Abstract Depth cameras measure the distance to a fruit's surface, whereas converting its silhouette into physical dimensions requires the depth of its center; corrections for this offset have so far been empirical, and therefore bound to the crop, sensor, and dataset they were fitted on. This paper derives the correction analytically. For a spherical fruit, integrating the surface-depth distribution over the visible hemisphere yields a closed-form sphere correction whose coefficient follows from sampling geometry, together with a theoretical justification of the median mask depth as the representative statistic. Combined with deep instance segmentation on RGB-D imagery of hydroponic melons, the empirically optimal coefficient coincided with the derived value, and the pipeline reached R 2 of 0.966 for fruit length (MAE 1.43 mm), 0.959 for width (1.84 mm), and 0.861 for end-to-end fresh weight (MAPE 4.6%). The analytical form made the measurement transferable. Applied unchanged to cylindrical mini-cucumbers, the pipeline held mm-level accuracy (width MAE 0.52 mm; fresh weight R 2 0.955 after coe cient refitting), with the correction's negligibility predicted in advance by an R / Z corollary; across active-stereo and time-of-flight cameras, the optimal coefficients proved non-interchangeable, identifying the coefficient as a physical parameter that absorbs geometry, sensor physics, and fruit shape. A field system that fuses and cross-verifies the two sensors, with an error-propagation confidence gate and parameterized grading logic, reproduced 2-3% fresh-weight error and 90.9% confirmed-judgment grading accuracy over four validation sessions in a commercial greenhouse unseen during development. Throughout, geometric components transferred unchanged while learned and regression components required recalibration - a boundary the model predicts and the system itself monitors.
Why it matches plant phenotyping methodsRGB-D画像と深度補正を用いて果実の寸法・重量を推定する手法を開発し、異なる果形・センサー・圃場で精度検証しており、植物表現型取得が中心である。
abstractThis paper derives the correction analytically.
Abstract Bacterial fruit blotch (BFB) caused by Acidovorax citrulli , is a destructive seed-transmitted disease that seriously threatens global cucurbit production. To address the need for detecting A. citrulli -infested seeds, this study developed a colony identification model and a seed infestation detection model based on multispectral imaging. The combined nMahalanobis and nCDA colony identification models achieved a high recall of 0.999 and a low false-positive rate of 0.149 when tested on samples. For infested melon seed detection, we evaluated and compared the classification performance of seven machine learning models. The results showed that LDA, logistic regression, and MLP exhibited stable performance on artificially infested seed samples. Furthermore, multi-cultivar modeling improved model generalizability and demonstrated the feasibility of using multispectral imaging to identify naturally infested seeds. When a qPCR Ct threshold of 37 was used to define seed infestation status, the logistic regression model achieved a validation accuracy of 0.82. Overall, these findings demonstrate the potential of multispectral imaging for colony identification and seed infestation detection, providing a new technical approach and a scientific basis for seed health testing of bacterial fruit blotch in cucurbit crops.
Why it matches plant phenotyping methodsマルチスペクトル画像と機械学習により、感染種子という植物器官の状態を検出する手法を開発・比較・検証しており、表現型取得が研究の中心である。
abstractthis study developed a colony identification model and a seed infestation detection model based on multispectral imaging.
Drought and water deficit have severely restricted melon ( Cucumis melo L.) production in Xinjiang, and large-scale systematic evaluations of drought tolerance at the germination stage are still extremely limited. Physiological and biochemical indicators related to the germination stage, including osmotic adjustment substances and antioxidant enzyme activities, have not yet been incorporated into prediction models for the rapid identification of germplasm drought resistance. To address these research gaps, this study selected 60 accessions of local melon germplasm resources in Xinjiang and used polyethylene glycol (PEG) solutions at four different concentrations (0%, 10%, 20% and 30%) to simulate drought stress conditions. Drought tolerance was evaluated to develop a method for the rapid screening of drought-tolerant germplasms. The findings demonstrated that PEG stress significantly suppressed seed germination and had both stimulatory and inhibitory effects on radicle growth. With the increase in PEG concentration, germination indices consistently exhibited a downward trend. Under 10% PEG treatment, the variation among different germplasms was relatively small, while 30% PEG completely inhibited seed germination. Notably, 20% PEG fell within the semi-lethal concentration range for all tested germplasms and yielded the maximum coefficient of variation for germination rate, which could maximally differentiate the drought resistance differences among germplasms. Therefore, 20% PEG was determined to be the optimal screening concentration. Under 20% polyethylene glycol (PEG) stress, the degree of membrane lipid peroxidation (malondialdehyde, MDA), contents of osmotic regulators (proline, Pro; soluble protein, SP), and activities of antioxidant enzymes (superoxide dismutase, SOD; peroxidase, POD; catalase, CAT; ascorbate peroxidase, APX) in the radicles of melon germplasms were universally elevated. However, the variation ranges and trends of biochemical indices among different germplasms exhibited significant differences. The proline content of melon accessions with strong drought resistance increased, the malondialdehyde (a product of membrane damage) was low, and the enzyme activities increased significantly. The proline content of non-drought-tolerant melon accessions increased less, malondialdehyde accumulated in large amounts, and the activity of some protective enzymes decreased. Correlation analysis demonstrated that Pro exerted a synergistic effect in conjunction with antioxidant enzymes (SOD, CAT) to mitigate drought stress. Cluster analysis classified the germplasm into 14 high-tolerance types, 10 medium-tolerance types, and 9 low-tolerance types. Based on extreme germination phenotypes, 27 germplasms were identified as drought-sensitive types. A prediction model for drought tolerance was established via stepwise regression: D = -0.309 + 0.053 × Pro (proline content) + 0.319 × RL (radicle length) + 0.469 × MDA (malondialdehyde) + 0.137 × SOD (superoxide dismutase), with four core indicators (RL, MDA, Pro, SOD) identified. These findings provide a scientific basis and technical support for drought tolerance breeding, parental selection, and large-scale, precise, and rapid drought tolerance screening of melon germplasms in the arid regions of Xinjiang.
Why it matches plant phenotyping methodsメロン遺伝資源の乾燥耐性を迅速にスクリーニングするため、最適PEG濃度の決定、指標選定、予測モデル構築を中心的に行っており、表現型取得・抽出法の開発に該当する。
abstractused polyethylene glycol (PEG) solutions at four different concentrations (0%, 10%, 20% and 30%) to simulate drought stress conditions. Drought tolerance was evaluated to develop a method for the rapid screening of drought-tolerant germplasms.
Melon (Cucumis melo L.) is a globally significant horticultural crop, characterized by high nutritional value and substantial commercial status. However, frequent outbreaks of powdery mildew severely threaten its yield and fruit quality. Current early detection methods primarily focus on detached leaf assays, which often lack sufficient model generalization. This study proposes a temporal 3D multispectral point cloud reconstruction method for melon plants by integrating multispectral imaging with 3D reconstruction technology. An Artificial Neural Network (ANN) model for 3D spatial light field distribution was developed based on a hemispherical white reference to achieve precise reflectance calibration of the multispectral point clouds. Post-calibration, the coefficient of variation (CV) for the spectral reflectance of the hemispherical reference in 3D space was reduced to less than 2.4%. On this basis, an early classification model for melon powdery mildew was constructed using Partial Least Squares Discriminant Analysis (PLS-DA) based on the mean reflectance spectra of individual plant point clouds. The results demonstrate that the average recognition accuracy reaches 85.94% from 4 days post-inoculation onwards, enabling disease early warning three days in advance. This research provides critical theoretical support and technical reference for the non-destructive early monitoring and precision smart plant protection of crops in facility agriculture.
Why it matches plant phenotyping methodsメロン個体の病徴状態を対象に、時系列3Dマルチスペクトル点群の再構成・反射率校正と早期病害分類を開発しており、植物表現型取得手法が中心である。
abstractThis study proposes a temporal 3D multispectral point cloud reconstruction method for melon plants by integrating multispectral imaging with 3D reconstruction technology.
Stomatal trait analysis is essential for optimizing crop photosynthesis and transpiration, yet deep learning studies have focused mainly on monocotyledons, leaving dicotyledonous crops such as melon (Cucumis melo L.) understudied. To bridge this gap, we established a dedicated melon stomatal dataset comprising 5,708 training images, 1,631 validation images, and 815 test images. On this basis, we developed an improved Mask R-CNN framework using Vision Transformer (ViT) as the backbone. Specifically, standard Multi-Head Attention (MHA) was replaced with Dynamically Composable Multi-Head Attention (DCMHA), which enhances information exchange across attention heads and alleviates the low-rank limitation of conventional attention. In addition, a modified effective Squeeze-and-Excitation (eSE) module was incorporated into the Feature Pyramid Network (FPN) to strengthen channel dependency modeling and multi-scale feature representation. On the melon dataset, the proposed model achieved a mean average precision (mAP) of 72.40 ± 0.09%, with AP50 and AP75 of 91.93 ± 0.14% and 84.59 ± 0.19%, respectively. Repeated-run statistical analyses showed that eSE significantly and consistently improved the main detection metrics across backbones, whereas DCMHA provided a more moderate gain within the ViT-based setting, with clearer support for AP50 than for mAP or AP75 under the baseline FPN setting. Overall, the combined configuration remained among the top-performing models for stomatal instance segmentation. Ellipse fitting further enabled automated quantification of stomatal length, width, count, area, and circumference, showing strong agreement with manual measurements (Pearson r = 0.978). The model also showed preliminary transferability to cucumber, watermelon, pumpkin, and loofah, with an average species-specific R² of 0.86, although each species was evaluated on a limited sample set.
Why it matches plant phenotyping methodsメロンの気孔形質を画像から自動抽出するデータセット、改良Mask R-CNN、インスタンスセグメンテーション、楕円フィッティングを開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractwe established a dedicated melon stomatal dataset comprising 5,708 training images, 1,631 validation images, and 815 test images.
Reproduction assets foundThe paper's authors explicitly state that the source code for model training and inference (including the key modules: DCMHA, eSE-enhanced FPN, Mask R-CNN/ViT pipeline) is publicly available at a GitHub repository, which matches an allowed URL. The melon stomatal image dataset (8,154 images) is described in detail but,Code · publicCode Availability
The source code for model training and inference, including the implementation of the
key modules, is publicly available at:
https://github.com/huangyao110/qk_maskrcnn_trsv2.gitOpen asset ↗huangyao110/qk_maskrcnn_trsv2pdf-page:22 lines:1-322Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 5 Sept 2026
Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 104 points/cm2 while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging.
Why it matches plant phenotyping methods種子の3D形態形質を取得する光学・計算イメージングシステムの開発と検証が研究の中心であり、フェノタイピング手法として明確に適格。
abstractThis paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation.
Automated quantification of plant-level development from multi-plant greenhouse scenes requires separating individual plants from shared scene-level reconstructions and quantifying organ-level development, a challenge that single-plant acquisition workflows do not directly address. This study presents an end-to-end phenotyping pipeline built on 3D Gaussian Splatting (3DGS) and a post-reconstruction extraction framework, LCR-GS, designed to isolate plant instances from full greenhouse scenes without scene-specific model retraining. LCR-GS integrates zero-shot 2D cues with multi-view lifting, geometric clustering, and chromatic refinement to convert large scene-level reconstructions (~2M Gaussians) into compact per-plant subsets (~16K Gaussians). Experiments on greenhouse-grown muskmelon at the early vegetative stage demonstrate high plant-extraction precision (0.933) and strong organ-level instance segmentation (mean AP50 = 0.924). Plant height and leaf count are validated against manual measurements (height R² = 0.98, RMSE = 1.88 cm; leaf count R² = 0.86), whereas additional morphological traits, including leaf area, leaf area index, mean internode length, and stem node count, are reported as pipeline-derived descriptors for within-cohort comparison. By decoupling semantic inference from reconstruction, the pipeline reduces scene-scale data by over 99% and provides a practical route to derive compact per-plant 3D representations from multi-plant greenhouse imagery for downstream organ-level analysis.
Why it matches plant phenotyping methods3DGS画像から個体・器官を抽出し、植物形質を定量化するフェノタイピング手法の開発と検証が中心である。
abstractThis study presents an end-to-end phenotyping pipeline built on 3D Gaussian Splatting (3DGS) and a post-reconstruction extraction framework, LCR-GS, designed to isolate plant instances from full greenhouse scenes without scene-specific model retraining.
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository containing the muskmelon 3DGS phenotyping dataset (scenes, Gaussian-level plant/background annotations, and point-level organ labels) used in this study.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/bblabNTU/3dgs-muskmelon-phenotyping-dataset.Open asset ↗bblabNTU/3dgs-muskmelon-phenotyping-datasethtml-lines:485-547Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
MelonThermalLeafStress / disease detectionDisease symptoms / severityPlant / canopy temperature
Powdery mildew, a disease caused by the biotrophic fungus Podosphaera xanthii, is one of the most destructive diseases affecting melon crops worldwide. This pathogen causes alterations in the physiology of the host plant even before visible symptoms appear, which in turn can be detected using non-invasive imaging techniques. In this piece of work, infrared thermography was used to evaluate the temperature dynamics of melon leaves infected with P. xanthii during the first 72 h after infection. Infected leaves showed a significant decrease in temperature compared to mock-controls from 18.5 hpi onwards, before the appearance of visible mycelium. This temperature difference between mock-control and P. xanthii-infected melon leaves remained significant throughout the experiment, suggesting a sustained disruption of water-balance regulation caused by the fungus. This imbalance could be linked to haustorium-mediated interference with stomatal function or epidermal osmotic homeostasis. Overall, these results highlight thermography as a powerful and sensitive tool for detecting early physiological responses during P. xanthii infection of melon leaves. Therefore, thermography could be used as a valuable complement to ‘omics’ and other image-based phenotyping methods, helping to provide a comprehensive view of the responses that different diseases trigger in host plants.
Why it matches plant phenotyping methodsメロン葉の感染に伴う温度変化を赤外線サーモグラフィーで非侵襲的に測定し、可視症状前の病態・生理状態を評価する方法の適用が中心である。
abstractwhich in turn can be detected using non-invasive imaging techniques
MelonGreenhouseFlowerFruitLeafObject detectionGrowth / development / phenology
Abstract Background Precision agriculture increasingly relies on computer vision systems to monitor crop growth; however, most existing approaches remain limited to frame-level object detection and do not support agronomic decision-making under uncertainty. To address this limitation, this study develops an interpretable and robust framework for cantaloupe ( Cucumis melo ) growth-stage assessment by integrating deep learning–based visual perception with fuzzy reasoning. Results A YOLOv7 detector was fine-tuned to identify healthy leaves, wilted leaves, flowers, and fruits from greenhouse imagery collected across eleven cultivation cycles at three production sites. The detected class counts were temporally aggregated and used as inputs to a Mamdani-type fuzzy inference system encoding expert agronomic knowledge and growth-stage expectations. Experimental evaluation showed that YOLOv7 achieved the highest mAP@0.5 (0.771) and balanced precision–recall performance compared with other YOLO variants, while the fuzzy reasoning layer transformed noisy object-level outputs into consistent crop-condition states with associated confidence levels. Real-world deployment on an edge device further demonstrated the system’s ability to generate actionable alerts, such as “Check Flower” and “Abnormal Condition,” aligned with expected phenological trends. Conclusions The proposed framework advances beyond conventional detection pipelines by enabling decision-level crop assessment that is interpretable, temporally aware, and robust to visual uncertainty. This approach provides a practical decision-support tool for greenhouse crop monitoring and supports the broader adoption of intelligent, confidence-aware systems in precision agriculture.
Why it matches plant phenotyping methodsカンタロープの葉・花・果実を画像から検出し、時系列集約とファジー推論で生育段階・作物状態を推定する手法が研究の中心であり、検出精度と実運用も評価している。
abstractthis study develops an interpretable and robust framework for cantaloupe ( Cucumis melo ) growth-stage assessment by integrating deep learning–based visual perception with fuzzy reasoning.
Wax gourd ( Benincasa hispida (Thunb.) Cogn.) and Mangalore Cucumber (Cucumis melo L. subsp. agrestis var. conomon) are nutritionally rich, mineral-dense crops with a short growing cycle, making them a preferred choice for cultivation among farmers across the country. The Mangalore cucumber, also known as the culinary cucumber, Indian yellow cucumber, or Japanese pickling melon, is widely used in Asian cuisine for pickling. While proper nutrient management is essential for optimal growth, disease control poses a significant challenge in ensuring healthy yields, as disease can rapidly spread from one leaf to another, affecting larger areas of the field and reducing crop yield. Since cucurbits grow close to the soil, they spread across the ground, exhibit dense canopies, and often overlap with neighboring plants. Early detection is crucial to ensure sustainable cultivation, food security, and increased crop productivity. To address this challenge, we collected a dataset comprising 3200 images that includes image samples of Wax gourd and Mangalore cucumber plants affected by leaf miner, pests and image samples of healthy leaves. The Cucurbitaceae datasets that are available in the public domain lack representation of the Mangalore cucumber and Wax gourd varieties. To the best of our knowledge, no publicly available dataset exists for the Wax gourd. Moreover, existing datasets typically contain images captured under controlled greenhouse conditions with plain backgrounds, featuring a single leaf per image. They exhibit low background complexity and limit the scope to detect diseases at the object level, including multiple diseases present on a single leaf or plant. The uniqueness of the proposed dataset lies in addressing this gap by providing field-level images of cucurbits. These images capture variations in soil, overlapped leaves, complex background, varying angles and distances, weeds, and human interference. This makes the dataset suitable for training object detection models capable of identifying single and multiple disease instances, and it can also be effectively used for classification tasks to distinguish between healthy and diseased leaves. It supports advancement in deep learning, feature extraction, segmentation and pattern recognition tasks. Additionally, the dataset serves as a valuable resource for plant pathologists, agronomists and agricultural experts in disease detection, monitoring and management, thereby promoting sustainable agricultural practices. By offering open access, this dataset promotes collaboration within the scientific community to facilitate the development of robust disease detection, identification, and disease control, thus enhancing farming practices and increasing agricultural yields and advancing food security.
Why it matches plant phenotyping methods植物の病害状態を画像から検出・分類する公開データセットが研究の中心であり、植物病害の画像ベース表現型解析に該当する。
abstractwe collected a dataset comprising 3200 images that includes image samples of Wax gourd and Mangalore cucumber plants affected by leaf miner, pests and image samples of healthy leaves.
Reproduction assets foundThe paper is a Data in Brief article describing the WMC-Leafset dataset of 3200 annotated field images of wax gourd and Mangalore cucumber plants for leaf miner/pest object detection. The dataset is publicly deposited on Mendeley Data with an explicit direct URL and DOI, making it a paper-specific, publicly actionable,Dataset · public6° 44′ 46″ east, latitude of 12.16999° or 12° 10′ 12″ north and Mangalore cucumber images were collected from Hulimahu village in longitude of 76.73329° or 76° 43′ 60″ east, latitude 12.1598° or 12° 9′ 35″ north
Data accessibility
Repository name: WMC-Leafset
Data identification number: 10.17632/8m2ytxd4dg.4
Direct URL to data: https://data.mendeley.com/datasets/8m2ytxd4dg/4
Related research article
[ 11 ] M. A. Keerthi Prasad, N. Shobha Rani, M. A. Sangamesha and K. V. Vinay, ``Identification and Detection of Leaf Miner, Pest Infestation in Cucurbitaceae Family in Real-Time Infield Scenarios using YOLOv5s Object Detection Model,'' 2024 11th International Conference on Computing for SustainaOpen asset ↗10.17632/8m2ytxd4dg.4lines:35-65Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Accurate fruit size estimation is crucial for plant phenotyping, as it enables precise crop management and enhances agricultural productivity by providing essential data for growth and resource efficiency analysis. In this study, we estimated the size of on-plant oriental melons grown in a vertical cultivation system to address the challenges posed by leaf occlusion. Data augmentation was achieved using a diffusion model to generate synthetic leaves to cover existing fruits and create an enriched dataset. Three instance segmentation models-mask region-based convolutional neural network (CNN), Mask2Former, and detection transformer (DETR)-and six de-occlusion models derived from these architectures were implemented. These models successfully inferred both visible and occluded areas of the fruit. Notably, Amodal Mask2Former and occlusion-aware RCNN (ORCNN) achieved average precision scores of 85.92 % and 85.35 %, respectively. The inferred masks were used to estimate the height and diameter of the fruit, with Amodal Mask2Former yielding a mean absolute error of 5.46 mm and 4.20 mm and a mean absolute percentage error of 4.86 % and 5.33 %, respectively. The results indicate enhanced performance of the transformer-based Amodal Mask2Former over CNN architectures in de-occlusion tasks and size estimation. Finally, the enhancement in de-occlusion models compared to conventional models was assessed and demonstrated across occlusion ratios ranging from 0 to 70 %. However, generating synthetic datasets with occlusion ratios over 70 % remains a limitation.
Why it matches plant phenotyping methods果実の遮蔽領域を復元し、画像から果実サイズを推定する手法の開発・評価が研究の中心であり、植物表現型計測に該当する。
abstractAccurate fruit size estimation is crucial for plant phenotyping
Reproduction assets foundThe authors explicitly state that the code for training/testing the segmentation models and the size estimation analysis is publicly available at their GitHub repository (https://github.com/sungjay-kim). The oriental melon image dataset itself is only available upon request from the corresponding author, so it is not aCode · publicThe code implemented for this study is publicly available at https://github.com/sungjay-kim . This repository contains code for training and testing segmentation models as well as algorithms for size estimation analysis.Open asset ↗https://github.com/sungjay-kimlines:553-617Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
Three-dimensional seed phenotyping demands imaging systems that simultaneously achieve the micron-level resolution in centimeter-level field-of-view (FOV), a challenge exacerbated by the intrinsic resolution-FOV trade-off in conventional light field architectures. This paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research. At the hardware level, we develop a tunable-focus lens group containing main lens with tunable lens, enabling the flexible adjustment on the effective focal length, coupled with a custom microlens array. A mathematic model is analyzed with FOV, lateral resolution, DOF, lens parameters and system configurations, which also help us on individual optical component design (i.e., MLA design). Computationally, we propose a hybrid aberration correction strategy: First, co-calibration of lens and microlens array aberrations via line-feature detection is developed. Subsequently, a conditional generative adversarial network (cGAN) with attention-guided residual learning enhances sub-aperture images, attaining PSNR 34.63 dB and SSIM 0.9570 on seed test. Experimentally, system achieves 6.2lp/mm resolution at MTF50 over 2~3cm FOV, a 307% improvement over 1.52 lp/mm at initial configurations. The reconstruction pipeline synergizes epipolar plane image (EPI) analysis with multi-view consistency constraints from the sub-aperture array, generating dense 3D point clouds surface (approximately 1.5×10^4 points/cm²) that preserve spectral-textural features at the mean time. Experimental validation of bitter melon seeds and rice grain seeds demonstrates that the accurate morphological parameters are extracted in large area and present in high-fidelity 3D reconstruction. This hardware/software co-optimization framework demonstrates unprecedented dynamic adjustability of resolution-FOV trade-off, overcoming the inherent limitations of conventional light-field systems, while establishing a field-reconfigurable scalable architecture for next-generation phenotyping, with potential extensions to robotic vision and biomedical imaging applications.
Why it matches plant phenotyping methods種子の3D表現型取得を目的に、可変焦点光学系、ライトフィールド撮像、計算再構成を統合したシステムを開発し、性能検証と形態パラメータ抽出を行っており、表現型取得法が研究の中心である。
abstractThis paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research.
Three-dimensional seed phenotyping demands imaging systems that simultaneously achieve the micron-level resolution in centimeter-level field-of-view (FOV), a challenge exacerbated by the intrinsic resolution-FOV trade-off in conventional light field architectures. This paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research. At the hardware level, we develop a tunable-focus lens group containing main lens with tunable lens, enabling the flexible adjustment on the effective focal length, coupled with a custom microlens array. A mathematic model is analyzed with FOV, lateral resolution, DOF, lens parameters and system configurations, which also help us on individual optical component design (i.e., MLA design). Computationally, we propose a hybrid aberration correction strategy: First, co-calibration of lens and microlens array aberrations via line-feature detection is developed. Subsequently, a conditional generative adversarial network (cGAN) with attention-guided residual learning enhances sub-aperture images, attaining PSNR 34.63 dB and SSIM 0.9570 on seed test. Experimentally, system achieves 6.2lp/mm resolution at MTF50 over 2~3cm FOV, a 307% improvement over 1.52 lp/mm at initial configurations. The reconstruction pipeline synergizes epipolar plane image (EPI) analysis with multi-view consistency constraints from the sub-aperture array, generating dense 3D point clouds surface (approximately 1.5×10^4 points/cm²) that preserve spectral-textural features at the mean time. Experimental validation of bitter melon seeds and rice grain seeds demonstrates that the accurate morphological parameters are extracted in large area and present in high-fidelity 3D reconstruction. This hardware/software co-optimization framework demonstrates unprecedented dynamic adjustability of resolution-FOV trade-off, overcoming the inherent limitations of conventional light-field systems, while establishing a field-reconfigurable scalable architecture for next-generation phenotyping, with potential extensions to robotic vision and biomedical imaging applications.
Why it matches plant phenotyping methods種子の3D形質を取得・抽出する光学系、計算再構成、検証を中心に開発した植物フェノタイピング手法研究。
abstractThis paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research.
In modern agriculture, ensuring plant health is essential for high crop yields and quality. Plant diseases pose risks to economies, communities, and the environment, making early and accurate diagnosis crucial. The internet of things (IoT) has revolutionized farming by enabling real-time crop monitoring and using drones and cameras for early disease detection. This technology helps farmers address challenges with precision and sustainability. This research propose an ensemble learning model incorporating multi-class capsule networks (MCCN) and other pre-trained model with majority voting system is implemented to predict plant diseases and pests early. The research aims to develop a robust MCCN-based ensemble prediction model for timely disease identification. To evaluate the performance of the ensemble model, various key metrics, including accuracy, and loss value, are assessed. Furthermore, a comparative analysis is conducted, benchmarking the MCCN model against other well-known pre-trained models such as residual network-101 (ResNet101), visual geometry group-19 (VGG19), and GoogleNet. This research signifies a substantial stride towards the realization of IoT-driven precision agriculture, where advanced technology and machine learning contribute to the early detection and mitigation of plant diseases, ultimately enhancing crop yield and environmental sustainability.
Why it matches plant phenotyping methods植物病害を画像等から識別するアンサンブル学習モデルの開発・比較が中心であり、罹病状態という植物表現型を推定する方法研究に該当する。
abstractThis research propose an ensemble learning model incorporating multi-class capsule networks (MCCN) and other pre-trained model with majority voting system is implemented to predict plant diseases and pests early.
Three-dimensional seed phenotyping demands imaging systems that simultaneously achieve the micron-level resolution in centimeter-level field-of-view (FOV), a challenge exacerbated by the intrinsic resolution-FOV trade-off in conventional light field architectures. This paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research. At the hardware level, we develop a tunable-focus lens group containing main lens with tunable lens, enabling the flexible adjustment on the effective focal length, coupled with a custom microlens array. A mathematic model is analyzed with FOV, lateral resolution, DOF, lens parameters and system configurations, which also help us on individual optical component design (i.e., MLA design). Computationally, we propose a hybrid aberration correction strategy: First, co-calibration of lens and microlens array aberrations via line-feature detection is developed. Subsequently, a conditional generative adversarial network (cGAN) with attention-guided residual learning enhances sub-aperture images, attaining PSNR 34.63 dB and SSIM 0.9570 on seed test. Experimentally, system achieves 6.2lp/mm resolution at MTF50 over 2~3cm FOV, a 307% improvement over 1.52 lp/mm at initial configurations. The reconstruction pipeline synergizes epipolar plane image (EPI) analysis with multi-view consistency constraints from the sub-aperture array, generating dense 3D point clouds surface (approximately 1.5×10^4 points/cm²) that preserve spectral-textural features at the mean time. Experimental validation of bitter melon seeds and rice grain seeds demonstrates that the accurate morphological parameters are extracted in large area and present in high-fidelity 3D reconstruction. This hardware/software co-optimization framework demonstrates unprecedented dynamic adjustability of resolution-FOV trade-off, overcoming the inherent limitations of conventional light-field systems, while establishing a field-reconfigurable scalable architecture for next-generation phenotyping, with potential extensions to robotic vision and biomedical imaging applications.
Why it matches plant phenotyping methods種子の3D形態形質を取得する光学・計算イメージングシステムを開発し、性能評価と実データでの検証を行っており、植物フェノタイピング手法が研究の中心である。
abstractThis paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research.
Rapid and accurate biomass, nutrients, and sugar estimation facilitates efficient plant phenotyping and site-specific crop management. The hyperspectral technique enabled rapid and non-destructive determination of Nitrogen, Potassium and sucrose concentration in melon ( C.melo ) leaves and fruit using a spectral reflectance. The best modal for Nitrogen and Potassium quantitative prediction was precisely assessed using the MobileNet-v3-l model that exhibited the highest accuracy, with R² being 0.958, MSE 12.188 and MAE 1.519. Compared with the highest accuracy model of RGB R², MSE decreased by 21 %, and MAE decreased by 16 %. Meanwhile, the ResNet18 model has the highest accuracy, R² is 0.921, MSE is 16.246, and MAE is 1.851. Compared with the highest accuracy model of RGB R², MSE is increased by 85 %, and MAE is increased by 8 % in the Potassium model. In the sucrose model, the RegNet-y-8gf model had the highest accuracy, with R² of 0.958, MSE of 8.776 and MAE of 1.707. Furthermore, in the reductive sugar model, the accuracy of using RGB hyperspectral imaging ResNet18 model is the highest, R² is 0.936, MSE is 0.517, and MAE is 0.471. The present study shows the potential of the use of HSI technology directly in the field by proximal measurements under natural light conditions for the prediction of the harvest time of the melon.
Why it matches plant phenotyping methodsメロンの葉・果実の栄養素や糖度をハイパースペクトル画像と機械学習で推定し、モデル精度を比較・評価しているため、植物形質取得法が中心である。
abstractRapid and accurate biomass, nutrients, and sugar estimation facilitates efficient plant phenotyping and site-specific crop management.
Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.
Why it matches plant phenotyping methodsメロン果実・花柄画像から11形質を抽出する深層学習・画像解析フレームワークを開発し、手動測定との検証とソフトウェア化まで行っており、植物表現型取得法が中心である。
abstracta deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel.
Tomato leaf curl New Delhi virus (ToLCNDV) is an emerging constraint in muskmelon production in India and other parts of the world. This study aims to identify the new sources of resistance against ToLCNDV from Indian melon germplasm, which has not been evaluated globally. Sixty melon germplasm comprising of both cultivated commercial types (vars. reticulatus and inodorus) from the subspecies melo and wild germplasm (vars. momordica, conomon, and callosus) from subspecies agrestis were screened in the field for two consecutive years under natural epiphytotic condition. The infected plants showed varying degrees of phenotypic symptoms, such as yellow mosaic, stunting of plant growth, and restricted fruiting. The disease response of ToLCNDV in melon genotypes were measured by a robust rating scale, which was developed by providing differential weightage to morphogenic symptoms on foliage, reduction of vine length and fruiting of the plant. The genotype DSM 132 (C.melo var. callosus) could be identified as highly resistant to ToLCNDV, which recorded the minimum disease severity index (DSI) of 0.00, 0.00, followed by DSM 19 (3.50, 4.50) and DSM-11-7 (7.00, 6.11) from C. melo var. momordica for two consecutive years. The resistance in these genotypes was further confirmed through challenge inoculation with viruliferous whitefly (Bemisia tabaci) carrying ToLCNDV in the greenhouse conditions, which showed a minimum vulnerability index in genotype DSM 132 (VI = 2.0) followed by DSM 19 (VI = 6.67) and DSM-11-7 (VI = 11.34). The molecular technique of virus detection through polymerase chain reaction (PCR) specific to ToLCNDV failed to detect the presence of tomato leaf curl New Delhi virus in resistant genotypes DSM 132, DSM 19, and DSM-11-7. Quantitative PCR (qPCR) showed very low viral titer in resistant genotypes DSM 132, DSM 19, and DSM-11-7 compared to susceptible genotypes. This study could identify three Indian melon genotypes with high levels of resistance to ToLCNDV, which will be useful for resistance breeding across the globe.
Why it matches plant phenotyping methodsメロンのウイルス抵抗性評価を目的に、症状・つる長・結実を統合した改良型の病害表現型評価尺度を開発・適用しており、植物病害状態の取得方法が中心的に扱われている。
titleby designing an improved method of field screening
Abstract Background The sweet potato whitefly ( Bemisia tabaci ) is a globally important insect pest that damages crops through direct feeding and by transmitting viruses. Current B. tabaci management revolves around the use of insecticides, which are economically and environmentally costly. Host plant resistance is a sustainable option to reduce the impact of whiteflies, but progress in deploying resistance in crops has been slow. A major obstacle is the high cost and low throughput of screening plants for B. tabaci resistance. Oviposition rate is a popular metric for host plant resistance to B. tabaci because it does not require tracking insect development through the entire life cycle, but accurate quantification is still limited by difficulties in observing B. tabaci eggs, which are microscopic and translucent. The goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato. Results We tested a selective staining process originally developed for leafhopper eggs: submerging the leaves in McBryde’s stain (acetic acid, ethanol, 0.2% aqueous acid Fuchsin, water; 20:19:2:1) for three days, followed by clearing under heat and pressure for 15 min in clearing solution (LGW; lactic acid, glycerol, water; 17:20:23). With a less experienced individual counting the eggs, B. tabaci egg counts increased after staining across all five crops. With a more experienced counter, egg counts increased after staining on melons, tomatoes, and cowpeas. For all five crops, there was significantly greater agreement on egg counts across the two counting individuals after the staining process. The staining method worked particularly well on melon, where egg counts universally increased after staining for both counting individuals. Conclusions Selective staining aids visualization of B. tabaci eggs across multiple crop plants, particularly species where leaf morphological features obscure eggs, such as melons and tomatoes. This method is broadly applicable to research questions requiring accurate quantification of B. tabaci eggs, including phenotyping for B. tabaci resistance.
Why it matches plant phenotyping methods植物葉上のコナジラミ卵を染色して定量し、計数値と計数者間一致を改善する方法を評価しており、抵抗性フェノタイピングへの応用が明示された中心的な手法研究。
abstractThe goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's egg-count datasets and the R Markdown analysis code in a Dryad repository, which is a public, paper-specific asset directly reproducing the phenotyping measurements and analysis.Dataset · publicThe datasets generated and analyzed during this study, and an R Markdown document containing the code used to perform these analyses are available in a Dryad repository (DOI: doi: https://doi.org/10.5061/dryad.vmcvdnd1m ).Open asset ↗Dryad · 10.5061/dryad.vmcvdnd1mlines:138-163Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Abstract Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.
Why it matches plant phenotyping methodsメロン果実・果梗を画像から分割し、特徴抽出によって11形質を推定する深層学習フレームワークを開発・検証し、ソフトウェア化しているため、植物フェノタイピング手法が中心である。
abstractBy integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAdditionally, we have developed a simple melon phenotypic traits extraction software, which can be downloaded via https://github.com/hongbinz13/Melon-Phenotype-Extractor/releases/tag/software .Open asset ↗https://github.com/hongbinz13/Melon-Phenotype-Extractor · Melon-Phenotype-Extractorlines:109-139Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published1 Jun 2023Revista Brasileira de Engenharia Agrícola e AmbientalCited by 8 · OpenAlex ↗
ABSTRACT The objective of this study was to analyze the feasibility of using thermal images to estimate the water status of melon plants (Cucumis melo L.) in tropical semi-arid climates. The study was conducted in a randomized block design with a split-plot arrangement. The plots comprised of soil cover (with and without mulching), and subplots were constructed using five irrigation regimes (120, 100, 80, 60, and 40% crop evapotranspiration), with five replicates. The following variables were evaluated: canopy temperature (Tcanopy), leaf water potential, air temperature (Tair), soil moisture, crop yield, and thermal index (ΔT), which is defined as the difference between Tcanopy and Tair. ΔT exhibited high correlations with crop yield and water consumption, indicating that thermography is an efficient tool for identifying the water status of melon plants, which could be employed for proper irrigation scheduling under tropical semi-arid scenarios. Moreover, thermal images identified the beneficial effects of soil cover on leaf water status and crop yield, primarily under moderate deficit irrigation. These results demonstrate that mulching is essential for increasing melon yield and water productivity in tropical regions.
Why it matches plant phenotyping methodsメロンの水分状態という植物生理形質を熱画像から推定する実現可能性を評価し、熱指標と収量・水消費との相関で手法を検証しているため、熱画像法が中心的である。
abstractThe objective of this study was to analyze the feasibility of using thermal images to estimate the water status of melon plants (Cucumis melo L.) in tropical semi-arid climates.
Global climate change and exponential population growth pose a challenge to agricultural outputs. In this scenario, novel techniques have been proposed to improve plant growth and increase crop yields. Wearable sensors are emerging as promising tools for the non-invasive monitoring of plant physiological and microclimate parameters. Features of plant wearables, such as easy anchorage to different organs, compliance with natural surfaces, high flexibility, and biocompatibility, allow for the detection of growth without impacting the plant functions. This work proposed two wearable sensors based on fiber Bragg gratings (FBGs) within silicone matrices. The use of FBGs is motivated by their high sensitivity, multiplexing capacities, and chemical inertia. Firstly, we focused on the design and the fabrication of two plant wearables with different matrix shapes tailored to specific plant organs (i.e., tobacco stem and melon fruit). Then, we described the sensors' metrological properties to investigate the sensitivity to strain and the influence of environmental factors, such as temperature and humidity, on the sensors' performance. Finally, we performed experimental tests to preliminary assess the capability of the proposed sensors to monitor dimensional changes of plants in both laboratory and open field settings. The promising results will foster key actions to improve the use of this innovative technology in smart agriculture applications for increasing crop products quality, agricultural efficiency, and profits.
Why it matches plant phenotyping methods植物の茎・果実の寸法変化を測定するFBGウェアラブルセンサーを設計・製作し、性能評価と実証を行った、中心的な植物フェノタイピング手法研究である。
abstractThis work proposed two wearable sensors based on fiber Bragg gratings (FBGs) within silicone matrices.
Flower morphologies shape the accessibility to nectar and pollen, two major traits that determine plant-pollinator interactions and reproductive success. Melon is an economically important crop whose reproduction is completely pollinator-dependent and, as such, is a valuable model for studying crop-ecological functions. High-resolution imaging techniques, such as micro-computed tomography (micro-CT), have recently become popular for phenotyping in plant science. Here, we implemented micro-CT to study floral morphology and honey bees in the context of nectar-related traits without a sample preparation to improve the phenotyping precision and quality. We generated high-quality 3D models of melon male and female flowers and compared the geometric measures. Micro-CT allowed for a relatively easy and rapid generation of 3D volumetric data on nectar, nectary, flower, and honey bee body sizes. A comparative analysis of male and female flowers showed a strong positive correlation between the nectar gland volume and the volume of the secreted nectar. We modeled the nectar level inside the flower and reconstructed a 3D model of the accessibility by honey bees. By combining data on flower morphology, the honey bee size and nectar volume, this protocol can be used to assess the flower accessibility to pollinators in a high resolution, and can readily carry out genotypes comparative analysis to identify nectar-pollination-related traits.
Why it matches plant phenotyping methodsマイクロCTを用いて花、蜜腺、蜜、ハナバチの3D形態・体積を取得し、花粉媒介関連形質を高精度に評価するプロトコルを実装・提示しており、表現型取得法が中心である。
abstractHere, we implemented micro-CT to study floral morphology and honey bees in the context of nectar-related traits without a sample preparation to improve the phenotyping precision and quality.
Reproduction assets foundThe paper deposits its Python image-processing/phenotyping pipeline on GitHub and provides a supplement containing raw nectar/nectary measurement data (Tables S1–S4). Both are paper-specific, public, and actionable.Supplement · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cells11213452/s1 . Figure S1: pollen on Stamens in ♂ and ⚥ flower types at different magnifications; Table S1: nectar-related traits in male and female flowers; Table S2: correlation analysis between nectary volume, nectary cross-section area, nectary surface, flower width and nectar volume in the respective male, female and pooled melon flowerOpen asset ↗lines:83-224Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
Different modeling techniques must be applied to manage production and statistical estimation to predict the expected harvest. By calculating advanced production methods and the rational valuation of different factors, we can accurately capture the variety of growth characteristics and the expected yield. This paper obtained 32 feature variables related to melons, including phenological features, shape features, and color features. The Gradient Boosted Decision Tree (GBDT) network and the Grid Search (GS) hyperparameter seeking method was applied to calculate the degree of importance of all melon fruits' characteristics and construct prediction models for three expected harvest indexes of melon yield, sugar content, and endocarp hardness. To facilitate growers to carry out prediction and estimation in the field without destroying the melon fruits. The reduced feature variables were selected as inputs. The GBDT model was used to provide a significant advantage in prediction compared to both Random Forest (RF) and Support Vector Regression (SVR) methods. In addition, to verify the feasibility of using only reduced feature variables as input for the evaluation work, this study also compares the predictive effects of the model when all feature variables and only reduced feature variables are used. The GBDT prediction model proposed in this paper predicted melon yield, sugar content, and hardness using reduced features as input, and the model R2 could reach more than 90%. Therefore, this method can effectively help growers carry out early non-destructive inspection and growth prediction of melons in the field.
Why it matches plant phenotyping methodsメロンの形態・色・生育特徴から収量、糖度、硬度を非破壊推定する学習モデルが研究の中心であり、植物形質の計算的推定手法に該当する。
abstractThe GBDT prediction model proposed in this paper predicted melon yield, sugar content, and hardness using reduced features as input
Summary One of the most significant factors that influence the globalized economy is agriculture. In order to address the requirement of increasing populations in terms of food necessities, modernizations and technological progressions in agriculture, it is necessary to implement a smart agricultural system. Various traditional techniques are still utilized by the farmers and their intuition in agriculture is not enough to furnish and deliver various issues namely soil management, plant disease identification, weed management, irrigation management, and so forth. Therefore, in this paper, a low‐cost smart farming automation system is evaluated and presented. Since the development of a smart farming automation system minimizes the labor cost and enhances agricultural production level, this paper proposes a deep neural network based Interactive fuzzy Bayesian search (DNN‐IFBS) algorithm for a low‐cost smart farming automation system. In addition to this, the crop water stress index (CWSI) is computed to determine the water status of the plant by employing solar irradiation, canopy temperature, and so forth. The data for analysis is collected simultaneously from three cameras containing image resolutions of about 650 × 460 pixels each. Two different plants namely the melon and sesame are utilized as datasets for experimentation. Finally, the performances of the proposed approach are determined according to the statistical performance measures namely accuracy, specificity as well as F ‐measure. The comparative analysis is carried out to evaluate the effectiveness of the proposed system. From the evaluation results, the accuracy rate obtained for the proposed approach is 98.7%.
Why it matches plant phenotyping methods低コスト農業自動化システムの開発が中心で、画像データとCWSIから植物の水分状態を推定する技術を評価しているため、植物フェノタイピング手法として含める。
abstractthis paper proposes a deep neural network based Interactive fuzzy Bayesian search (DNN‐IFBS) algorithm for a low‐cost smart farming automation system
The generation of realistic plant and animal images from marker information could be a main contribution of artificial intelligence to genetics and breeding. Since morphological traits are highly variable and highly heritable, this must be possible. However, a suitable algorithm has not been proposed yet. This paper is a proof of concept demonstrating the feasibility of this proposal using ‘decoders’, a class of deep learning architecture. We apply it to Cucurbitaceae, perhaps the family harboring the largest variability in fruit shape in the plant kingdom, and to tomato, a species with high morphological diversity also. We generate Cucurbitaceae shapes assuming a hypothetical, but plausible, evolutive path along observed fruit shapes of C. melo . In tomato, we used 353 images from 129 crosses between 25 maternal and 7 paternal lines for which genotype data were available. In both instances, a simple decoder was able to recover expected shapes with large accuracy. For the tomato pedigree, we also show that the algorithm can be trained to generate offspring images from their parents’ shapes, bypassing genotype information. Data and code are available at https://github.com/miguelperezenciso/dna2image .
Why it matches plant phenotyping methodsDNA配列や親の形状から植物果実形状画像を生成する深層学習手法の概念実証であり、植物形態の取得・推定が研究の中心です。
titleComputer generation of fruit shapes from DNA sequence
Reproduction assets foundThe paper's cucurbit shape phenotyping inputs and analysis code are publicly available in the authors' dna2image GitHub repository, explicitly cited in the methods and data availability statement.Dataset · publichways. One pathway would be wild gourd (akin to pumpkin shape) scallop acorn; a
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second pathway would be wild gourd marrow straightneck zucchini cocozelle
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(Figure 1B). See also Figure 17 in (Paris 1989). We extracted contours from the
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‘contours.png’ file, based in (Paris 1989) and available in GitHub
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(https://github.com/miguelperezenciso/dna2image/blob/main/images/contours.png), using
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OpenCV library (Bradski 2000). Contours were centered and 500 pseudo-landmarks were
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obtained with the algorithm in Zingaretti et al. (2021). Next, contours were aligned with a
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generalized procrustes algorithm implemented in python package ‘procrustes’ (Meng et al.
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2022Open asset ↗https://github.com/miguelperezenciso/dna2image · contours.pngpdf-raw-page:5 lines:1-76Code · publicy, we have shown that very simple networks can be successfully trained in small
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datasets to accurately predict fruit images. Although much work remains to be done, this
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research opens new possibilities in the area of prediction of complex traits.
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Data availability statement
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All data and code are available at https://github.com/miguelperezenciso/dna2image.327
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CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted September 22, 2022.
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https://doi.org/10.1101/2022.09.19.Open asset ↗https://github.com/miguelperezenciso/dna2image.327 · dna2image.327pdf-raw-page:10 lines:1-73Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
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
In precision agriculture, the nitrogen level is significantly important for establishing phenotype, quality and yield of crops. It cannot be achieved in the future without appropriate nitrogen fertilizer application. Moreover, a convenient and real-time advance technology for nitrogen nutrition diagnosis of crops is a prerequisite for an efficient and reasonable nitrogen-fertilizer management system. With the development of research on plant phenotype and artificial intelligence technology in agriculture, deep learning has demonstrated a great potential in agriculture for recognizing nondestructive nitrogen nutrition diagnosis in plants by automation and high throughput at a low cost. To build a nitrogen nutrient-diagnosis model, muskmelons were cultivated under different nitrogen levels in a greenhouse. The digital images of canopy leaves and the environmental factors (light and temperature) during the growth period of muskmelons were tracked and analyzed. The nitrogen concentrations of the plants were measured, we successfully constructed and trained machine-learning- and deep-learning models based on the traditional backpropagation neural network (BPNN), the emerging convolution neural network (CNN), the deep convolution neural network (DCNN) and the long short-term memory (LSTM) for the nitrogen nutrition diagnosis of muskmelon. The adjusted determination coefficient (R2) and mean square error (MSE) between the predicted values and measured values of nitrogen concentration were adopted to evaluate the models’ accuracy. The values were R2 = 0.567 and MSE = 0.429 for BPNN model; R2 = 0.376 and MSE = 0.628 for CNN model; R2 = 0.686 and MSE = 0.355 for deep convolution neural network (DCNN) model; and R2 = 0.904 and MSE = 0.123 for the hybrid model DCNN–LSTM. Therefore, DCNN–LSTM shows the highest accuracy in predicting the nitrogen content of muskmelon. Our findings highlight a base for achieving a convenient, precise and intelligent diagnosis of nitrogen nutrition in muskmelon.
Why it matches plant phenotyping methods画像と環境情報からメロンの窒素栄養状態を推定する深層学習手法を構築・比較・評価しており、植物状態の取得・推定が研究の中心である。
abstractTo build a nitrogen nutrient-diagnosis model, muskmelons were cultivated under different nitrogen levels in a greenhouse.
Cucurbit powdery mildew (CPM) is caused most frequently by well‐differentiated obligate erysiphaceous ectoparasites Golovinomyces orontii and Podosphaera xanthii, which vary in their ecology and virulence. All economically important cucurbit crops host both of these CPM species. Breeding of cucurbits for CPM resistance is highly important worldwide, but adequate knowledge of CPM species determination, as well as virulence structure, population dynamics, and spatiotemporal variation of these pathogens, has not yet been achieved. New tools have been developed to enhance research on CPM virulence variation for more efficient breeding and seed and crop production. A set of differential genotypes of Cucumis melo, with high differentiation capacity, may contribute substantially to understanding of variation in CPM virulence at both individual and population levels. Long‐term observations (2001–2012) of CPM pathogens in the Czech Republic were used to analyse virulence variation within and among annual CPM populations and demonstrate the utility of recently developed tools for studying species variability and virulence variation of CPM pathogens worldwide. Detailed analyses of diversity and spatiotemporal fluctuations in the composition of CPM populations provide crucial information for shaping breeding programmes and predicting the most effective sources of race‐specific resistance. The primary aim of this work was to create a uniform framework for determination of CPM species structure and diversity, virulence phenotypes, virulence and phenotype frequencies, phenotype complexity, dynamics, and variation within and among CPM populations. In addition, practical advice is presented on how to select the most relevant data and interpret them for use in cucurbit resistance breeding.
Why it matches plant phenotyping methodsウリ類うどんこ病菌の病原性表現型を評価するための差別品種・統一フレームワーク・ツールを中心に扱い、長期データで有用性を検証しているため、植物病害表現型の方法論研究に該当する。
abstractNew tools have been developed to enhance research on CPM virulence variation for more efficient breeding and seed and crop production.
Abstract Deficit irrigation (DI) strategies and soil cover are highly effective to improve the the water productivity in semi-arid regions. However, the effective monitoring of plant water status under DI strategies becomes crucial. The main objective of this study was to evaluate the use of thermal images to estimate the water status of melon plants cultivated in soil with and without mulching under different irrigation regimes. The experience was carried out from October to December 2018. The study was carried out in a randomized block design, in a split plot arrangement. Plots were composed by soil cover (with and without mulching with plant material), and subplots by 5 irrigation regimes (120, 100, 80, 60 and 40% of crop evapotranspiration-ETc), with five replicates. The following variables were evaluated: canopy temperature (T canopy ), leaf water potential (Ψ leaf ), air temperature (T air ), soil moisture, crop yield and the thermal index (ΔT), this being defined as the difference between T canopy and T air . ΔT showed high correlations with crop yield and crop water consumption, evidencing that thermography is an efficient tool to identify the water status of melon plants and could be employed for a proper irrigation scheduling under the tropical semi-arid scenarios. Moreover, the use of thermal images also allowed the identification of beneficial effects of soil cover on leaf water status and crop yield, mainly under moderate DI. The obtained results also demonstrate that mulching is essential to increase melon yield and water productivity in tropical regions.
Why it matches plant phenotyping methods熱画像でメロンの水分状態を推定する方法を中心に評価し、熱指標と収量・水分状態の相関を検証しているため、植物フェノタイピング手法の実質的応用・検証に該当する。
abstractThe main objective of this study was to evaluate the use of thermal images to estimate the water status of melon plants cultivated in soil with and without mulching under different irrigation regimes.
In this study, a non-destructive method using visible/near infrared (Vis/NIR) spectroscopy was investigated to predict the soluble solids content (SSC) of intact melons (Cucumis melo L.) cv. ‘Manao’, ‘Jinhongbao’, ‘Xizhoumi’. A set of 360 samples (120 melons of each cultivar) was used to develop the calibration model, and two location (stylar-end and equatorial locations) models were investigated independently. The samples' spectra were obtained by a portable Vis/NIR photo-diode array spectrometer operated in reflectance mode. Multiplicative scatter correction (MSC), first derivative and Savizky-Golay (SG) smoothing in turn were applied to the obtained spectra. The region from 750 to 950 nm was selected to develop NIR models combined with the partial least squares (PLS) regression method. The results indicated that the stylar-end of the intact melon was the proper location to evaluate the SSC in the intact melon due to its suitable and exclusive physiological structure. A competitive adaptive reweighted sampling (CARS) algorithm was used to select effective wavelengths. Results showed that the CARS algorithm had great potential for simplifying the variables. Furthermore, another 195 samples were used for external prediction to evaluate the CARS-PLS model's accuracy and stability, which resulted in a high determination coefficient (R2p = 0.83) and a low root mean square error (RMSEP = 0.73 ºBrix).
Why it matches plant phenotyping methods携帯型Vis/NIR分光法で intact melon の可溶性固形分を非破壊推定する校正モデルを開発し、外部予測で精度・安定性を検証しており、植物器官の形質取得法が中心である。
abstracta non-destructive method using visible/near infrared (Vis/NIR) spectroscopy was investigated to predict the soluble solids content (SSC) of intact melons
Internal morphological damage can have critical effects on the development and germination power of seeds. This study investigates the morphological characteristics of naturally aged muskmelon seed in relation to germination ability. An X-ray microCT scanner was employed to generate CT images and then several image processing techniques such as re-slicing, contrast enhancement, noise reduction, and segmentation were performed on the images. Afterwards, fifteen preprocessed images were nominated from each sample, and features of interest (i.e., local binary pattern, Gabor, local Fourier (FFT), texture, contrast and Haralick textural (Tx) features) were extracted. The sequential forward selection (SFS) method was applied as a search strategy to identify the most relevant features using a variety of different objective functions. It was determined that the Fisher discriminant objective function performed the best. A germination test was performed to evaluate the seed viability and the information was used to construct the training and validation data set. The seeds were divided into 2 groups: viable (group-1) and non-viable (group-0). Different classifiers were probed to determine the optimal performer, where the linear discriminant classifier resulted in an accuracy of 98.9%, with 10-fold cross-validation using eighteen selected features. The findings of this study indicate that CT imaging is a potential tool for the classification of seeds based on the characterisation of internal morphologically.
Why it matches plant phenotyping methodsX-ray microCT画像と画像処理・特徴抽出・分類を組み合わせ、種子内部形態から生存性を推定する手法が研究の中心であるため、植物表現型計測手法として採用する。
abstractAn X-ray microCT scanner was employed to generate CT images and then several image processing techniques such as re-slicing, contrast enhancement, noise reduction, and segmentation were performed on the images.
The bacterium Dickeya dadantii is responsible of important economic losses in crop yield worldwide. In melon leaves, D. dadantii produced multiple necrotic spots surrounded by a chlorotic halo, followed by necrosis of the whole infiltrated area and chlorosis in the surrounding tissues. The extent of these symptoms, as well as the day of appearance, was dose-dependent. Several imaging techniques (variable chlorophyll fluorescence, multicolor fluorescence, and thermography) provided spatial and temporal information about alterations in the primary and secondary metabolism, as well as the stomatal activity in the infected leaves. Detection of diseased leaves was carried out by using machine learning on the numerical data provided by these imaging techniques. Mathematical algorithms based on data from infiltrated areas offered 96.5 to 99.1% accuracy when classifying them as mock vs. bacteria-infiltrated. These algorithms also showed a high performance of classification of whole leaves, providing accuracy values of up to 96%. Thus, the detection of disease on whole leaves by a model trained on infiltrated areas appears as a reliable method that could be scaled-up for use in plant breeding programs or precision agriculture.
Why it matches plant phenotyping methods複数の画像・熱画像・蛍光計測と機械学習を用いて、メロン葉の感染症状を定量的に検出・分類する方法が研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractSeveral imaging techniques (variable chlorophyll fluorescence, multicolor fluorescence, and thermography) provided spatial and temporal information about alterations in the primary and secondary metabolism, as well as the stomatal activity in the infected leaves.
Ethylene is a gaseous plant hormone involved in defense, adaptations to environmental stress and fruit ripening. Its relevance to the latter makes its detection highly useful for physiologists interested in the onset of ripening. Produced as a sharp peak during the respiratory burst, ethylene is biologically active at tens of nl L -1 . Reliable quantification at such concentrations generally requires specialized instrumentation. Here we present a rapid, high-sensitivity method for detecting ethylene in attached fruit using a conventional gas chromatography-mass spectrometry (GC-MS) system and in situ headspace collection chambers. We apply this method to melon (Cucumis melo L.), a unique species consisting of climacteric and non-climacteric varieties, with a high variation in the climacteric phenotype among climacteric types. Using a population of recombinant inbred lines (RILs) derived from highly climacteric ('Védrantais', cantalupensis type) and non-climacteric ('Piel de Sapo', inodorus type) parental lines, we observed a significant variation for the intensity, onset and duration of the ethylene burst during fruit ripening. Our method does not require concentration, sampling times over 1 h or fruit harvest. We achieved a limit of detection of 0.41 ± 0.04 nl L -1 and a limit of quantification of 1.37 ± 0.13 nl L -1 with an analysis time per sample of 2.6 min. Validation of the analytical method indicated that linearity (>98%), precision (coefficient of variation ≤2%) and sensitivity compared favorably with dedicated optical sensors. This study adds to evidence of the characteristic climacteric ethylene burst as a complex trait whose intensity in our RIL population lies along a continuum in addition to two extremes.
Why it matches plant phenotyping methods果実に付着した状態でエチレン放出を定量するGC-MS法とヘッドスペース採取法を開発し、検出限界・定量限界・直線性・精度・感度を検証している。果実成熟に関わる生理形質の取得法が研究の中心である。
abstractHere we present a rapid, high-sensitivity method for detecting ethylene in attached fruit using a conventional gas chromatography-mass spectrometry (GC-MS) system and in situ headspace collection chambers.
Early production of melon plant (Cucumis melo) is carried out using tunnels structures, where extreme temperatures lead to high reactive oxygen species production and, hence, oxidative stress. Malondialdehyde (MDA) is a recognized biomarker of the advanced oxidative status in a biological system. Thus a reliable, sensitive, simple, selective, and rapid separative strategy based on ultra-high-performance liquid chromatography coupled to positive electrospray-tandem mass spectrometry (UPLC-(+)ESI-MS/MS) was developed for the first time to measure MDA, without derivatization, in leaves of melon plants exposed to stress conditions. The detection and quantitation limits were 0.02 μ g·L -1 and 0.08 μ g·L -1 , respectively, which was demonstrated to be better than the methodologies currently reported in the literature. The accuracy values were between 96% and 104%. The precision intraday and interday values were 2.7% and 3.8%, respectively. The optimized methodology was applied to monitoring of changes in MDA levels between control and exposed to thermal stress conditions melon leaves samples. Important preliminary conclusions were obtained. Besides, a comparison between MDA levels in melon leaves quantified by the proposed method and the traditional thiobarbituric acid reactive species (TBARS) approach was undertaken. The MDA determination by TBARS could lead to unrealistic conclusions regarding the oxidative stress status in plants.
Why it matches plant phenotyping methodsメロン葉の酸化ストレス状態を表すMDAを測定する分析法を開発・性能検証し、既存法との比較とストレス葉への適用まで行っており、植物状態の取得法が中心である。
abstracta reliable, sensitive, simple, selective, and rapid separative strategy based on ultra-high-performance liquid chromatography coupled to positive electrospray-tandem mass spectrometry (UPLC-(+)ESI-MS/MS) was developed for the first time to measure MDA, without derivatization, in leaves of melon plants exposed to stress conditions.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 11 Sept 2026
The negative impact of conventional farming on environment and human health make improvements on farming management mandatory. Imaging techniques are implemented in remote sensing for monitoring crop fields and plant phenotyping programs. The increasingly large size and complexity of the data obtained by these techniques, makes the implementation of powerful mathematical tools necessary in order to identify informative parameters and to apply them in precision agriculture. Multicolor fluorescence imaging is a useful approach for the study of plant defense responses to stress factors at bench scale. However, it has not been fully applied to plant phenotyping. This work evaluates the possible application of multicolor fluorescence imaging in combination with thermography for the particular case of zucchini plants affected by soft rot, caused by Dickeya dadantii. Several statistical models -based on logistic regression analysis (LRA) and artificial neural networks (ANN)- were obtained for the experimental system zucchini-D. dadantii, which classify new samples as “healthy” or “infected”. The LRA worked best in identifying high dose-infiltrated leaves (in infiltrated and non-infiltrated areas) whereas ANN offered a higher accuracy at identifying low dose-infiltrated areas. To assess the applicability of these results to cucurbits in a more general way, these models were validated for melon infected by the same pathogen, achieving accurate predictions for the infiltrated areas. The values of accuracy achieved are comparable to those found in the literature for classifiers identifying other infections based on data obtained by different techniques. Thus, MCFI in combination with thermography prove useful at providing data at lab scale that can be analyzed by machine learning. This approach could be scaled up to be applied in plant phenotyping.
Why it matches plant phenotyping methodsマルチカラー蛍光画像と熱画像を用いて感染植物の健康・感染状態を分類し、統計モデルとANNを構築・検証しており、植物病害状態の取得・推定手法が中心である。
abstractThis work evaluates the possible application of multicolor fluorescence imaging in combination with thermography for the particular case of zucchini plants affected by soft rot, caused by Dickeya dadantii.