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.
Eggplant (Solanum melongena) is a major cash crop, yet field detection of its pests and diseases remains difficult because disease evidence is simultaneously occluded by foliage, blurred at lesion boundaries, and highly variable in scale. Fruit rot is especially challenging: the waxy epidermis and purple anthocyanin-rich surface reduce chromatic contrast, while infection spreads gradually from water-soaked tissue to necrotic tissue, producing diffuse borders between diseased and healthy regions. In this work, we reframe eggplant disease detection through a context-boundary-scale coupling principle, which states that accurate field detection should jointly model incomplete contextual cues, ambiguous lesion boundaries, and scale-varying symptom morphology rather than optimize these cues independently. ISA-YOLO is proposed as an implementation of this principle on top of YOLOv13 through coordinated context modeling, boundary-aware aggregation, and progressive multi-scale fusion. Experiments on two public datasets show that ISA-YOLO achieves 78.1 and 77.7% mAP at 30.66 and 31.74 FPS, outperforming mainstream detectors in overall trade-off between accuracy and speed. After pruning and quantization, inference speed increases to about 75 FPS while maintaining strong accuracy. These results indicate that the proposed principle provides an effective pathway for accurate and deployable eggplant pest and disease detection in smart agriculture.
Why it matches plant phenotyping methodsナスの病害・害虫を画像から検出するISA-YOLOモデルの開発と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フェノタイピング手法に該当する。
titleA high-performance detection model ISA-YOLO for eggplant pests and diseases.
Reproduction assets foundThe paper uses four public Roboflow image datasets (BISU, UTM, Papaya, Tomato) and states that supporting data and code are publicly available on Zenodo, all with explicit URLs in the Data availability section.Dataset · publicwas supported by National Natural Science Foundation of China grants (62472269, 62072291).
Data availability
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these public datasets complied with relevant institutional, national, and international guidelines and legislation when collecting the data. No newOpen asset ↗eggplant-disease-detectionlines:1509-1560Dataset · publicty
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these public datasets complied with relevant institutional, national, and international guidelines and legislation when collecting the data. No new data collection was performed for this research. The authors confirm that the use of these datasets in thOpen asset ↗eggplant-disease-detection-5fuqvlines:1509-1560Dataset · publicof the outcomes of the Provincial Undergraduate Training Program on Innovation and Entrepreneurship (Number: S202510108100). This work was supported by National Natural Science Foundation of China grants (62472269, 62072291).
Data availability
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these publicOpen asset ↗papaya-eswlk-r2ydylines:1509-1560Dataset · publicnovation and Entrepreneurship (Number: S202510108100). This work was supported by National Natural Science Foundation of China grants (62472269, 62072291).
Data availability
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these public datasets complied with relevant institutional, national, and internatOpen asset ↗tomato-rottenlines:1509-1560Code · publicarch. The authors confirm that the use of these datasets in this study is fully compliant with their original licenses and ethical guidelines. The final images presented in the article accurately reflect the original data and meet community standards. The data and code supporting the conclusions of this article are available at https://zenodo.org/records/19425300 .
Declarations
Ethics approval and consent to participateOpen asset ↗Zenodo · 19425300lines:1509-1560Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Brinjal (eggplant) is a critical crop in South Asia, especially in Bangladesh, but its production is drastically affected by numerous diseases that inhibit yield and quality. Manual diagnosis of disease is time-consuming, subjective, and prone to errors, necessitating automated, scalable technology. To address these issues, this paper proposes PD-ViCo, a lightweight, efficient transformer-based model for brinjal fruit disease classification using Simple Vision Transformer (ViT) with Patch Dropout and Contrastive Captioner (CoCa) methods. One new dataset of 1,823 field-harvested brinjal images encompassing five disease classes including Phomopsis Blight, Fruit and Shoot Borer, Fruit Cracking, Wet Rot, and Healthy samples were prepared through real-world agricultural data collection from Bangladesh. The approach includes extensive preprocessing, class balancing (under-sampling/oversampling), and resilient augmentation methods. The PD-ViCo model significantly improves classification performance under data imbalance with patch dropout regularization and CoCa-style aggregation, resulting in better generalization and robustness. On a range of imbalanced, under-sampled, and oversampled datasets, PD-ViCo achieved a classification accuracy of 99.12% and F1-score of 97.76%, outperforming both ViT and Swin Transformer across all key evaluation metrics. Explainability was also applied using Grad-CAM and Grad-CAM + + , generating visual explanations of model decisions and maintaining conformity to disease-affected regions in the images. These visualizations ensure the credibility of the model and its usability for real agricultural conditions. This study demonstrates that PD-ViCo is a highly accurate, interpretable, and lightweight model for multi-class brinjal disease diagnosis. Not only does it advance state-of-the-art in agricultural AI, but it also provides a valuable dataset and an understandable decision-making protocol that can be applied directly by farmers, agronomists, and agricultural technologists.
Why it matches plant phenotyping methods植物画像から病害状態を分類するモデル、データセット、説明可能性評価を中心に開発・検証しており、植物フェノタイピング手法として適格。
abstractthis paper proposes PD-ViCo, a lightweight, efficient transformer-based model for brinjal fruit disease classification
Reproduction assets foundThe paper's own field-harvested brinjal disease image dataset (1,823 images, five classes) is publicly deposited on Mendeley Data, with an explicit availability statement and URL matching an allowed entry. No code or model checkpoint deposit is stated.Dataset · publicThe data utilized in this study is publicly accessible on Mendeley Data Repository at the following link: [ https://data.mendeley.com/datasets/ngc58fsxgd/1 ].Open asset ↗Mendeley Data · ngc58fsxgd/1lines:226-251Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published31 May 2026International Journal for Research in Applied Science and Engineering TechnologyCited by 0 · OpenAlex ↗
Plant diseases are one of the major challenges faced in modern agriculture, as they directly impact crop productivity, food quality, and the overall economic stability of farmers. Various environmental factors such as climate change, excessive moisture, poor soil conditions, and pest attacks contribute to the rapid spread of plant diseases. Traditional methods of disease identification mainly rely on manual inspection by agricultural experts, which is time-consuming, costly, and often inaccurate during the early stages of infection. Therefore, there is a growing need for an automated, fast, and reliable plant disease detection system that can assist farmers in identifying diseases at an early stage and taking appropriate preventive actions. This project presents an intelligent plant disease detection system that utilizes image processing, Machine learning, and machine learning techniques for accurate disease identification in crops such as Onion, Brinjal, Mango, Papaya, and Guava. The system is designed to analyze images of plant leaves captured through cameras or mobile devices. Using advanced image preprocessing methods, the captured leaf images are enhanced and processed to extract important features such as color, texture, and disease patterns. These features are then analyzed using a Convolutional Neural Network (CNN) model, which classifies the plant as either healthy or diseased with high accuracy. If a disease is detected, the system further identifies the specific type of disease affecting the plant and provides suitable recommendations for treatment and prevention. These recommendations include appropriate fertilizers, pesticides, organic supplements, and preventive agricultural practices customized for each crop type. The system also helps farmers understand the severity of the disease and suggests measures to minimize its spread to nearby plants. By providing real-time analysis and accurate predictions, the proposed solution reduces dependency on manual monitoring and expert consultation. The main objective of this project is to support precision agriculture by enabling early disease diagnosis, improving crop management efficiency, and increasing agricultural productivity. The automated detection process saves time, reduces crop losses, minimizes excessive pesticide usage, and promotes sustainable farming practices. Furthermore, this system can be integrated into smart farming applications and mobile-based agricultural support systems, making it accessible and beneficial for farmers in rural and urban areas alike
Why it matches plant phenotyping methods植物葉画像から健康・罹病状態と病害種を推定する画像処理・CNN手法が研究の中心であり、植物病害状態の表現型取得に該当する。ただし処置推奨は付随的である。
abstractThis project presents an intelligent plant disease detection system that utilizes image processing, Machine learning, and machine learning techniques for accurate disease identification in crops such as Onion, Brinjal, Mango, Papaya, and Guava.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Eggplant (Solanum melongena L.) is a widely cultivated vegetable crop worldwide, occupying an important position in the agricultural industries of Asia, the Middle East, and Southern Europe. Its significance extends beyond agricultural economics to diverse dimensions such as dietary nutrition, rendering it of considerable research and application value. Traditional crop phenotyping methods suffer from low efficiency, substantial manual errors, and a tendency to damage tender seedlings, while existing three-dimensional phenotyping techniques face challenges including strong background interference and large data volumes. These dual constraints limit the accuracy and application feasibility of seedling phenotyping. To address these issues, this study proposes a non-destructive phenotyping method for eggplant seedlings, with the improvement of the PointNet++ architecture as its core and point cloud background purification as a key preprocessing step, aiming to enhance eggplant breeding efficiency and seedling screening accuracy. The raw point clouds first undergo background purification to actively remove seedling tray points, thereby improving point cloud purity and reducing data size. Concurrently, based on the PointNet++ model, we develop an improved point cloud segmentation model, EggplantPointNet++, by introducing multi-scale residual blocks, integrating channel attention mechanisms, incorporating a global context module, and refining the feature propagation layer. In conjunction with the DBSCAN clustering algorithm, this approach achieves semantic and instance segmentation of eggplant seedling point clouds, with certain improvements in segmentation accuracy and model efficiency under small-scale and occluded scenarios. To validate the technical effectiveness, multiple comparative experiments and ablation studies were conducted. The results demonstrate that EggplantPointNet++ outperforms the original model, background purification preprocessing provides positive gains, and each improved module contributes positively. The final model achieves improvements in core metrics including Recall and F1-score. Based on the segmented point cloud data, this study calculates core phenotypic parameters including plant height, stem diameter, cotyledon angle, and cotyledon area. Using the technical system established in this study, we completed the time-series measurement of three-dimensional morphological changes in eggplant seedlings during the cotyledon stage, providing quantitative references for seedling growth assessment and superior plant selection.
Why it matches plant phenotyping methodsナス幼苗の3D点群から形質を抽出する非破壊フェノタイピング手法を開発し、比較実験・アブレーションで技術性能を検証しているため、方法が中心的である。
abstractthis study proposes a non-destructive phenotyping method for eggplant seedlings
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.
Plant 3D reconstruction using optical imaging often suffers from incomplete point clouds due to viewpoint occlusion and sensor limitations. This incompleteness hinders accurate structural representation and subsequent feature extraction for plant analysis. To address these challenges, we propose a Multi-Scale Dual-Discriminator Generative Adversarial Network (MSDDG) for plant point cloud completion. A multi-scale point cloud generator (MSPG) that integrates local and global features from raw incomplete point clouds is used for MSDDG to reconstruct complete shapes. The dual-discriminators-a multi-view projected silhouette discriminator and a spatial distance discriminator-are designed to ensure geometric realism and spatial plausibility from multiple perspectives. To train MSDDG, we created the Plant4L dataset containing four plant species (sunflower, pumpkin, luffa, and eggplant) with high-quality 3D models augmented via 3D thin plate spline transformations and virtual occlusion simulation to generate incomplete point clouds and multi-view silhouettes. Experimental results on Plant4L demonstrate that MSDDG achieves superior completion performance, with Chamfer Distance (CD), Hausdorff Distance (HD), and Uniformity Chamfer Distance (UCD) all below 0.41. Comparative evaluations confirm MSDDG's superiority over previous point cloud completion methods. The application of MSDDG for 3D reconstruction from single view further validate its effectiveness in restoring occluded plant structures.
Why it matches plant phenotyping methods植物の不完全点群を補完して3D構造を再構成する手法を開発し、植物データセット上で比較評価・検証しており、表現型取得ワークフローが中心です。
abstractwe propose a Multi-Scale Dual-Discriminator Generative Adversarial Network (MSDDG) for plant point cloud completion.
Reproduction assets foundThe paper's data availability statement explicitly states that the source code and datasets (including the Plant4L point cloud completion dataset) are publicly available at the authors' GitHub repository.Code · publicThe source code and datasets used in this study are publicly available at https://github.com/Amuro-Aznable/MSCGPCN.git .Open asset ↗https://github.com/Amuro-Aznable/MSCGPCN.git · MSCGPCNlines:415-421Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published23 Apr 2026International Journal of Applied Agricultural SciencesCited by 0 · OpenAlex ↗
this research investigates the use of artificial neural networks (ANNs) and image processing techniques for monitoring black eggplant crops, including for classification, disease detection, and potential yield estimation. A dataset of eggplant images was analysed, image pre-processing was performed, features were extracted via convolutional neural networks (CNNs), and classification/regression models were built. The results show that CNN-based methods achieve high accuracy in disease classification and crop classification tasks. The implications for precision agriculture and reduced environmental impact are discussed. The aim of this study is to use artificial intelligence, specifically networks, to examine diseases affecting eggplant, given its importance as a crop. Practitioners should begin with transfer learning using pre-trained CNNs for disease detection, progressively integrating multispectral sensors and recurrent networks for temporal modeling. The development of a dedicated black eggplant monitoring case report would significantly advance precision horticulture for this economically vital crop . The study could potentially be extended to other crops.
Why it matches plant phenotyping methods画像解析とCNNを用いたナスの病害分類・作物分類が研究の中心であり、植物の病害状態を画像から推定するフェノタイピング手法の適用に該当する。
abstractthe use of artificial neural networks (ANNs) and image processing techniques for monitoring black eggplant crops, including for classification, disease detection, and potential yield estimation
Conventional approaches to measuring stomatal conductance (gs) and transpiration often rely on instruments that interfere with plant physiology. Porometers, for example, restrict natural leaf movement, apply pressure, and introduce dry airflow that can alter stomatal behaviour, thereby reducing the relevance of such measurements. Prior studies report discrepancies among devices attributable to such interferences (Toro et al. 2019). To minimise artefacts, transpiration should be estimated remotely without physical contact, which theoretically can be achieved via a thermal leaf energy-balance approach that infers gs from leaf temperature, radiative load, and boundary-layer terms. In this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely. Approaches to estimate stomatal conductance based on the energy-balance equation were developed recently to aid phenotyping of plantss. Most methods either imposed rapid changes in air humidity to perturb transpiration and, consequently, leaf temperature (Driever et al. 2023), or relied on ‘dry’ and ‘wet’ reference surfaces (as in Leinonen et al. 2006) to compute stress indices (Vialet-Chabrand and Lawson 2020). These methods require reference materials to assess surface temperatures under maximum and zero transpiration, showing the effect of longwave radiation. However, reference-material methods were constrained by heterogeneity in light interception caused by variation in leaf angle and orientation, because reference surfaces could not reorient like real leaves (Zhang et al. 2025). In this study, we addressed this challenge by using thermal imaging and 3D photogrammetry to capture leaf temperature and geometry noninvasively, allowing parameter estimation for each leaf individually. Here, ρ is the density of air (kg m−3), cp is the specific heat capacity of air (J kg−1 K−1) and rHR is the parallel resistance to heat and radiative transfer on the leaf surface (s m−1), s is the slope of the curve relating saturating water vapour pressure to temperature (Pa °C−1). TL and TA are leaf and air temperatures (°C), respectively, δe is air vapour pressure deficit (Pa), γ is the psychrometric constant (Pa K−1) and rva is the boundary layer resistance to water vapour (s m−1) (Supporting Information S2: Equation S1). The net radiative energy Rn in the energy-balance term was obtained from the same 3D light interception model, which integrates measured direct and lateral scattered irradiance (W m−2) (Supplement Material and Methods, File S2). Stomatal conductance gs (m s−1) is the inverse of stomatal resistance rs (s m−1). To experimentally obtain a wide range of gs values, we grew eggplant (Solanum melongena L.) plants in hydroponic units in growth chambers under four sets of environmental conditions (Table 1). Thirty-day-old plants (4–5-leaf stage) were placed on balances (Supplementary Materials and Methods, File S2). Units were sealed with plastic film to minimise evaporation. Mass loss attributable to transpiration was logged automatically every 30 s. To induce short-term changes in stomatal conductance, we imposed an acute osmotic stress by delivering a saline NaCl solution with high electrical conductivity (60 mS cm−1) to the root zone, producing a steep drop in root osmotic potential. This created rapid physiological and morphological responses that altered incident irradiance at the leaves, leaf temperature, and consequently energy balance, stomatal conductance and transpiration. We chose this stressor for operational simplicity. Any perturbation that modifies transpiration dynamics and thus gas exchange could have served our purpose. The total transpiration of a leaf, Et (kg s−1), is the product of the total conductance to water vapour from the mesophyll to the atmosphere, gv (m s−1), calculated from the estimated stomatal resistance rs (s m−1) and the boundary layer conductance gva (m s−1), the difference between water vapour concentration inside the leaf Cvs (dimensionless), and in surrounding air Cva (dimensionless), the leaf area A (m2), and the density of water ρw (kg/m3) (Jones 1992). Estimated stomatal conductance was obtained from leaf energy balance calculation (Equation 1). Boundary-layer conductance was computed from measured wind speed and leaf dimensions (leaf area, length, width) extracted from structure-from-motion 3D reconstructions (Supporting Information S1: Equation S6; Grace et al. 1980). Transpiration was then calculated for each leaf at each thermal 3D imaging time point, and whole-plant transpiration for comparison with gravimetric logs was the sum of all per-leaf estimates. As a non-invasive approach, we evaluated the plausibility or our model derived stomatal conductance (Equation 2) indirectly by comparing calculated and measured whole plant transpiration. We emphasise that this is not a direct validation of gs. Rather, the close agreement between modelled and measured transpiration across the wide range of environmental treatments, both stressed and non-stressed, provides confidence that the inferred gs is realistic. RGB and thermal images acquired before, during, and after stress application enabled dynamic tracking of leaf position and temperature (Supplementary Material and Methods, File S2). As expected, osmotic stress application had immediate effects on morphology and physiology. While control leaves maintained an angle of around 110° throughout, osmotic shock induced immediate turgor loss and drooping in all environments except one (Figure 1A,B). Leaf angles recovered to pre-stress positions within 1 h, indicating adaptation to the osmotic shock and restoration of turgor. Only environment 4 (high light, low air temperature and low humidity) maintained turgor during stress. Angle shifts were most pronounced in older leaves, which drooped and reduced light interception; younger leaves better preserved structure and turgor (Supporting Information S1: Figure S2). These angle changes also altered incident irradiance at the leaf surface (Supporting Information S1: Figure S3). These morphological responses coincided with increases in leaf temperature, consistent with altered water fluxes and stomatal regulation after stress. Across environments, plants showed a uniform rise in leaf temperature following osmotic stress, regardless of initial temperature (Supporting Information S1: Figure S4). This response held across leaf ages, encompassing older (Figure 1C) and younger (Figure 1D) leaves. Stomatal conductance estimated with our method followed the same pattern, dropping rapidly after osmotic shock in both older (Figure 1E) and younger (Figure 1F) leaves (Supporting Information S1: Figure S5). We estimated no stomatal conductance recovery to pre-stress conditions over the time course of stress exposure. Model-estimated and gravimetrically measured transpiration showed identical time courses across all four environmental conditions (Figure 1G–J). Transpiration rates did not recover to the same extent as leaf turgor, indicating long-term effects of the osmotic shock. Across environments and time points, correlation between model estimated and measured whole-plant transpiration was high (Figure 1K). In this study, stomatal conductance (gs) is a model-derived quantity inferred from the same physically constrained framework and model (leaf temperature, boundary-layer conductance and vapour pressure deficit). Since we did not measure gs directly, we cannot validate gs directly. Instead, we used a non-invasive check via transpiration. Model predictions closely tracked measured transpiration across the four controlled environments. This agreement increases confidence that the inferred gs is realistic, while we acknowledge that transpiration agreement alone is not a rigorous validation and cannot fully rule out compensating errors. Our study demonstrated the potential of our approach to estimate transpiration accurately by combining 3D imaging and thermography with physiological modelling without the use of reference materials that imitate real leaves. This remote approach enables simultaneous assessment of morphological and physiological responses to stress, yielding a more integrated view on plant transpiration and gas exchange. In contrast to chamber and porometer measurements or IR methods requiring wet and dry references or calibration plates, our workflow is reference-free. Absorbed shortwave radiation is derived from measured irradiance and a 3D reconstruction of leaf geometry, with no external reference materials. Moreover, remote measurements avoid continuous pressure from clamp-on porometers, permitting long-term observation and capture of rapid stress responses without sustained damage or microclimate artifacts. Further, the approach is not limited by any clamp on sensors and as such enables multi-leaf tracking. Applied to crop canopies, this approach could improve understanding of canopy processes that influence productivity and enable remote estimation of canopy transpiration. Future research could further improve by replacing our strong saline solution stress by gradual soil drying to depict a more realistic and natural stress while testing the approach under long-term conditions. Recent studies indicate that, with rising atmospheric CO2 concentrations, breeding for reduced stomatal conductance could increases WUE without affecting photosynthetic capacity (Srivastava et al. 2024). As such, remote systems for high-throughput plant phenotyping (HTP) are required to scan vast quantities of plants. We see a potential use of our system for such purposes to quickly estimated whole plant and individual leaf transpiration, as initial image capturing is very fast. A large bottleneck in our work was 3D model generation speed and manual extraction of leaf parameters from these 3D models. Both could be streamlined with more automated software, possibly including neural network solutions. The authors have nothing to report. The authors declare no conflict of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Why it matches plant phenotyping methods3D画像、熱画像、光遮断モデル、エネルギーバランスモデルを統合し、葉ごとの蒸散と気孔コンダクタンスを非侵襲的に推定する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractIn this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely.
The cultivation of brinjal is seriously affected by various pathogens which affect crop yield and quality hence the importance of automated disease detection systems in precision agriculture. The conventional image-based diagnostic methods fail to differentiate between similar diseases classes that look similar and this results in misdiagnosis and poor crop management. This research paper advances to suggest a lightweight deep learning model, IEMA-YOLOv11 (Improved Efficient Multi-Scale Attention-based YOLOv11), to successfully identify and detect Brinjal disease successfully in real-time. The model combines an EMA (Efficient Multi-Scale Attention) system, IRMB (Inverted Residual Mobile Blocks), and a LDFM (Local Detail Feature Module) to obtain fine-grained lesion features, as well as a refined MPDIoU loss to achieve a better object localization. The framework was tested against a curated multi-class brinjal disease data of six fungal, two bacterial, three viral and one nematode infection. IEMA-YOLOv11 had a precision of 96.5, a recall of 95.7, mAP@50 of 95.6 and mAP@50:95 of 94.9, with only 5.12M parameters and 17.1 GFLOPs. In comparison to the current benchmarks, IEMA-YOLOv11 was superior to YOLOv8-n by a margin of 2.4% in mAP@50, which has a potential to be used on a large scale to detect brinjal disease in a sustainable manner.
Why it matches plant phenotyping methods植物病害の病斑特徴を画像から抽出・検出する深層学習手法を開発し、データセット上で性能検証しているため、植物フェノタイピング手法が研究の中心である。
abstractThis research paper advances to suggest a lightweight deep learning model, IEMA-YOLOv11 (Improved Efficient Multi-Scale Attention-based YOLOv11), to successfully identify and detect Brinjal disease successfully in real-time.
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.
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.
In natural planting environments, pest and disease detection on eggplant fruits is characterized by small lesion sizes, weak edge feature information, significant scale variations, and complex backgrounds. Particularly, fruit borer holes, fruit rot lesions, and melon thrips bite marks exhibit obvious differences in size, edge structure, and spatial distribution, posing considerable challenges for real-time accurate detection. This paper proposes the DFSNet, a lightweight improved network for pest and disease detection on eggplant fruits in natural scenes. First, PConv is introduced in the P1, P2 shallow feature extraction stages of the baseline model's backbone network to enhance the modeling capability for fine-grained directional textures and weak edge information. Subsequently, an MSDA (Multi-Scale Directional Aggregation) module is designed and embedded into the feature enhancement modules at the P3, P4, and P5 layers of the backbone, which effectively improves the perception capability for insect hole edges and lesion contours through multi-directional depthwise separable convolution and Directional Edge Enhancer (DEE). Furthermore, a CSP-MSLA structure is introduced into the neck network, combining multi-scale linear attention mechanism with cross-stage partial connections to achieve selective enhancement of key pest and disease regions while maintaining low computational complexity. Finally, an SDDH (Shape-based Dynamic Detection Head) is introduced, which enhances the model's adaptive capability to different pest and disease geometric features and scale variations by introducing Scale-based Dynamic Loss. Experimental results demonstrate that the model achieves Precision of 81.0%, Recall of 78.3%, and mAP@50 of 80.5% on a self-constructed eggplant pest and disease dataset under natural scenes, representing improvements of 6.9, 8.8%, and 7.8% percentage points respectively compared to the baseline model. Meanwhile, the model parameters and computational cost are compressed to 1.8M and 5.4G respectively, with an inference speed of up to 378.13 FPS. The proposed method effectively improves small target detection accuracy and robustness under complex backgrounds while ensuring real-time performance, demonstrating particularly significant advantages in detecting small targets such as fruit borer holes and melon thrips bite marks, proving that this model is an efficient and robust real-time detection model for eggplant fruit pests and diseases.
Why it matches plant phenotyping methods卵果実の病斑・食害痕を画像から検出する深層学習手法を開発し、データセット上で性能評価しており、植物の病害状態の取得が中心的です。
abstractThis paper proposes the DFSNet, a lightweight improved network for pest and disease detection on eggplant fruits in natural scenes.
Eggplant was one of the most widely grown vegetable crops in the Jaipur region and plays an important role in the income of local farmers. The eggplant vegetable crop was often affected by different leaf diseases such as insect pest infestation, leaf spot, mosaic virus, white mold, and wilt, which reduce yield and quality. Early and accurate recognition of these diseases was necessary for reducing losses and improving sustainable farming practices. In current study, different modern computer-based approaches were tested for identifying multiple eggplant diseases from leaf images. Several well-known image recognition models were first applied individually, but their accuracy remained low, ranging between 30% and 35%. A combined model using image features with an advanced decision system improved the accuracy to about 76%. Further, a voting-based approach that merges results from multiple models reached around 60% accuracy. The most effective solution was a stacking-based hybrid system that combined the strengths of all models. Present approach achieved a very high accuracy of 99.8%, with precision, recall, F1-score, and area under curve values also close to 1.0. The statistical analysis confirmed that current hybrid method was significantly better than other tested methods, with Wilcoxon signed-rank tests showing clear improvements in most pairwise comparisons. Sample predictions further demonstrated that the hybrid model was able to correctly identify almost all cases of healthy and diseased leaves, while other models often confused between classes. These results show that a carefully designed hybrid system can provide reliable disease recognition in eggplant crops. The proposed method can help farmers and extension workers in Jaipur to monitor plant health more effectively and reduce the economic losses caused by leaf diseases, supporting sustainable vegetable production in the region.
Why it matches plant phenotyping methods卵plant葉画像から病害状態を推定するハイブリッド画像認識手法の開発・比較検証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。
abstractdifferent modern computer-based approaches were tested for identifying multiple eggplant diseases from leaf images
• ToF camera was evaluated for 3D phenotyping and canopy volume estimation. • Strong correlation was observed between ToF imagery and manual methods. • ToF technology could accurately estimate canopy volume in horticultural crops. • Study highlights the potential of ToF imagery for smart farm management. Noninvasive and accurate quantification of plant architectural traits remains a persistent challenge in horticultural crop breeding and high-throughput phenotyping (HTPP), largely due to labor constraints and limited availability of scalable 3D sensing technologies. Time-of-Flight (ToF) imaging offers a promising active sensing approach capable of generating high-resolution digital replicas of plant canopies for structural trait analysis. This study evaluated the feasibility of deploying 3D ToF imaging for digital phenotyping of three horticultural and ornamental crops, guava, brinjal, and jasmine with a focus on canopy volume estimation. High-resolution 3D point clouds were acquired for five plants per crop across three growth stages during the summer season of 2024. Structural parameters including plant height, width North–South (W NS ), width East–West (W EW ), and 3D aspect ratio were extracted and used to compute canopy volume via a voxel grid method. To validate accuracy, ToF-derived canopy volumes were compared against manually measured volumes estimated using the prolate spheroid volume (PSV) method. The ToF-based digital phenotyping framework demonstrated strong agreement with manual measurements across all crops. Guava exhibited the highest accuracy at the intermediate growth stage (R² = 1.0; RMSE = 0.0004), while brinjal (R² = 0.908; RMSE = 0.004) and jasmine (R² = 0.935; RMSE = 0.4351) showed robust performance at the full-grown stage. Those regression relationships were statistically significant ( p < 0.05), confirming the reliability of ToF-based canopy reconstruction for structural phenotyping. Overall, the findings demonstrate that ToF imaging provides a robust, noninvasive, and scalable framework for digital canopy phenotyping in horticultural crops, supporting precision breeding and structural trait monitoring applications in field environments.
Why it matches plant phenotyping methodsToFによる3D画像取得とキャノピー体積・構造形質の抽出を開発・評価し、手動測定との比較検証を行っており、植物表現型取得法が研究の中心である。
abstractToF camera was evaluated for 3D phenotyping and canopy volume estimation.
Brinjal (Solanum melongena) or eggplant is one of the four most essential vegetable crops that are grown in Bangladesh and contribute significantly to the agricultural industry of the country. Brinjal supports the livelihood of numerous small farmers; however, brinjal is severely susceptible to various fruit diseases, which have serious impacts on yield quality and may cause considerable economic losses. While most existing plant disease datasets primarily focus on leaf-related disorders, only a limited number include fruit-related diseases and even those contain very few classes. This gap is significant because fruit diseases directly affect crop quality, market value, and overall yield. This is why we present here a new and comprehensive dataset that is unparalleled, exclusively for brinjal fruit diseases. This data set consists of 1823 high-quality, labelled images, across five distinct classes: Phomopsis Blight, Shoot and Fruit Borer, Fruit Cracking, Wet Rot, and Healthy Fruit. The images were collected from real farm conditions in numerous areas of Bangladesh to ensure a robust sample of varied environmental and farming practices impacting the growth of diseases. This dataset is designed with the unique aim to support plant disease research and enhance training of deep learning models for autonomous disease detection. Lastly, the dataset will allow early disease detection, enhancing crop management practice, reduction of losses, and increasing farmers' economic returns. The release of this dataset will encourage agricultural research as well as practical use in precision agriculture.
Why it matches plant phenotyping methodsナス果実の病徴を対象とした画像データセットの構築・公開が中心であり、植物の病害状態を画像から識別する再利用可能な表現型データ資源に該当する。
abstractwe present here a new and comprehensive dataset that is unparalleled, exclusively for brinjal fruit diseases.
Reproduction assets foundThe paper's brinjal fruit disease image dataset (1823 labeled images, five classes) is publicly deposited on Mendeley Data, and the authors' model training/augmentation code is publicly available on GitHub.Code · publicThe complete code, along with augmentation scripts and model development, is publicly available in our GitHub repository [12].Open asset ↗GitHubhtml-lines:299-357Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Jan 2026Computers and Electronics in Agriculture.
Addressing the challenges of variable target morphology, small critical regions, and complex background interference in eggplant picking point detection within complex agricultural scenarios, this study proposes MDAD-YOLO (Multi-dimensional Attention and DySample YOLO), a detection model improved based on the YOLOv10n-pose framework. First, the model’s cross-dimensional perception ability for fruits and picking points is enhanced by integrating the collaborative mechanism of regional receptive field attention with channel-space joint attention. Next, within the Neck structure, coordinate attention is incorporated to optimize the spatial localization accuracy of fine-grained features, enhancing sensitivity to minute regions such as the fruit stem apex. Additionally, dynamic pixel reorganization is applied to enhance feature map reconstruction details, addressing the detail loss caused by traditional interpolation methods. Finally, cascading adaptive fine-grained channel attention with position-sensitive attention enables multi-level modeling of channel dependencies and collaborative spatial context enhancement. Through a seven-tier validation framework, the model’s effectiveness, robustness, and generalizability have been comprehensively demonstrated. Experimental results show that the model achieves 93.6% mAP@50 for object detection, 94.7% mAP@50 and 92.1% mAP for keypoints detection, and an average pixel Euclidean distance error of 19.41 on the self-built eggplant dataset, outperforming YOLOv12 and other high-performance models. Additionally, cross-crop experiments on the pepper dataset showed a 2.1% and 2.7% improvement in mAP for object and picking point detection, respectively, compared to the baseline model, confirming its cross-crop robustness. This study reveals the synergistic enhancement of dynamic upsampling and attention mechanisms in agricultural object detection, providing new insights for lightweight model design in complex scenarios.
Why it matches plant phenotyping methods果実と収穫点の画像ベース検出・キーポイント推定モデルを開発し、複数データセットで性能と頑健性を検証しているため、植物形質取得手法が中心である。
abstractthis study proposes MDAD-YOLO (Multi-dimensional Attention and DySample YOLO), a detection model improved based on the YOLOv10n-pose framework.
Precision agriculture progressively relies on remote sensing (RS) technologies to enhance crop classification and monitoring. Among various RS platforms, spectroradiometer offers the highest spectral precision, making them essential for validating the accuracy and performance of other RS methods. Each crop exhibits a unique spectral signature that corresponds to its biophysical characteristics. This spectral information plays a crucial role in accurately classifying crop types and assessing their health status, including water and nutrient availability. Specifically, evaluating crop chlorophyll content enables effective nitrogen management and yield optimization. This study focuses on collecting spectral data using a spectroradiometer (350-1050 nm) at a height of 30 cm above the crop canopy from eight crops, i.e., rice, finger millet, cotton, sunflower, sweet corn, broccoli, cauliflower, and brinjal, classifying the collected data, and measuring chlorophyll content using a Soil Plant Analysis Development (SPAD) meter and predicting the same using key spectral bands and machine learning (ML) techniques. Six supervised ML algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP) were employed for crop classification. The feature selection process revealed that the spectral range of 710-750 nm is the most significant for crop classification. The MLP model achieved the highest accuracy of 97% during training, 93% in testing, and 85% during validation stage, outperforming other ML classifiers. For chlorophyll content prediction, the RF demonstrated the best performance, with coefficient of determination values of 0.92 for training and 0.72 for testing stage. The ML-based framework, developed in this study, can be applied to various RS platforms, including satellites and unmanned aerial vehicles (UAVs), for crop classification and prediction of chlorophyll content. The developed modelling framework would assist government agencies and policymakers in identifying crop types accurately, enhancing agricultural planning, and optimizing resource allocation to support sustainable on-farm practices.
Why it matches plant phenotyping methods分光反射センシングと機械学習により作物のクロロフィル含量という植物形質を推定する枠組みが研究の中心であり、モデル性能の検証も行っているため。
abstractpredicting the same using key spectral bands and machine learning (ML) techniques
The accurate quantification of plant organ color remains a major challenge in plant variety identification, particularly when adjacent expression states exhibit subtle visual differences in color. This study addressed this challenge by integrating colorimetric, phenotypic, and genomic analyses of 137 eggplant germplasm resources to characterize their fruit color. The CIELAB color parameters accurately represented fruit coloration and exhibited strong correlations with DNA fingerprinting results, while also aligning with the visual description of color characteristics based on Distinctness, Uniformity, and Stability (DUS) test guidelines. The color transition from harvest maturity to physiological ripeness was effectively captured by shifts in these values. Furthermore, the purple fruits at harvest maturity were subdivided into violet and red subcategories based on their CIELAB parameter distributions, and the yellow, ochre, and brown fruits at physiological ripeness were clearly separated using K-means clustering. Consequently, this study defined precise CIELAB ranges for each color category, offering a robust, multidimensional approach for the objective identification of eggplant varieties and enhancing the reproducibility of color-based DUS evaluations.
Why it matches plant phenotyping methodsナス果実という植物器官の色をCIELAB値とクラスタリングで定量・分類し、品種識別とDUS評価の再現性を高める方法が研究の中心である。
abstractThe accurate quantification of plant organ color remains a major challenge in plant variety identification
The growing global population and rising concerns about food security highlight the critical need for intelligent agriculture. Among various technologies, plant disease detection is vital but faces challenges in balancing data privacy and model accuracy. To address this, we propose a novel privacy-preserving eggplant disease detection system with high accuracy. First, we introduce a lightweight 3D chaotic cube-based image encryption method that ensures security with low computational cost. Second, a streamlined YOLOv11n-12D framework is employed to optimize detection performance on resource-constrained devices. Finally, the encryption and detection modules are integrated into a real-time, secure, and accurate identification system.Experimental results show our framework achieves near-ideal encryption security (entropy=7.6195, Number of Pixel Change Rate(NPCR)=99.63%, Unified Average Changing Intensity(UACI)=32.85%) with 23× faster encryption (0.0127s) versus existing methods. The distilled YOLOv11n-12D model maintains teacher-level accuracy (mAP@0.5=0.849) at 3.6× the speed of YOLOv12s (2.7ms/inference), with +6.5% mAP improvement for small disease detection (e.g., thrips). This system balances privacy and real-time performance for smart agriculture applications.
Why it matches plant phenotyping methodsナスの病害を画像から検出するモデルと、暗号化を含むリアルタイム検出システム自体が研究の中心であり、植物の病害状態を推定するフェノタイピング手法に該当する。
abstractwe propose a novel privacy-preserving eggplant disease detection system with high accuracy.
The Eggplant Leaf Disease Dataset was meticulously developed to address challenges in accurately identifying diseases that threaten eggplant crops, a vital agricultural resource worldwide. This dataset includes 3116 high-resolution images captured between March and May 2024 from two major agricultural regions in Bangladesh, representing real-world conditions. It comprises 10 distinct disease classes-Aphids, Cercospora Leaf Spot, Defect Eggplant, Flea Beetles, Fresh Eggplant, Fresh Eggplant Leaf, Leaf Wilt, Phytophthora Blight, Powdery Mildew, and Tobacco Mosaic Virus-making it the most comprehensive dataset for eggplant diseases to date. To enhance its utility, rigorous data augmentation techniques, including flipping, rotating, shearing, shifting, noise addition, and brightness adjustment, were applied. This expanded the dataset to 10,000 images, ensuring its robustness for machine learning applications. Expert annotations further enhance its quality, providing critical insights for precise disease classification. Our Proposed CBAM-EfficientNetB0 model had an amazing classification accuracy of 98.70 %, which was much better than the baseline architectures. ResNet50 only got 32.60 %, VGG16 got 73.00 %, and VGG19 got 68.00 %. The proposed model's better performance shows that combining channel and spatial attention through CBAM with EfficientNetB0's feature extraction abilities works well. This architecture does a good job of picking out the distinguishing features in eggplant leaf images, which makes it possible to accurately identify diseases. The dataset and model work together to make AI-powered early disease detection, automated monitoring, and decision support in precision agriculture possible. These tools help farmers use sustainable farming methods by making timely interventions, reducing the need for manual inspection, and increasing crop productivity and food security.
Why it matches plant phenotyping methodsナス葉の病害状態を画像から分類するデータセットと解析モデルを開発・評価しており、植物病害フェノタイピング手法が中心である。
titleA comprehensive annotated image dataset for deep learning analysis of eggplant leaf diseases.
Reproduction assets foundThe paper's eggplant leaf disease image dataset (3116 annotated images, augmented to 10,000) is publicly deposited on Mendeley Data with a direct URL and DOI provided in the article.Dataset · publicSeed Certification Agency, Ministry of Agriculture, Bangladesh, for his invaluable feedback and cooperation .
Data source location
Town/City/Region: Dhaka, Musnshigonj and Jhenaidah Sadar.
Country: Bangladesh
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/5drkk544k8.1
Direct URL to data: https://data.mendeley.com/datasets/5drkk544k8/1Open asset ↗Mendeley Data · 10.17632/5drkk544k8.1lines:1-43Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
With the development of smart agriculture and the growth of the global population, vegetable production is facing the dual challenges of diversified planting environments and increased concealment of diseases. Eggplant, as an important economic crop, has its disease detection accuracy directly affecting yield and quality. However, traditional detection methods fail to effectively capture small diseased areas. To address this issue, this paper proposes an improved deep learning small target detection model-the Efficient Deep-fusion Detection Model (EDDet), which is specifically optimized for the recognition of small diseased spots in eggplant disease detection. In the detection network, we innovatively designed the Pinwheel Fusion Feature Extractor (PFFE) framework, replacing the standard convolutions of the first two layers with Pinwheel Convolutions (PConv). By using asymmetric padding and parallel convolution kernel design, the receptive field is effectively expanded, the ability to capture underlying features is enhanced, and the detection of small diseased areas in eggplants is more precise. In the feature fusion stage, this paper designs a Cross-layer Attention Module (CAM), including Cross-layer Channel Attention (CCA) and Cross-layer Spatial Attention (CSA), which can efficiently interact and fuse features of different scales without additional sampling, alleviating the information loss caused by semantic gaps. In addition, to solve the instability caused by IoU fluctuations in the bounding box regression process, the model introduces Scale-based Dynamic Loss (SD Loss), which dynamically adjusts the loss weight based on the size of the target. By adaptively adjusting the proportion of IoU-based loss and location constraint loss, more precise localization and stable regression of small diseased areas in eggplants are achieved. Experimental results demonstrate that EDDet achieves a notable improvement in mAP50 (85.4%), outperforming the baseline by 2.8%.Importantly, EDDet also Maintains excellent efficiency with only 2.75 M parameters, 9.1 GFLOPs, and a high inference speed of 288.3 FPS, which is 37.5 FPS higher than the baseline.These results highlight the model's strong potential for real-time deployment in complex agricultural scenarios where both precision and speed are critical.
Why it matches plant phenotyping methodsナス葉の病斑という植物の病害状態を画像から検出・局在化する深層学習手法を開発し、精度と速度を実験的に評価しており、表現型取得・抽出手法が中心である。
abstractthis paper proposes an improved deep learning small target detection model-the Efficient Deep-fusion Detection Model (EDDet), which is specifically optimized for the recognition of small diseased spots in eggplant disease detection.
Reproduction assets foundThe paper used a publicly available eggplant fruit disease dataset hosted on Roboflow, with an explicit public URL in the Data Availability statement; no author code or model checkpoints are stated as publicly available.Dataset · publicThis study analyzed a combination of publicly available datasets and data collected by the authors. The publicly available datasets can be accessed at https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection . If you wish to request the complete dataset, please email the corresponding authorOpen asset ↗bohol-island-state-university-vgjlb/eggplant-disease-detectionlines:1561-1651Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Published30 Sept 2025International Journal of Electronics and Communication EngineeringCited by 0 · OpenAlex ↗
Over 295 million people in 53 countries experience acute food insecurity due to factors like famine, war, climate change, and conflict zones. Sustainable Development Goal 2: Zero Hunger aims to achieve food security, improve nutrition, end hunger, and promote sustainable agriculture. Balancing farming with environmental protection is crucial, especially in the face of climate change and globalization. Studying plant phenomics, which focuses on how plants grow and react to climate change, can help develop more productive and stronger crops. Advanced technology, such as High-throughput plant phenotyping, can provide detailed data for accurate predictions and better disease control. This article aims to explore the use of AI and machine learning in plant phenotyping, the integration of imaging technologies, IoT, and sensors, and the application of various technologies, including Brinjal, in vegetable phenotyping. Artificial Intelligence, IoT devices, edge computing, computer vision, and advanced sensor technologies are revolutionizing sustainable agriculture. These technologies provide real-time data, early detection of diseases, and improved nutrient, water, and pest management. Auto Machine Learning, Explainable AI, and Deep Learning enhance understanding and optimize breeding cycles. This combination of multi-omics data, machine learning, and smart tools is crucial for smart and sustainable agriculture, promoting farmer-based innovation and cross-sector collaboration.
Why it matches plant phenotyping methods植物フェノタイピングにおけるAI、機械学習、画像技術、IoT、センサーを主題とするレビューであり、方法論の整理が中心です。
abstractThis article aims to explore the use of AI and machine learning in plant phenotyping, the integration of imaging technologies, IoT, and sensors, and the application of various technologies, including Brinjal, in vegetable phenotyping.
In the context of the rapid development of smart agriculture, the detection of crop diseases remains a critical and challenging task. The diversity in eggplant disease scales, disease edge features, and the complexity of planting backgrounds significantly impact disease detection effectiveness. To address these challenges, we propose an eggplant disease detection network with edge feature enhancement based on multi-scale learning. The overall network adopts a "backbone-neck-head" architecture: the backbone extracts features, the neck performs feature fusion, and a three-scale detection head produces the final predictions. First, we designed the Multi-scale Edge Information Enhance (CSP-MSEIE) module to extract features from different disease scales and highlight edge information to obtain richer target features. Second, the Multi-source Interaction Module (MSIM) and Dynamic Interpolation Interaction Module (DIIM) sub-modules were designed further to enhance the model's capacity for multi-scale feature representation. By leveraging dynamic interpolation and feature fusion strategies, these sub-modules significantly improved the model's ability to detect targets in complex backgrounds. Then, leveraging these sub-modules, we designed the Multi-scale Context Reconstruction Pyramid Network (MCRPN) to facilitate spatial feature reconstruction and hierarchical context extraction. This framework efficiently combines feature information across multiple levels, strengthening the model's ability to capture and utilize contextual details. Finally, we validated the effectiveness of the proposed model on two disease datasets. It is noteworthy that on the eggplant disease data, the proposed disease detection model achieved improvements of 4.7% and 7.2% in mAP50 and mAP50-95 metrics, respectively, and the model's frames per second (FPS) reached 270.5. This detection network provides an effective solution for the efficient detection of crop diseases.
Why it matches plant phenotyping methods植物病害の画像ベース検出ネットワークを開発し、病害状態の推定をデータセットで検証しており、表現型取得・抽出手法が中心である。
abstractwe propose an eggplant disease detection network with edge feature enhancement based on multi-scale learning.
Reproduction assets foundThe paper's two evaluation datasets are publicly available: the PlantDoc dataset and the Roboflow-hosted eggplant disease detection dataset, both explicitly linked in the data availability statement. No author code or model checkpoints are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://public.roboflow.com/object-detection/plantdoc/ ; https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection .
Author contributions
HS: Investigation, Formal Analysis, Methodology, Writing – original draft, Data curation, Conceptualization. RF: Formal Analysis, Data curation, Writing – review & editing, Methodology. DK: Writing – review & edOpen asset ↗PlantDoclines:371-486Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://public.roboflow.com/object-detection/plantdoc/ ; https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection .
Author contributions
HS: Investigation, Formal Analysis, Methodology, Writing – original draft, Data curation, Conceptualization. RF: Formal Analysis, Data curation, Writing – review & editing, Methodology. DK: Writing – review & editing, Funding acquisition, Formal Analysis, Supervision.
Conflict of interest
The authors declOpen asset ↗eggplant-disease-detectionlines:371-486Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Verticillium wilt is a major threat to eggplant production, and there is an urgent need for the rapid and accurate screening of resistant varieties to enhance breeding efficiency. Owing to the long incubation period of Verticillium wilt before visible symptoms appear, early disease detection remains challenging. In this study, a simple and efficient leaf injection method was established and optimized by evaluating key factors, such as seedling age and inoculum concentration. The results showed that the two-leaf-one-heart stage, combined with a spore concentration of 1 × 10⁷ spores/mL, provided optimal conditions for inoculation. Chlorophyll fluorescence (Chl-F) was used to monitor the early physiological changes in plants under pathogen stress. After pathogen inoculation, changes in Chl-F parameters, such as the effective quantum yield of photosystem II (ΦPSII) and the relative electron transport rate (rETR), were negatively correlated with the genotype’s resistance. These parameters served as early indicators for resistance screening and grading. Furthermore, our findings confirmed the high correlation (R = 0.92, P < 0.01) between the leaf injection and root-dipping inoculation methods in a resistance-segregating population, validating the reliability of the leaf injection method for resistance screening. This study demonstrated the effectiveness of combining the leaf injection method with Chl-F analysis for precise and early disease detection and offered valuable insights into enhancing eggplant breeding strategies for Verticillium wilt resistance.
Why it matches plant phenotyping methods葉注入接種法とクロロフィル蛍光による早期病害・抵抗性評価を開発、最適化し、既存接種法との相関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。
abstractIn this study, a simple and efficient leaf injection method was established and optimized by evaluating key factors, such as seedling age and inoculum concentration.
Photosynthesis plays a pivotal role in vegetable growth. However, its intricate interplay with plant physiology and environmental factors complicates precise prediction of photosynthetic rates (Pn). Current predictive models primarily focus on environmental influences on photosynthesis, limiting their applicability to leaves exhibiting different physiological traits. To address the challenge, we introduce a novel approach that incorporates chlorophyll fluorescence (ChlF) parameters into a model for predicting Pn across diverse leaf ontogenies. Eggplant leaves were used as experimental samples. We collected 5280 Pn data of leaves with different ChlF parameters under controlled changes in temperature, [CO₂], and light intensity. The Fₒ (initial fluorescence) and Fᵥ/Fₘ (Maximum light energy conversion efficiency of PSII system) were selected as key ChlF indicators using the entropy method. Fₒ and Fᵥ/Fₘ, along with temperature, [CO₂], and light intensity, are key features, while Pn serves as a label, forming a robust modeling dataset. Then, we proposed a Convolutional Neural Network Regression model with Input Encoding and Genetic Algorithm optimization (CNNR-IEGA) to train these environment and fluorescence data and develop the predictive model for eggplant Pn.The results indicate that the model exhibits excellent performance in predicting Pn. On unknown datasets, the root mean square error of the model is only 0.97 μmol·m⁻²·s⁻¹, with a high coefficient of determination reaching 0.99. Compared to models established by other algorithms (including multiple nonlinear regression, support vector regression, and back propagation neural network), the proposed model demonstrates superior performance across training, testing, and validation sets. Furthermore, compared to models without ChlF parameters and those with single ChlF parameters, the proposed model has the highest accuracy. This demonstrates the validity of using fluorescence to characterize crop photosynthetic performance. CNNR-IEGA can serve as a basis for crop growth environment assessment, greenhouse control, and production warning, offering new theories and opportunities for the development of precision agriculture.
Why it matches plant phenotyping methods植物の光合成速度という生理形質を、クロロフィル蛍光・環境データから予測するモデルを開発し、比較検証しているため、表現型取得・推定手法が中心です。
abstractwe introduce a novel approach that incorporates chlorophyll fluorescence (ChlF) parameters into a model for predicting Pn across diverse leaf ontogenies
Eggplant seed vigor is a crucial indicator of its germination rate and seedling growth quality. In response to the need for efficient and nondestructive assessment methods, this study explores the use of hyperspectral imaging combined with advanced feature selection and classification algorithms to evaluate eggplant seed viability. Hyperspectral imaging was employed to collect spectral data from eggplant seeds, covering 360 bands within a wavelength range of 395.24-1008.20 nm. The seeds underwent microwave heating and constant-temperature water bath aging treatments. Data preprocessing involved three techniques: Multiplicative Scatter Correction (MSC), Savitzky-Golay (SG) smoothing, and Standard Normal Variate (SNV) transformation. An Enhanced Information Acquisition Optimization (EIAO) algorithm was proposed for feature selection, which successfully identified a minimal set of 23 key wavelengths. Seed vigor classification models were developed using Extreme Learning Machine (ELM), Random Forest (RF), and Support Vector Machine (SVM).The optimal classification accuracies achieved were 90.0% for ELM, 91.45% for RF, and 90.5% for SVM. The MSC-EIAO-RF model demonstrated the best performance, achieving an accuracy of 91.45%, which is 9.04% higher than the MSC-IAO model (82.41%).Validation on four UCI datasets further confirmed the EIAO algorithm's superiority over conventional feature selection methods. These results verify the robustness and generalizability of hyperspectral imaging combined with EIAO for nondestructive seed viability detection, offering an intelligent and efficient solution for seed quality assessment.
Why it matches plant phenotyping methodsナス種子の活力・生存性を非破壊評価するためのハイパースペクトル画像法、波長選択アルゴリズム、分類モデルを中心に開発・検証しており、植物形質測定法に該当する。
abstractthis study explores the use of hyperspectral imaging combined with advanced feature selection and classification algorithms to evaluate eggplant seed viability
In recent years, researchers have focused on the automated identification of diseases using hyperspectral images, which is one of the most significant and basic difficulties for sustainable farming. The technology and approaches that have been employed up to this point are narrowly focused and entirely dependent on deep learning models. The most efficient technique for detecting and predicting illnesses from Brinjal pictures is convolutional neural networks. The current study examined several of the neural network processing methods already in use with the goal of identifying Brinjal illnesses. In order to handle the given imaging data, a number of deep learning models and architectures, image processing methods, and data gathering sources were first examined. After presenting the findings of the assessment of a number of currently used deep learning models, the study's conclusion included potential future applications of hyperspectral data processing. In order to identify diseases of the brinjal plant and facilitate further study into the wider possibilities of deep learning, this survey aims to enhance system performance and accuracy. Numerous image sensors and data collection devices were analyzed in order to identify plant illnesses. Lastly, we spoke about how deep learning models can do better than humans when it comes to generalization. In order to create an automated end-to-end plant disease management system, we conclude that realistic plant disease analysis will discover a number of crops, their related diseases early in the season, and correct disease severity assessment.
Why it matches plant phenotyping methodsナス植物の画像から病害を検出・重症度評価する深層学習および画像取得手法を体系的にレビューしており、植物の状態を推定するフェノタイピング手法が中心である。
titleSYSTEMATIC REVIEW OF DEEP LEARNING TECHNIQUES IN EGG PLANT DISEASE DETECTION
A novel eggplant disease detection method based on multimodal data fusion and attention mechanisms is proposed in this study, aimed at improving both the accuracy and robustness of disease detection. The method integrates image and sensor data, optimizing the fusion of multimodal features through an embedded attention mechanism, which enhances the model's ability to focus on disease-related features. Experimental results demonstrate that the proposed method excels across various evaluation metrics, achieving a precision of 0.94, recall of 0.90, accuracy of 0.92, and mAP@75 of 0.91, indicating excellent classification accuracy and object localization capability. Further experiments, through ablation studies, evaluated the impact of different attention mechanisms and loss functions on model performance, all of which showed superior performance for the proposed approach. The multimodal data fusion combined with the embedded attention mechanism effectively enhances the accuracy and robustness of the eggplant disease detection model, making it highly suitable for complex disease identification tasks and demonstrating significant potential for widespread application.
Why it matches plant phenotyping methodsナスの病害状態を画像・センサーデータから推定する手法を開発し、アブレーション実験と性能評価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す。
abstractA novel eggplant disease detection method based on multimodal data fusion and attention mechanisms is proposed in this study
This dataset on eggplant leaf diseases has been meticulously developed to provide a valuable resource for agricultural research and the advancement of automated disease detection systems. It comprises 4,089 high-resolution images of eggplant leaves, systematically categorized into six distinct classes: Healthy Leaf, Insect Pest Disease, Leaf Spot Disease, Mosaic Virus Disease, White Mold Disease, and Wilt Disease. The images were captured using smartphone cameras under controlled conditions with a consistent white background to ensure clarity and uniformity. To reflect real-world agricultural scenarios, data collection was conducted across multiple geographic locations and in varying lighting conditions. This approach enhances the dataset's diversity and applicability. The dataset underwent thorough manual labelling and preprocessing to ensure accuracy and consistency across all samples. Each image is clearly labelled according to its respective disease class, making the dataset readily usable for machine learning applications. The balanced representation of healthy and diseased leaves allows for comprehensive training and testing of classification models. Designed to support the development of machine learning models for the early detection and classification of eggplant diseases, this dataset holds significant reuse potential in various research domains. It is particularly suitable for applications in plant pathology, precision agriculture, and disease forecasting, where timely and accurate diagnosis is crucial. The dataset is freely available for academic and research purposes, making it a valuable resource for researchers and developers aiming to innovate in agricultural technology and crop management. With its robust design and practical focus, the dataset has the potential to drive advancements in sustainable farming practices and enhance agricultural productivity.
Why it matches plant phenotyping methodsナス葉の病害状態を画像から分類するための大規模データセットであり、植物病害表現型の取得・再利用可能な基盤が中心です。
titleA comprehensive image dataset for the identification of eggplant leaf diseases and computer vision applications.
Reproduction assets foundThe paper is a Data in Brief article describing an eggplant leaf disease image dataset (4,089 images, six classes) publicly deposited on Mendeley Data, plus an authors' GitHub repository containing the preprocessing code. Both are paper-specific, public, and directly actionable.Dataset · publiclant field in Char Keshabpur, Shibchar, Madaripur (Latitude: 23°21′32.9″N, Longitude: 90°11′48.5″E)
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Eggplant field in Daffodil Smart City, Khagan, Ashulia (Latitude: 23°52′37.6″N, Longitude: 90°19′16.2″E).
Data accessibility
Repository name: Mendeley Data.
Data identification number: 10.17632/d3ypkphghb.2
Direct URL to data: https://data.mendeley.com/datasets/d3ypkphghb/2
Access the dataset at https://data.mendeley.com/datasets/d3ypkphghb/2 and cite using Data ID 10.17632/d3ypkphghb.2 .
Related research articleOpen asset ↗Mendeley Data · 10.17632/d3ypkphghb.2lines:1-49Code · publicand facilitate classification tasks.
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Classification: Images were organized into six predefined classes: Healthy Leaf, Insect Pest, Leaf Spot, Mosaic Virus, White Mold, and Wilt, forming a structured dataset ready for analysis.
4.5.
Code used for data preprocessing
GitHub Repository name: Data_Preprocessing
Direct URL of Code: https://github.com/paradoxicalProfessor/Data_Preprocessing
Limitations
The Eggplant Leaf Disease dataset has some limitations. It was collected from specific regions in Bangladesh, which may limit its applicability to other environments. Our dataset includes only six disease classes, which may not represent all eggplant diseases in different regions. Some disease clasOpen asset ↗Data_Preprocessinglines:223-258Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Internet of Things (IoT) assisted smart farming techniques are gradually being used efficiently for identification and classification of vegetable plant diseases. Detection and classification of diseases in these plant families like Solanaceae are still problematic using DCNN due to variations in environmental conditions, genome variation, type of disease, etc. In this paper, two methods for spotting and diagnosing diseases of brinjal and tomato plants leaves named as Optimal Environmental Traversing Alert (OETA) and Optimum diagnosis of Solanaceae leaf diseases (ODSLD) respectively have been proposed. The OETA machine learning (ML) based method is used first to detect the disease, and then the ODSLD deep convolutional neural networks (DCNN) method is used to classify it. An analysis of the proposed method experiments showed that OETA disease detection for brinjal plant (eggplants) was 97.81 percent and for tomato plants was 99.03 percent. For disease classification by ODSLD method, the VGG-16 for brinjal plant and ResNet-50 for tomato plants outperformed other existing DCNN computer vision methods.
Why it matches plant phenotyping methodsトマトおよびナス葉の病徴を画像・機械学習で検出／分類する手法の提案と性能評価が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
abstracttwo methods for spotting and diagnosing diseases of brinjal and tomato plants leaves named as Optimal Environmental Traversing Alert (OETA) and Optimum diagnosis of Solanaceae leaf diseases (ODSLD) respectively have been proposed.
In the field of modern agriculture, plant phenotype analysis is crucial. It directly impacts the accurate assessment of photosynthesis, the refinement of farmland management, and serves as the foundation for crop breeding optimization, effective monitoring of diseases and pests, and reasonable adjustment of planting density. To deepen the application of precision agriculture, this study, combined with deep learning technology, proposed a low-cost hardware and software integration solution for non-contact acquisition of field eggplant plant point cloud data, achieving high-precision phenotypic information extraction.Utilizing depth camera technology, this study designed a multi-view point cloud scanning device capable of capturing point cloud data at different growth stages of eggplant plant in the field, accurately reflecting its natural growth state. In terms of data processing, this study improved the PointMLP model by introducing an attention mechanism and multi-scale feature extraction. These enhancements increased the model's ability to learn complex spatial relationships, making it more suitable for crops with intricate geometric forms. Experimental results indicated that the model achieved excellent performance in eggplant plant stem and leaf segmentation tasks, with Precision, F1-score, Recall, and mIoU reaching 92.35%, 90.4%, 88.7%, and 85.82%, respectively, demonstrating significant competitiveness compared to existing advanced networks.To further improve the accuracy of leaf point cloud extraction, the concepts of "erosion" and "dilation" from image processing were integrated into the DBSCAN algorithm, achieving high-precision extraction of single eggplant leaf point clouds in complex scenes. The Delaunay algorithm was utilized to calculate the area of each eggplant leaf, with an R² coefficient as high as 0.87, verifying the accuracy and reliability of the approach.The innovative solution proposed in this study not only enhances the efficiency and accuracy of plant phenotypic analysis but also reduces labor costs and crop damage, providing robust support for plant phenotypic analysis in large-scale field operations.
Why it matches plant phenotyping methods深度カメラによる圃場ナスの点群取得、セグメンテーション、葉面積推定を開発・検証しており、植物表現型取得手法が研究の中心である。
abstractproposed a low-cost hardware and software integration solution for non-contact acquisition of field eggplant plant point cloud data, achieving high-precision phenotypic information extraction
In the intelligent harvesting of eggplant, the lack of in situ identification technology makes it challenging to determine the maturity of purple eggplant fruit. The length of the fruit-setting date can determine when the eggplant is ready to be harvested. This study uses deep learning techniques to predict the date of fruit maturity. First, we proposed a fruit-setting days prediction method based on fruit spectroscopy and neural networks. Second, we collected the field in situ spectral data of purple eggplant fruit during 15-33 days of fruit setting using a portable spectrometer, covering 500-1000 nm. A fruit-setting time regression network combining multi-scale convolution, multi-head attention mechanism, and long short-term memory recurrent neural network was constructed using the collected in situ spectral data. The model demonstrated better fitting performance than traditional machine learning models such as backpropagation neural network, random forest, support vector machine, and partial least squares regression in the regression task of fruit-setting days. After testing various spectral preprocessing methods, the best fitting effect was found on the standard normal variate-processed dataset, with R 2 of 0.876 and RMSE(root mean square error) of 2.148 days. Furthermore, the feasibility of each model module was analyzed in depth through ablation experiments, confirming each component's role in improving the model's performance. The network attention weight was also analyzed, and the model has strong detail mining ability in a specific spectral interval. In summary, the combination of visible and near-infrared spectroscopy and attention cycle neural network is an effective method to predict the fruit-setting days of purple eggplant fruit. PRACTICAL APPLICATION: A prediction method of fruit-setting days based on fruit spectral characteristics and recurrent neural network regression was proposed. A novel approach to detecting and disclosing in situ surface VIS-NIRS reflectance data of eggplant fruit during ripening is presented for the first time. A set of long-term and short-term memory networks based on multi-scale convolution and multi-head attention mechanisms was constructed for spectral data fitting. Through the ablation test method and attention weight analysis, the function of each module in the network and the interpretability of feature extraction are explored.
Why it matches plant phenotyping methods紫ナス果実の成熟時期（果実設定日数）をVIS-NIRSスペクトルと深層学習で推定する手法を開発・検証しており、植物形質の取得・推定方法が研究の中心である。
abstractFirst, we proposed a fruit-setting days prediction method based on fruit spectroscopy and neural networks.
Abstract Crop damage and monetary losses occur due to plant diseases. At specified periods, farmers closely monitor their crops in the field to look for infections or diseases. In place of manual diagnosis, computers have been used to provide computerized tracking and recognition of a variety of diseases.This study makes a contribution to denoising algorithms that improve contrast, edge details and picture details. To isolate the leaf affected area, segmentation was carried out. Image fusion is performed using the segmented image obtained from several segmentation techniques, and features are extracted according to structural, texture, and color criteria. Furthermore, an image fusion technique based on Discrete Shearlet Transforms (DST) is applied to improve imaging quality while reducing redundancy. The color, texture, and structural elements of the fused images are retrieved and fed into the Artificial Neural Network (ANN) for classification, resulting in improved performance. When compared to other classification methods (SVM, MSVM, FFNN, and RFNN), the accuracy required for the DST-based fusion approach and the Radial Basis Function Neural Network is relatively high. The classification accuracy was 99.35%, with 89.24% sensitivity, 94.67% specificity, and 90.12% precision.
Why it matches plant phenotyping methodsナス葉の病害領域を画像から分離・特徴抽出し、病害を分類する画像解析手法が中心であり、植物の病徴状態を直接推定している。
abstractTo isolate the leaf affected area, segmentation was carried out.
Background As one of the world's most important vegetable crops, eggplant production is often severely affected by verticillium wilt, leading to significant declines in yield and quality. Traditional multispectral disease-imaging equipment is expensive and complicated to operate. Low-cost multispectral devices cannot capture images and cover less information. The traditional approach to early disease diagnosis involves using multispectral disease-imaging equipment in conjunction with machine learning technology. However, this approach has significant limitations in early disease diagnosis, including challenges such as high costs, complex operation, and low model performance. Results The aim of this study was to combine low-cost multispectral cameras with deep learning technology to detect early stage Verticillium wilt in eggplant effectively. Using the Manual FS-3200T-10GE-NNC multispectral camera to perform multispectral imaging of the leaves of eggplant seedlings at the early infection stage, information fusion was performed on the collected multispectral images, and a five-channel image information fusion model was established. Image information fusion technology was combined with deep learning technology, among which the VGG16-triplet attention model performed the best, achieving a precision of 86.73% on the test set. Model validation on 48- and 72-hour data reached a precision of 75% and 82%, respectively, achieving an early diagnosis of Verticillium wilt. This highlighted the potential of multispectral cameras for early disease detection. Conclusions In this study, we successfully developed a method for the non-destructive detection of the early stages of eggplant wilt disease by combining multispectral imaging technology with deep learning algorithms. While ensuring high accuracy, this method significantly reduces the cost of experimental equipment. The application of this method can reduce the cost of agricultural equipment and provide a scientific basis for agricultural production practices, helping to reduce losses caused by diseases.
Why it matches plant phenotyping methodsナス葉の病徴をマルチスペクトル画像と深層学習で非破壊検出する手法の開発・検証が中心であり、植物の病害状態を直接推定している。
abstractThe aim of this study was to combine low-cost multispectral cameras with deep learning technology to detect early stage Verticillium wilt in eggplant effectively.
This study presents the development and evaluation of machine learning models for detecting leaf spot disease in brinjal crops using spectral sensor data. The spectral reflectance of diseased and healthy tissues was recorded across nine wavelength bands (F1: 415 nm, F2: 445 nm, F3: 480 nm, F4: 515 nm, F5: 555 nm, F6: 590 nm, F7: 630 nm, F8: 680 nm, and F9: NIR-750 nm). The data revealed distinct spectral signatures, particularly between F5 (555 nm) and F9 (NIR), where diseased tissues consistently showed lower reflectance compared to healthy tissues. Two machine learning algorithms, Decision Tree (DT) and Support Vector Machine (SVM), were employed to classify the spectral data. The DT model achieved a maximum testing accuracy of 88.2 %, with a Gini index and a depth of 4 as optimal hyperparameters. The confusion matrix indicated that the DT model correctly identified 883 diseased instances and 667 healthy cases, while misclassifying 213 healthy tissues as diseased and 25 diseased tissues as healthy. The SVM model, configured with a cost parameter of 10.0 and a tolerance of 0.01, outperformed the DT model, achieving a testing accuracy of 92.4 %. The SVM model correctly classified 99.3 % of diseased instances and 94.1 % of healthy cases. The results demonstrate the potential of spectral sensor data combined with ML algorithms for precise disease detection, facilitating targeted pesticide application, and reducing input costs. The high accuracy of the SVM model underscores its utility in agricultural disease management, enabling early intervention and enhancing crop health monitoring. Future research may explore integrating multiple sensors and advanced feature extraction methods to further improve the efficiency and accuracy of these systems.
Why it matches plant phenotyping methods植物の病斑状態をスペクトルセンサで取得し、機械学習による検出モデルを開発・評価しており、病害表現型の取得手法が中心である。
abstractThis study presents the development and evaluation of machine learning models for detecting leaf spot disease in brinjal crops using spectral sensor data.
Accurate assessing leaf nitrogen content (LNC) is crucial for actual production and fertilizer management. In this research, a portable device was designed to rapidly and non-destructively evaluate LNC with precision. Using hydroponically grown eggplants exposed to different nitrogen content nutrient solutions as experimental samples, we conducted various measurements, including chlorophyll fluorescence (ChlF) induction curves, hyperspectral images, and LNC values. Correlations between LNC and ChlF parameters were calculated, and the parameter qN obtained the highest correlation with LNC. False color images of qN were segmented using the K-Means algorithm to obtain three regions. The spectral data and the measured LNC of the corresponding region in the leaf were matched, and a LNC prediction model was developed using the partial least square regression (PLSR) algorithm with the processed spectral data as input and the measured LNC as output. The results showed that the model using standard normal variate-iteratively retains informative variables- successive projections algorithm (SNV-IRIV-SPA-PLSR) yielded the best performance, with a correlation coefficient of prediction (R²) of 0.9332, a root mean square error (RMSE) of 2.6890 mg/g, a residual prediction deviation (RPD) of 3.97 and a ratio of performance to interquartile distance (RPIQ) of 7.28. Based on the selected wavelengths from the SNV-IRIV-SPA-PLSR-VIP model, six narrow-band light emitting diodes (LEDs) were chosen as the light source for the designed device. Inexpensive modules were employed to assemble the device, and accuracy tests were conducted. The PLSR algorithm was employed to develop the device’s LNC evaluation model with the reflectance of the leaf under 6 LEDs as input (resulting in R², RMSE, RPD, and RPIQ values of 0.8075 6.6242 mg/g, 2.30 and 4.26, respectively). The model was then embedded in the core processor. To validate the device’s performance, an independent set was used, resulting in R² of 0.7559, RMSE of 7.4771 mg/g, RPD of 2.07, and RPIQ of 3.57, respectively. The proposed device could rapidly and accurately determine LNC in plants, surpassing other devices in terms of portability and cost. This research offers a potential solution for plant fertilizer management.
Why it matches plant phenotyping methods葉の窒素含量という植物形質を対象に、Vis/NIRS・蛍光・ハイパースペクトル計測、画像分割、特徴抽出、予測モデル、携帯デバイス化と独立検証を中心的に開発・評価しているため。
abstracta portable device was designed to rapidly and non-destructively evaluate LNC with precision
Bangladesh's agricultural landscape is significantly influenced by vegetable cultivation, which substantially enhances nutrition, the economy, and food security in the nation. Millions of people rely on vegetable production for their daily sustenance, generating considerable income for numerous farmers. However, leaf diseases frequently compromise the yield and quality of vegetable crops. Plant diseases are a common impediment to global agricultural productivity, adversely affecting crop quality and yield, leading to substantial economic losses for farmers. Early detection of plant leaf diseases is crucial for improving cultivation and vegetable production. Common diseases such as Bacterial Spot, Mosaic Virus, and Downy Mildew often reduce vegetable plant cultivation and severely impact vegetable production and the food economy. Consequently, many farmers in Bangladesh struggle to identify the specific diseases, incurring significant losses. This dataset contains 12,643 images of widely grown crops in Bangladesh, facilitating the identification of unhealthy leaves compared to healthy ones. The dataset includes images of vegetable leaves such as Bitter Gourd (2223 images), Bottle Gourd (1803 images), Eggplants (2944 images), Cauliflowers (1598 images), Cucumbers (1626 images), and Tomatoes (2449 images). Each vegetable class encompasses several common diseases that affect cultivation. By identifying early leaf diseases, this dataset will be invaluable for farmers and agricultural researchers alike.
Why it matches plant phenotyping methods植物葉の画像から健康状態と病徴を識別するデータセットを提供しており、植物の病害状態を観測する再利用可能な画像ベースのフェノタイピング資源が中心です。
abstractThis dataset contains 12,643 images of widely grown crops in Bangladesh, facilitating the identification of unhealthy leaves compared to healthy ones.
Reproduction assets foundThe paper is a Data in Brief article describing a public smartphone image dataset of vegetable leaf diseases hosted on Mendeley Data, with an explicit direct URL matching an allowed URL.Dataset · publicRepository name: Mendeley Data
Data identification number: DOI: 10.17632/n67gctmjyj.3
Direct URL to data: https://data.mendeley.com/datasets/n67gctmjyj/3Open asset ↗Mendeley Data · 10.17632/n67gctmjyj.3lines:1-48Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Ensuring the healthy growth of eggplants requires the precise detection of leaf diseases, which can significantly boost yield and economic income. Improving the efficiency of plant disease identification in natural scenes is currently a crucial issue. This study aims to provide an efficient detection method suitable for disease detection in natural scenes. A lightweight detection model, YOLOv5s-BiPCNeXt, is proposed. This model utilizes the MobileNeXt backbone to reduce network parameters and computational complexity and includes a lightweight C3-BiPC neck module. Additionally, a multi-scale cross-spatial attention mechanism (EMA) is integrated into the neck network, and the nearest neighbor interpolation algorithm is replaced with the content-aware feature recombination operator (CARAFE), enhancing the model's ability to perceive multidimensional information and extract multiscale disease features and improving the spatial resolution of the disease feature map. These improvements enhance the detection accuracy for eggplant leaves, effectively reducing missed and incorrect detections caused by complex backgrounds and improving the detection and localization of small lesions at the early stages of brown spot and powdery mildew diseases. Experimental results show that the YOLOv5s-BiPCNeXt model achieves an average precision (AP) of 94.9% for brown spot disease, 95.0% for powdery mildew, and 99.5% for healthy leaves. Deployed on a Jetson Orin Nano edge detection device, the model attains an average recognition speed of 26 FPS (Frame Per Second), meeting real-time requirements. Compared to other algorithms, YOLOv5s-BiPCNeXt demonstrates superior overall performance, accurately detecting plant diseases under natural conditions and offering valuable technical support for the prevention and treatment of eggplant leaf diseases.
Why it matches plant phenotyping methodsナス葉の病斑を画像から検出・局在化する軽量モデルを開発し、精度とリアルタイム性能を検証しており、植物の病害状態を推定する方法が中心である。
abstractA lightweight detection model, YOLOv5s-BiPCNeXt, is proposed.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Remote sensing has been increasingly used in precision agriculture. Buoyed by the developments in the miniaturization of sensors and platforms, contemporary remote sensing offers data at resolutions finer enough to respond to within-farm variations. LiDAR point cloud, offers features amenable to modelling structural parameters of crops. Early prediction of crop growth parameters helps farmers and other stakeholders dynamically manage farming activities. The objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops. LiDAR point clouds were acquired using a terrestrial laser scanner on five dates during the growth cycles of tomato, eggplant and cabbage on the experimental research farms of the University of Agricultural Sciences, Bengaluru, India. We implemented a hybrid deep learning framework combining distinct features of long-term short memory (LSTM) and Gated Recurrent Unit (GRU) for the predictions of plant height and crown area. The predictions are validated with reference ground truth measurements. These predictions were validated against ground truth measurements. The findings demonstrate that plant-level structural parameters can be predicted well ahead of crop growth stages with around 80% accuracy. Notably, the LSTM and the GRU models exhibited limitations in capturing variations in structural parameters. Conversely, the hybrid model offered significantly improved predictions, particularly for crown area, with error rates for height prediction ranging from 5 to 12%, with deviations exhibiting a more balanced distribution between overestimation and underestimation This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications. However, the prediction quality is relatively low at the advanced growth stage, closer to the harvest. In contrast, the prediction quality is stable across the three different crops. The results indicate the presence of a robust relationship between the features of the LiDAR point cloud and the auto-feature map of the deep learning methods adapted for plant-level crop structural characterization. This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications.
Why it matches plant phenotyping methodsLiDAR点群と深層学習を用いて、植物体レベルの草丈・樹冠面積を推定する手法を開発・検証しており、表現型取得・抽出が研究の中心です。
abstractThe objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops.
Accurately detecting the anthocyanin content in eggplant peel is essential for effective eggplant breeding. The present study aims to present a method that combines hyperspectral imaging with advanced computational analysis to rapidly, non-destructively, and precisely measure anthocyanin content in eggplant fruit. For this purpose, hyperspectral images of the fruits of 20 varieties with diverse colors were collected, and the content of the anthocyanin were detected using high performance liquid chromatography (HPLC) methods. In order to minimize background noise in the hyperspectral images, five preprocessing algorithms were utilized on average reflectance spectra: standard normalized variate (SNV), autoscales (AUT), normalization (NOR), Savitzky-Golay convolutional smoothing (SG), and mean centering (MC). Additionally, the competitive adaptive reweighted sampling (CARS) method was employed to reduce the dimensionality of the high-dimensional hyperspectral data. In order to predict the cyanidin, petunidin, delphinidin, and total anthocyanin content of eggplant fruit, two models were constructed: partial least squares regression (PLSR) and least squares support vector machine (LS-SVM). The HPLC results showed that eggplant peel primarily contains three types of anthocyanins. Furthermore, there were significant differences in the average reflectance rates between 400-750 nm wavelength ranges for different colors of eggplant peel. The prediction model results indicated that the model based on NOR CARS LS-SVM achieved the best performance, with a squared coefficient of determination (R 2 ) greater than 0.98, RMSEP and RMSEC less than 0.03 for cyanidin, petunidin, delphinidin, and total anthocyanin predication. These results suggest that hyperspectral imaging is a rapid and non-destructive technique for assessing the anthocyanin content of eggplant peel. This approach holds promise for facilitating the more effective eggplant breeding.
Why it matches plant phenotyping methodsナス果皮のアントシアニン含量という植物形質を、ハイパースペクトル画像と前処理・特徴選択・回帰モデルで非破壊推定する手法が研究の中心であり、性能評価も明示されている。
abstractThe present study aims to present a method that combines hyperspectral imaging with advanced computational analysis to rapidly, non-destructively, and precisely measure anthocyanin content in eggplant fruit.
Plant leaf infection recognition using supervised machine learning has emerged as a promising solution to address the pressing challenges in agriculture and plant pathology. This innovative approach leverages supervised learning techniques to develop robust models capable of accurately identifying diseases and abnormalities in plant leaves based on input images. The proposed process involves several key steps. Initially, a diverse real time data's of brinjal images containing both infected and normal plant leaf is collected and meticulously labeled. The real time dataset covered healthy brijal leafs (HL), Cercospora solani(CS) diseases, Tobacco Mosaic Virus (TMV) diseases, Pythium aphanidermatum (PA) diseases, Pseudomonas solanacearum (PS) deseases and Alternaria melongenea (AM) diseases. Data pre-processing stage, such as filtering, noise removal, resizing and extraction are then evaluated to ensure consistency and enhance the dataset's diversity. Next, meaningful information are taken out from the preprocessed brinjal frames to serve as inputs for the machine learning model. Leaf Intensity Vector (LIV) + Principle Component Analysis + Gray Level Co-occurrence Matrix (GLCM) + Support Vector Machine are employed for brinjal leaf disease reorganization. Finally, the extracted proposed features are classified using Polynomial and RBF kernel of SVM, KNN, Random Forests (RF) and Decision Trees (DTs). The performance of the proposed brinjal leaf diseases classification system gives higher accuracy of SVM RBF (98.48%) on brinjalleaf disordered models.
Why it matches plant phenotyping methods植物葉の画像から病害状態を認識・分類する機械学習手法の開発が中心であり、植物の病害表現型を直接推定している。
abstractThe proposed process involves several key steps.
Different diseases are observed in vegetables, fruits, cereals, and commercial crops by farmers and agricultural experts. Nonetheless, this evaluation process is time-consuming, and initial symptoms are primarily visible at microscopic levels, limiting the possibility of an accurate diagnosis. This paper proposes an innovative method for identifying and classifying infected brinjal leaves using Deep Convolutional Neural Networks (DCNN) and Radial Basis Feed Forward Neural Networks (RBFNN). We collected 1100 images of brinjal leaf disease that were caused by five different species (Pseudomonas solanacearum, Cercospora solani, Alternaria melongenea, Pythium aphanidermatum, and Tobacco Mosaic Virus) and 400 images of healthy leaves from India's agricultural form. First, the original plant leaf is preprocessed by a Gaussian filter to reduce the noise and improve the quality of the image through image enhancement. A segmentation method based on expectation and maximization (EM) is then utilized to segment the leaf's-diseased regions. Next, the discrete Shearlet transform is used to extract the main features of the images such as texture, color, and structure, which are then merged to produce vectors. Lastly, DCNN and RBFNN are used to classify brinjal leaves based on their disease types. The DCNN achieved a mean accuracy of 93.30% (with fusion) and 76.70% (without fusion) compared to the RBFNN (82%-without fusion, 87%-with fusion) in classifying leaf diseases.
Why it matches plant phenotyping methodsブリンジャル葉の病斑領域を画像から抽出・分類する手法が研究の中心であり、植物病害状態の表現型測定に該当する。
abstractThis paper proposes an innovative method for identifying and classifying infected brinjal leaves using Deep Convolutional Neural Networks (DCNN) and Radial Basis Feed Forward Neural Networks (RBFNN).
High-resolution remote sensing data has expanded the scope, precision, and scale of remote sensing applications in agriculture. Availability of spatial information at actionable field units is vital for using remote sensing data in agriculture. Crop discrimination and biophysical characterisation sensitive to nutrient levels have not been addressed at the patch level. This work investigates the synergetic application of high-resolution satellite imagery and terrestrial LiDAR point cloud for object-level discrimination and biophysical characterisation of a few crops at different nitrogen (N) levels. To this end, cabbage, eggplant, and tomato at three levels of N were grown on the experimental fields of the University of Agricultural Sciences, Bengaluru, India, in 2017. Fusing the multispectral imagery (WorldView-III) and LiDAR point cloud (terrestrial laser scanner) at the feature level, object-level supervised classification and estimation of two critical biophysical parameters (crown area and biomass) were performed using the support vector machine (SVM) and Random Forests (RF) algorithms with reference to different N levels. Results suggest discrimination of vegetable crops with high accuracy (92%), about 20% higher than the individual sensors, from the fused imagery sensitive to N levels. The quality of retrievals indicates a contrasting pattern wherein the accuracy of the crown area is high with the LiDAR point cloud at various N levels. For the biomass, there is no perceptible differentiation of N levels within a crop. The accuracy of crop classification with reference to N levels is similar from both RF and SVM algorithms. However, RF algorithm offered substantially higher classification results when the N status is ignored. In contrast, the quality of biophysical modelling is very high and is similar from both the algorithms. Weather conditions and sub-field level environment-induced variations in the crop growth likely are the factors responsible for the reduced sensitivity of remote sensing data to crop N levels at the patch level.
Why it matches plant phenotyping methodsマルチスペクトル画像とLiDARを融合し、作物の冠面積とバイオマスという植物形質を推定する手法が研究の中心であり、センサー融合と推定精度を評価している。
abstractFusing the multispectral imagery (WorldView-III) and LiDAR point cloud (terrestrial laser scanner) at the feature level, object-level supervised classification and estimation of two critical biophysical parameters (crown area and biomass) were performed
Proximal remote sensing devices are novel tools that enable the study of plant health status through the measurement of specific characteristics, including the color or spectrum of light reflected or transmitted by the leaves or the canopy. The aim of this study is to compare the RGB and multispectral data collected during five years (2016–2020) of four fruiting vegetables (melon, tomato, eggplant, and peppers) with trial treatments of non-grafted and grafted onto resistant rootstocks cultivated in a Meloidogyne incognita (a root-knot nematode) infested soil in a greenhouse. The proximal remote sensing of plant health status data collected was divided into three levels. Firstly, leaf level pigments were measured using two different handheld sensors (SPAD and Dualex). Secondly, canopy vigor and biomass were assessed using vegetation indices derived from RGB images and the Normalized Difference Vegetation Index (NDVI) measured with a portable spectroradiometer (Greenseeker). Third, we assessed plant level water stress, as a consequence of the root damage by nematodes, using stomatal conductance measured with a porometer and indirectly using plant temperature with an infrared thermometer, and also the stable carbon isotope composition of leaf dry matter.. It was found that the interaction between treatments and crops (ANOVA) was statistically different for only four of seventeen parameters: flavonoid (p
Why it matches plant phenotyping methods植物の健康状態を複数の近接リモートセンシング機器で測定・比較し、葉・群落・個体レベルの形質抽出を技術的に評価しているため、手法の実質的応用に該当します。
abstractProximal remote sensing devices are novel tools that enable the study of plant health status through the measurement of specific characteristics
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Plant-parasitic nematodes are a significant cause of yield losses and food security issues. Specifically, nematodes of the genus Meloidogyne can cause significant production losses in horticultural crops around the world. Understanding the mechanisms of the ever-changing physiology of plant roots by imaging the galls induced by nematodes could provide a great insight into their control. However, infected roots are unsuitable for light microscopy investigation due to the opacity of plant tissues. Thus, samples must be cleared to visualize the interior of whole plants in order to make them transparent using clearing agents. This work aims to identify which clearing protocol and microscopy system is the most appropriate to obtain 3D images of tomato cv. Durinta and eggplant cv. Cristal samples infected with Meloidogyne incognita to visualize and study the root–nematode interaction. To that extent, two clearing solutions (BABB and ECi), combined with three different dehydration solvents (ethanol, methanol and 1-propanol), are tested. In addition, the advantages and disadvantages of alternative imaging techniques to confocal microscopy are analyzed by employing an experimental custom-made setup that combines two microscopic techniques, light sheet fluorescence microscopy and optical projection tomography, on a single instrument.
Why it matches plant phenotyping methods根部の感染状態を可視化するための透明化プロトコルと3D顕微鏡法を開発・比較しており、植物病害状態の画像取得手法が中心である。
abstractThis work aims to identify which clearing protocol and microscopy system is the most appropriate to obtain 3D images of tomato cv. Durinta and eggplant cv. Cristal samples infected with Meloidogyne incognita to visualize and study the root–nematode interaction.
This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable's non-contact measurement problems and can promote the application of computer vision in agricultural automation.
Why it matches plant phenotyping methods野菜の長さ・直径という植物器官形質を、ステレオカメラ、深度画像、キーポイント検出で非接触推定する手法が研究の中心であるため。
abstractThis work focuses on the problem of non-contact measurement for vegetables in agricultural automation.
Reproduction assets foundThe authors publicly released both the vegetable keypoint dataset (1600 COCO-format images with ROI boxes and six keypoints) and the implementation code for their size estimation method on GitHub. Labelme is a generic third-party annotation tool and is excluded.Code · publicThe implementation code of our size estimation method can be accessed on https://github.com/BourneZ130/VegetableDetection , accessed on 15 February 2022.Open asset ↗BourneZ130/VegetableDetectionlines:76-141Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Recent trends in the application of deep learning techniques for crop disease classification is gaining international attention among experts of various domains. Many of the pioneering works have been carried out using the leaf images in laboratory condition with several shortcomings for implementation in field condition. Some of the other key issues are the presence of multiple disorders and the similarity of symptoms which can be addressed by using local symptomatic regions of the disease symptoms. In addition, although many studies discussed on the AI-based smartphone application for disease classification, very few studies have actually implemented it. This study has explored the classification of five diseases in Solanum melongena (also commonly known as eggplant, aubergine or brinjal) with the creation of the dataset consisting of local symptomatic region, utilised one of the popular deep learning model VGG16 for classification and optimised it for deployment in a smartphone. The VGG16 model was trained with fine-tuned hyperparameter and evaluated with a test dataset which resulted in the accuracy of 94.3%. This study also analysed the feature parameters from several layers using Multi-class Support Vector Machine (MSVM) to understand the learning process as it approaches top layers. Further, the feature parameters of dominant channels that significantly influenced the classification process were identified and analysed. Finally, VGG16 model was customised and implemented in a smartphone. It was tested in a trial condition which resulted in the classification accuracy of 91.3%. Discussions on the possible reasons for misclassification and scope for improvement have been provided.
Why it matches plant phenotyping methodsナス葉の局所的な病徴画像から病害状態を分類する画像解析手法を開発・最適化し、データセット、精度評価、スマートフォン実装まで扱っており、植物病害フェノタイピングが中心である。
abstractThis study has explored the classification of five diseases in Solanum melongena (also commonly known as eggplant, aubergine or brinjal) with the creation of the dataset consisting of local symptomatic region, utilised one of the popular deep learning model VGG16 for classification and optimised it for deployment in a smartphone.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Estimation of biophysical parameters at various crop growth stages is vital for precision agricultural crop production. Spatial delineation of crops’ responses to various levels of nutrients helps optimise resources and reduce nutrient leaching. This paper explores the potential of 3D terrestrial laser scanning (TLS) for the estimation of plant height, crown area, and biomass of vegetable crops at various N levels. Experimental setup of growing three vegetable crops: tomato (Solanumlycopersicum L.), eggplant (Solanummelongena L.) and cabbage (Brassica oleracea L.) with three levels of N fertilization was laid out at the University of Agricultural Sciences, Bengaluru, India in 2017. LiDAR point clouds using a terrestrial laser scanner were collected at different growth stages. A methodology which included, among other processing steps, adaptive spatial filtering, canopy height modelling, watershed segmentation, and support vector regression has been adapted for the estimation of plant height, crown area, and biomass. Validation with ground measurements show high prediction accuracies for plant height (lowest coefficient of determination (R²), 0.96; highest symmetric mean absolute percentage error (SMAPE) of 3.18), and crown area (lowest R², 0.82; highest SMAPE, 8.82) for all the three crops across growth stages. The combined use of plant height and the crown area has enabled accurate and consistent estimation of biomass (lowest R², 0.92; highest SMAPE, 7.53) throughout the growing season. However, the mapping of a specific range of biomass to a specific N level is ambiguous due to wider variations in the crop growth due to rainfall, and wind interferences.
Why it matches plant phenotyping methodsTLSと点群処理・回帰を用いて植物形質（草丈、冠面積、バイオマス）を推定し、地上計測で精度検証しており、フェノタイピング手法が研究の中心である。
abstractThis paper explores the potential of 3D terrestrial laser scanning (TLS) for the estimation of plant height, crown area, and biomass of vegetable crops at various N levels.
Eggplant / aubergineLiDAR / point cloudLeafMorphology / geometry measurementLeaf traits
Leaf is an important organ for photosynthesis and transpiration associated with the plants’ growth. Through the study of leaf phenotype, it the physiological characteristics produced by the interaction of the morphological parameters with the environment can be understood. In order to realize the assessment of the spatial morphology of leaves, a method based on three-dimensional stereo vision was introduced to extract the shape information, including the length and width of the leaves. Firstly, a depth sensor was used to collect the point cloud of plant leaves. Then, the leaf coordinate system was adjusted by principal component analysis to extract the region of interest; and compared with a cross-sectional method, the geodesic distance method, we proposed a method based on the cutting plane to obtain the intersecting line of the three-dimensional leaf model. Eggplant leaves were used to compare the accuracy of these methods in the measurement of a single leaf.
Why it matches plant phenotyping methods三次元ステレオビジョンと点群処理により葉長・葉幅を抽出する測定法を開発し、既存手法と精度比較しており、植物フェノタイピング手法が研究の中心である。
abstracta method based on three-dimensional stereo vision was introduced to extract the shape information, including the length and width of the leaves
Aiming at the diseases of tomato and eggplant, we present a solanaceae disease recognition model based on SE-Inception. Our model uses batch normalization layer (BN) to accelerate network convergence. Besides, SE-Inception structure and multi-scale feature extraction module is adopted to improve accuracy of this model. Our sample data set consists of 4 disease categories including whitefly, powdery mildew, yellow smut, cotton blight. We also add healthy leaves into it. In order to reduce overfitting, the data set is expanded by the data enhancement method of translation, rotation and flip. Experiments show that the average recognition accuracy of this model is 98.29% and the model size is 14.68 MB on our constructed dataset. In addition, in order to verify the robustness of this model, it was also verified on the public data set of PlantVillage, and the top-1, top-5 accuracy and the size of our proposed model is 99.27%, 99.99% and 14.8 MB respectively. Moreover, we implemented a solanaceae disease image recognition system using this model based on the Android. The accuracy of average recognition and the recognition time of a single photo are 95.09% and 227 ms, respectively. Our constructed model has a small number of parameters with maintaining high accuracy, which can meet the needs of automatic recognition of disease images on mobile devices. Data and code are available at https://github.com/Jujube-sun/diseaseRecognition.
Why it matches plant phenotyping methodsトマト・ナスの病害画像から植物の病気状態を推定する認識モデルを開発し、複数データセットで精度検証しているため、植物フェノタイピング手法が中心である。
abstractwe present a solanaceae disease recognition model based on SE-Inception.
Remote sensing (RS) has been an effective tool to monitor agricultural production systems, but for vegetable crops, precision agriculture has received less interest to date. The objective of this study was to test the predictive performance of two types of RS data—crop height information derived from point clouds based on RGB UAV data, and reflectance information from terrestrial hyperspectral imagery—to predict fresh matter yield (FMY) for three vegetable crops (eggplant, tomato, and cabbage). The study was conducted in an experimental layout in Bengaluru, India, at five dates in summer 2017. The prediction accuracy varied strongly depending on the RS dataset used. For all crops, a good predictive performance with cross-validated prediction error
Why it matches plant phenotyping methodsRGB UAV 3Dデータと地上ハイパースペクトル画像を用いた作物高さ・反射情報から、野菜作物の生体重収量を推定し、予測性能を検証することが研究の中心であるため。
abstractThe objective of this study was to test the predictive performance of two types of RS data—crop height information derived from point clouds based on RGB UAV data, and reflectance information from terrestrial hyperspectral imagery—to predict fresh matter yield (FMY) for three vegetable crops (eggplant, tomato, and cabbage).
Currently, the application of deep learning in crop disease classification is one of the active areas of research for which an image dataset is required. Eggplant (Solanum melongena) is one of the important crops, but it is susceptible to serious diseases which hinder its production. Surprisingly, so far no dataset is available for the diseases in this crop. The unavailability of the dataset for these diseases motivated the authors to create a standard dataset in laboratory and field conditions for five major diseases. Pre-trained Visual Geometry Group 16 (VGG16) architecture has been used and the images have been converted to other color spaces namely Hue Saturation Value (HSV), YCbCr and grayscale for evaluation. Results show that the dataset created with RGB and YCbCr images in field condition was promising with a classification accuracy of 99.4%. The dataset also has been evaluated with other popular architectures and compared. In addition, VGG16 has been used as feature extractor from 8 th convolution layer and these features have been used for classifying diseases employing Multi-Class Support Vector Machine (MSVM). The analysis depicted an equivalent or in some cases produced better accuracy. Possible reasons for variation in interclass accuracy and future direction have been discussed.
Why it matches plant phenotyping methodsナスの病害状態を画像から分類するデータセットを構築し、VGG16・MSVM等を比較評価しており、植物病害フェノタイピング手法とデータセットが研究の中心である。
abstractThe unavailability of the dataset for these diseases motivated the authors to create a standard dataset in laboratory and field conditions for five major diseases.
3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).
Why it matches plant phenotyping methodsUAV画像の3D点群から作物高・バイオマスを推定する手法を開発・評価し、成長段階の影響と精度を検証しているため、植物表現型取得が研究の中心である。
abstract3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 10 Sept 2026
Background and aims Global agriculture is facing the challenge of a phenotyping bottleneck due to large-scale screening/breeding experiments with improved breeds. Phenotypic analysis with high-throughput, high-accuracy and low-cost technologies has therefore become urgent. Recent advances in image-based 3D reconstruction offer the opportunity of high-throughput phenotyping. The main aim of this study was to quantify and evaluate the canopy structure of plant populations in two and three dimensions based on the multi-view stereo (MVS) approach, and to monitor plant growth and development from seedling stage to fruiting stage. Methods Multi-view images of flat-leaf cucumber, small-leaf pepper and curly-leaf eggplant were obtained by moving a camera around the plant canopy. Three-dimensional point clouds were reconstructed from images based on the MVS approach and were then converted into surfaces with triangular facets. Phenotypic parameters, including leaf length, leaf width, leaf area, plant height and maximum canopy width, were calculated from reconstructed surfaces. Accurate evaluation in 2D and 3D for individual leaves was performed by comparing reconstructed phenotypic parameters with referenced values and by calculating the Hausdorff distance, i.e. the mean distance between two surfaces. Key results Our analysis demonstrates that there were good agreements in leaf parameters between referenced and estimated values. A high level of overlap was also found between surfaces of image-based reconstructions and laser scanning. Accuracy of 3D reconstruction of curly-leaf plants was relatively lower than that of flat-leaf plants. Plant height of three plants and maximum canopy width of cucumber and pepper showed an increasing trend during the 70 d after transplanting. Maximum canopy width of eggplants reached its peak at the 40th day after transplanting. The larger leaf phenotypic parameters of cucumber were mostly found at the middle-upper leaf position. Conclusions High-accuracy 3D evaluation of reconstruction quality indicated that dynamic capture of the 3D canopy based on the MVS approach can be potentially used in 3D phenotyping for applications in breeding and field management.
Why it matches plant phenotyping methodsMVS画像から3Dキャノピーを再構成し、葉形質や草冠構造を定量化・検証する手法が研究の中心である。
abstractThe main aim of this study was to quantify and evaluate the canopy structure of plant populations in two and three dimensions based on the multi-view stereo (MVS) approach
This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves.
Why it matches plant phenotyping methodsナス葉の病徴をハイパースペクトル画像とテクスチャ特徴から検出する画像解析手法が研究の中心であり、植物病害状態の表現型推定に該当する。
abstractThis study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves.