success of grafting and transplant establishment. This study aimed to investigate temperature control strategies in a closed transplant production system (CTPS) and their integration with imaging-based prediction to produce grafting-ready seedlings. Scion ‘Dotaerang Dia’ and rootstock ‘B-Blocking’ were grown under combinations of average daily temperatures (ADTs; 24 and 26 °C) and difference between day and night temperatures (DIFs; –8, –4, 0, +4, and +8 °C). Morphological traits crucial for grafting, including the epicotyl length (EPL) and diameter (EPD) of scions and hypocotyl length (HYL) and diameter (HYD) of rootstocks, and canopy traits, including leaf area index (LAI) and canopy height (CH), were evaluated. Higher ADTs and positive DIFs promoted elongation, whereas lower ADTs and negative DIFs restricted elongation and improved compactness. Compact seedlings with a higher dry matter content are advantageous for grafting, whereas seedlings with greater elongation and dimensional synchrony better meet the requirements of robotic grafting. Imaging-based monitoring using multispectral-derived LAI and light detection and ranging (LiDAR)-derived CH accurately predicted grafting-related traits ( R 2 > 0.9 for EPL, EPD, and HYD); however, HYL predictions were less reliable under negative DIFs. Leave-one-environment-out cross-validation confirmed robust performance for diameter traits across environments. These findings collectively indicate that CTPS enable precise morphological regulation of tomato scions and rootstocks through temperature control, whereas imaging-based phenotyping allows a basis for real-time prediction of grafting suitability. This integration establishes a scalable and automation-ready framework for grafted transplant production, offering a technological foundation for developing automated grafting strategies.

Why it matches plant phenotyping methods画像計測（マルチスペクトルによるLAI、LiDARによる樹冠高）から接ぎ木関連形態形質を予測・検証し、リアルタイムな接ぎ木適性評価を実現する方法が研究の中心である。

abstractCanopy traits imaging predicted grafting traits with high accuracy (R 2 > 0.9).

Plant phenomics-assisted selection of Trichoderma spp. strains effective in the biocontrol of tomato soil-borne fungal diseases →

The genus Trichoderma is a valuable source of biological control agents: useful means for sustainable crop disease management. Speeding up screening phase in the set-up of new microbial means is crucial to meet needs for managing pathogens. Functional phenomics expressing through objective spectral data the result of the plant genotype's interactions with the environment, can contribute to the performance-based selection. In this study nineteen Trichoderma spp., including strains belonging to the species T. atroviride , T. harzianum , T. longibrachiatum and T. rifaii , were screened for the biocontrol of F. oxysporum f. sp. lycopersici and S. rolfsii infections on tomato plants, with the help of phenomics measures on infected plants subjected to symptom-reducing effects of the beneficial microbial treatments, alongside the traditional method. Trichoderma spp. isolates showed an antagonistic behavior in plate assays against the two pathogens, while four and five out of total isolates significantly lowered, respectively, wilt and Southern blight symptoms on plants. Detection of the plant phenotype closer to that of the healthy ideotype, defined through the overall computation of biometric and spectral traits acquired by a multispectral dual scan platform, individuated T. harzianum T2 and PB3 as the best performing strains in controlling both tomato pathogens. Leaf area and Normalized Chlorophyll Pigment Ratio Index Average acted as engine vectors for phenotypic clustering with non-infected plants in both systems. Voxel Volume Total, 3D-Leaf area, Green Leaf Index Average, Hue Average, and Surface Angle Average assumed importance under F. oxysporum assay. Specifically, the phenomics assisted procedure contributed to prompt results in the individuation of the best performing Trichoderma strains against F. oxysporum and S. rolfsii .

Why it matches plant phenotyping methods感染植物からマルチスペクトル画像および3D・生理形質を取得し、計算的に健全表現型との近さを評価するフェノミクス手順が、Trichoderma株選抜の中心的手法として用いられている。

abstractDetection of the plant phenotype closer to that of the healthy ideotype, defined through the overall computation of biometric and spectral traits acquired by a multispectral dual scan platform

Construction of an industrial detection model for tomato organic acids based on low-rank adaptation and spatial feature fusion →

Tomato (Solanum lycopersicum L.) is not only a globally important crop for human consumption but also a potential source of industrially valuable organic acids. Organic acids such as citric and malic acid have widespread applications in bio-based chemical synthesis, fermentation, biodegradable plastics, and green solvents. In this study, we develop a novel non-destructive framework combining Low-Rank Adaptation (LoRA) with Adaptive Spatial Feature Fusion Network (ASFFN) to accurately predict organic acid content using hyperspectral imaging (HSI). The model integrates advanced feature fusion strategies and robust outlier detection to improve generalizability across tomato varieties. Comparative experiments demonstrate the superior performance of the proposed LoRA-ASFFN over conventional CNN and PLSR baselines. The model enables rapid and precise identification of tomatoes with high organic acid concentrations, providing an efficient pathway for industrial processing and extraction. This research contributes to advancing bio-based chemical supply chains and improving the economic value of tomato crops through data-driven, precision screening techniques.

Why it matches plant phenotyping methodsハイパースペクトル画像からトマト果実の有機酸含量を非破壊推定するモデル開発が研究の中心であり、植物器官の化学的形質を抽出するフェノタイピング手法に該当する。

abstractwe develop a novel non-destructive framework combining Low-Rank Adaptation (LoRA) with Adaptive Spatial Feature Fusion Network (ASFFN) to accurately predict organic acid content using hyperspectral imaging (HSI).

Label-Free Rapid Quantification of Abscisic Acid in Xylem Sap Samples Using Surface Plasmon Resonance. →

The phytohormone abscisic acid (ABA) plays a central role in organizing adaptive responses in plants to various abiotic stresses, helping the plant minimize the negative impact on growth and development. Rapid and direct detection of ABA is valuable for investigating plant responses to abiotic stress. In this work, we propose a novel label-free, non-competitive immunoassay for detecting and quantifying ABA easily and rapidly using a surface plasmon resonance (SPR) biosensor. The SPR sensor chip was functionalized with a commercial anti-ABA antibody, characterized for its affinity, binding kinetics, and specificity using the same platform. The direct assay demonstrated high specificity and sensitivity, with a calculated limit of detection of 1.36 ng/mL in buffer. The new immunosensor was applied to determine ABA concentrations directly in xylem sap samples from tomato plants subjected to abiotic stress (drought and high salinity) and was able to accurately reflect ABA levels corresponding to the applied stress. The results were comparable to the reference method, ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS), establishing this new immunosensor as a novel detection method for rapid and reliable monitoring of ABA levels associated with abiotic stress in tomato plants.

Why it matches plant phenotyping methods植物の干ばつ・高塩ストレスに関連するABA濃度を迅速測定するSPR免疫センサーを開発し、性能評価とUPLC-MS/MS比較検証を行っているため、植物の生理状態を取得する方法が中心である。

abstractwe propose a novel label-free, non-competitive immunoassay for detecting and quantifying ABA easily and rapidly using a surface plasmon resonance (SPR) biosensor.

Contrast limited adaptive histogram equalization (CLAHE) and colour difference histogram (CDH) feature merging capsule network (CCFMCapsNet) for complex image recognition. →

To enhance crop yield, detecting leaf diseases has become a crucial research focus. Deep learning and computer vision excel in digital image processing. Various techniques grounded in deep learning have been utilized for detecting plant leaf diseases; however, achieving high accuracy remains a challenge. Basic convolutional neural networks (CNNs) in deep learning struggle with issues such as the abnormal orientation of images, rotation, and others, resulting in subpar performance. CNNs also need extensive data covering a wide range of variations to deliver strong performance. CapsNet is an innovative deep-learning architecture designed to address the limitations of CNNs. It performs well without needing a vast amount of data in various variations. CapsNets have their limitations, such as the encoder network considering every element in the image and the crowding issue. Due to this, they perform well on simple image recognition tasks but struggle with more complex images. To address these challenges, we introduced a new CapsNet model known as CCFM-CapsNet. This model incorporates CLAHE to reduce image noise and CDH to extract crucial features. Also, max-pooling and dropout layers are incorporated in the original CapsNet model for identifying and classifying diseases in apples, bananas, grapes, corn, mangoes, pepper, potatoes, rice, tomato and also for classifying fashion-MNIST and CIFAR-10 datasets. The proposed CCFM-CapsNet demonstrates significantly high validation accuracies, achieving 99.53%, 95.24%, 99.75%, 97.40%, 99.13%, 100%, 99.77%, 100%, 98.54%, 93.48%, and 82.34% with corresponding parameters in millions(M) 4.68M, 4.68M, 4.68M, 4.68M, 4.79M, 4.63M, 4.66M, 4.68M, 4.84M, 2.39M, and 4.84M for the datasets aforementioned respectively, outperforming the traditional CapsNet and other advanced CapsNet models. Consequently, the CCFM-CapsNet model can be utilized effectively as a smart tool for identifying plant diseases and also in achieving Sustainable Development Goal 2 (Zero Hunger), which aims to end global hunger by the year 2030.

Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習モデルを提案・評価しており、画像取得・分類手法が研究の中心であるため。

abstractTo address these challenges, we introduced a new CapsNet model known as CCFM-CapsNet.

Interpretable deep multimodal-based tomato disease diagnosis and severity estimation. →

Plant diseases pose a significant threat to global food security, particularly in regions that rely heavily on crops that are vulnerable to disease, such as tomatoes. This research addresses the inefficiencies of traditional farming solutions by presenting a novel multimodal deep learning algorithm. The algorithm leverages EfficientNetB0 for image-based disease classification and utilizes Recurrent Neural Networks (RNN) to predict disease severity based on environmental data. By integrating visual and climatological inputs, our model addresses the limitations of unimodal systems, enhancing classification accuracy and interpretability. The model achieved a disease classification accuracy of 96.40% and a severity prediction accuracy of 99.20%. Additionally, the use of LIME and SHAP explainable AI techniques improves the understanding of disease severity classification outcomes. The contributions of this study align with precision agriculture practices and advance the resilience of local food systems, particularly in economies heavily dependent on tomato production. The proposed approach has the potential to mitigate the impacts of plant diseases and enhance food security by utilizing innovative technological solutions.

Why it matches plant phenotyping methodsトマト病害の画像分類と病害重症度推定を行うマルチモーダル手法が研究の中心であり、植物状態の測定・推定に直接関わるため含める。

abstractpresenting a novel multimodal deep learning algorithm

Visual-language transformer-based tomato leaf disease detection for portable greenhouse monitoring device. →

Tomato leaf diseases pose a significant threat to global food security, necessitating accurate and efficient detection methods. This paper introduces the Tomato Leaf Disease Visual Language Model (TLDVLM), a novel approach based on the BLIP-2 architecture enhanced with Low-Rank Adaptation (LoRA), for precise classification of 10 distinct tomato leaf diseases. Our methodology integrates a sophisticated image preprocessing pipeline, utilizing GroundingDINO for robust leaf detection and SAM-2 for pixel-level segmentation, ensuring that the model focuses solely on relevant plant tissue. The TLDVLM leverages the powerful multimodal understanding of BLIP-2, with LoRA applied to its Q-Former module, enabling parameter-efficient fine-tuning without compromising performance. Comparative experiments demonstrate that the TLDVLM significantly outperforms baseline models, including CLIP-LoRA and ConvNeXT-tiny, achieving an accuracy of 97.27%, a precision of 0.9587, a recall of 0.9789, and an F1-score of 0.9681. Beyond classification, the finetuned TLDVLM checkpoints are integrated into a practical application for new image inference. This application displays the raw and segmented images, the predicted disease, and offers functionalities to fetch comprehensive information on disease causes and remedies using external APIs (e.g., OpenAI), with an option to download a PDF summary for offline access on a portable device. This research highlights the potential of LoRA-adapted Vision-Language Models in developing highly accurate, efficient, and user-friendly agricultural diagnostic tools.

Why it matches plant phenotyping methodsトマト葉の病害状態を画像から分類する手法を開発・比較評価しており、植物病害表現型の取得が中心である。

abstractThis paper introduces the Tomato Leaf Disease Visual Language Model (TLDVLM), a novel approach based on the BLIP-2 architecture enhanced with Low-Rank Adaptation (LoRA), for precise classification of 10 distinct tomato leaf diseases.

Systematic Review of a Convolutional Neural Network for Detecting Tomato Leaf Disease →

Abstract The agricultural sector is facing an increasing number of diseases of plants, particularly factors that have a significant impact on tomato plants, which can have a major effect on their quality and productivity. Timely management and action depend on accurate disease detection. Image classification tasks have made extensive use of Convolutional Neural Networks (CNNs). However, they face limitations in capturing global contextual information, which can lead to potential inaccuracies. This study reviews existing literature on the use of CNNs and hybrid models for tomato leaf disease detection, covering literature published between 2014 to 2024. A structured database search initially identified 2,591 records, of which 29 peer-reviewed studies met the inclusion criteria for detailed analysis. The study also examines the role of the nutrients present in tomato leaves, symptoms of disease, and their impact on productivity. The review evaluates CNN architectures, transfer learning models, lightweight networks, and hybrid approaches, focusing on datasets, preprocessing methods, and performance outcomes. Reported accuracies often exceed 95% on benchmark datasets, but performance declines sharply in field conditions due to variable environments, class imbalance, and limited dataset diversity. Three major challenges emerged: weak generalization beyond controlled data, high computational costs for deployment, and the absence of robust, field-oriented datasets. Recent advances, including transformer-enhanced CNNs, attention mechanisms, lightweight architectures, and pruning techniques, show promise in addressing these gaps. This review consolidates evidence, identifies limitations, and outlines future directions for plant disease detection that are resource-efficient, explainable, and real-time systems for sustainable agriculture.

Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出するCNN手法を体系的にレビューしており、植物病害状態の画像ベース表現型計測手法が中心です。

titleSystematic Review of a Convolutional Neural Network for Detecting Tomato Leaf Disease

Early Detection and Quantification of Fusarium Wilt in Greenhouse-Grown Tomato Plants Using Water-Relation Measurements. →

Visual estimates of plant symptoms are traditionally used to quantify disease severity. Yet, the methodologies used to assess these phenotypes are often subjective and do not allow tracking of disease progression from very early stages. Here, we hypothesized that quantitative analysis of whole-plant physiological vital functions can be used to objectively determine plant health, providing a more sensitive way to detect disease. We studied the tomato wilt that is caused by Fusarium oxysporum f. sp. lycopersici . Physiological performance of infected and noninfected tomato plants was compared using a whole-plant pot-based lysimeter functional phenotyping system in a semi-environmentally controlled greenhouse. Water-balance traits of the plants were measured continuously and simultaneously in a quantitative manner. Infected plants exhibited early reductions in transpiration and biomass gain, which preceded visual disease symptoms. These changes in transpiration proved to be effective quantitative indicators for assessing both plant susceptibility to infection and virulence of the fungus. Physiological changes linked to fungal outgrowth and toxin release contributed to reduced hydraulic conductance during initial infection stages. The functional phenotyping method objectively captures early-stage disease progression, advancing plant disease research and management. This approach emphasizes the potential of quantitative whole-plant physiological analysis over traditional visual estimates for understanding and detecting plant diseases.

Why it matches plant phenotyping methods全植物の水収支を連続定量する機能的フェノタイピング法を用い、Fusarium萎凋病の早期進行と感受性を客観評価する手法が中心である。

abstractusing a whole-plant pot-based lysimeter functional phenotyping system

Tomato Leaf Disease Detection Method Based on Multi-Scale Feature Fusion. →

Tomato is a key economic crop whose yield and quality depend heavily on the early and accurate detection of leaf diseases. Conventional diagnosis based on manual observation is labor-intensive and prone to subjective bias. To overcome the limitations of disease detection under complex environmental conditions, this study presents an enhanced YOLO11n-based detection framework for tomato leaf diseases. The proposed model integrates an EfficientMSF module in the backbone to strengthen multi-scale feature extraction, introduces a C2CU module to enhance global contextual representation, and employs a CAFMFusion module to achieve efficient fusion of local and global features. Experiments were conducted on a self-constructed dataset containing nine tomato leaf categories, including eight disease types and healthy samples. The proposed approach achieves an average Recall of 71.0%, mAP@0.5 of 76.5%, and mAP@0.5-0.95 of 60.5%, outperforming the baseline YOLO11n by 3.4%, 1.3%, and 2.0%, respectively. In particular, for the challenging Leaf Mold class, mAP@0.5 improved by 3.4%. These results demonstrate that the proposed method possesses strong robustness and practical applicability in complex field conditions, offering an effective solution for intelligent tomato disease monitoring and precision agricultural management.

Why it matches plant phenotyping methodsトマト葉の病徴・病害状態を画像から検出する深層学習法を開発し、データセット上で性能比較・検証しているため、植物フェノタイピング手法が中心である。

abstractthis study presents an enhanced YOLO11n-based detection framework for tomato leaf diseases.

Performance Evaluation of Plant Leaf Disease Detection using Structural Modification of the Traditional Convolutional Neural Networks →

Plant diseases reduce agricultural productivity, and farmers struggle to precisely identify and control these diseases which leads to crop losses. The early identification of plant diseases is a challenging task in agriculture due to the variation in the size, shape, colour, and texture caused by environmental changes. This research employs convolutional neural networks (CNNs) for the identification of plant leaf diseases, namely, corn common rust, potato early blight, and tomato bacterial spot. The model was trained with the augmented dataset on 15 epochs with and without the additional convolution layer in the CNN architecture. The accuracy achieved by CNN without adding additional layers is 99.4% and after adding additional layers, the accuracy is 75.5% at 10 epoch. These results show that the proposed method can be optimized in the future and can be used as the most effective method in predicting plant leaf disease. This model may lead to enhanced crop yields and quality by rapid prediction of leaf diseases.

Why it matches plant phenotyping methods植物葉の病害状態を画像から推定するCNN手法が研究の中心であり、構造変更と性能比較による評価も行っているため、植物フェノタイピング手法として採用する。

abstractThis research employs convolutional neural networks (CNNs) for the identification of plant leaf diseases, namely, corn common rust, potato early blight, and tomato bacterial spot.

Flexible sensor based on composites of hafnium diselenide, carboxylated single walled carbon nanotubes, and carboxylated graphene for in situ rutin detection in plants. →

Rutin, a natural flavonoid with anti-inflammatory and antioxidant properties, is widely found in plants and foods. To meet the demand for in situ detection of Ru in soft plant tissues, in this paper, a thin laser-induced graphene (LIG) electrode was prepared on polyimide (PI) and transferred to Ecoflex substrate, obtaining LIG/Ecoflex electrodes with good flexibility and tensile resistance. To further improve the electrochemical catalytic performance, nanocomposites of hafnium diselenide (HfSe 2 ), carboxylated single-walled carbon nanotubes (COOH-SWCNT), carboxylated graphene (COOH-GR) and nafion were also modified on the surface of LIG by a one-step dropping method. The HfSe 2 -COOH-SWCNT-COOH-GR-nafion/LIG/Ecoflex sensor could detect Ru in the range of 1 μM-700 μM under different pH values (4.5-7.4). It was also successfully used to in situ detection of Ru content in tomato leaves under different salt stress. The as-prepared sensor has important practical application prospects in the monitoring of plant physiological information in situ.

Why it matches plant phenotyping methods植物組織内のルチンをin situ測定する柔軟電気化学センサーを開発し、検出範囲とトマト葉での実利用を検証しており、植物生理状態の取得手法が中心である。

abstractTo meet the demand for in situ detection of Ru in soft plant tissues, in this paper, a thin laser-induced graphene (LIG) electrode was prepared on polyimide (PI) and transferred to Ecoflex substrate, obtaining LIG/Ecoflex electrodes with good flexibility and tensile resistance.

A Novel Hyperspectral Imaging Approach for Early Detection of Broomrape Infestation in Tomato Plants →

Abstract Broomrape (Orobanche spp.) is a root-parasitic weed that severely threatens tomato crops by siphoning nutrients during subterranean development, often causing irreversible yield losses before above-ground symptoms appear. This study investigates a non-invasive approach for early detection of broomrape in tomato by combining hyperspectral imaging with narrow-band vegetation indices. Tomato plants were imaged with a ground-based hyperspectral camera. We applied statistical and machine-learning models to distinguish infested from healthy plants at stages prior to broomrape emergence. We found that vegetation indices sensitive to chlorophyll and canopy vigor (e.g. NDVI, NDVIre, PSNDb, GNDVI) showed significant declines in infested plants, reflecting parasite-induced stress. Using these indices in a classifier yielded high accuracy in early infestation detection. Our results demonstrate that hyperspectral-derived indices can reveal the physiological impact of Orobanche parasitism on tomato leaves before visible symptoms. This approach offers a promising tool for precision agriculture, enabling targeted management of broomrape (e.g. site-specific herbicide application) and improving crop protection strategies.

Why it matches plant phenotyping methodsトマト葉の生理的ストレスと寄生状態を、ハイパースペクトル画像・植生指数・分類モデルで非侵襲的に推定する手法が研究の中心である。

abstractThis study investigates a non-invasive approach for early detection of broomrape in tomato by combining hyperspectral imaging with narrow-band vegetation indices.

Plant stem occlusion inpainting with Deep Reinforcement Learning →

Growth monitoring of tomato plants in large greenhouse environments is critical for quality and efficient production. Stem diameter and elongation are key phenotypic traits for plant growth monitoring. Traditional methods, however, rely on manual operations, which are time-consuming and labor-intensive and do not apply to large-scale greenhouses. Currently, automated image-based methods exemplified by three-dimensional (3D) point cloud technology are among the preferred solutions. Nevertheless, the occlusion of plant structures during the information acquisition process is challenging for practical applications. To address this challenge, this study proposes a novel method for plant stem occlusion inpainting using Deep Reinforcement Learning (DRL). Unlike most existing 3D reconstruction approaches that require depth data from multiple viewpoints, our solution captures 3D point cloud data from a single direction. The DRL model is applied to inpaint the incomplete stem for accurate stem reconstruction and phenotypic measurements. Specifically, our approach consists of two parts, structural completion and stem diameter completion. First, we extract the point cloud of incomplete stems from the RGB-D camera data. Second, we obtain the spatial structure of the stems by inpainting the 3D stem centerline with the DRL model. Finally, we add shape features (stem diameters) by inpainting the two edge lines of the stem occlusion part with the DRL model. For stem inpainted 3D point cloud data, we conducted validation experiments by measuring several commonly used stem phenotypic traits in tomato plants, including stem diameter, stem length, and stem inclination. The experimental results show that the Mean Absolute Percentage Error (MAPE) of the occluded main stem diameter is 9.7%, stem length is 5.7%, and tilt angle is 1%. For the occluded branch stem, the MAPE of stem diameter is 23.1%, stem length is 7.9%, and tilt angle is 1.5%. The accuracy of these measurements for occluded stems is acceptable compared to that obtained from 3D point clouds of unoccluded stems. This highlights the significant potential of using DRL to effectively inpaint occluded 3D point cloud data of plants.

Why it matches plant phenotyping methods植物茎の遮蔽部分を3D点群と深層強化学習で補完し、茎径・茎長・傾斜角という表現型形質の測定精度を検証しており、フェノタイピング手法が中心的です。

abstractTo address this challenge, this study proposes a novel method for plant stem occlusion inpainting using Deep Reinforcement Learning (DRL).

Temporal semantic multispectral point cloud generation and feature fusion pipeline for comprehensive trait estimation in greenhouse tomatoes →

Accurate estimation of comprehensive traits such as yield and quality is crucial for optimizing agricultural management practices across the tomato industry chain. Traditional manual methods are time-consuming, labor-intensive, and prone to errors, reducing estimation accuracy. In contrast, modern intelligent estimation approaches based on multi-temporal spatial and spectral feature fusion offer improved efficiency and accuracy but still face challenges such as non-generalizable segmentation models, asynchronous feature extraction and weak correlations. This study proposes a novel pipeline for estimating yield and quality of greenhouse tomatoes using temporal semantic multispectral (TSM) point clouds. An unsupervised deep learning model was designed to register RGB-D images and multispectral (MS) images collected by an unmanned ground vehicle (UGV) plant phenotyping platform. The digital number (DN) point clouds of tomato organs were reconstructed based on the masks predicted by SegFormer with fusion of multispectral and depth modalities (MSD-SF). These point clouds were then radiometrically calibrated using neural reference field with sparse viewpoints (NeREF-S) to generate accurate reflectance point clouds. Finally, multi-temporal spatial-spectral features of tomatoes were extracted from the TSM point clouds, and random forest regression models were developed to estimate traits such as fruit flavor preference, water content, brix, acidity, brix-to-acid ratio, vitamin C content, single-fruit mass, and single-plant yield. The image registration model achieved high accuracy on the test set, with average structural similarity index measure, peak signal-to-noise ratio and learned perceptual image patch similarity of 0.238, 13.116 dB, and 0.374, respectively. The MS point clouds calibrated by NeREF-S significantly improved the signal-to-noise ratio to 11.56 dB. The average rRMSE for all trait estimations was 9.03 %. The results indicate that the proposed estimation method is efficient and accurate, holding promise to become a new paradigm for estimating the comprehensive traits of greenhouse tomatoes.

Why it matches plant phenotyping methods温室トマトの収量・品質形質を推定するため、UGVフェノタイピングプラットフォーム、マルチスペクトル点群生成、画像登録・放射較正、特徴抽出および回帰推定パイプラインを中心的に開発・評価している。

abstractThis study proposes a novel pipeline for estimating yield and quality of greenhouse tomatoes using temporal semantic multispectral (TSM) point clouds.

Using 3D reconstruction from image motion to predict total leaf area in dwarf tomato plants →

Accurate estimation of total leaf area (TLA) is essential for assessing plant growth, photosynthetic activity, and transpiration, but remains a challenge for bushy plants like dwarf tomatoes. Traditional destructive methods and imaging-based techniques often fall short due to labor intensity, plant damage, or the inability to capture complex canopies. This study evaluated a non-destructive method combining sequential 3D reconstructions from RGB images and machine learning to estimate TLA for three dwarf tomato cultivars—Mohamed, Hahms Gelbe Topftomate, and Red Robin—grown under controlled greenhouse conditions. Two experiments, conducted in spring–summer and autumn–winter, included 73 plants, yielding 418 TLA measurements using an “onion” approach, where layers of leaves were sequentially removed and scanned. High-resolution videos were recorded from multiple angles for each plant, and 500 frames were extracted per plant for 3D reconstruction. Point clouds were created and processed, four reconstruction algorithms (Alpha Shape, Marching Cubes, Poisson’s, and Ball Pivoting) were tested, and meshes were evaluated using seven regression models: Multivariable Linear Regression (MLR), Lasso Regression (Lasso), Ridge Regression (Ridge-Reg), Elastic Net Regression (ENR), Random Forest (RF), extreme gradient boosting (XGBoost), and Multilayer Perceptron (MLP). The Alpha Shape reconstruction (α = 3) combined with XGBoost yielded the best performance, achieving an R² of 0.80 and MAE of 489 cm², with significant results across other model combinations. Results were lower when using data from different experiments as train and test datasets (R² = 0.56 and MAE = 579 cm²). Feature importance analysis identified height, width, and surface area as the most predictive features. These findings demonstrate the robustness of our approach across variable environmental conditions and canopy structures. This scalable, automated TLA estimation method is particularly suited for urban farming and precision agriculture, offering practical implications for automated pruning, improved resource efficiency, and sustainable food production.

Why it matches plant phenotyping methodsRGB画像からの3D再構成と機械学習により植物の総葉面積を推定する手法を開発・評価しており、表現型取得が研究の中心です。

abstractThis study evaluated a non-destructive method combining sequential 3D reconstructions from RGB images and machine learning to estimate TLA

Visible-near infrared hyperspectral imaging for non-destructive estimation of leaf nitrogen content under water-saving irrigation in protected tomato cultivation. →

Accurate estimation of leaf nitrogen content (LNC) is critical for optimizing fertilization strategies in greenhouse tomato production. This study developed a robust hyperspectral-based framework for non-destructive LNC prediction by combining advanced spectral preprocessing, feature selection, and machine learning. Hyperspectral reflectance data were collected across five nitrogen and irrigation treatments over key growth stages. Signal quality was enhanced through Savitzky-Golay smoothing (SG) and Standard Normal Variate normalization (SNV). Key nitrogen-sensitive wavelengths-centered around 725 nm and 730 - 780 nm-were identified using Competitive Adaptive Reweighted Sampling (CARS) and Principal Component Analysis (PCA). Four predictive models were compared, among which a hybrid Stacked Autoencoder-Feedforward Neural Network (SAE-FNN) achieved the highest accuracy (test R² = 0.77, RPD = 2.06), effectively capturing nonlinear spectral-nitrogen interactions. In contrast, Support Vector Machine (SVM) exhibited overfitting and Partial Least Squares Method (PLSR) underperformed due to its linear constraints. These results underscore the potential of integrating hyperspectral sensing with deep learning for intelligent nitrogen monitoring in controlled-environment agriculture.

Why it matches plant phenotyping methods植物の葉窒素含量という形質を、ハイパースペクトル画像と機械学習で非破壊推定する手法を開発・比較検証しており、フェノタイピング手法が中心である。

abstractThis study developed a robust hyperspectral-based framework for non-destructive LNC prediction by combining advanced spectral preprocessing, feature selection, and machine learning.

Influence of tomato storage period on the generalization of a near-infrared spectroscopy-based brix prediction mode →

To mitigate the impact of storage period variations on fruit sugar content prediction models and further enhance the universality of sorting models, this study investigated the influence of different storage periods on tomato brix prediction using near-infrared (NIR) spectroscopy and established a partial least squares (PLS) brix content prediction model. Experiments revealed that when the storage periods of the calibration set and the prediction set differed, the predictive performance of the PLS model significantly declined. To address this issue, the study found that optimizing spectral data with standard normal variate (SNV) transformation and adopting a mixed-modeling strategy incorporating multiple storage periods substantially improved the accuracy of the universal model: the correlation coefficient of the prediction set (Rp) increased from 0.803 to 0.934, the root mean square error of prediction (RMSEP) decreased from 0.476 to 0.375, and the residual predictive deviation (RPD) rose from 2.11 to 3.26. Finally, the competitive adaptive reweighted sampling (CARS) algorithm was employed to screen key wavelengths, effectively reducing data dimensionality while minimizing interference from storage period differences. Compared with the successive projections algorithm (SPA), the CARS method demonstrated superior performance, ultimately establishing a highly robust universal prediction model for tomato brix.

Why it matches plant phenotyping methodsトマト果実の糖度（Brix）をNIR分光で推定する予測モデルを開発・比較・検証しており、表現型取得手法が研究の中心である。

abstractthis study investigated the influence of different storage periods on tomato brix prediction using near-infrared (NIR) spectroscopy and established a partial least squares (PLS) brix content prediction model.

Electrical signalling in tomato — Oidium neolycopersici pathosystem for detection of powdery mildew →

Plants are subjected to a plethora of biotic stresses caused by various pathogens; among them, fungal pathogens represent the most destructive ones. In order to preserve the health status of plants, especially under the influence of climate change, the need to develop new sustainable, inexpensive, in-field and non-destructive diagnostic methods for plant pathogens is of great importance. In this direction, spectroscopic and molecular methods have made progress, while others, such as electrical diagnostic methods are still in the early stages of development. In this work, electrical signals in tomato plants infected with the fungal pathogen Oidium neolycopersici, the causative agent of powdery mildew, were measured. Differences in electrical responses were observed between healthy and infected plants during the entire monitoring period, and infected plants showed overall lower values of the electrical potential in comparison with healthy plants. Measurement of electrical potential allowed the successful differentiation between infected and healthy plants before the onset of symptoms (3.2 days in advance). A significant difference in electrical signals was obtained not only between infected and healthy plants, but also concerning the growing substrate: A stronger electrical potential was measured in plants grown in the peat-based substrate compared to those cultivated in the water substrate allowing a 97.5% discrimination. Based on the results of this study, measurements of electrical signals may become the basis for an alternative non-destructive diagnosis of tomato powdery mildew and other plant diseases. With the possibility of directly applying the technique in the field followed by remote monitoring of electrical signals, it may become useful for supporting timely disease control.

Why it matches plant phenotyping methodsトマトの感染状態を電気信号から非破壊的に識別する診断手法を測定・評価しており、植物病害状態のフェノタイピング手法が中心です。

abstractelectrical diagnostic methods are still in the early stages of development

Crop Disease Prediction Using Deep Learning Algorithm →

Plant diseases pose a significant threat to global agricultural productivity, particularly affecting key crops like tomato and potato. Traditional disease detection methods are often slow, subjective, and labour-intensive, leading to delayed responses and increased crop losses. This study proposes a hybrid machine learning framework that integrates ResNet9 for classification and U-Net for segmentation to detect and localize leaf diseases in tomato and potato plants. A comprehensive dataset of over 22,500 images spanning 13 classes, including healthy and diseased samples, was compiled from multiple sources and preprocessed using image normalization, histogram equalization, and data augmentation techniques. The model was trained using a 70:20:10 data split and optimized through early stopping and cyclic learning rates. Evaluation metrics including accuracy, precision, recall, F1-score, and ROC-AUC were used to assess performance, with the proposed model achieving a remarkable accuracy of 96.3%, F1-score of 95.2%, and ROC-AUC of 97.1%. The use of U-Net enabled accurate segmentation of infected regions, improving model interpretability and trustworthiness. Confusion matrix analysis revealed minimal misclassifications, and visual tools such as saliency maps confirmed the model’s attention to disease-prone areas. Real-world testing demonstrated the system’s robustness across different environments and lighting conditions. Comparative results showed superior performance of the hybrid model over VGG16, EfficientNet-B0, and baseline CNNs in both accuracy and inference speed. This approach offers a scalable, real-time solution for automated plant disease detection and diagnosis, particularly suited for use in resource-constrained agricultural settings. The hybrid model not only supports early intervention and precision agriculture practices but also bridges the gap between advanced machine learning and practical farming needs.

Why it matches plant phenotyping methods植物葉の病変領域を画像から検出・分類・分割する手法が研究の中心であり、病害状態という植物表現型を直接推定しているため含める。

abstractThis study proposes a hybrid machine learning framework that integrates ResNet9 for classification and U-Net for segmentation to detect and localize leaf diseases in tomato and potato plants.

Enhanced-RICAP: a novel data augmentation strategy for improved deep learning-based plant disease identification and mobile diagnosis →

Introduction Plant diseases pose a significant threat to global food security and agricultural productivity, making accurate and timely disease identification essential for effective crop management and minimizing economic losses. Although data augmentation techniques such as RICAP improve model robustness, their reliance on randomly extracted image regions can introduce label noise, potentially misleading the training of deep learning models. Methods This study introduces Enhanced-RICAP, an advanced data augmentation technique designed to improve the accuracy of deep learning models for plant disease detection. Enhanced-RICAP replaces random patch selection with an attention module guided by class activation maps, focusing on discriminative regions, Enhanced-RICAP reduces label noise and improves model accuracy for plant disease detection, addressing a key limitation of traditional augmentation methods. The method was evaluated using several deep learning architectures, such as ResNet18, ResNet34, ResNet50, EfficientNet-b, and Xception, on the cassava leaf disease and PlantVillage tomato leaf disease datasets. Results The experimental results demonstrate that Enhanced-RICAP consistently outperforms existing augmentation methods, including CutMix, MixUp, CutOut, Hide-and-Seek, and RICAP, across key evaluation metrics: accuracy, precision, recall, and F1-score. The ResNet18+Enhanced-RICAP configuration achieved 99.86% accuracy on the tomato leaf disease dataset, whereas the Xception+Enhanced-RICAP model attained 96.64% accuracy in classifying four cassava leaf disease categories. Discussion and Conclusion To bridge the gap between research and practical application, the ResNet18+Enhanced-RICAP model was deployed in PlantDisease, a mobile application that enables real-time disease identification and management recommendations. This approach supports sustainable agriculture and strengthens food security by providing farmers with accessible and reliable diagnostic tools.

Why it matches plant phenotyping methods植物病害画像から病害状態を識別するデータ拡張法を開発し、複数モデル・データセットで性能検証しているため、植物フェノタイピング手法が中心である。

abstractThis study introduces Enhanced-RICAP, an advanced data augmentation technique designed to improve the accuracy of deep learning models for plant disease detection.

CGA-ASNet: an RGB-D amodal segmentation network for restoring occluded tomato regions →

Obtaining the complete morphology of tomato fruits under non-destructive conditions is essential for phenotype research, yet fruit occlusions often hinder deep learning-based image segmentation methods from capturing the true shape of occluded regions. This limitation reduces prediction accuracy and adversely impacts phenotype data acquisition. To overcome this challenge, we propose CGA-ASNet, an RGB-D amodal segmentation network incorporating a Contextual and Global Attention (CGA) module. A synthetic tomato dataset (Tomato-sim) was constructed using NVIDIA Isaac Sim's Replicator Composer (ISRC) to realistically simulate tomato morphology and greenhouse environments, and the network was trained on this dataset. To evaluate generalization, CGA-ASNet was tested on both the synthetic and a separate real-world dataset. While no explicit domain adaptation techniques were adopted, diverse lighting conditions (strong, normal, and weak illumination) were simulated to implicitly reduce the domain gap, and a mean coordinate fusion algorithm was introduced to improve annotation completeness in real-world occlusion scenarios. By leveraging contextual information among feature input keys for self-attention learning, capturing global information, and expanding the receptive field, CGA-ASNet enhanced representation capacity, semantic understanding, and localization accuracy. Experimental results demonstrated that CGA-ASNet achieved an F@0.75 score of 94.2 and a mean Intersection over Union (mIoU) of 82.4% in greenhouse amodal segmentation tasks. These findings indicate that training with well-designed synthetic datasets can effectively support accurate occlusion-aware segmentation in real environments, providing a practical solution for tomato phenotyping in greenhouse conditions.

Why it matches plant phenotyping methodsトマト果実の遮蔽領域を復元して完全形態を取得するRGB-D画像解析手法を開発し、合成・実画像データセットで技術検証しているため、植物表現型取得が中心的である。

abstractObtaining the complete morphology of tomato fruits under non-destructive conditions is essential for phenotype research

Edge-Preserving Multi-Scale Network for Plant Point Cloud Segmentation →

Accurate plant organ segmentation is essential for enabling high-throughput extraction of plant phenotypes, as it provides the foundational data for both trait measurement and structural modeling. Although existing studies have made significant progress, plant semantic segmentation (stems and leaves) across multiple species remains underexplored. To this end, this paper introduces an edge-aware downsampling algorithm and a novel network for segmenting plant point clouds at multiple scales, named MSPlantSegNet. Experimental results on a dataset of tobacco, tomato, and sorghum demonstrate that MSPlantSegNet attained superior performance across all four key metrics-precision (97.13 %), recall (95.63 %), F1-score (96.20 %), and IoU (93.14 %). MSPlantSegNet surpasses a set of leading models, including PointNet++, PointNet, ASIS, DGCNN, PlantNet, PSegNet, and PointNeXt. This research has valuable implications for plant phenotyping, the development of smart agriculture, and ideal type selection.

Why it matches plant phenotyping methods植物点群から茎・葉を分割する新規ネットワークとダウンサンプリング法を開発し、複数種データで性能比較しており、表現型抽出の基盤手法が中心である。

abstractAccurate plant organ segmentation is essential for enabling high-throughput extraction of plant phenotypes

Review: Artificial Intelligence and Deep Transfer Learning for Plant Disease Detection and Classification →

The persistent threat of plant disease epidemics poses significant challenges to global agriculture, making crops susceptible to catastrophic diseases that compromise food security and nutritional well-being. This review critically examines the application of deep transfer learning and convolutional neural networks (CNNs) in classifying plant diseases, such as tomato leaf diseases. By synthesizing recent advancements in the field, the article highlights how pre-trained models, trained on large-scale image datasets, can be adapted to recognize disease-specific patterns in agricultural contexts. The discussion encompasses key methodologies, including the integration of custom architectures and shallow classifiers, as exemplified by works such as Fruit and Vegetable Leaf Disease Recognition based on a Novel Custom Convolutional Neural Network and Shallow Classifier and An Integrated Framework of Two-Stream Deep Learning Models Optimal Information Fusion for Fruits Disease Recognition. A critical analysis of existing approaches is provided, addressing their strengths, limitations, and the role of dataset quality and diversity in model performance, including the use of publicly available datasets of labelled plant disease images, such as PlantVillage. The review underscores the transformative potential of automation and robotics in reducing disease spread while emphasizing unresolved challenges, such as the need for cost-effective, scalable frameworks. By identifying gaps in current research and proposing future directions, this article aims to guide the development of sustainable, AI-driven solutions for agricultural productivity.

Why it matches plant phenotyping methods植物病害を画像から検出・分類するAI手法を体系的に検討するレビューであり、植物の病徴・病害状態を推定するフェノタイピング手法が中心です。

titleReview: Artificial Intelligence and Deep Transfer Learning for Plant Disease Detection and Classification

A multi-scale detection model for tomato leaf diseases with small target detection head. →

In tomato cultivation, various diseases significantly impact tomato quality and yield. The substantial scale differences among diseased leaf targets pose precise detection and identification challenges. Moreover, early detection of disease infection in small leaves during the initial growth stages is crucial for implementing timely intervention and prevention strategies. To address these challenges, we propose a novel tomato disease detection method called TomatoLeafDet, which integrates multi-scale feature processing techniques and small object detection technologies.Initially, we designed a Cross Stage Partial -Serial Multi-kernel Feature Aggregation (CSP-SMKFA) module to extract feature information from targets at different scales, enhancing the model's perception of multi-scale objects. Next, we introduced a Symmetrical Re-calibration Aggregation (SRCA) module, incorporating a bidirectional fusion mechanism between highresolution and low-resolution features. This approach facilitates more comprehensive information transmission between features, further improving the efficacy of multi-scale feature fusion. Finally, we proposed a Re-Calibration Feature Pyramid Network with a small object detection head to consolidate the multi-scale features extracted by the backbone network. This network provides richer multi-scale feature information input for detection heads at various scales. Results indicate that our method outperforms YOLOv9 and YOLOv10 on two datasets. Notably, on the CCMT tomato dataset, the proposed model achieved improvements in mean Average Precision (mAP50) of 4.4%, 1.9%, and 2.3% compared to the baseline model, YOLOv9s, and YOLOv10n, respectively, exhibiting significant efficacy.

Why it matches plant phenotyping methodsトマト葉の病害状態を画像から検出・識別する新規深層学習手法を開発し、既存モデルおよび複数データセットで性能比較しているため、植物フェノタイピング手法が中心である。

abstractwe propose a novel tomato disease detection method called TomatoLeafDet

Deep learning-based approach for phenotypic trait extraction and computation of tomato under varying water stress. →

Introduction: With the advancement of imaging technologies, the efficiency of acquiring plant phenotypic information has significantly improved. The integration of deep learning has further enhanced the automatic recognition of plant structures and the accuracy of phenotypic parameter extraction. To enable efficient monitoring of tomato water stress, this study developed a deep learning-based framework for phenotypic trait extraction and parameter computation, applied to tomato images collected under varying water stress conditions. Methods: Based on the You Only Look Once version 11 nano (YOLOv11n) object detection model, adaptive kernel convolution (AKConv) was integrated into the backbone's C3 module with kernel size 2 convolution (C3k2), and a recalibration feature pyramid detection head based on the P2 layer was designed. Results and discussion: Results showed that the improved model achieved a 4.1% increase in recall, a 2.7% increase in mAP50, and a 5.4% increase in mAP50-95 for tomato phenotype recognition. Using the bounding box information extracted by the model, key phenotype parameters were further calculated through geometric analysis. The average relative error for plant height was 6.9%, and the error in petiole count was 10.12%, indicating good applicability and accuracy for non-destructive crop phenotype analysis. Based on these extracted traits, multiple sets of weighted combinations were constructed as input features for classification. Seven classification algorithms-Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Naive Bayes, and Gradient Boosting-were used to differentiate tomato plants under different water stress conditions. The results showed that Random Forest consistently performed the best across all combinations, with the highest classification accuracy reaching 98%. This integrated approach provides a novel approach and technical support for the early identification of water stress and the advancement of precision irrigation.

Why it matches plant phenotyping methodsトマト画像から植物高や葉柄数などの表現型形質を抽出・計算する深層学習フレームワークを開発し、精度評価も行っており、表現型取得手法が研究の中心である。

abstractthis study developed a deep learning-based framework for phenotypic trait extraction and parameter computation

High-Performance Deep Learning for Instant Pest and Disease Detection in Precision Agriculture. →

Global farm productivity is constantly under attack from pests and diseases, resulting in massive crop loss and food insecurity. Manual scouting, expert estimation, and laboratory-based microscopy are time-consuming, prone to human error, and labor-intensive. Although traditional machine learning classifiers such as SVM, Random Forest, and Decision Trees provide better accuracy, they are not field deployable. This article presents a high-performance deep learning fusion model using MobileNetV2 and EfficientNetB0 for real-time detection of pests and diseases in precision farming. The model, trained on the CCMT dataset (24,881 original and 102,976 augmented images in 22 classes of cashew, cassava, maize, and tomato crops), attained a global accuracy of 89.5%, precision and recall of 95.68%, F1-score of 95.67%, and ROC-AUC of 0.95. For supporting deployment in edge environments, methods such as quantization, pruning, and knowledge distillation were employed to decrease inference time to below 10 ms per image. The suggested model is superior to baseline CNN models, including ResNet-50 (81.25%), VGG-16 (83.10%), and other edge lightweight models (83.00%). The optimized model is run on low-power devices such as smartphones, Raspberry Pi, and farm drones without the need for cloud computing, allowing real-time detection in far-off fields. Field trials using drones validated rapid image capture and inference performance. This study delivers a scalable, cost-effective, and accurate early pest and disease detection framework for sustainable agriculture and supporting food security at the global level. The model has been successfully implemented with TensorFlow Lite within Android applications and Raspberry Pi systems.

Why it matches plant phenotyping methods植物画像から病害状態を分類する深層学習手法の開発・比較検証と、エッジ実装・フィールド検証が中心であり、植物病害フェノタイピング手法に該当する。

abstractThis article presents a high-performance deep learning fusion model using MobileNetV2 and EfficientNetB0 for real-time detection of pests and diseases in precision farming.

In-situ biological ozone detection by measuring electrochemical impedances of plant tissues. →

This work demonstrates the biological detection of low-level by measuring electrochemical impedances of stem tissues in tobacco and tomato plants, both indoor and outdoor. Ozone concentrations as low as 30 above ambient levels were detected via physiological responses, enabling the use of phytosensors as biodetectors of environmental pollutants. exposure affects stomatal regulation that in turn alters the hydrodynamics of fluid transport system in plants. The measurement results indicate a reaction of hydrodynamic system to changes in concentration with a delay of 10-20 min between the onset of exposure and biological response. The probability of false-negative responses from a plant is 0.15 ± 0.06. Pooling data from at least three plants allows for 92% confidence in detecting excess . Measurements on days with low and high ozone levels of 80 to 130 result in a 2.33-fold difference in sensor readings at these levels, underscoring the sensitivity of the method. Statistical robustness is supported by 948 plant-sensor measurements with 9 plants over 51 days, totaling 10 7 samples via automated monitoring. Long-term field tests demonstrate the reliability of electrochemical methods. This approach has applications in environmental monitoring, biological pollution detection and biosensing.

Why it matches plant phenotyping methods植物組織の電気化学インピーダンスからオゾン曝露に対する生理応答を検出するセンサー手法を開発・検証しており、植物状態の取得方法が研究の中心である。

abstractThis work demonstrates the biological detection of low-level by measuring electrochemical impedances of stem tissues in tobacco and tomato plants

A Journey on The Exploration of Village Plant Dataset Using ‎Machine Learning Models →

This article is coined for investigating the Village Plant dataset. Many researchers worldwide, carrying out their research in ‎the domain of agriculture, are dependent on this open source dataset. A plant is vulnerable to several infirmities during its period of growth. ‎Detection of the plant’s ill health and monitoring the environmental parameters is the most challenging task in agriculture. Plant disease epidemic may have a significant effect on crop production, reducing the country’s wealth. Early diagnosis of the occurrence of ill health in plants ‎and the remedies are feasible using Artificial Intelligence (AI). Currently, methods like Deep Learning (DL) algorithms, machine vision ‎techniques, and robotics play an important role in monitoring plant diseases and the growth status. This dataset contains multi-fold in-‎information about the plants. They include the normal and diseased images of plants like Bell Pepper, Tomato, Cucumber, and Potato. An Internet ‎of Things (IoT) based plant data collection and integration system will provide data for this research, which optimizes the feature set through ‎Ant Colony Optimization (ACO) for improving prediction in feature selection using deep learning models like DenseNet, ResNet 50, ‎VGG 19, and Long Short-Term Memory (LSTM) networks, which in turn enhances plant productivity with advances in AI-driven agricul‎tural diagnostics for plant stress prediction‎.

Why it matches plant phenotyping methods植物の正常・罹病画像から植物の病気・ストレス状態を推定する画像解析ワークフローとデータセット利用が研究の中心であり、植物状態のフェノタイピング手法に該当する。

abstractThis dataset contains multi-fold in-‎information about the plants. They include the normal and diseased images of plants like Bell Pepper, Tomato, Cucumber, and Potato.

Plant Leaf Disease Prediction using Deep Learning Algorithm with Fertilizer Suggestion →

Motivation: Early disease detection and accurate diagnosis of tomato leaf diseases are crucial for enhancing crop yield and ensuring food security in modern agriculture. Problem: One of the challenges faced by the agriculture sector is the spread of viruses, fungi and bacteria that cause plant diseases. Traditional diagnostic methods require manual inspection by farmers or experts, which is time-consuming and prone to inaccuracies. Approach: Proposed Convolutional Neural Network (CNN) system aims to classify ten classes of tomato leaf diseases using sophisticated deep learning techniques. The input images were pre-processed with a Wiener filter to enhance clarity and suppress noise without compromising important edge information. Computational complexity is decreased and precision is increased by selecting features through the Particle Swarm Optimisation (PSO) algorithm. Finally, the proposed CNN model can be utilised to effectively detect relevant plant leaf diseases based on the selected features. Result: It achieved promising results, with 96.5% precision, 94.7% recall, and 95.6% F1-score, to evidence its effectiveness. With the incorporation of pre-processing, PSO-based feature selection, and CNN-based classification this method offers a practical and easy-to-use tool that assists farmers with their decision-making, minimising crop loss.

Why it matches plant phenotyping methodsトマト葉画像から病害状態をCNNで推定する手法を開発・評価しており、植物病害表現型の取得・分類が研究の中心である。

abstractProposed Convolutional Neural Network (CNN) system aims to classify ten classes of tomato leaf diseases using sophisticated deep learning techniques.

Automated Detection and Classification of Tomato Crop Diseases Using Convolutional Neural Networks →

Tomato plants, a globally significant horticultural crop, are frequently threatened by a range of diseases that compromise yield and quality. Traditional disease detection methods based on manual inspection by experts are often labour-intensive, time-consuming, and susceptible to human error. This study presents a machine learning-based approach that leverages Convolutional Neural Networks (CNNs) to automate the identification and classification of common tomato diseases using leaf images. A comprehensive dataset, including both healthy and diseased leaf images, was collected, pre-processed, and augmented to enhance model performance under various environmental conditions. A custom-designed CNN model was then trained and evaluated using standard metrics such as accuracy, precision, recall, and F1-score. The model demonstrated high classification accuracy and robustness across multiple disease categories including early blight, late blight, and bacterial spot. Furthermore, the system was deployed as a user-friendly web and mobile application interface, allowing real-time diagnosis in the field. This enables farmers especially in resource-constrained settings to identify and respond to infections early, thereby reducing yield losses and limiting excessive pesticide use. The project underscores the potential of AI-driven solutions in modernizing agricultural practices and promoting sustainable crop management. Recommendations are made for future work to improve model adaptability, extend its disease coverage, and integrate environmental sensor data for multimodal analysis.

Why it matches plant phenotyping methodsトマト葉画像から病害状態を分類するCNN手法を開発・評価しており、植物の病徴・病害状態の取得が研究の中心であるため。

abstractThis study presents a machine learning-based approach that leverages Convolutional Neural Networks (CNNs) to automate the identification and classification of common tomato diseases using leaf images.

Automatic 3D Plant Organ Instance Segmentation Method Based on PointNeXt and Quickshift++ →

Organ instance segmentation of 3D plant point clouds is a crucial prerequisite for organ-level phenotype estimation. However, most current cloud segmentation methods are usually designed for specific crop, hardly fit for both monocotyledonous and dicotyledonous crops which have significant structural differences. This study therefore proposed a two-stage method with higher generalization ability for single-plant organ instance segmentation based on PointNeXt and Quickshift++. The effectiveness of this method was tested on different types of crops. The dataset includes point clouds of 122 self-acquired sugarcanes, 49 open-accessed maizes, and 77 open-accessed tomatoes. The improved PointNeXt model was trained to implement the semantic segmentation of stems and leaves. The average mOA and mIoU on the test set reaches 96.96 ​% and 87.15 ​%, respectively. The Quickshift++ algorithm was then applied to encode the global spatial structure and local connections of plants for rapid localization and segmentation of leaf instance. Our approach outperformed four SOTA methods, ASIS, JSNet, DFSP, and PSegNet in terms of both quantitative and qualitative segmentation results, achieving average values for mPrec, mRec, mF1, and mIoU of 93.32 ​%, 85.60 ​%, 87.94 ​%, and 81.46 ​%, respectively. The proposed method also yields excellent results for several other plants in their early stages, indicating its generalization ability and applicability for organ instance segmentation for different plants, thus providing a powerful tool for plant phenotypic research.

Why it matches plant phenotyping methods植物の3D点群から茎・葉の器官インスタンスを分割する手法を開発・比較検証しており、器官レベルの表現型推定に直接つながる中心的な方法研究である。

abstractOrgan instance segmentation of 3D plant point clouds is a crucial prerequisite for organ-level phenotype estimation.

Plant leaf disease detection and classification using CNN and VGG16 models →