Fluorescence in situ hybridisation (FISH) is a valuable technique for visualising RNA molecules in their native cellular context. Still, its application in plant tissues is often limited by tissue autofluorescence and the lack of optimised protocols. Here, we developed and validated a simple, reproducible FISH workflow to detect Apple scar skin viroid (ASSVd) in cucumber. Systematic optimisation of probe chemistry, tissue selection, and sampling stage significantly improved assay sensitivity and reproducibility. The AZDye594-labelled antisense riboprobe produced higher signal-to-background ratios and lower background fluorescence than fluorescein-labelled probes, enabling reliable detection of ASSVd in vascular-associated tissues. The optimised workflow consistently detected ASSVd in both leaves and stems. High-resolution confocal imaging further revealed predominant nuclear accumulation of ASSVd RNA in infected cells. Together, this study establishes a sensitive and accessible FISH workflow for localisation of ASSVd in cucumber and provides a practical platform for investigating the spatial distribution of viroid and other plant RNA pathogens.
Why it matches plant phenotyping methods植物組織内の病原体RNAの空間局在を可視化・定量可能にするFISHワークフローを開発・検証しており、植物状態の画像取得法が研究の中心である。
abstractHere, we developed and validated a simple, reproducible FISH workflow to detect Apple scar skin viroid (ASSVd) in cucumber.
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.
Monitoring continuous agricultural canopies is fundamentally limited by the geometric constraints and computational bottlenecks of traditional 3D reconstruction. This study presents a 3D volumetric phenotyping pipeline integrating semantic mask generation with 3D Gaussian Splatting (3DGS) to quantify greenhouse cucumber canopy architecture. To drive component-specific optimization, we evaluated custom-trained convolutional networks (YOLO11) against a zero-shot foundation model (SAM3), determining that SAM3 provided the necessary boundary precision for accurate spatial isolation. The optimized 3DGS model outperformed implicit NeRF baselines, preserving fine-scale morphological details at real-time rendering speeds ( ≈ 48 FPS). To enable actionable measurement, a uniform voxelization protocol was applied to the point cloud, successfully neutralizing algorithmic densification bias. This technical framework yielded highly accurate physical geometry, achieving a Root Mean Square Error (RMSE) of ≤ 0.59 cm against in situ leaf measurements. Transitioning to agronomic interpretation, the pipeline was deployed to quantify complex canopy architecture. It mathematically mapped structural congestion zones and provided a temporal validation of a standard pruning intervention, explicitly capturing the geometric increase in lower-canopy porosity and the upward translation of biomass. This framework provides a robust, scale-accurate tool for monitoring plant architecture and guiding dynamic canopy management.
Why it matches plant phenotyping methods植物キャノピーの3D形状を取得・定量化する画像ベースの表現型解析パイプラインを開発し、実測葉寸法で精度検証しているため、方法が研究の中心である。
abstractThis study presents a 3D volumetric phenotyping pipeline integrating semantic mask generation with 3D Gaussian Splatting (3DGS) to quantify greenhouse cucumber canopy architecture.
Plant height is a key 3D phenotypic trait for assessing crop growth, biomass accumulation, and lodging resistance. To overcome the practical limitations of conventional plant height measurement methods, this study proposes a novel vision foundation model-based framework named Depth for Plant Height (Depth4PH) for plant height estimation in agricultural scenes using low-cost monocular RGB imaging. As part of our contributions, a synthetic–real coupled multimodal dataset was constructed by integrating Blender virtual agricultural scenes (Blender VAS) with real field images. Building upon the existing Depth Anything V2 foundation model, we developed a novel module called Transfer-based Agricultural Metric Depth Anything V2 (TAM-Depth V2) for absolute metric depth estimation through parameter-efficient fine-tuning, depth decoder reconstruction, and joint loss optimization. Furthermore, we designed a novel multi-source prompt-based segmentation framework, MSP-SAM2, to generate positive and negative prompts for zero-shot crop instance segmentation. Finally, a new inverse physical plant height estimation algorithm, RANSAC-Per, was introduced to estimate plant height by combining truncated percentile statistics with local RANSAC micro-plane fitting, thereby reducing the effects of depth noise and field microtopographic variation. The result showed that TAM-Depth V2 achieved stable absolute depth estimation, with an RMSE of 0.1162 m and an AbsRel of 4.25%. Compared to the original box-prompted SAM 2, MSP-SAM2 achieved a 4.4% improvement in mIoU, reaching 91.6% and a recall of 93.2%. On a 350-plant multi-crop test set, Depth4PH achieved R 2 = 0.948, RMSE = 12.23 cm, and MAE = 8.82 cm, and MAPE =10.15%, with crop-specific RMSEs ranging from 3.99 cm (cucumber) to 20.73 cm (maize), significantly outperforming the traditional Global-MinMax baseline (which had an RMSE of 22.62 cm). These results indicate that Depth4PH provides a promising foundational pathway for high-throughput crop phenotyping. With future optimization for edge deployment, it holds significant potential to support high-throughput monitoring in precision agriculture.
Why it matches plant phenotyping methods植物高の画像取得・深度推定・セグメンテーション・高さ抽出アルゴリズムを一体化した植物表現型計測フレームワークの開発と検証が中心であり、データセット構築と性能評価も含む。
abstractthis study proposes a novel vision foundation model-based framework named Depth for Plant Height (Depth4PH) for plant height estimation in agricultural scenes using low-cost monocular RGB imaging.
The present study validates a custom multi-sensor system for high-resolution, non-phenotyping. The photogrammetric workflow was optimised by evaluating image density, algorithms, and camera calibration. Beyond validation, a case study demonstrated the system’s capacity to monitor early plant development following biostimulant treatment. Technical assessment revealed that prior internal camera calibration was unnecessary for the optics used. A reduced dataset of 120 images yielded reconstruction accuracies statistically comparable to full 360-image sets ( p > 0.05), confirming potential for maximised throughput efficiency by reducing processing time by approximately 66% without compromising data integrity. Analysis demonstrated that algorithmic settings determined reconstruction accuracy. Active parameter tweaks were essential for maximising surface area precision across all plant species ( R 2 ≥ 0.98). Conversely, volumetric accuracy required deactivation of these tweaks to maintain mesh consistency. While surface area estimation remained robust, volumetric precision showed species-specific variability driven by morphological complexity. Baseline parameters for accurate plant health scaling were established by defining species-specific vegetation index ranges (e.g., 0.38–0.82 for C. sativus ). The platform’s robustness was validated through a longitudinal study evaluating four treatments: yeast autolysate (A), a fungal biostimulant (F), their combination (AF), and a control (C). The system captured distinct morpho-physiological responses, demonstrating that autolysate-based treatments (A and AF) significantly enhanced biomass growth. The developed multi-sensor system recorded surface area expansions of 122% and 102% relative to the control ( p < 0.001), alongside a 110% increase in biological height. Fidelity of these 3D reconstructions was substantiated by a strong correlation ( R 2 = 0.96) between the 3D-derived leaf area index and ground-truth measurements. A key innovation of the pipeline is the integration of vertical distribution metrics as descriptive statistical tools, enabling high-resolution characterisation of canopy architecture. The plant health status metric evidenced enhanced physiological resilience in variants A and AF. Gravimetric analysis corroborated the non-destructive findings, confirming significant increases ( p < 0.001) in dried shoot weight of 77% (A) and 79% (AF). The validated system decoupled structural biomass from physiological health, offering broad utility across diverse phenotyping tasks. Such functionality streamlines the valorisation of industrial by-products into biopreparations, driving progress in sustainable agriculture.
Why it matches plant phenotyping methodsフォトグラメトリとマルチスペクトル計測による植物表現型取得システムの最適化・技術検証が研究の中心であり、植物形態・生理状態の測定性能を評価している。
abstractThe present study validates a custom multi-sensor system for high-resolution, non-phenotyping.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Introduction: Cucumber diseases severely affect yield and quality. Deep learning-based analysis of microscopic pathogen images enables high-throughput identification and counting of pathogens, thereby facilitating early disease detection. However, most existing pathogen-recognition methods focus mainly on qualitative identification and cannot quantitatively characterize pathogen morphology, which limits their ability to reveal the developmental characteristics and functional differentiation of different infection structures from the perspective of pathogen morphology-function adaptability. Methods: To address this issue, this study focused on cucumber powdery mildew and downy mildew and achieved precise extraction and characterization of pathogen morphological features based on microscopic image instance segmentation. First, an in situ stained microscopic image dataset of cucumber pathogens was constructed. Second, an instance segmentation model, SWS-YOLO11n, was developed for cucumber pathogen infection structures to accurately identify and segment different infection structures in microscopic images. Finally, morphological analysis methods were used to quantitatively extract and characterize pathogen infection-structure features. Results: values greater than 0.90. In addition, category-wise morphological distribution analysis showed that different infection-structure types exhibited clear differentiation in size, contour complexity, and elongation. Discussion: This study provides an effective tool for high-throughput phenotyping of cucumber pathogen infection structures. The proposed method offers methodological support for disease diagnosis, pathogen morphological phenotyping, and precision disease management in horticultural production.
Why it matches plant phenotyping methods顕微鏡画像のインスタンスセグメンテーションを開発し、キュウリ病原体の感染構造の形態形質を定量抽出する手法が中心である。
abstractan instance segmentation model, SWS-YOLO11n, was developed for cucumber pathogen infection structures to accurately identify and segment different infection structures in microscopic images.
Commercial greenhouse cucumber production is graded by fruit length, which drives harvest scheduling, labour allocation, and logistics. Manual measurement with thread or caliper is accurate but infeasible at commercial scale. This paper presents CucumberVision, a non-contact length estimation framework using an Intel RealSense D435 RGB-D camera. A YOLO26n instance segmentation model locates cucumbers, and SAM (ViT-B backbone) refines each detection to a pixel-precise mask. Five methods are evaluated under matched conditions: (M1) a dominant-axis skeleton scan-line baseline; (M2) PCA on the bounding-box depth point cloud; (M3) SAM mask with medial-axis skeletonisation; (M4) a hybrid keypoint-guided approach using a YOLO26-pose model predicting five anatomical landmarks (KP0--KP4) with piecewise 3D arc-length; and (M5) a novel medial arc spline method fitting a cubic spline through the 3D medial axis of the SAM mask and computing arc length by trapezoidal integration -- the first such application to elongated vegetable measurement. All methods share five-frame burst depth averaging, colour-stream intrinsic alignment, and adaptive method selection with cascading fallbacks ensuring 100% coverage. A benchmark of 48 captures across seven cucumbers in three size categories (small ~8 cm, medium ~13 cm, large ~25 cm) with thread-based ground truth establishes a significant accuracy hierarchy: M1 (MAPE 9.68%) > M2 (5.31%) > M4 (5.51%) > M3 (5.82%) > M5 (4.13%). M5 significantly outperforms all competitors at Bonferroni-corrected alpha=0.0125. A secondary contribution is identifying a 12--18% length underestimation caused by using depth-stream rather than colour-stream intrinsics after rs.align(rs.stream.color) -- an under-reported error source. The complete system is released open source and runs in real time on a single consumer-grade GPU.
Why it matches plant phenotyping methodsRGB-D画像からキュウリ果実長を推定する手法を開発し、複数手法との比較検証、実測値によるベンチマーク、誤差要因分析まで行っており、植物形質取得が研究の中心です。
abstractThis paper presents CucumberVision, a non-contact length estimation framework using an Intel RealSense D435 RGB-D camera.
Accurate and efficient identification of cucumber leaf diseases is a critical step in preventing losses and facilitating timely intervention in agricultural activi-ties. However, most state-of-the-art plant disease recognition models, including those employing deep learning, often fail to identify spatial dependencies among symptomatic leaf feature regions, require high computational resources, and lack robustness in their predictions. To overcome these challenges, this paper pro-poses MobileGraph, a graph-aided deep learning model that jointly reasons local texture patterns and spatial dependencies among CNN-derived cucumber leaf feature regions using MobileNetV3 as a lightweight feature extractor. Experi-ments on a publicly available cucumber leaf disease dataset containing 5 classes and 4,000 images show that the proposed model achieves 99.75% accuracy, 99.75% macro F1-score, and 99.69% MCC, outperforming several state-of-the-art models including ResNet-152, EfficientNet-B7, DenseNet-201, ConvNeXt, and VGG16, while having a significantly lower computational cost of 0.465 GFLOPs. Explainability results from Grad-CAM and LIME indicate that the model is focused on biologically important regions of plant lesions. Furthermore, a proto-type mobile application illustrates the feasibility of real-time cucumber disease diagnosis for practical agricultural monitoring. These results indicate that Mobi-leGraph provides an efficient and interpretable solution for intelligent crop health surveillance.
Why it matches plant phenotyping methodsキュウリ葉の病斑という植物状態を画像から診断する深層学習手法を開発し、複数モデルとの性能比較・検証まで行っており、病害表現型の取得・推定が研究の中心である。
abstractExplainability results from Grad-CAM and LIME indicate that the model is focused on biologically important regions of plant lesions.
Reproduction assets foundThe paper's experiments use the publicly available Cucumber Disease Recognition Dataset (4,000 images, 5 classes) hosted on Mendeley Data, which is a paper-specific public phenotype/image asset. The MobileGraph source code is only available upon request, so it does not qualify as a public asset.Dataset · publicThe dataset analysed of this study, titled ”Cucumber Disease Recognition
Dataset” is publicly available in the Mendeley Data repository at
(https://data.mendeley.com/datasets/y6d3z6f8z9/1).Open asset ↗Mendeley Data · y6d3z6f8z9pdf-page:36 lines:1-71Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
This study proposes a multivariable quality prediction framework for cucumbers based on hyperspectral imaging, addressing the limitations of single-indicator approaches through chemometric analysis. Experiments were conducted under varying nitrogen levels and growth stages, with principal component analysis identifying nitrate, soluble sugar, and soluble solids as core indicators significantly correlated with nitrogen content. Spectral data underwent preprocessing via SG smoothing, MSC, SNV, and their paired combinations. Feature wavelengths were selected using CARS, UVE, and SPA algorithms, followed by comparative modeling with PLSR, SVR, and CNN approaches. Results demonstrated optimal performance for the CNN model utilizing full-spectrum input, achieving calibration set R 2 values exceeding 0.913 for all three indicators. This model enabled visualization of spatial distribution patterns, revealing spatial heterogeneity in cucumber quality under different nitrogen treatments. The method offers systematic rigor and high accuracy, providing a technical foundation for precision nitrogen management and vegetable quality enhancement.
Why it matches plant phenotyping methodsキュウリの品質形質をハイパースペクトル画像から推定・可視化する手法が研究の中心であり、前処理、波長選択、機械学習モデル比較まで技術的に評価している。
abstractThis study proposes a multivariable quality prediction framework for cucumbers based on hyperspectral imaging
Cucumber is a core cultivated facility vegetable in China. Drought stress at the seedling stage severely inhibits its growth and development. The regulatory mechanism and optimal application concentration of SiO 2 nanoparticles in alleviating drought stress in cucumber seedlings remain unclear. Moreover, traditional manual measurement and classic point cloud segmentation models struggle to achieve high-throughput accurate detection of cucumber seedling phenotypes under drought stress. To address these issues, this study focused on phenotypic detection under drought stress and analysis of the regulatory effects of SiO 2 nanoparticles. An improved compact and low-redundancy segmentation model, DDCANet, was proposed based on PointNet++-SSG. Combined with 3D point cloud technology and the Euclidean clustering algorithm, it enables automatic extraction of phenotypic parameters from cucumber seedlings treated with SiO 2 nanoparticles under drought stress. In this study, Trailing cucumber seedlings were used as experimental materials. 3D point cloud data of cucumber seedlings were collected under treatments with different concentrations of SiO 2 nanoparticles and PEG-simulated drought stress. A dataset containing 70 valid samples was constructed and labeled into two categories: Stem and Leaf. The core optimizations of the DDCANet model are as follows: Firstly, an Adaptive Density-Aware Feature Enhancement (ADFE) module is embedded to accurately capture point cloud density heterogeneity induced by SiO 2 ;Secondly, a Channel Attention and Normalization-enhanced SA Layer (CANL) is designed to strengthen the coupling of local and global drought-related phenotypic features. Thirdly, a Drought-Aware Hybrid Loss (DHL) function is constructed to alleviate the class imbalance of seedling stem and leaf point clouds under drought stress. Results show that the DDCANet model achieves a mean Intersection over Union (mIoU) of 89.01 ± 0.32% and a Stem IoU of 83.6 ± 0.45%, representing improvements of 6.55% and 9.6% respectively compared with the baseline PointNet++-SSG model, and a 30.5% improvement in stem segmentation accuracy compared with the classic PointNet model. It thus enables high-throughput, non-destructive detection of drought phenotypes in cucumber seedlings under SiO 2 nanoparticle treatment. Ablation experiments verified the positive contributions of the ADFE, CANL, and DHL modules. Furthermore, instance segmentation and phenotype extraction were completed using the Euclidean clustering algorithm to analyze the drought-alleviating effects of SiO 2 nanoparticles under PEG-simulated drought stress. Results indicate that a low concentration of 20 mg/L exhibits a weak alleviating effect, medium concentrations of 40-60 mg/L show bidirectional regulatory characteristics, and a high concentration of 100 mg/L causes negative physiological effects. The optimal application concentration is 80 mg/L, which comprehensively improves key phenotypes such as seedling height and volume under drought stress and exerts a positive regulatory effect on seedling growth under drought conditions. The DDCANet model constructed in this study provides an efficient technical tool for the accurate phenotypic detection of crop seedlings treated with SiO 2 nanoparticles under drought stress. It clarifies the optimal application concentration of SiO 2 nanoparticles, offers a precise concentration threshold and theoretical support for the scientific application of SiO 2 nanoparticles in drought-stressed cultivation of protected cucumber, and establishes a novel methodological reference for the research on phenotypic regulation of crops under drought stress via nano-agricultural technology.
Why it matches plant phenotyping methods3D点群分割モデルを開発・検証し、キュウリ幼苗の茎葉分離と形質抽出を自動化することが研究の中心であるため、植物フェノタイピング手法論文として採用。
abstracttraditional manual measurement and classic point cloud segmentation models struggle to achieve high-throughput accurate detection of cucumber seedling phenotypes under drought stress.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Accurate and standardized phenotyping of complex, environmentally sensitive quantitative traits remains a major bottleneck for reliable locus discovery and breeding applications. Here, we established a skeleton-guided 3D digital phenotyping framework that generates standardized digital replicas and enables precise quantification of fruit and plant architecture traits in cucumber. The workflow was applied to a permanent recombinant inbred line (RIL) population (n = 211) evaluated across two seasons (2023-2024), from which nine traits were extracted from 3D models. All 211 RILs were whole-genome resequenced to generate genome-wide SNPs, enabling construction of a high-density linkage map and subsequent QTL mapping, complemented by GWAS for physical anchoring of association signals. Using this integrated design, we identified 29 QTLs across the nine traits and resolved cross-season major-effect loci with consistent genetic signals. Notably, two cross-season loci were detected as novel: FL4.1/FSL4.1 affecting fruit length and fruit stalk length, and NLB1.1/LLB1.1 affecting branching. GWAS further anchored lead variants to physical coordinates and supported cross-season associations. Together, these results demonstrate that standardized 3D phenotyping provides a reproducible and interoperable trait definition framework that supports cross-season locus discovery and downstream marker development for quantitative genetic dissection in cucumber.
Why it matches plant phenotyping methodsキュウリの果実・植物体形態を3Dモデルから定量化する標準化フェノタイピング手法の構築と応用が研究の中心であり、再現可能な形質抽出枠組みとして評価されている。
abstractwe established a skeleton-guided 3D digital phenotyping framework that generates standardized digital replicas and enables precise quantification of fruit and plant architecture traits in cucumber.
Abstract Premise There is a knowledge gap regarding how foliar injury and restricted water uptake can be detected by measuring root dielectric response. This pot study nondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying. Methods Root dielectric properties were recorded on a minute scale in control and glyphosate‐treated maize, cucumber, and pea. Chlorophyll, stomatal conductance, and biomass measurements were taken to interpret the dielectric changes. Results Electrical capacitance and conductance varied diurnally due to the circadian regulation of water uptake and hydraulic conductance. Glyphosate application reduced capacitance, indicating the impeded root growth and activity caused by impaired amino acid synthesis, foliar damage, and restricted transpiration. The dissipation factor decreased in response to glyphosate due to impeded apoplastic water flow, suppressed root lignification, and hampered water absorption. The enhanced leaf and root hydraulic resistance caused by glyphosate was manifested in sharply reduced electrical conductance. Changes in the species’ dielectric response were consistent with physiological symptoms and biomass loss. Discussion Real‐time dielectric measurement proved suitable for the nondestructive monitoring of plant responses to foliar stress through altered root traits. This method could be employed to evaluate herbicide tolerance in crops and to develop and determine dosage of herbicide ingredients.
Why it matches plant phenotyping methods植物の根の誘電特性をリアルタイム・非破壊で測定し、ストレス応答や根形質を評価する方法が研究の中心であるため。
abstractnondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying
Accurate pixel-level delineation of crop disease in greenhouse images is challenging due to weak lesion margins, scale variations, leaf-vein interference, specular highlights and partial occlusion. This paper introduces a boundary-refined DeepLabV3+ model that keeps the encoder-decoder efficiency of the original framework and adds a boundary supervision branch, uncertainty-aware cross-scale fusion, and an adaptive refinement gate. The network is tested on a set of 6,840 curated greenhouse images that include leaves of tomatoes, cucumbers, peppers and eggplants, as well as 8 disease categories and healthy tissue. Under a fixed split by greenhouse compartment, the proposed model has achieved a mean intersection over union of 89.7%, a mean F1 score of 94.8%, and a boundary F1 of 86.9% at a two-pixel tolerance. The corresponding values are 3.8, 2.4 and 7.6 percentage points higher than those of the standard DeepLabV3+. The mean Intersection over Union (IoU) under low-illumination and condensation-blur conditions are 3.1 and 2.7, respectively. Ablation studies show that boundary supervision is responsible for most of the contour improvement, and uncertainty-aware fusion reduces false lesion expansion along veins. The model has 31.6 million parameters and, after mixed-precision optimisation, runs at 18.7 frames per second on an embedded graphics chip. Based on the above results, explicit boundary reasoning can improve the precision of disease-area estimation without sacrificing the efficiency required in practice; it is thus suitable for greenhouse scouting, targeted spraying and longitudinal severity assessment.
Why it matches plant phenotyping methods温室画像から作物病斑の画素レベル境界と病害面積を推定する画像解析手法を開発・検証しており、植物病害状態の表現型取得が中心である。
abstractThis paper introduces a boundary-refined DeepLabV3+ model that keeps the encoder-decoder efficiency of the original framework and adds a boundary supervision branch, uncertainty-aware cross-scale fusion, and an adaptive refinement gate.
Currently operating commercial photovoltaics (PV) systems integrated with agricultural production (Agrivoltaics) offer immense potential for the dual harvest of renewable energy and agro-products. Within controlled-environment agriculture (CEA), the use of semi-transparent photovoltaics (ST-PV) and the ability to control the microclimate and shading are beneficial for the production of high-value crops such as cucumbers. The objective of this research was to commence the cultivation of cucumbers under evolving CEA-PV systems by combining greenhouse experiments with computer vision (CV) based driven phenotyping to create an analytical framework and system control framework for the cultivation of cucumbers in an evolving CEA-PV system. The method involved using the monitored plant vigor to control in real time the irrigation and shading of the cucumber plants. The control of irrigation and shading was based on the monitored plant vigor as determined by a U-Net++ implementation for canopy segmentation, an EfficientNet-B3 implementation for stress detection, and a CNN regressor for growth trait estimation. Within the greenhouse, uniform environmental and fertigation conditions were established to evaluate the effect of four shading regimes (0%, 20%, 40%, 60%) on the cucumbers. Simulated, yet representative results predicted cucumber yields to be stable (within ±4% of full yield) with a 20% shading and a 15-20% reduction in water use compared to full sun. Yield was also observed to drop by 10-14% under higher shading of 40 to 60% due to insufficient photosynthetic activity for fruiting. The CNN based models were robust, (segmentation IoU 0.91, stress-class F1 0.92, LAI regression R²≈0.93), allowing for precise and comprehensive monitoring in an annual non-invasive fashion. The greenhouse's annual photovoltaic (PV) output was estimated to be 1,550 to 1,750 kWh/kWp which is able to exceed the energy demand resulting to a net energy surplus. The outcome demonstrates that the cucumber crop can be successfully combined with controlled environment agrovoltaic systems with moderate shading for optimum cucumber yield. Moreover, informed supervision through Artificial Intelligence (AI) helps to navigate closed-loop systems and enhance the water-use efficiency and yield stability.
Why it matches plant phenotyping methodsCNNによるキャノピー分割、ストレス検出、成長形質推定を中核とする植物フェノタイピングおよび閉ループ制御フレームワークであり、性能指標も報告されているため。
abstractcombining greenhouse experiments with computer vision (CV) based driven phenotyping to create an analytical framework and system control framework for the cultivation of cucumbers
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.
Precision greenhouse agriculture enhances plant health and crop yields by continuously monitoring key plant parameters. Stem diameter is such a parameter and is monitored to support decisions on plant care. However, traditional contact-based methods induce thigmomorphogenic effects that impact plant growth. Here, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement. The OC operates by projecting a collimated light beam to cast a shadow of the stem onto a high-resolution image sensor. The shadow size is a measure for the stem diameter. Controlled laboratory tests show the OC offers an accuracy comparable to that of a Digital Caliper (DC). Field trials on irregular tomato and cucumber stems demonstrate a repeatability of 0.1-0.2 mm. The OC's non-invasive design and high repeatability exceed the performance of a DC, making it particularly suited for accurately monitoring soft, variable plant structures. Bringing the advantage of avoiding thigmomophogenic effects and thus optimizing crop yield, the OC is a promising tool for high-throughput plant phenotyping and precision agriculture applications.
Why it matches plant phenotyping methods植物の茎径を非接触・高精度に測定する光学デバイスを開発し、実験室および圃場で精度・再現性を検証しており、植物表現型取得法が研究の中心です。
abstractHere, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement.
Reproduction assets foundThe paper's measurement data (optical caliper, digital caliper, and micrometer readings on reference cylinders and tomato/cucumber stems) is openly deposited on the SURF data repository of The Hague University of Applied Sciences. No author analysis code or trained models are explicitly deposited; other allowed URLs (DDataset · publicThe data gathered during this study is openly available via https://hhs.data.surf.nl/s/nqnFYBf42KPA75P (accessed on 10 February 2026).Open asset ↗hhs.data.surf.nlhtml-lines:282-314Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Common beanCucumberMaizePeaPotatoTomatoWheatMultispectral / hyperspectralLeafPhysiological trait estimation
The objective of this study was to assess the predictability of leaf dry matter content across a diverse range of plant species using hyperspectral reflectance data. The dataset encompassed leaves from multiple crops, including potatoes, beans, wheat, maize, peas, tomatoes, basil, and cucumbers, collected under varying growth conditions, cultivation systems, seasonal contexts, and developmental stages. As an initial benchmark, commonly used narrow-band spectral indices and their combinations were evaluated, but they exhibited limited predictive performance for dry matter content. Consequently, several full-spectrum machine learning models were trained and compared to assess their individual predictive ability. Given their complementary strengths, these models were integrated into a stacked ensemble framework to enhance overall accuracy. The resulting ensemble, combining the outputs of multiple base learners through a meta-learner, achieved a coefficient of determination of R2=0.896 on an independent test set, outperforming all individual models. The findings highlight the potential of a multi-model stacking approach to improve the accuracy and robustness of leaf biochemical property estimation from hyperspectral data.
Why it matches plant phenotyping methodsハイパースペクトル反射データから葉乾物含量を推定する機械学習手法を開発・比較・検証しており、植物形質の取得方法が研究の中心である。
abstractassess the predictability of leaf dry matter content across a diverse range of plant species using hyperspectral reflectance data
• XGBoost classified healthy and spider mite–infested leaves of cucumber and strawberry • Classification accuracy remained above 70% even with a reduced set of wavelengths • A combined model detected spider mite infestations across two crop species effectively The two-spotted spider mite is a globally significant pest affecting over 150 crop species, including cucumbers and strawberries. Its feeding activity leads to chlorophyll degradation and physiological changes in leaf tissue, which alter spectral reflectance properties and enable image-based detection. In this study, hyperspectral imaging (HSI) under controlled conditions was used to classify healthy and spider mite-infested leaves of cucumber and strawberry plants, including asymptomatic infested leaves. Spectral data were analyzed and classified with three supervised machine learning algorithms built on extreme gradient boosting (XGBoost) models. The study had three objectives: (1) to assess the ability of XGBoost to classify multiple infestation states, (2) to evaluate model performance with a reduced set of effective wavelengths, and (3) to determine whether infestation across both crops can be classified using a single, merged model. Using all wavelengths, results showed that classification accuracy was 93% for cucumber leaves, 84% for strawberry leaves, and 87% when combined. With five most effective wavelengths, classification accuracy reached 70% for cucumber leaves, 65% for strawberry leaves, and 65% for cucumber and strawberry leaves combined. The most effective wavelengths were consistently selected from the red-edge and near-infrared (NIR) spectral regions, which highlights their importance for early detection. To the best of our knowledge, this is the first known study to successfully apply a combined machine learning model for early spider mite detection across two different crop species using hyperspectral data under controlled conditions. The results show the potential of machine learning for multi-crop pest detection and could lay the groundwork for practical, sensor-based tools in precision agriculture.
Why it matches plant phenotyping methodsハイパースペクトル画像とXGBoostにより、植物葉のダニ感染状態を直接推定し、波長削減と作物間モデル性能を評価しているため、病害・害虫状態のフェノタイピング手法が中心である。
titleMulti-crop early detection of spider mite damage using hyperspectral data and XGBoost
Powdery mildew represents a significant threat to cucumber yield, with its infection process encompassing stages such as “attachment, colonisation, and dispersal.” With the advancement of deep learning, computer vision techniques are increasingly applied to study powdery mildew infection patterns. However, existing biological methods are low-throughput, subjective, and struggle to capture the dynamic characteristics of pathogen infection throughout the entire process. Current microscopic image analysis methods also fail to simultaneously recognise and segment various infection structures across different stages of infection, making it difficult to reveal the evolving infection patterns over time. To overcome these limitations, this paper proposes an integrated SR-QC-TA framework for modelling the infection behaviour of cucumber powdery mildew. First, a time-series dataset of microscopic images covering all stages of infection was constructed, systematically documenting the evolution of key infection structures from attachment to dispersal. Second, an instance segmentation algorithm, FS-YOLOv8s, was developed to achieve high-precision, multi-class recognition of pathogen structures in complex backgrounds. Additionally, a multi-dimensional quantitative characterisation method for pathogen infection features was designed, describing infection characteristics in terms of quantity, morphology, and location. Finally, based on these recognition and characterisation results, a temporal analysis framework was established to quantify dynamic changes in infection and reveal the stages of infection behaviour. Experimental results demonstrate that FS-YOLOv8s achieved mAPᵇᵒˣ@0.5 and mAPᵐᵃˢᵏ@0.5 scores of 91.8 % and 92.3 %, respectively, enabling high-precision segmentation across all infection stages. This research advances intelligent monitoring and control of cucumber powdery mildew and drives disease monitoring in horticultural crops toward bioengineering systems.
Why it matches plant phenotyping methodsキュウリうどんこ病の感染状態を顕微鏡画像からインスタンスセグメンテーションで抽出し、感染構造の数量・形態・位置と時間変化を定量化する手法が研究の中心であるため。
abstractan instance segmentation algorithm, FS-YOLOv8s, was developed to achieve high-precision, multi-class recognition of pathogen structures in complex backgrounds.
The study is a design and development of a strong disease detection system of cucumber and grape leaves with noisy image data, focusing on the ability to withstand salt-and-pepper and Gaussian noises. The image datasets used in agriculture are usually affected by noise because of changes in light, sensor defects, and environmental conditions, which may lead to lower diagnostic accuracy. In order to address this, the proposed system incorporates high noise reduction methods whereby a median filter and a Gaussian filter are used to restore the image quality without compromising on the important leaf texture information. After processing, colour, texture, and shape are used to extract features, which are effective in extracting disease-specific visual representations. These fine features are then trained on various optimized machine learning models, such as Light Gradient Boosted Machine (LGBM), Quantum Support Vector Machine (QSVM), a Modified Random Forest (MRF) with adaptive weighted features, and a Multi-SVM classifier with a custom kernel to map nonlinear features. Through experimental analyses, the proposed ensemble framework is shown to be highly accurate, robust, and noise-tolerant as opposed to the traditional frameworks. The hybrid method is effective in recognizing the significant cucumber diseases and grapes, including powdery mildew, downy mildew, and anthracnose, which will be utilized in the noisy agricultural conditions in the real world. In general, this system offers a noise-resistant, reliable, and computationally efficient system to detect early signs of plant diseases, which can be used in sustainable crop monitoring and precision farming.
Why it matches plant phenotyping methods植物葉の画像から病徴を推定する画像処理・特徴抽出・機械学習システムの設計開発が中心であり、植物病害状態のフェノタイピング手法に該当する。
abstractThe study is a design and development of a strong disease detection system of cucumber and grape leaves with noisy image data
Reproduction assets foundThe paper uses two public Kaggle leaf-image datasets (cucumber and grape) as its phenotyping inputs; both are publicly accessible with URLs given in the references. No author code or model checkpoints are reported.Dataset · publicGrape Disease Dataset, which was collected on Kaggle
[17], is an extensive collection of images created for the
classification and analysis of different diseases in grape
leaves.Open asset ↗Kagglepdf-raw-page:7 lines:1-64Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
This study addresses the challenges of early recognition of fruit and vegetable diseases and pests in facility horticultural greenhouses and the difficulty of real-time deployment on edge devices, and proposes a lightweight cross-scale intelligent recognition network, Light-HortiNet, designed to achieve a balance between high accuracy and high efficiency for automated greenhouse pest and disease detection. The method is built upon a lightweight Mobile-Transformer backbone and integrates a cross-scale lightweight attention mechanism, a small-object enhancement branch, and an alternative block distillation strategy, thereby effectively improving robustness and stability under complex illumination, high-humidity environments, and small-scale target scenarios. Systematic experimental evaluations were conducted on a greenhouse pest and disease dataset covering crops such as tomato, cucumber, strawberry, and pepper. The results demonstrate significant advantages in detection performance, with mAP@50 reaching 0.872, mAP@50:95 reaching 0.561, classification accuracy reaching 0.894, precision reaching 0.886, recall reaching 0.879, and F1-score reaching 0.882, substantially outperforming mainstream lightweight models such as YOLOv8n, YOLOv11n, MobileNetV3, and Tiny-DETR. In terms of small-object recognition capability, the model achieved an mAP-small of 0.536 and a recall-small of 0.589, markedly enhancing detection stability for micro pests such as whiteflies and thrips as well as early-stage disease lesions. In addition, real-time inference performance exceeding 20 FPS was achieved on edge platforms such as Jetson Nano, demonstrating favorable deployment adaptability.
Why it matches plant phenotyping methods植物の病変・病害状態を画像から検出する軽量モデルの開発と性能評価が中心であり、単なる生物学的実験の routine 測定ではない。害虫検出も含むが、早期病変検出という植物状態の推定を技術的に評価しているため含める。
abstractproposes a lightweight cross-scale intelligent recognition network, Light-HortiNet, designed to achieve a balance between high accuracy and high efficiency for automated greenhouse pest and disease detection.
Cucumber downy mildew, angular leaf spot, and powdery mildew represent three predominant fungal diseases that substantially compromise cucumber yield and quality. To address the challenges posed by the irregular morphology, prominent multi-scale characteristics, and ambiguous lesion boundaries of cucumber foliar diseases in complex field environments—which often lead to insufficient detection accuracy—along with the existing models’ difficulty in balancing high precision with lightweight deployment, this study presents YOLOv11n-DSU (a lightweight hierarchical detection model engineered using the YOLOv11n architecture). The proposed model integrates three key enhancements: deformable convolution (DEConv) for optimized feature extraction from irregular lesions, a spatial and channel-wise attention (SCSA) mechanism for adaptive feature refinement, and a Unified Intersection over Union (Unified-IoU) loss function to improve localization accuracy. Experimental evaluations demonstrate substantial performance gains, with mean Average Precision at 50% IoU threshold (mAP50) and mAP50–95 increasing by 7.9 and 10.9 percentage points, respectively, and precision and recall improving by 6.1 and 10.0 percentage points. Moreover, the computational complexity is markedly reduced to 5.8 Giga Floating Point Operations (GFLOPs). Successful deployment on an embedded platform confirms the model’s practical viability, exhibiting robust real-time inference capabilities and portability. This work provides an accurate and efficient solution for automated disease grading in field conditions, enabling real-time and precise severity classification, and offers significant potential for advancing precision plant protection and smart agricultural systems.
Why it matches plant phenotyping methodsキュウリ葉の病斑を画像から検出・重症度分類するYOLOモデルを開発し、精度・計算量・組込み実装を評価しており、植物病害表現型の取得・推定が中心です。
abstractthis study presents YOLOv11n-DSU (a lightweight hierarchical detection model engineered using the YOLOv11n architecture).
With the continuous improvement of the intelligence level of facility agriculture, agricultural robots are undertaking more and more autonomous tasks in greenhouse environments, and the multifunctional integration of visual perception systems has become a key technological bottleneck. A perception system for agricultural robots that integrates visual navigation and phenotype recognition is developed to address issues such as path recognition being susceptible to environmental interference and poor real-time plant detection. The system consists of a path navigation module and a plant detection module. The former introduces an image segmentation method based on visual transformation structure to extract agricultural path information. The latter adopts a lightweight instance segmentation structure to achieve precise segmentation and structural localization of crop phenotype regions. In the navigation model test, in the rain and fog disturbance scene, the average delay is 51.0 ms, the frame rate is 44.2 FPS, and the control jitter amplitude is 1.36°. The test results of the detection module show that its boundary F1 values for Tomato, Cucumber, Pepper, and Lettuce crops are 90.2%, 87.6%, 88.8%, and 86.3%, respectively. The experimental results show that the proposed scheme reduces inference delay while ensuring accuracy, has good environmental adaptability and edge deployment potential, and demonstrates good robustness and practicality in complex greenhouse environments.
Why it matches plant phenotyping methods植物の表現型領域を画像分割・認識する手法を開発し、複数作物で精度と実時間性能を評価しており、表現型取得が中心的な技術貢献です。
abstractA perception system for agricultural robots that integrates visual navigation and phenotype recognition is developed
Low temperatures have a significant impact on the growth, development, and productivity of cucumber plants. The potential of near-infrared spectroscopy and the aquaphotomics approach for investigating chilling stress was studied in Voreas F1 and Gergana cultivars. Changes in the spectral patterns of cucumber plants were compared with physiological and metabolic data. Voreas plants were unable to survive seven days of low-temperature stress due to a drastic increase in electrolyte leakage and a decrease in the net photosynthesis rate, stomatal conductance, and transpiration rate. Gergana plants survived chilling by preserving cell membrane integrity and photosynthesis efficiency. During chilling treatment, the content of most metabolites in both cultivars was reduced compared to the controls, yet it was much more pronounced in Voreas. We observed an increased accumulation of cinnamic acid on the seventh day only in the Gergana cultivar. A MicroNIR spectrometer was used for in vivo spectral measurements of cotyledons and the first two leaves. Differences in absorption spectra were observed among control, stressed, and recovered plants, across different days of stress, and between the studied cultivars. The most significant differences were in the 1300-1600 nm range, much smaller for Gergana than Voreas. Aquagrams of the two cultivars also reveal differences in their responses to low temperatures and changes in water molecular structure in the leaves. The errors of prediction for the days of chilling by using PLS models were from 0.96 to 1.14 days for independent validation, depending on the spectral data of different leaves used. Near-infrared spectroscopy and aquaphotomics can be used as additional tools for early detection of stress and investigation of low-temperature tolerance in cucumber cultivars.
Why it matches plant phenotyping methods近赤外分光法とアクアフォトミクスを用いて、キュウリ葉の低温ストレス状態を非破壊的に検出・予測する手法を評価しており、植物表現型取得が中心です。
abstractA MicroNIR spectrometer was used for in vivo spectral measurements of cotyledons and the first two leaves.
Cucumber (Cucumis sativus. L) is economically valuable vegetable crop worldwide. Although cucumber genomic sequence has been completed, the functions of most genes have not yet been characterized. Virus-induced gene silencing (VIGS) is an efficient system for investigating gene function in plants, however, detection of VIGS efficiency by PCR is a time-consuming method. In this study, a vacuum-agroinfiltrated Tobacco ringspot virus (TRSV)-based gene silencing method was developed in cucumber, and CsGLABROUS3 (CsGL3), which functions in initiation of trichome, was cloned into pTRSV2 vector to develop a TRSV-CsGL3 system. Gene silenced cucumbers were visible using trichome as a selected marker, and their glabrous phenotype exhibited throughout the life cycle in TRSV-CsGL3 system. Thereafter, a flower morphogenesis gene (UNUSUAL FLORAL ORGANS, CsUFO) was selected to silence by the TRSV-CsGL3 system, and CsUFO silenced cucumbers produced the flower defect phenotype as expected. In summary, TRSV-CsGL3 is a real-time visualized VIGS system using trichome as a selected marker, which simplify the VIGS identification procedure, and it can be used to investigate gene function throughout the life cycle in cucumber.
Why it matches plant phenotyping methodsキュウリのVIGS効率をトライコーム形態でリアルタイムに可視化・判定する手法の開発が中心であり、植物表現型の取得方法として適格です。
abstracta vacuum-agroinfiltrated Tobacco ringspot virus (TRSV)-based gene silencing method was developed in cucumber
Vegetation indices (VIs) are a widely adopted and straightforward tool for non-contact estimation of chlorophyll and carotenoid content in plant leaves. However, VI-based method accuracy depends critically on instrument configuration and calibration procedures. This study aimed to evaluate the sensitivity of VI-based pigment assessment to variations in spectral channel parameters (central wavelength and bandwidth) as well as to changes in calibration details defined by the specific VI formula. Pigment content was measured in leaves of Lactuca sativa L. and Cucumis sativus L. at contrasting developmental stages using VI-based reflection spectroscopy across the 450-950 nm spectral range with various protocols and spectrophotometry as the reference method. VI values were calculated with varying central wavelength and widths of spectral bands, and across different VI formulas. Comparative analysis of the obtained measurements revealed that even minor shifts in central wavelengths of less than 20 nm or the use of an alternative index formula could lead to relative errors of 42-77% in the estimation of chlorophylls and carotenoids content, while changes in bandwidth had a much smaller impact, resulting in only 2-5% relative errors. Even with identical parameters of spectral channels, the choice of an appropriate VI and its regression model could introduce significant errors, ranging from 36% to 86%. These findings highlight the critical role of instrument specifications and calibration models in the VIs-based method accuracy and stability, as measurement errors can lead to suboptimal agronomic decisions. Moreover, our study underscores that comparing results from different sensors or platforms can be unreliable unless the channel parameters and calibration details are clearly specified. Therefore, standardization and transparency in VIs assignment is vital to ensure reproducibility and cross-compatibility in non-destructive pigment monitoring by using various devices.
Why it matches plant phenotyping methods葉の色素量を推定する反射分光・植生指数法について、波長帯、帯域幅、校正モデルによる精度と再現性を体系的に評価しており、植物表現型取得法の技術検証が中心です。
abstractThis study aimed to evaluate the sensitivity of VI-based pigment assessment to variations in spectral channel parameters (central wavelength and bandwidth) as well as to changes in calibration details defined by the specific VI formula.
CucumberGreenhouseRGB / grayscaleFruitPhysiological trait estimationWater status / transpiration
Water loss during storage is a major cause of postharvest quality deterioration in cucumber, yet existing methods to monitor hydration are often destructive or require expensive instrumentation. We developed a low-cost, non-destructive approach for estimating fruit relative water content (RWC) using visible-light color imaging combined with an ensemble machine-learning model (Random Forest). A total of 1200 fruits were greenhouse-grown, harvested at market maturity, and equally divided between optimal and ambient storage temperature (10 and 25 °C, respectively). Digital images were acquired at harvest and at 7 d intervals during storage, and color parameters from four standard color systems (RGB, CMYK, CIELAB, HSV) were extracted separately for the neck, mid, and blossom regions as well as for the whole fruit. During storage, fruit RWC decreased from 100% (fully hydrated condition) to 15.3%, providing a broad dynamic range for assessing color–hydration relationships. Among the 16 color features evaluated, the mean cyan component (μC) of the CMYK space showed the strongest relationship with measured RWC (R2 up to 0.70 for whole-fruit averages), reflecting the cyan region’s heightened sensitivity to dehydration-induced changes in pigments, cuticle properties and surface scattering. The Random Forest regression model trained on these features achieved a higher predictive accuracy (R2 = 0.89). Predictive accuracy was also consistently higher when μC was calculated over the entire fruit surface rather than for individual anatomical regions, indicating that whole-fruit color information provides a more robust hydration signal than region-specific measurements. Our findings demonstrate that simple visible-range imaging coupled with ensemble learning can provide a cost-effective, non-invasive tool for monitoring postharvest hydration of cucumber fruit, with direct applications in quality control, shelf-life prediction and waste reduction across the fresh-produce supply chain.
Why it matches plant phenotyping methodsキュウリ果実の相対含水量という植物状態を、可視光画像と機械学習で非破壊推定する手法を開発しており、表現型取得・推定が研究の中心である。
abstractWe developed a low-cost, non-destructive approach for estimating fruit relative water content (RWC) using visible-light color imaging combined with an ensemble machine-learning model (Random Forest).
Introduction The rapid growth of the global population and intensive agricultural activities has posed serious environmental challenges. In response, there is an increasing demand for sustainable agricultural solutions that ensure efficient resource utilization while maintaining ecological balance. Among these, intercropping has gained prominence as a viable method, promoting enhanced land use efficiency and fostering environment for crop development. However, disease management in intercropping systems remains complex due to the potential for cross-infection and overlapping disease symptoms among crops. Early and precise illness recognition is, therefore, critical for sustaining crop condition and efficiency. Methods This study introduces an intelligent intercropping framework for early leaf disease detection, utilizing hyperspectral imaging and hybrid deep learning models for precision agriculture. Hyperspectral imaging captures intricate biochemical and structural variations in crops like maize, soybean, pea, and cucumber-subtle markers of disease that are otherwise imperceptible. These images enable accurate identification of diseases such as rust, leaf spot, and complex co-infections. To refine disease region segmentation and improve detection accuracy, the proposed model employs the synergistic swarm optimization (SSO) algorithm. A phase attention fusion network (PANet) is utilized for deep feature extraction, minimizing false detection rates. Furthermore, a dual-stage Kepler optimization (DSKO) algorithm addresses the challenge of high-dimensional data by choosing the most applicable landscapes. The disease classification is performed using a random deep convolutional neural network (R-DCNN). Results and discussion Experimental evaluations were conducted using publicly available hyperspectral datasets for maize-soybean and pea-cucumber intercropping systems. The suggested ideal attained remarkable organization accuracies of 99.676% and 99.538% for the respective intercropping systems, demonstrating its potential as a robust, non-invasive tool for smart, sustainable agriculture.
Why it matches plant phenotyping methods植物の葉の病徴をハイパースペクトル画像から検出・分類する画像解析手法が研究の中心であり、病害状態という植物表現型を直接推定しているため含める。
abstractThis study introduces an intelligent intercropping framework for early leaf disease detection, utilizing hyperspectral imaging and hybrid deep learning models for precision agriculture.
Solar greenhouses are energy-efficient facilities for year-round crop production. To better understand the effects of shading caused by the insulation quilts on the microclimate and canopy light interception, this study systematically investigated summer cucumber production inside an insulated plastic greenhouse in Beijing, China. Monitoring experiments were conducted across the seedling, flowering, fruiting, and maturity stages, and were combined with 3D simulation modeling. Specifically, the radiation transmittance of greenhouse roof was analyzed at different solar altitude angles. A functional-structural plant model (FSPM) was employed to evaluate the spatiotemporal distribution of light within the greenhouse. Shaded areas resulting from the insulation quilts were calculated, and the daily light integral (DLI) on the canopy under various shading patterns was quantified. Results showed that insulation quilt shading reduced indoor temperature by an average of 4.2 °C and increased relative humidity by 7.5 %, but also caused a significant reduction in canopy light availability. The average DLI during the seedling, flowering, fruiting, and maturity stages was merely 9.1, 10.8, 16.8 and 20.8 mol/m²/d, respectively, which was significantly lower than the commonly recommended range of 20–30 mol/m²/d for optimal growth. Considering the combined effects of temperature, humidity and photosynthetic requirements, supplementary lighting at night is necessary to ensure optimal crop development. The modeling framework proposed in this study represents an initial step toward quantitatively evaluating shading effects in greenhouse. This approach is not constrained by geographic location and can be directly applied to other greenhouse types and crop species.
Why it matches plant phenotyping methods温室内のキャノピー光環境・光遮蔽を3Dモデルで定量化する再利用可能な計算手法が中心であり、植物キャノピーの光利用状態を推定しているため。
abstractA functional-structural plant model (FSPM) was employed to evaluate the spatiotemporal distribution of light within the greenhouse.
This article is coined for investigating the Village Plant dataset. Many researchers worldwide, carrying out their research in the domain of agriculture, are dependent on this open source dataset. A plant is vulnerable to several infirmities during its period of growth. Detection of the plant’s ill health and monitoring the environmental parameters is the most challenging task in agriculture. Plant disease epidemic may have a significant effect on crop production, reducing the country’s wealth. Early diagnosis of the occurrence of ill health in plants and the remedies are feasible using Artificial Intelligence (AI). Currently, methods like Deep Learning (DL) algorithms, machine vision techniques, and robotics play an important role in monitoring plant diseases and the growth status. This dataset contains multi-fold in-information about the plants. They include the normal and diseased images of plants like Bell Pepper, Tomato, Cucumber, and Potato. An Internet of Things (IoT) based plant data collection and integration system will provide data for this research, which optimizes the feature set through Ant Colony Optimization (ACO) for improving prediction in feature selection using deep learning models like DenseNet, ResNet 50, VGG 19, and Long Short-Term Memory (LSTM) networks, which in turn enhances plant productivity with advances in AI-driven agricultural diagnostics for plant stress prediction.
Why it matches plant phenotyping methods植物の正常・罹病画像から植物の病気・ストレス状態を推定する画像解析ワークフローとデータセット利用が研究の中心であり、植物状態のフェノタイピング手法に該当する。
abstractThis dataset contains multi-fold in-information about the plants. They include the normal and diseased images of plants like Bell Pepper, Tomato, Cucumber, and Potato.
Detecting heavy metals in plants is highly important for diagnosing plant health and understanding the stress mechanisms induced by heavy metals. However, the minimally invasive detection of heavy metals in plants remains a challenge. A novel paper-based sap enrichment device (PBSED), combined with laser-induced breakdown spectroscopy (LIBS) was proposed for the minimally invasive detection of Cd(Ⅱ) and Pb(Ⅱ) in plants. The PBSED included a stainless-steel capillary and heavy metal ion enrichment filter paper (HMIE-FP). The stainless-steel capillary was inserted into the plant stem, where plant sap was transported onto the paper substrate through capillary action. The heavy metal ions (HMIs) in the plants were enriched on the HMIE-FP, and LIBS was used to detect Cd(Ⅱ) and Pb(Ⅱ) on the HMIE-FP to determine the Cd(Ⅱ) and Pb(Ⅱ) concentration within the plant. COMSOL simulations were employed to analyse the flow dynamics of plant sap within the PBSED. To increase the heavy metal enrichment amount, the HMIE-FP was modified with AuAg bimetallic nanoparticles (AuAgBNPs). The PBSED–LIBS method was applied to detect Cd(Ⅱ) and Pb(Ⅱ) in cucumber plants, and the results were strongly correlated with the inductively coupled plasma mass spectrometry (ICP–MS) results (R² = 0.99 for Cd(Ⅱ) and 0.96 for Pb(Ⅱ)). The proposed PBSED–LIBS method demonstrated high sensitivity and minimal invasiveness; thus, it is suitable for rapid, in vivo detection of HMIs in plants. These findings provide valuable insights for the development of efficient, nondestructive tools for environmental applications.
Why it matches plant phenotyping methods植物体内の重金属濃度という状態を、PBSEDとLIBSで低侵襲・in vivoに測定する手法を開発し、ICP-MSとの相関で検証しており、フェノタイピング手法が中心である。
abstractA novel paper-based sap enrichment device (PBSED), combined with laser-induced breakdown spectroscopy (LIBS) was proposed for the minimally invasive detection of Cd(Ⅱ) and Pb(Ⅱ) in plants.
To address the issues of low efficiency and large errors in traditional manual cucumber fruit phenotyping methods, this paper proposes the application of keypoint detection technology for cucumber phenotyping and designs an improved lightweight model called YOLO11m-SCFPose. Based on YOLO11m-pose, the original backbone network is replaced with the lightweight StarNet-S1 backbone, reducing model complexity. Additionally, an improved C3K2_PartialConv neck module is used to enhance information interaction and fusion among multi-scale features while maintaining computational efficiency. The Focaler-IoU loss function is employed to improve keypoint localization accuracy. Results show that the improved model achieves an mAP50-95 of 0.924, with a floating-point operation count (GFLOPs) of 32.1, and reduces the model size to 1.229 × 107 parameters. This model demonstrates better computational efficiency and lower resource consumption, providing an effective lightweight solution for crop phenotypic analysis.
Why it matches plant phenotyping methodsキュウリ果実の表現型取得を目的に、キーポイント検出モデルを設計・改良し、精度と計算効率を評価しているため、植物フェノタイピング手法が中心である。
abstractthis paper proposes the application of keypoint detection technology for cucumber phenotyping and designs an improved lightweight model called YOLO11m-SCFPose.
In agricultural scenarios, the images obtained are often affected by factors such as weather and environmental conditions, which can introduce varying levels of noise to the images. This requires computer vision models to possess a degree of robustness. Generally, model with strong robustness comes with higher computational complexity and model size, which place greater demands on hardware computing resources during deployment. Hence, this research proposes PConv BiFormer (PCBT) based on the BiFormer architecture for the purpose of identifying crop diseases. Prior to inputting images into the network, an additional convolutional layer is introduced to use feature maps generated through convolutional operations as the model’s input. Furthermore, the Depthwise Convolution in BiFormer is replaced with Partial Convolution (PConv) to encode relative positional information. The Convolutional Gated Linear Unit was introduced as the model’s channel mixer to filter global information, aiming to enhance the model’s robustness. PCBT-small has classification accuracies of 0.998, 0.878, and 0.919 on three datasets with computational load of 2.16G FLOPs and had a parameter count of 10.20M. PCBT maintains accuracy of above 0.711 even when detecting photos with various random noise. Compared to Biformer, PCBT reduced the parameter count by 22.4%. Additionally, it achieved improvements in recognition accuracy on the noiseless validation sets for cucumber, banana, and grape by 3.2%, 10.8%, and 1.5%, respectively. On the validation sets with 0-200 random pixel noise, the recognition accuracy also increased by 13.2%, 7.8%, and 17.3%, respectively. When compared to other lightweight models mentioned in the experiments, such as MobileNet, EfficientNet, and MobileFormer, PCBT demonstrates superior robustness. Furthermore, in comparison to more robust models like Swin Transformer, ConvNeXtV2, and DeepViT, PCBT not only maintains excellent robustness but also has fewer model parameters and lower FLOPs. Our proposed model aligns better with the practical requirements of agricultural applications.
Why it matches plant phenotyping methods作物画像から病害を識別する新規Transformerモデルを開発し、複数データセットおよびノイズ条件で精度・頑健性を比較検証しており、植物病害状態の画像ベース表現型取得が中心である。
titlePartial convolutional biformer: A transformer architecture for diagnosing crop diseases under complex backgrounds
This study explores the development and evaluation of high-throughput 3D plant reconstruction and 2D feature detection and segmentation methods for plant phenotyping. A robotic system was employed to collect datasets of individual cucumber plants, utilizing automated mechanisms for efficient, high-throughput data acquisition. Three types of 3D reconstruction methods called Instant-NGP, Nerfacto, and 3D Gaussian Splatting were adopted and compared in terms of rendering quality and speed.Among them, 3D Gaussian Splatting performed the best, achieving PSNR: 25, SSIM: 0.84, LPIPS: 0.20, and also an impressive rendering speed of 6.39 FPS. Novel viewpoint renderings and depth maps further demonstrated its ability to generate accurate and photo-realistic representations of plants. Additionally, rendered images were utilized for training YOLO models to segment plant features into two classes: leaf and fruit. The YOLOv11s model achieved the highest F1 Score (0.932), balancing speed and accuracy. Ultra-view renderings and segmentation provided valuable insights into plant morphology, including leaf and fruit counts, paving the way for scalable, automated phenotyping applications. This study highlights the potential of integrating 3D Gaussian Splatting with advanced segmentation models for precise and efficient plant phenotyping.
Why it matches plant phenotyping methods植物の高スループット3D再構成、画像セグメンテーション、ロボット計測を開発・比較評価し、葉・果実数や形態の抽出に用いており、植物フェノタイピング手法が中心である。
abstractThis study explores the development and evaluation of high-throughput 3D plant reconstruction and 2D feature detection and segmentation methods for plant phenotyping.
This paper introduces a novel fuzzy deep convolutional neural network architecture for cucumber plant disease detection, contributing to precision farming in Industry 5.0. The proposed architecture incorporates 40 convolutional layers, 4 pooling layers, 4 inverted bottleneck blocks, 4 bottleneck blocks, 5 fuzzy layers, and a fully connected layer designed to enhance accuracy and stability when analyzing remotely sensed data. The fuzzy optimistic formula is used for activation in four blocks, enabling effective information fusion. At the same time, the ReLU transfer function ensures robustness, mainly when dealing with noisy or incomplete image segments. Feature vector optimization is performed using a chaotic particle swarm algorithm, enhancing the model’s overall accuracy, reliability, and ease of implementation. The architecture achieves 98% classification accuracy, outperforming leading models like VGG-19, DarkNet-19, and ResNet-50. Moreover, the computational time per run (40–90 s) is significantly lower than these models, which use higher learnable parameters (5.7 million). The proposed approach is efficient and feasible, offering a more stable and accurate disease detection system while utilizing fewer resources. This work demonstrates the potential of AI-driven solutions in agriculture, particularly in improving disease detection and crop yield through advanced machine learning techniques.
Why it matches plant phenotyping methodsキュウリ植物の病害を画像から検出・分類する深層学習手法を開発・評価しており、感染植物の状態を推定する方法が研究の中心である。
abstractThis paper introduces a novel fuzzy deep convolutional neural network architecture for cucumber plant disease detection
Reproduction assets foundThe paper's cucumber leaf disease image dataset is publicly available on Kaggle (base dataset), though the authors note two additional classes are only available upon request. No author analysis code, trained models, or other paper-specific assets are disclosed.Dataset · publicdy was funded by the National Natural Science Foundation of China (nos. 71762010) and Hainan Provincial
Natural Science Foundation of China (nos. 621RC1059).
Data availability
We used Kaggle dataset for our experiments and additionally added two more classes, which are available upon request
from the corresponding author. Link: https://www.kaggle.com/datasets/kaushigihanml/cucumber-leaf-disease-dataset.Declarations
Ethics approval and consent to participate
There are no ethical implications regarding the public dataset.
Consent for publication
There are no ethical implications regarding the public dataset.
Competing interests
The authors declare no competing interests.
Received: 8 January 20Open asset ↗Kaggle · kaushigihanml/cucumber-leaf-disease-datasetpdf-raw-page:20 lines:1-44Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Smart agriculture and environmental monitoring claim innovative wearable sensing technologies suitable for real-time, in-situ biochemical analysis for non-specialized users in plants. Current strategies measure physical parameters, ions or hormones by amperometry or potentiometry. Among these, plant hormones serve as stress biomarkers due to their role in stress response mechanisms. While electrocatalysis has been explored for their detection, early-stage stress monitoring at low concentrations demands higher selectivity and specificity. Therefore, new strategies integrating biorecognition elements, such as antibodies, with autonomous sample collection and bioassay performance are required. In this regard, this work proposes a novel wearable immunosensor device based on an electrochemical lateral flow assay (eLF) that includes an autonomous microsampling technology for minimally invasive in-situ sap extraction and abscisic acid (ABA) detection. This sap device collects, processes and analyzes plant sap with low sample volume (<10 μL) and short assay time (9min) using immunosensing for the first time in ABA wearable detection. Validation in drought-stressed cucumber plants demonstrated 78 % sensitivity and 71 % specificity in detecting subtle water stress with 77 % accuracy. These findings highlight the potential of this plant-wearable biosensor for early stress detection and its versatility to be adapted for the detection of other relevant molecules (proteins or DNA), key for smart agriculture and environmental monitoring.
Why it matches plant phenotyping methods植物体内のABAを低侵襲に採取・測定するウェアラブル電気化学センサーを開発し、乾燥ストレス検出性能を検証しており、植物状態の取得方法が中心である。
abstractthis work proposes a novel wearable immunosensor device based on an electrochemical lateral flow assay (eLF) that includes an autonomous microsampling technology for minimally invasive in-situ sap extraction and abscisic acid (ABA) detection.
CucumberTomatoFruitLeafStem / branchGrowth / time-series analysisGrowth / development / phenology
Wearable electronics have been applied to plants for various applications, including microclimate detection, health diagnosis, and growth rate measurement. However, previously reported plant growth strain sensors have limitations in the strain sensing range, optical transparency, and uncertain stability and reproducibility. Our recent work reported a transparent, conjugated polymer-based strain sensor that achieved above 400% operating strain in measurements of growth in a grass. In this work, we develop second-generation plant strain sensors to broaden their application scope in plant growth monitoring by (1) imparting photostability through device engineering and (2) boosting stretchability through direct ink writing. We first fabricate a strain sensor using room-temperature-cured Au-C-Al electrodes, which drastically improve sensor stability under direct light illumination. The sensors are successfully applied to leaves and stems of tomatoes as well as cotyledons and fruits of cucumbers to track the elongation or radial growth rate in day/night cycles. Notably, the cucumber cotyledon is so far the youngest plant organ (3 days after germination) on which a strain sensor has been used for growth monitoring. Moreover, we attain a significantly improved strain sensing range by patterning and encapsulating strain sensors using direct ink writing. An "elastic rope" model is proposed, which revealed that the shape and contour length of patterned sensors jointly determine the strain sensing range along with the intrinsic stretchability of the material. The most stretchable long-horseshoe pattern reaches a maximum apparent operating nominal strain of 1000% tested on grass leaves, a new record in strain sensors applied to plant growth monitoring.
Why it matches plant phenotyping methods植物の伸長・肥大成長を測定するウェアラブルひずみセンサーを開発し、複数の植物器官で性能と適用性を実証しており、植物フェノタイピング手法が中心である。
abstractIn this work, we develop second-generation plant strain sensors to broaden their application scope in plant growth monitoring
Detecting heavy metals in plants is highly important for diagnosing plant health and understanding the stress mechanisms induced by heavy metals. However, the minimally invasive detection of heavy metals in plants remains a challenge. A novel paper-based sap enrichment device (PBSED), combined with laser-induced breakdown spectroscopy (LIBS) was proposed for the minimally invasive detection of Cd(Ⅱ) and Pb(Ⅱ) in plants. The PBSED included a stainless-steel capillary and heavy metal ion enrichment filter paper (HMIE-FP). The stainless-steel capillary was inserted into the plant stem, where plant sap was transported onto the paper substrate through capillary action. The heavy metal ions (HMIs) in the plants were enriched on the HMIE-FP, and LIBS was used to detect Cd(Ⅱ) and Pb(Ⅱ) on the HMIE-FP to determine the Cd(Ⅱ) and Pb(Ⅱ) concentration within the plant. COMSOL simulations were employed to analyse the flow dynamics of plant sap within the PBSED. To increase the heavy metal enrichment amount, the HMIE-FP was modified with AuAg bimetallic nanoparticles (AuAgBNPs). The PBSED-LIBS method was applied to detect Cd(Ⅱ) and Pb(Ⅱ) in cucumber plants, and the results were strongly correlated with the inductively coupled plasma mass spectrometry (ICP-MS) results (R² = 0.99 for Cd(Ⅱ) and 0.96 for Pb(Ⅱ)). The proposed PBSED-LIBS method demonstrated high sensitivity and minimal invasiveness; thus, it is suitable for rapid, in vivo detection of HMIs in plants. These findings provide valuable insights for the development of efficient, nondestructive tools for environmental applications.
Why it matches plant phenotyping methods植物体内の重金属濃度という状態を、PBSED-LIBSで低侵襲・迅速に取得する手法を開発し、ICP-MSとの相関で検証しており、植物フェノタイピング手法が中心である。
abstractA novel paper-based sap enrichment device (PBSED), combined with laser-induced breakdown spectroscopy (LIBS) was proposed for the minimally invasive detection of Cd(Ⅱ) and Pb(Ⅱ) in plants.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Accurate diagnosis of crop nutritional status is critical for optimizing yield and quality in modern agriculture. This study enhances the accuracy of Raman spectroscopy-based nutrient diagnosis, improving its application in precision agriculture. We propose a method to identify optimal diagnostic positions on cucumber leaves for early detection of nitrogen (N), phosphorus (P), and potassium (K) deficiencies, thereby providing a robust scientific basis for high-throughput phenotyping using Raman spectroscopy (RS). Using a dot-matrix approach, we collected RS data across different leaf positions and explored the selection of diagnostic positions through spectral cosine similarity analysis. These results provide critical insights for developing rapid, non-destructive methods for nutrient stress monitoring in crops. Results show that spectral similarity across positions exhibits higher instability during the early developmental stages of leaves or under short-term (24 h) nutrient stress, with significant differences in the stability of spectral data among treatment groups. However, visual analysis of the spatial distribution of positions with lower similarity values reveals consistent spectral similarity distribution patterns across different treatment groups, with the lower similarity values predominantly observed at the leaf margins, near the main veins, and at the leaf base. Excluding low-similarity data significantly improved model performance for early (24 h) nutrient deficiency diagnosis, resulting in higher precision, recall, and F1 scores. Based on these results, the efficacy of the proposed method for selecting diagnostic positions has been validated. It is recommended to avoid collecting RS data from areas near the leaf margins, main veins, and the leaf base when diagnosing early nutrient deficiencies in plants to enhance diagnostic accuracy.
Why it matches plant phenotyping methodsキュウリ葉の栄養欠乏をRamanスペクトルで診断する際の最適測定位置選択法を開発・検証しており、植物状態の取得精度向上が中心的な方法論的貢献である。
abstractWe propose a method to identify optimal diagnostic positions on cucumber leaves for early detection of nitrogen (N), phosphorus (P), and potassium (K) deficiencies
Cucumbers play a significant role as a greenhouse crop globally. In numerous countries, they are fundamental to dietary practices, contributing significantly to the nutritional patterns of various populations. Due to unfavorable environmental conditions, they are highly vulnerable to various diseases. Therefore the accurate detection of cucumber diseases is essential for maintaining crop quality and ensuring food security. Traditional methods, reliant on human inspection, are prone to errors, especially in the early stages of disease progression. Based on a VGG19 architecture, this paper uses an innovative transfer learning approach for detecting and classifying cucumber diseases, showing the applicability of artificial intelligence in this area. The model effectively distinguishes between healthy and diseased cucumber images, including Anthracnose, Bacterial Wilt, Belly Rot, Downy Mildew, Fresh Cucumber, Fresh Leaf, Pythium Fruit Rot, and Gummy Stem Blight. Using this novel approach, a balanced accuracy of 97.66% on unseen test data is achieved, compared to a balanced accuracy of 93.87% obtained with the conventional transfer learning approach, where fine-tuning is employed. This result sets a new benchmark within the dataset, highlighting the potential of deep learning techniques in agricultural disease detection. By enabling early disease diagnosis and informed agricultural management, this research contributes to enhancing crop productivity and sustainability.
Why it matches plant phenotyping methodsキュウリ画像から健全・罹病状態を深層学習で分類する手法が研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。未見データで性能評価も行っている。
abstractBased on a VGG19 architecture, this paper uses an innovative transfer learning approach for detecting and classifying cucumber diseases
Reconstructing 3D architecture of cucumber populations for multi-scale phenotypic analysis poses significant challenges in greenhouse crop research. Cucumber canopy architecture directly impacts light interception and the plant growth conditions. Terrestrial Laser Scanning (TLS) was employed to capture the 3D point cloud of cucumber plants at various growth stages, named as real plant canopy (RPC). A novel method, CP-FEC-RG, combining Fast Euclidean Clustering with Region Growing algorithm, was developed to segment cucumber plants and extract phenotypic traits both at plant and leaf scales. The virtual plant canopies (VPCs), namely VPC-H, VPC-M and VPC-L were constructed representing high, medium, and low growth potentials based on the data collected via TLS. A radiative transfer model was adopted to compare the radiation interception capabilities of both RPC and VPCs. An average recall rate of 92.2% was achieved for leaf segmentation. Growth differences were observed among the segmented individual plants and leaves, with coefficients of variations for phenotypic traits ranging from 0.13 to 0.48 for individual plants and from 0.21 to 0.54 for leaves. For daily cumulative light interception, VPC-L showed a reduction of 17.1% compared to RPC, whereas VPC-M and VPC-H exhibited increases of 18.2% and 30.1%, respectively. These findings highlight the importance of using the RPC for the accurate calculations of light interception and provide a solid foundation for applying TLS in the 3D phenotypic analysis of crops in solar greenhouses.
Why it matches plant phenotyping methodsTLSによる3D形状取得、植物・葉の分割、表現型形質抽出手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractA novel method, CP-FEC-RG, combining Fast Euclidean Clustering with Region Growing algorithm, was developed to segment cucumber plants and extract phenotypic traits both at plant and leaf scales.
It is difficult and costly to obtain large-scale, labeled crop disease data in the field of agriculture. How to use small samples of unlabeled data for feature learning has become an urgent problem that needs to be solved. The emergence of self-supervised contrastive learning methods and self-supervised mask learning methods can solve the problem of missing labels on the training data. However, each of these paradigms comes with its own advantages and drawbacks. At the same time, the features learned by dataset in a single modality are limited, ignoring the correlation with other modal information. Hence, this paper introduced an effective framework for multimodal self-supervised learning, denoted as MMSSL, to address the task of identifying cucumber diseases with small sample sizes. Integrating image self-supervised mask learning, image self-supervised contrastive learning, and multimodal image-text contrastive learning, the model can not only learn disease feature information from different modalities, but also capture global and local disease feature information. Simultaneously, the mask learning branch was enhanced by introducing a prompt learning module based on a cross-attention network. This module aided in approximately locating the masked regions in the image data in advance, facilitating the decoder in making accurate decoding predictions. Experimental results demonstrate that the proposed method achieves a 95% accuracy in cucumber disease identification in the absence of labels. The approach effectively uncovers high-level semantic features within multimodal small-sample cucumber disease data. GradCAM is also employed for visual analysis to further understand the decision-making process of the model in disease identification. In conclusion, the proposed method in this paper is advantageous for enhancing the classification accuracy of small-sample cucumber data in a multimodal, unlabeled context, demonstrating good generalization performance.
Why it matches plant phenotyping methodsキュウリ病害の画像から植物の病徴状態を推定するマルチモーダル自己教師あり学習法を開発・評価しており、病害識別という植物フェノタイプ推定が中心的な技術貢献である。
abstractthis paper introduced an effective framework for multimodal self-supervised learning, denoted as MMSSL, to address the task of identifying cucumber diseases with small sample sizes.
Due to the small and irregular shapes of vegetable seeds, modeling them is challenging, and the imprecision of physical parameters hinders the performance of vegetable seeders, impeding simulation development. In this study, seeds of cucumber, pepper, and tomato were seen as examples. A 3D point cloud reconstruction method based on Structure-from-Motion Multi-View Stereo (SfM-MVS) was employed to accurately extract 3D models of small and irregularly shaped seeds. Corresponding discrete element models were established. Combining physical and simulation experiments on seed angle of repose(AOR), significant parameters influencing seed AOR and their ranges were identified through Plackett-Burman Design (PBD) and steepest ascent test. Within this range, the GA-BP-GA algorithm was used to accurately inverse the optimal parameter combination. The results indicate that the SfM-MVS 3D point cloud reconstruction method can extract more detailed shape information of small and irregularly shaped seeds. The GA-BP-GA algorithm achieved an inversion of physical parameters with the smallest relative error of cucumber, pepper, and tomato seeds being 0.26%, 0.98%, and 0.51%, respectively. Through experimental comparative analysis, the feasibility and accuracy of this method in calibrating discrete element parameters for small and irregularly shaped seeds were validated. The established seed models and calibrated parameters in this study can be implemented to the simulation optimization design of vegetable seeders, enhancing development efficiency and operational performance.
Why it matches plant phenotyping methodsSfM-MVSによる種子の3D形状取得と、形状情報を用いたDEMパラメータ校正が中心的な技術貢献であり、種子という植物器官の形態計測を扱っている。
abstractA 3D point cloud reconstruction method based on Structure-from-Motion Multi-View Stereo (SfM-MVS) was employed to accurately extract 3D models of small and irregularly shaped seeds.
Measurement of internode elongation just below the shoot apex or growing point of the main stem is important for assessing plant growth. However, it is difficult to directly measure internode elongation on climbing plants with many leaves, such as cucumber plants. It is also difficult to measure the stem length of tall leafy plants in the field, and is prone to measurement errors. In addition, touching plants to measure them can stress them. Here, we measured internodal growth just below the shoot apex by using a 3D point cloud model reconstructed using Structure from Motion and Multi-View Stereo (SfM/MVS) methods under greenhouse conditions. The SfM/MVS method could nondestructively measure the internode elongation of multiple plants accurately and simultaneously with a root mean square error of 3.1 mm. Elongation was most active in the top two internodes and ceased in older internodes. Average elongation lengths of internodes 1 and 2 as counted from the top (6.7-7.7 mm day-1) were significantly greater than that of internode 3 (3.37 mm day-1), which was significantly greater than those of internodes 4 to 6 (0.0-0.5 mm day-1). These growth rates of two top internodes are the indicator of plant growth, which can be used for plant diagnosis. Our quantitative method for assessing internode elongation can be used under normal greenhouse conditions. Traditional 2D measurements face challenges due to occlusion, which this 3D method overcomes by digitally removing leaves for clear node visibility. 3D measurements enable time series analysis of internode elongation, which is difficult to measure in situ. The 3D data can be stored for later reanalysis.
Why it matches plant phenotyping methodsSfM/MVSによるキュウリの節間伸長を非破壊・3D・同時測定する方法を開発し、精度（RMSE）を検証している。植物形質の取得法が研究の中心である。
abstractHere, we measured internodal growth just below the shoot apex by using a 3D point cloud model reconstructed using Structure from Motion and Multi-View Stereo (SfM/MVS) methods under greenhouse conditions.
Quantitative analysis for plant growth attributes has gained prominence in plant science and agriculture. Despite the availability of automated phenotyping systems as a solution to labor-intensive manual measurement techniques, these systems often require specialized knowledge and face challenges in scaling for high-throughput applications. This research introduces a scalable high-throughput plant phenotyping technique utilizing a Pan Tilt Zoom (PTZ) camera. The primary objective is to assess the application of a PTZ camera in a plant phenotyping system. By integrating open-source software and hardware technologies, the method captures images of cucumber plants in a controlled greenhouse environment. The operational procedure of the robot consists of a series of steps. It begins with the robot’s initial movement to capture infrared images, followed by an analysis to detect Aruco markers serving as location identifiers for capturing plant images. Subsequently, the PTZ camera is adjusted to capture specific plant traits from predefined viewpoints. The captured images with location IDs, preset viewpoints, and timestamps are then sent to a remote server. Validation of the system’s dependability includes manual measurements on fundamental operations and the evaluation of the effectiveness of zoomed images captured by the PTZ camera, tested through plant feature detection. Experimental results demonstrate promising outcomes, achieving a mean average precision (mAP) of 94%, 97.6%, 98.4%, 90.1%, and 97.6% for apical buds, male flowers, female flowers, tiny cucumbers, and mature cucumbers respectively when using the trained YOLOv8s on an augmented dataset tested on highly zoomed images validation set, outperforming less zoomed or less detailed validation sets. These findings underscore the efficacy of this innovative approach in capturing real-time plant images. Leveraging the PTZ camera’s zoom, pan, and tilt capabilities enables comprehensive visualization of plant traits and adaptability to evolving growth patterns, thereby improving the results of plant feature detection. The amassed imagery serves a dual purpose by acting as training data for AI models, highlighting their potential to facilitate future research endeavors demanding extensive and scalable plant information.
Why it matches plant phenotyping methodsPTZカメラとロボットを用いた植物フェノタイピングシステムの開発・検証が研究の中心で、植物器官の画像取得と特徴検出を評価している。
abstractThis research introduces a scalable high-throughput plant phenotyping technique utilizing a Pan Tilt Zoom (PTZ) camera.
Quantitative analysis for plant growth attributes has gained prominence in plant science and agriculture. Despite the availability of automated phenotyping systems as a solution to labor-intensive manual measurement techniques, these systems often require specialized knowledge and face challenges in scaling for high-throughput applications. This research introduces a scalable high-throughput plant phenotyping technique utilizing a Pan Tilt Zoom (PTZ) camera. The primary objective is to assess the application of a PTZ camera in a plant phenotyping system. By integrating open-source software and hardware technologies, the method captures images of cucumber plants in a controlled greenhouse environment. The operational procedure of the robot consists of a series of steps. It begins with the robot’s initial movement to capture infrared images, followed by an analysis to detect Aruco markers serving as location identifiers for capturing plant images. Subsequently, the PTZ camera is adjusted to capture specific plant traits from predefined viewpoints. The captured images with location IDs, preset viewpoints, and timestamps are then sent to a remote server. Validation of the system’s dependability includes manual measurements on fundamental operations and the evaluation of the effectiveness of zoomed images captured by the PTZ camera, tested through plant feature detection. Experimental results demonstrate promising outcomes, achieving a mean average precision (mAP) of 94%, 97.6%, 98.4%, 90.1%, and 97.6% for apical buds, male flowers, female flowers, tiny cucumbers, and mature cucumbers respectively when using the trained YOLOv8s on an augmented dataset tested on highly zoomed images validation set, outperforming less zoomed or less detailed validation sets. These findings underscore the efficacy of this innovative approach in capturing real-time plant images. Leveraging the PTZ camera’s zoom, pan, and tilt capabilities enables comprehensive visualization of plant traits and adaptability to evolving growth patterns, thereby improving the results of plant feature detection. The amassed imagery serves a dual purpose by acting as training data for AI models, highlighting their potential to facilitate future research endeavors demanding extensive and scalable plant information. • Introducing a high-throughput plant phenotyping method for capturing real-time imagery of plants. • Proposing PTZ camera’s imaging mechanisms for visualizing diverse and detailed plant features. • Improved results arise from applying plant feature detection to zoomed images taken with a PTZ camera.
Why it matches plant phenotyping methodsPTZカメラを用いた植物表現型取得システムを開発し、画像による器官・生育特徴検出で有効性を検証しており、方法が研究の中心である。
abstractThis research introduces a scalable high-throughput plant phenotyping technique utilizing a Pan Tilt Zoom (PTZ) camera.
Plant phenotyping plays a pivotal role in observing and comprehending the growth and development of plants. In phenotyping, plant organ segmentation based on 3D point clouds has garnered increasing attention in recent years. However, using only the geometric relationship features of Euclidean space still cannot accurately segment and measure plants. To this end, we mine more geometric features and propose a segmentation network based on a multiview geometric graph encoder, called SN-MGGE. First, we construct a point cloud acquisition platform to obtain the cucumber seedling point cloud dataset, and employ CloudCompare software to annotate the point cloud data. The GGE module is then designed to generate the point features, including the geometric relationships and geometric shape structure, via a graph encoder over the Euclidean and hyperbolic spaces. Finally, the semantic segmentation results are obtained via a downsampling operation and multilayer perceptron. Extensive experiments on a cucumber seedling dataset clearly show that our proposed SN-MGGE network outperforms several mainstream segmentation networks (e.g., PointNet++, AGConv, and PointMLP), achieving mIoU and OA values of 94.90% and 97.43%, respectively. On the basis of the segmentation results, 4 phenotypic parameters (i.e., plant height, leaf length, leaf width, and leaf area) are extracted through the K-means clustering method; these parameters are very close to the ground truth, and the R 2 values reach 0.98, 0.96, 0.97, and 0.97, respectively. Furthermore, an ablation study and a generalization experiment also show that the SN-MGGE network is robust and extensive.
Why it matches plant phenotyping methods3D点群からキュウリ幼苗を分割し、草丈・葉長・葉幅・葉面積を抽出するネットワークと取得基盤を開発・検証しており、植物表現型取得が中心である。
abstractwe construct a point cloud acquisition platform to obtain the cucumber seedling point cloud dataset
Abstract In the realm of global food security, plants serve as the primary source of sustenance. However, plant diseases pose a significant threat to this security. The process of diagnosing these diseases forms the bedrock of disease control efforts. The precision and expediency of these diagnoses wield substantial influence over disease management and the consequent reduction of economic losses. Conversely, incorrect diagnoses can render interventions ineffective, leading to agricultural crop deterioration and compounding economic hardships for both farmers and their respective nations. This research endeavors to diagnose the prevalent crops in Jordan, as identified by the Jordanian Department of Statistics for the year 2019. These crops encompass four key agricultural varieties: cucumbers, tomatoes, lettuce, and cabbage. To facilitate this, a novel dataset known as "Jordan 22" was meticulously curated. Jordan 22 was painstakingly compiled through the collection of images featuring both diseased and healthy plants, captured within the confines of Jordanian farms. These images underwent meticulous classification by a panel of three agricultural specialists, well-versed in plant disease identification and prevention. The Jordan 22 dataset comprises a substantial size, amounting to 3210 images. Following the compilation of this dataset, a series of preprocessing steps were executed. These encompassed the standardization of image backgrounds and the uniformization of image dimensions. Furthermore, image augmentation techniques were applied to the dataset to expand its diversity. Subsequently, a deep learning model, the Convolutional Neural Network (CNN), was meticulously trained on the augmented dataset. The results yielded by the CNN were nothing short of remarkable, with a test accuracy rate reaching an impressive 0.9712. Optimal performance was observed when images were resized to 256x256 dimensions, and max pooling was employed in lieu of average pooling within the pooling layer. Furthermore, the initial convolutional layer was set at a size of 32, with subsequent convolutional layers standardized at 128 in size. In conclusion, this research represents a pivotal step towards enhancing plant disease diagnosis and, by extension, global food security. Through the creation of the Jordan 22 dataset and the meticulous training of a CNN model, we have achieved substantial accuracy in disease detection, paving the way for more effective disease management strategies in agriculture.
Why it matches plant phenotyping methods植物画像から病害状態を推定するCNNとデータセットを開発・評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis research endeavors to diagnose the prevalent crops in Jordan
Reproduction assets foundThe paper's Jordan22 plant disease image dataset (2310 RGB leaf images of cucumber, tomato, cabbage, and lettuce collected in Jordan and expert-classified) is explicitly stated as openly available on the authors' public GitHub repository. No separate analysis code or trained model checkpoint is explicitly deposited.Dataset · publicThe data that support the findings of this study are openly available in [Jordan22_Dataset] at
[https://github.com/shahd1995913/Jordan22_Dataset], reference number [17].Open asset ↗Jordan22_Datasetpdf-page:25 lines:1-40Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
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 15 Sept 2026
Fruits and vegetables are among the most nutrient-dense cash crops worldwide. Diagnosing diseases in fruits and vegetables is a key challenge in maintaining agricultural products. Due to the similarity in disease colour, texture, and shape, it is difficult to recognize manually. Also, this process is time-consuming and requires an expert person. We proposed a novel deep learning and optimization framework for apple and cucumber leaf disease classification to consider the above challenges. In the proposed framework, a hybrid contrast enhancement technique is proposed based on the Bi-LSTM and Haze reduction to highlight the diseased part in the image. After that, two custom models named Bottleneck Residual with Self-Attention (BRwSA) and Inverted Bottleneck Residual with Self-Attention (IBRwSA) are proposed and trained on the selected datasets. After the training, testing images are employed, and deep features are extracted from the self-attention layer. Deep extracted features are fused using a concatenation approach that is further optimized in the next step using an improved human learning optimization algorithm. The purpose of this algorithm was to improve the classification accuracy and reduce the testing time. The selected features are finally classified using a shallow wide neural network (SWNN) classifier. In addition to that, both trained models are interpreted using an explainable AI technique such as LIME. Based on this approach, it is easy to interpret the inside strength of both models for apple and cucumber leaf disease classification and identification. A detailed experimental process was conducted on both datasets, Apple and Cucumber. On both datasets, the proposed framework obtained an accuracy of 94.8% and 94.9%, respectively. A comparison was also conducted using a few state-of-the-art techniques, and the proposed framework showed improved performance.
Why it matches plant phenotyping methods葉の病徴を画像から分類・認識する深層学習フレームワークが研究の中心であり、植物の病害状態を直接推定する画像ベース表現型解析手法に該当する。
abstractWe proposed a novel deep learning and optimization framework for apple and cucumber leaf disease classification
CucumberRootSeed / grainMorphology / geometry measurementObject detectionGrowth / development / phenologyRoot system architectureStress response / tolerance
Seed germination vigor is one of the important indexes reflecting the quality of seeds, and the level of its germination vigor directly affects the crop yield. The traditional manual determination of seed germination vigor is inefficient, subjective, prone to damage the seed structure, cumbersome and with large errors. We carried out a cucumber seed germination experiment under salt stress based on the seed germination phenotype acquisition platform. We obtained image data of cucumber seed germination under salt stress conditions. On the basis of the YOLOv8-n model, the original loss function CIoU_Loss was replaced by ECIOU_Loss, and the Coordinate Attention(CA) mechanism was added to the head network, which helped the model locate and identify the target. The small-target detection head was added, which enhanced the detection accuracy of the tiny target. The precision P, recall R, and mAP of detection of the model improved from the original values of 91.6%, 85.4%, and 91.8% to 96.9%, 97.3%, and 98.9%, respectively. Based on the improved YOLOv8-ECS model, cucumber seeds under different concentrations of salt stress were detected by target detection, cucumber seed germination rate, germination index and other parameters were calculated, the root length of cucumber seeds during germination was extracted and analyzed, and the change characteristics of root length during cucumber seed germination were obtained, and finally the germination activity of cucumber seeds under different concentrations of salt stress was evaluated. This work provides a simple and efficient method for the selection and breeding of salt-tolerant varieties of cucumber.
Why it matches plant phenotyping methods改良YOLOv8と种子萌发表型采集平台是核心方法贡献，并验证了检测性能、提取根长及萌发相关表型。
abstractWe carried out a cucumber seed germination experiment under salt stress based on the seed germination phenotype acquisition platform.
Two-spotted spider mite (Tetranychus urticae) is an important greenhouse pest. In cucumbers, heavy infestations lead to the complete loss of leaf assimilation surface, resulting in plant death. Symptoms caused by spider mite feeding alter the light reflection of leaves and could therefore be optically detected. Machine learning methods have already been employed to analyze spectral information in order to differentiate between healthy and spider mite-infested leaves of crops such as tomatoes or cotton. In this study, machine learning methods were applied to cucumbers. Hyperspectral data of leaves were recorded under controlled conditions. Effective wavelengths were identified using three feature selection methods. Subsequently, three supervised machine learning algorithms were used to classify healthy and spider mite-infested leaves. All combinations of feature selection and classification methods yielded accuracy of over 80%, even when using ten or five wavelengths. These results suggest that machine learning methods are a powerful tool for image-based detection of spider mites in cucumbers. In addition, due to the limited number of wavelengths, there is also substantial potential for practical application.
Why it matches plant phenotyping methodsキュウリ葉のハイパースペクトル計測、波長選択、機械学習分類を用いてダニ被害状態を直接推定する方法が研究の中心であり、植物フェノタイピング手法に該当する。
abstractHyperspectral data of leaves were recorded under controlled conditions. Effective wavelengths were identified using three feature selection methods. Subsequently, three supervised machine learning algorithms were used to classify healthy and spider mite-infested leaves.
Background In complex agricultural environments, the presence of shadows, leaf debris, and uneven illumination can hinder the performance of leaf segmentation models for cucumber disease detection. This is further exacerbated by the imbalance in pixel ratios between background and lesion areas, which affects the accuracy of lesion extraction. Results An original image segmentation framework, the LS-ASPP model, which utilizes a two-stage Atrous Spatial Pyramid Pooling (ASPP) approach combined with adaptive loss to address these challenges has been proposed. The Leaf-ASPP stage employs attention modules and residual structures to capture multi-scale semantic information and enhance edge perception, allowing for precise extraction of leaf contours from complex backgrounds. In the Spot-ASPP stage, we adjust the dilation rate of ASPP and introduce a Convolutional Attention Block Module (CABM) to accurately segment lesion areas. Conclusions The LS-ASPP model demonstrates improved performance in semantic segmentation accuracy under complex conditions, providing a robust solution for precise cucumber lesion segmentation. By focusing on challenging pixels and adapting to the specific requirements of agricultural image analysis, our framework has the potential to enhance disease detection accuracy and facilitate timely and effective crop management decisions.
Why it matches plant phenotyping methodsキュウリ葉の病斑を画像から抽出・分割する手法の開発が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として収録する。
abstractAn original image segmentation framework, the LS-ASPP model, which utilizes a two-stage Atrous Spatial Pyramid Pooling (ASPP) approach combined with adaptive loss to address these challenges has been proposed.
Published4 Jul 20242024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)Cited by 5 · OpenAlex ↗
Abstract-Deep learning (DL) has recently gained a wide attention in detection of plant leaf diseases. However, the effectiveness of DL depends on the vast amount of data to derive the meaningful patterns which is crucial for accurate detection. This led to the appreciation of pretrained models, which are trained on large data for improved detection despite limited data in the required domain. This paper presents an ensemble-based DL model for detecting diseases in cucumber plant leaves. The proposed ensemble learning framework includes Resnet V50, MobileNetV2 and EfficientNet-B0 as the pretrained models which aggregate the output of these models by weighted ensemble averaging. Data augmentation and Hyperparameter tunings were performed to increase the model generalization and capability. The proposed ensemble approach was validated on Cucumber Disease Recognition Dataset and achieved an accuracy of 99.351%, precision of 99.363% and F1 Score of 99.352% that outperforms the underlying learning framework. Additionally, to provide more insights into the extent of a model to predict each class, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed that produces heat maps highlighting the regions which contributed most to the prediction.
Why it matches plant phenotyping methodsキュウリ葉の病害状態を画像から推定する深層学習モデルを開発・検証しており、植物フェノタイピング手法が中心である。
abstractThis paper presents an ensemble-based DL model for detecting diseases in cucumber plant leaves.
Cucumber is an important vegetable crop that has high nutritional and economic value and is thus favored by consumers worldwide. Exploring an accurate and fast technique for measuring the morphological traits of cucumber fruit could be helpful for improving its breeding efficiency and further refining the development models for pepo fruits. At present, several sets of measurement schemes and standards have been proposed and applied for the characterization of cucumber fruits; however, these manual methods are time-consuming and inefficient. Therefore, in this paper, we propose a cucumber fruit morphological trait identification framework and software called CucumberAI, which combines image processing techniques with deep learning models to efficiently identify up to 51 cucumber features, including 32 newly defined parameters. The proposed tool introduces an algorithm for performing cucumber contour extraction and fruit segmentation based on image processing techniques. The identification framework comprises 6 deep learning models that combine fruit feature recognition rules with MobileNetV2 to construct a decision tree for fruit shape recognition. Additionally, the framework employs U-Net segmentation models for fruit stripe and endocarp segmentation, a MobileNetV2 model for carpel classification, a ResNet50 model for stripe classification and a YOLOv5 model for tumor identification. The relationships between the image-based manual and algorithmic traits are highly correlated, and validation tests were conducted to perform correlation analyses of fruit surface smoothness and roughness, and a fruit appearance cluster analysis was also performed. In brief, CucumberAI offers an efficient approach for extracting and analyzing cucumber phenotypes and provides valuable information for future cucumber genetic improvements.
Why it matches plant phenotyping methodsキュウリ果実の形態形質を画像処理・深層学習で抽出するフレームワークとソフトウェアを開発し、手動測定との相関による検証も行っており、植物フェノタイピング手法が中心である。
abstractwe propose a cucumber fruit morphological trait identification framework and software called CucumberAI, which combines image processing techniques with deep learning models to efficiently identify up to 51 cucumber features
Cucumber is one of the important salad vegetables cultivated worldwide and is highly sensitive to water stress. However, till date, no drought-tolerant cucumber genotypes have been identified using a large set of diverse germplasms via high-throughput phenotyping methods. This study involved screening of a large set of Indian-origin cucumber germplasms for drought stress response using water-deficit and polyethylene glycol (PEG)-induced stress conditions in hydroponic solutions. Water-deficit and PEG-induced methods were optimized before screening of an entire set of germplasm for key physiological traits. Pearson’s correlation revealed non-significant difference between these two methods. Hierarchical cluster analysis and drought tolerance matrix score (DTMS) were calculated based on key physiological traits for ranking of the genotypes. Furthermore, the entire set of genotypes was exposed to water stress (< 8% soil moisture) for 15 d under field conditions to record yield-related traits for validation of the hydroponic-based ranking. Finally, eight tolerant genotypes were identified with high seedling survivability percentage, minimum reduction in root–shoot dry weight, fresh weight and water content percentage, highest DTMS and minimum yield reduction under field stress conditions compared with seven identified sensitive genotypes. The optimized rapid phenotyping method and identified drought-tolerant lines will be instrumental in understanding the physiological and molecular basis of drought tolerance and in facilitating the development of climate-resilient improved cucumber genotypes in the future.
Why it matches plant phenotyping methods水ストレス条件下での迅速な表現型測定法を最適化し、大規模遺伝資源のスクリーニングに適用するとともに、圃場条件で順位を検証しており、表現型取得法が研究の中心です。
abstractWater-deficit and PEG-induced methods were optimized before screening of an entire set of germplasm for key physiological traits.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract The root dielectric response was measured on a minute scale to assess its efficiency for monitoring short-term cadmium (Cd) toxicity non-destructively. Electrical capacitance (C R ), dissipation factor (D R ) and electrical conductance (G R ) were detected during the 24 to 168 h after Cd treatment (0, 20, 50 mg Cd 2+ kg –1 substrate) in potted maize, cucumber and pea. Stress was also evaluated by measuring leaf chlorophyll content, F v /F m and stomatal conductance (g s ) in situ , and shoot and root mass and total root length after harvest. C R showed a clear diurnal pattern, reflecting the water uptake rate, and decreased significantly in response to excessive Cd due to impeded root growth, the reduced tissue permittivity caused by accelerated lignification, and root ageing. Cd exposure markedly increased D R , indicating greater conductive energy loss due to oxidative membrane damage and enhanced electrolyte leakage. G R , which was coupled with root hydraulic conductance and varied diurnally, was increased transiently by Cd toxicity due to enhanced membrane permeability, but declined thereafter owing to stress-induced leaf senescence and transpiration loss. The time series of impedance components indicated the comparatively high Cd tolerance of the applied maize and the sensitivity of pea cultivar, which was confirmed by visible shoot symptoms, repeated physiological investigations and biomass measurements. The results demonstrated the potential of single-frequency dielectric measurements to follow certain aspects of the stress response of different species on a fine timescale without plant injury. The approach can be combined with widely used plant physiological methods and could contribute to breeding crop genotypes with improved stress tolerance.
Why it matches plant phenotyping methods植物のCd毒性・ストレス状態を、無傷根系の誘電応答で非破壊かつ高時間分解能に測定する手法が研究の中心であり、他の生理測定や生体重による確認も行っている。
abstractThe root dielectric response was measured on a minute scale to assess its efficiency for monitoring short-term cadmium (Cd) toxicity non-destructively.
In light of the prevalent pest and disease issues faced by greenhouse cucumbers, a staple vegetable during winter, this study introduces a detection method based on the enhanced YOLOv8s model. This method aims to provide technical support for detecting and classifying pests and diseases in cucumber agricultural production. The model integrates the 'MultiCat' module for multiscale feature fusion and employs the 'C2fe' and 'ADC2f'modules to strengthen spatial and channel attention. The 'Block2d' function also facilitates the choice between average pooling and attention-based spatial pooling. Channel fusion is achieved through additive and multiplicative operations, allowing the model to delve deeper into feature learning. Experimental results confirm that our approach outperforms the original YOLOv8s model in pest detection, particularly excelling in the identification of small-scale and overlapping afflictions.
Why it matches plant phenotyping methodsキュウリの病害・害虫を植物画像から検出・分類するYOLOv8改良モデルの開発と性能比較が中心であり、植物の病害状態を推定する画像ベースの表現型計測法に該当する。
abstractthis study introduces a detection method based on the enhanced YOLOv8s model.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.
Why it matches plant phenotyping methods植物病害の画像取得・分割・特徴抽出・分類による症状検出手法を中心にレビューしており、植物状態の推定方法が主題である。
abstractThis review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Background The measurement of root dielectric response is a useful non-destructive method to evaluate root growth and function. Previous studies tracked root development throughout the plant growing cycle by single-time electrical measurements taken repeatedly. However, it is known that root conductivity and uptake activity can change rapidly, coupled with the day/night cycles of photosynthetic and transpiration rate. Therefore, the low-frequency dielectric monitoring of intact root–substrate systems at minute-scale temporal resolution was tested using a customized impedance measurement system in a laboratory environment. Electrical capacitance (C R ) and conductance (G R ) and the dissipation factor (D R ) were detected for 144 h in potted maize, cucumber and pea grown under various light/dark and temperature conditions, or subjected to progressive leaf excision or decapitation. Photosynthetic parameters and stomatal conductance were also measured to evaluate the stress response. Results The C R and G R data series showed significant 24-h seasonality associated with the light/dark and temperature cycles applied. This was attributed to the diurnal patterns in whole-plant transpiration (detected via stomatal conductance), which is strongly linked to the root water uptake rate. C R and G R decreased during the 6-day dark treatment, and dropped proportionally with increasing defoliation levels, likely due to the loss of canopy transpiration caused by dark-induced senescence or removal of leaves. D R showed a decreasing trend for plants exposed to 6-day darkness, whereas it was increased markedly by decapitation, indicating altered root membrane structure and permeability, and a modified ratio of apoplastic to cell-to-cell water and current pathways. Conclusions Dynamic, in situ impedance measurement of the intact root system was an efficient way of following integrated root water uptake, including diurnal cycles, and stress-induced changes. It was also demonstrated that the dielectric response mainly originated from root tissue polarization and current conduction, and was influenced by the actual physiological activity of the root system. Dielectric measurement on fine timescale, as a diagnostic tool for monitoring root physiological status and environmental response, deserves future attention.
Why it matches plant phenotyping methods根系の誘電特性を高時間分解能で測定するシステムを開発・検証し、根の吸水や生理状態を非破壊的に推定する手法が研究の中心である。
abstractThe measurement of root dielectric response is a useful non-destructive method to evaluate root growth and function.
CucumberGreenhouseRootSegmentationYield / biomass estimationRoot system architectureYield / yield components
The root system is important for the absorption of water and nutrients by plants. Cultivating and selecting a root system architecture (RSA) with good adaptability and ultrahigh productivity have become the primary goals of agricultural improvement. Exploring the correlation between the RSA and crop yield is important for cultivating crop varieties with high-stress resistance and productivity. In this study, 277 cucumber varieties were collected for root system image analysis and yield using germination plates and greenhouse cultivation. Deep learning tools were used to train ResNet50 and U-Net models for image classification and segmentation of seedlings and to perform quality inspection and productivity prediction of cucumber seedling root system images. The results showed that U-Net can automatically extract cucumber root systems with high quality (F1_score ≥ 0.95), and the trained ResNet50 can predict cucumber yield grade through seedling root system image, with the highest F1_score reaching 0.86 using 10-day-old seedlings. The root angle had the strongest correlation with yield, and the shallow- and steep-angle frequencies had significant positive and negative correlations with yield, respectively. RSA and nutrient absorption jointly affected the production capacity of cucumber plants. The germination plate planting method and automated root system segmentation model used in this study are convenient for high-throughput phenotypic (HTP) research on root systems. Moreover, using seedling root system images to predict yield grade provides a new method for rapidly breeding high-yield RSA in crops such as cucumbers.
Why it matches plant phenotyping methods根系画像の自動セグメンテーションと収量予測モデルを開発・評価し、高スループット表現型解析への適用を中心に扱うため。
abstractDeep learning tools were used to train ResNet50 and U-Net models for image classification and segmentation of seedlings and to perform quality inspection and productivity prediction of cucumber seedling root system images.
Reproduction assets foundThe paper reports cucumber root-image phenotyping (U-Net segmentation, ResNet50 yield-grade classification) and states that the segmentation and classification model code was uploaded to a public GitHub repository under the author's account. No public phenotype/image dataset deposit is stated in the supplied blocks.Code · publicsis of variance was used to compare trait differences
between the different yield grades. Deep learning model train-
ing and testing were conducted using the PyTorch framework,
mainly running on a cloud platform (https://www.autodl.com).The codes for the segmentation and classification models used
in this study were uploaded to https://github.com/zhucuifang/.AUTHOR CONTRIBUTIONS
Cuifang Zhu: Investigation, Data collection and analysis;
Writing – original draft; Hongjun Yu: Investigation, Data
collection, Funding acquisition; Tao Lu and Yang Li: Super-
vision; Weijei Jiang: Methodology, Guidance, Funding
acquisition; Qiang Li: Review and editing, Guidance.
Ó 2024 Society for Experimental BOpen asset ↗zhucuifangpdf-raw-page:19 lines:112-171Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
To investigate the relationship between stem puncture mechanical characteristics and NK stress diagnosis, the microstructure, surface morphology, cellulose and lignin content, puncture mechanical characteristics, and epidermal cell morphology of cucumber stems were measured herein. The results indicated that the middle stem, which had a diameter of approximately 7000 μm, was more suitable for puncturing due to its lower amount of epidermal hair, and its gradual regularity in shape. Further, the cucumber stems were protected from puncture damage due to their ability to rapidly heal within 25 h.. The epidermal penetration of the cucumber stems increased with the increase in cellulose and lignin, though cellulose played a more decisive role. The epidermal break distance increased with an increase in N application and decreased with an increase in K + application, but the change in intercellular space caused by K + supply was the most critical factor affecting the epidermal break distance. In addition, a decrease in K + concentration led to a decrease in epidermal brittleness, whereas the factors affecting epidermal toughness were more complex. Finally, we found that although the detection of epidermal brittleness and toughness on nutrient stress was poor under certain treatment, the puncture mechanical characteristics of the stem still had a significant indicative effect on N application rate. Therefore, elucidating of the relationship between the puncture mechanical characteristics of the stems and crop nutritional stress is not only beneficial for promoting stem stress physiology research but also for designing on-site nutritional testing equipment in the future.
Why it matches plant phenotyping methodsキュウリ茎の穿刺力学特性を用いた栄養ストレス診断を主題とし、測定部位の適性、診断性能、栄養条件との関係を評価しているため、植物表現型取得法の応用・検証に該当する。
titlePreliminary study on the diagnosis of NK stress based on the puncture mechanical characteristics of cucumber stem.
Downy mildew of cucumber caused by the oomycete Pseudoperonospora cubensis (P. cubensis) is currently the most destructive disease of this crop, causing high yield losses. Visual evaluation by experts is the most established method for the detection of P. cubensis infection, but depends on visual disease symptoms. Detection of fungal infection before visual symptoms appear is highly desirable, allowing to deploy disease control measures before extensive crop damage occurs. The capacity of remote sensing approaches, such as hyperspectral imaging or high-resolution spectrometry, have proved to be powerful tools for detecting plant fungal diseases at pre-symptomatic stages over the last years. However, these approaches are expensive and processing the massive amount of multidimensional data, in case of spectroscopy and hyperspectral imaging, remains complex. Affordable, easy to handle, devices enabling the identification of P. cubensis infected cucumber plants at the pre-symptomatic stage in-situ may represent a high-value alternative for farmers. This study compared the performance of a high-cost high-resolution spectroradiometer (FieldSpec4) and a lower-cost leaf-clip sensor (DUALEX) for the early detection of P. cubensis infection in cucumber. Screening of cucumber plants, grown in a growth chamber, 24 h and 48 h post inoculation, allowed to identify a subset of spectral bands and leaf pigments for differentiating between infected and healthy cucumber plants at the pre-symptomatic stage. In the case of the FieldSpec 4, models based on spectral bands at 402, 576, 690, 708, and 723 nm differentiated between cucumber plants infected with P. cubensis and healthy plants with a F1 value of 0.67. The use of the lower-cost leaf-clip DUALEX sensor differentiated between infected and healthy cucumber plants with a F1 value of 0.60. Furthermore, the epidermal flavonol content obtained with the +leaf-clip DUALEX sensor was shown to be decreased in cucumber plants infected with P. cubensis compared to those of healthy plants, highlighting its role as biomarker for detecting P. cubensis infection at the pre-symptomatic stage. Results of this study will help to develop affordable remote sensing tools suitable for detecting P. cubensis infection at the pre-symptomatic stage in-situ.
Why it matches plant phenotyping methodsキュウリの病害状態を非破壊スペクトル・葉クリップセンサーで検出し、高価・低価格センサーの性能比較と検証を主目的とするため、植物フェノタイピング手法として中心的である。
abstractThis study compared the performance of a high-cost high-resolution spectroradiometer (FieldSpec4) and a lower-cost leaf-clip sensor (DUALEX) for the early detection of P. cubensis infection in cucumber.
Anthracnose, caused by species in the Colletotrichum orbiculare complex, is a major foliar disease in open‐field cucumber farming worldwide. This study aimed to develop and validate a standard area diagram (SAD) set to estimate the severity of anthracnose on cucumber leaves. For this purpose, a SAD set with nine levels of severity (1; 3; 5; 10; 20; 30; 40; 50 and 60%) is proposed. The SAD set was validated by 16 raters with no experience in evaluating plant diseases. Both accuracy and precision improved when the proposed SAD set was employed. The statistical parameters were bias coefficient factor‐Cb (no SAD set = 0.891, with SAD set = 0.982); correlation coefficient‐r (no SAD set = 0.851, with SAD set = 0.941); and Lin's concordance correlation coefficient‐ρc (no SAD set = 0.755, with SAD set = 0.924). In addition, estimates were more reliable: intra‐class correlation coefficient‐ρ (no SAD set = 0.646, with SAD set = 0.887). The SAD set proposed here is a useful tool for improving visual assessments of anthracnose severity on cucumber leaves.
Why it matches plant phenotyping methodsキュウリ葉の病害重症度を評価する標準面積図を開発し、評価者による精度・再現性を検証した、植物表現型測定法が中心の研究。
abstractThis study aimed to develop and validate a standard area diagram (SAD) set to estimate the severity of anthracnose on cucumber leaves.
CucumberPepper / chilliTomatoThermalLeafPhysiological trait estimationPlant / canopy temperature
Image-based high-throughput phenotyping promises the rapid determination of functional traits in large plant populations. However, interpretation of some traits - such as those related to photosynthesis or transpiration rates - is only meaningful if the irradiance absorbed by the measured leaves is known, which can differ greatly between different parts of the same plant and within canopies. No feasible method currently exists to rapidly measure absorbed irradiance in three-dimensional plants and canopies. We developed a method and protocols to derive absorbed irradiance at any visible part of a canopy with a thermal camera, by fitting a leaf energy balance model to transient changes in leaf temperature. Leaves were exposed to short light pulses (30 s) that were not long enough to trigger stomatal opening but strong enough to induce transient changes in leaf temperature that was proportional to the absorbed irradiance. The method was successfully validated against point measurements of absorbed irradiance in plant species with relatively simple architecture (sweet pepper, cucumber, tomato, and lettuce). Once calibrated, the model was used to produce absorbed irradiance maps from thermograms. Our method opens new avenues for the interpretation of plant responses derived from imaging techniques and can be adapted to existing high-throughput phenotyping platforms.
Why it matches plant phenotyping methods熱画像と葉エネルギーバランスモデルにより、植物キャノピー内の吸収光量を迅速に推定・可視化するフェノタイピング手法を開発し、点測定で検証しているため。
abstractWe developed a method and protocols to derive absorbed irradiance at any visible part of a canopy with a thermal camera, by fitting a leaf energy balance model to transient changes in leaf temperature.
Reproduction assets foundThe paper's authors explicitly state that all analysis code (R/STAN energy-balance fitting and Julia absorbed-irradiance mapping) is publicly available on their GitHub repository, which directly reproduces the paper's phenotyping computations. No separate phenotype dataset or image deposit is stated in the supplied.Code · publicData analysis
Calculations to solve Eqns 2 and 3 were run in R (R project,
v.4.2.0). The absorbed irradiance map was calculated in JULIA
(v.1.40.1; https://julialang.org/). All codes are available on
GitHub (https://github.com/jiayu0903/leaf-absorbed-irradiance.git). Statistical analysis was performed using a Student’s t-test for
paired samples to determine significant differences (P < 0.05)
between means.
Results
The temperature of sweet pepper (Fig. 2a), cucumber (Fig. S6a),
and tomato leaves (Fig. S7) showed a near-linear increase when
exposed to a brief 30 s period of irradiance fromOpen asset ↗https://github.com/jiayu0903/leaf-absorbed-irradiance.gitpdf-raw-page:7 lines:1-87Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Dec 2023Computers and Electronics in Agriculture.
The latest advances in deep learning technology make it possible to recognize vegetable diseases from leaf images. The existing disease recognition methods based on computer vision have shown exciting achievements in terms of accuracy, stability, and portability. However, these methods cannot provide a decision-making basis for the final results, and lack a text basis to support the users’ judgement. Disease diagnosis is a risky decision. If the detection method lacks transparency, the users will not be able to fully trust the recognition results, which greatly limits the application of various recognition methods based on deep learning. Aiming at the problem of low “man–machine” credibility due to the fact that deep learning-based methods are unable to provide decision-making basis, this paper proposed a two-stage image dense captioning model named “DFYOLOv5m-M²Transformer”, which can generate description sentences of visualized disease features on the basis of the recognized diseased area. Firstly, we established a target detection dataset and a dense captioning dataset containing leaf images of 10 diseases, involving 2 vegetables, i.e., cucumber and tomato. Secondly, we chose the DFYOLOv5m network as the disease detector to extract the diseased area from the image, and the M²-Transformer network as the decision basis generator to generate description sentences of disease features. Then, the Bi-Level Routing Attention module was introduced to extract fine-grained features under complex backgrounds in order to resolve the problem of poor feature extraction in case of mixed diseases. Finally, we used Atrous Convolution to expand the receptive field of the model, and fused NWD and CIoU to improve the model’s performance in detecting small targets. The experimental results show that the IoU and Meteor joint evaluation indicator of DFYOLOv5m-M²Transformer achieved a mean Average Precision (mAP) of 94.7 % on the dense captioning dataset, which was 7.2 % higher than that of the best-performing model Veg-DenseCap in the control group. Moreover, the decision basis that is automatically generated by the proposed model is characterized by the advantages of high accuracy, correct grammar and large sentence variety. The outcome of this study provides a new idea for optimizing the user experience in using vegetable disease recognition models.
Why it matches plant phenotyping methods植物葉の病徴領域を画像から検出・説明する手法を開発し、疾患画像データセットで評価しており、病害状態の表現型取得が中心である。
abstractthis paper proposed a two-stage image dense captioning model named “DFYOLOv5m-M²Transformer”, which can generate description sentences of visualized disease features on the basis of the recognized diseased area.
With rich annotation information, object detection-based automated plant disease diagnosis systems (e.g., YOLO-based systems) often provide advantages over classification-based systems (e.g., EfficientNet-based), such as the ability to detect disease locations and superior classification performance. One drawback of these detection systems is dealing with unannotated healthy data with no real symptoms present. In practice, healthy plant data appear to be very similar to many disease data. Thus, those models often produce mis-detected boxes on healthy images. In addition, labeling new data for detection models is typically time-consuming. Hard-sample mining (HSM) is a common technique for re-training a model by using the mis-detected boxes as new training samples. However, blindly selecting an arbitrary amount of hard-sample for re-training will result in the degradation of diagnostic performance for other diseases due to the high similarity between disease and healthy data. In this paper, we propose a simple but effective training strategy called hard-sample re-mining (HSReM), which is designed to enhance the diagnostic performance of healthy data and simultaneously improve the performance of disease data by strategically selecting hard-sample training images at an appropriate level. Experiments based on two practical in-field eight-class cucumber and ten-class tomato datasets (42.7K and 35.6K images) show that our HSReM training strategy leads to a substantial improvement in the overall diagnostic performance on large-scale unseen data. Specifically, the object detection model trained using the HSReM strategy not only achieved superior results as compared to the classification-based state-of-the-art EfficientNetV2-Large model and the original object detection model, but also outperformed the model using the HSM strategy in multiple evaluation metrics.
Why it matches plant phenotyping methods植物病害の症状位置・診断を対象に、物体検出モデルの再学習戦略HSReMを開発し、キュウリ・トマトの実データセットで評価しているため、画像ベースの病害表現型取得手法が中心である。
titleTowards robust plant disease diagnosis with hard-sample re-mining strategy
A method to efficiently and quantitatively study the delivery of a pesticide-surfactant formulation in a water solution to plant leaves is presented. The methodology of measurement of the surface of the leaf wet area is used instead of the more problematic measurement of the contact angle. A method based on a Deep Learning model was used to automatically measure the wet area of cucumber leaves by processing the frames of video footage. We have individuated an existing Deep Learning model, called HED-UNet, reported in the literature for other applications, and we have applied it to this different task with a minor modification. The model was selected because it combines edge detection with image segmentation, which is what is needed for the task at hand. This novel application of the HED-UNet model proves effective, and opens a wide range of new applications, the one presented here being just a first example. We present the measurement technique, some details of the Deep Learning model, its training procedure and its image segmentation performance. We report the results of the wet area surface measurement as a function of the concentration of a surfactant in the pesticide solution, which helps to plan the surfactant concentration. It can be concluded that the most effective concentration is the highest in the range tested, which is 11.25 times the CMC concentration. Moreover, a validation error on the Deep Learning model, as low as 0.012 is obtained, which leads to the conclusion that the chosen Deep Learning model can be effectively used to automatically measure the wet area on leaves.
Why it matches plant phenotyping methods植物葉面の濡れ面積という観測可能な葉形質を、動画画像と深層学習で自動抽出・定量する手法が研究の中心であり、学習手順と性能検証も示されているため。
abstractA method based on a Deep Learning model was used to automatically measure the wet area of cucumber leaves by processing the frames of video footage.
CucumberLeafStress / disease detectionStress response / toleranceWater status / transpiration
Background Specific detection of the type and severity of plant abiotic stresses helps prevent yield loss by considering timely actions. This study introduces a novel method to detect the type and severity of stress in cucumber plants under salinity and drought conditions. Various features, i.e., morphological (image textural features), physiological/biochemical (relative water content, chlorophyll, catalase activity, anthocyanins, phenol content, and proline), as well as miRNA characteristics (the concentration of miRNA-156a, miRNA-166i, miRNA-399g, and miRNA-477b) were extracted from plant leaves, and machine learning methods were used to predict the type and severity of stress by having these features. Support vector machine (SVM) with parameters optimized by genetic algorithm (GA) and particle swarm optimization (PSO) was used for machine learning. Results The coefficient of determination of predicting the stress type and severity in plants under both stresses was 0.61, 0.82, and 0.99 using morphological, physiological/biochemical, and miRNA characteristics, respectively. This reveals machine learning methods optimized by metaheuristic optimization techniques can provide specific detection of salt and drought stresses in cucumber plants based on miRNA characteristics. Among the study miRNAs, miRNA-477b and miRNA-399g had the highest and lowest contribution to salt and drought stresses, respectively. Conclusions Comapred to conventional plant traits, miRNAs are more reliable features for providing us with valuable information about plant abiotic diseases at early stages. Using an electrochemical miRNA biosensor similar to one used in this work to measure the miRNA concentration in plant leaves and using a machine learning algorithm such as SVM enable farmers to detect the salt and drought stress at early stages in cucumber plants with very high accuracy.
Why it matches plant phenotyping methodsキュウリ葉から抽出した画像・生理・miRNA特徴を用いて、機械学習で塩・乾燥ストレスの種類と重症度を推定する手法が研究の中心であり、植物の状態を直接評価するフェノタイピング手法に該当する。
abstractThis study introduces a novel method to detect the type and severity of stress in cucumber plants under salinity and drought conditions.
Computer-vision based methods contribute to the availability of high-quality phenotypic datasets. Most computer-vision based methods for plant phenotyping are based on analysis of 2D images. However, previous research showed that for traits related to plant architecture, like internode length, a main limitation of 2D methods was that plants with a curved growing pattern could not be accurately measured. In this work, it was hypothesised that methods based on 3D data can overcome this limitation, while increasing the overall accuracy of the internode length measurements. To test the hypothesis, a method was proposed to estimate internode lengths from 3D point clouds of cucumber plants. First, a deep neural network based on PointNet++ was trained to segment the point clouds into plant parts. The points that were predicted as ‘node’ were then selected and a clustering algorithm was used to group points belonging to the same node. The Euclidean distance between the detected nodes was used as an estimate of the internode length. The results were compared to the results of a previously published method based on 2D images. The results of the 3D method were significantly more accurate than the results of the 2D method. Moreover, in contrast to the 2D method, the internode length estimates of the 3D method were equally accurate for curved plants as well as for straight plants. The results clearly demonstrated that computer-vision based methods to measure plant architecture in general, and more specifically to measure internode length, greatly benefit from the availability of 3D data.
Why it matches plant phenotyping methodsキュウリの3D点群から節間長を推定する画像解析手法を開発し、既存の2D手法と精度比較しているため、植物表現型取得法が研究の中心である。
abstractIn this work, it was hypothesised that methods based on 3D data can overcome this limitation, while increasing the overall accuracy of the internode length measurements.
Glycine betaine (GB) is a bioactive molecule protecting plants from abiotic stress. This study fabricated an ultrasensitive molecular imprinted polymer (MIP) electrochemical sensor to perform in vivo measurements of GB. Polydopamine (PDA) was formed on the carboxylated multi-walled carbon nanotubes (COOH-MWCNTs) by spontaneous polymerisation of dopamine (DA). Then MIP-coated MWCNTs were fabricated on a Au nanoparticles (NP) and thionine (Thi) modified screen-printed electrode (SPE). The MIP-COOH-MWCNTs/pThi/AuNPs/SPE exhibited an ultrasensitive GB detection response between 1 fmol/L and 10 mmol/L (R 2 = 0.996) with a low detection limit (0.707 fmol/L, S/N = 3). In vivo measurement of GB in cucumber seedling leaves under different salinity stress conditions confirmed the practical applicability of the MIP sensor. Thus, this study proposed a novel and promising fabrication method for an electrochemical MIP sensor that has broad application prospects in precision agriculture.
Why it matches plant phenotyping methods植物体内のグリシンベタインという生理状態を測定する電気化学センサーの作製・性能評価が研究の中心であり、植物フェノタイピング手法に該当する。
abstractThis study fabricated an ultrasensitive molecular imprinted polymer (MIP) electrochemical sensor to perform in vivo measurements of GB.
Automated disease inspection of greenhouse vegetables is less limited by the visibility of heavily infected leaves and more so by the reliable location of early lesions covering only a few dozen pixels. SE-YOLOv8 is a small-object-enhanced detector in this study that maintains a high-resolution P2 path, performs adaptive bidirectional cross-scale fusion, adds a lightweight coordinate attention module, and uses a scale-balanced localization objective. A greenhouse dataset with 12,480 images of tomato, cucumber, pepper and lettuce was divided into five disease categories plus a healthy background, and 31,762 annotated lesions were included. Under a fixed 640 × 640 input, the proposed model achieved 94.8% precision, 92.6% recall, 95.7% , and 72.4% . Relative to YOLOv8s, recall for lesions smaller than 32 × 32 pixels increased by 8.9 percentage points while parameters rose from 11.2 M to 12.7 M. The model sustained 48.6 frames/s on an NVIDIA Jetson Orin NX and reduced the expected calibration error from 6.8% to 3.9%. Ablation and robustness experiments show that the high-resolution branch contributes the most to the gain, and adaptive fusion and coordinate attention improve discrimination under glare, leaf overlap and clutter. Therefore, by maintaining fine spatial information and regulating cross-scale flow of information, early disease localisation can be achieved without reducing the throughput of practical greenhouses.
Why it matches plant phenotyping methods温室作物の病斑を画像から検出・位置推定する深層学習手法を開発し、比較、アブレーション、頑健性、速度、較正を評価しており、植物病害状態の表現型取得が中心である。
abstractSE-YOLOv8 is a small-object-enhanced detector in this study that maintains a high-resolution P2 path, performs adaptive bidirectional cross-scale fusion, adds a lightweight coordinate attention module, and uses a scale-balanced localization objective.
Abstract Crop protection from diseases through applications of plant protection products is crucial to secure worldwide food production. Nevertheless, sustainable management of plant diseases is an open challenge with a major role in the economic and environmental impact of agricultural activities. A primary contribution is expected to come from precision crop protection approaches, with treatments tailored to spatial and time-specific needs of the crop, in contrast to the current practice of applying treatments uniformly to fields. In view of this, image-based automatic detection of early disease symptoms is considered a key enabling technology for high throughput scouting of the crop, in order to timely target the treatments on emerging infection spots. Thanks to the unprecedented performance in image-recognition problems, Deep Learning (DL) methods based on Convolutional Neural Networks (CNNs) have recently entered the domain of plant disease detection. This work develops two DL approaches for automatic recognition of powdery mildew disease on cucumber leaves, with a specific focus on exploring unsupervised techniques to overcome the need of large training set of manually labelled images. To this aim, autoencoder networks were implemented for unsupervised detection of disease symptoms through: i) clusterization of features in a compressed space; ii) anomaly detection. The two proposed approaches were applied to multispectral images acquired during in-vivo experiments, and the obtained results were assessed by quantitative indices. The clusterization approach showed only partially capability to provide accurate disease detection, even if it gathered some relevant information. Anomaly detection showed instead to possess a significant potential of discrimination which could be further exploited as a prior step to train more powerful supervised architectures with a very limited number of labelled samples.
Why it matches plant phenotyping methodsキュウリ葉の病徴をマルチスペクトル画像から自動検出する深層学習手法を開発・評価しており、植物病害状態の表現型取得が中心である。
abstractThis work develops two DL approaches for automatic recognition of powdery mildew disease on cucumber leaves
Plant disease can cause reduce quality and quantity of agriculture crops. In some countries farmers spend considerable time to consult with plant protection, while the time is an important factor to controlling of disease. Due to the fact that Powdery Mildew and Anthracnose fungal diseases cause the most damage in cucumber greenhouses, in this study, by presenting a non-destructive method based on image processing technique and artificial neural network, these two types of fungal diseases have been diagnosed. The steps related to the implementation of the proposed method are divided into three parts: segmentation, separation of damaged parts from the leaf and classification of the disease type class. After color and texture features were extracted from cucumber leaf samples, a multilayer perceptron neural network with error post-diffusion learning algorithm was used to separate different classes of images. Network input is the average of the main color components (R, G, B) of the images and the output is zero as a healthy leaf, number one as Powdery Mildew and number two as Anthracnose. The structure of this network was 24-3-4-3, which uses the tansig transfer function for the hidden and output layer, and among the educational functions. So back propagation (BP) algorithm in neural network by using lovenerg marquart (LM) function training has been successfully to diagnosis and classifies plant diseases in 6 second with 99.95% accuracy.
Why it matches plant phenotyping methodsキュウリ葉の画像から病斑を抽出し、うどんこ病・炭疽病・健全状態を分類する画像処理とニューラルネットワーク手法が研究の中心であるため。
abstractin this study, by presenting a non-destructive method based on image processing technique and artificial neural network, these two types of fungal diseases have been diagnosed.
Objectives A well-known drawback to the implementation of Convolutional Neural Networks (CNNs) for image-recognition is the intensive annotation effort for large enough training dataset, that can become prohibitive in several applications. In this study we focus on applications in the agricultural domain and we implement Deep Learning (DL) techniques for the automatic generation of meaningful synthetic images of plant leaves, which can be used as a virtually unlimited dataset to train or validate specialized CNN models or other image-recognition algorithms. Methods Following an approach based on DL generative models, we introduce a Leaf-to-Leaf Translation (L2L) algorithm, able to produce collections of novel synthetic images in two steps: first, a residual variational autoencoder architecture is used to generate novel synthetic leaf skeletons geometry, starting from binarized skeletons obtained from real leaf images. Second, a translation via Pix2pix framework based on conditional generator adversarial networks (cGANs) reproduces the color distribution of the leaf surface, by preserving the underneath venation pattern and leaf shape. Results The L2L algorithm generates synthetic images of leaves with meaningful and realistic appearance, indicating that it can significantly contribute to expand a small dataset of real images. The performance was assessed qualitatively and quantitatively, by employing a DL anomaly detection strategy which quantifies the anomaly degree of synthetic leaves with respect to real samples. Finally, as an illustrative example, the proposed L2L algorithm was used for generating a set of synthetic images of healthy end diseased cucumber leaves aimed at training a CNN model for automatic detection of disease symptoms. Conclusions Generative DL approaches have the potential to be a new paradigm to provide low-cost meaningful synthetic samples. Our focus was to dispose of synthetic leaves images for smart agriculture applications but, more in general, they can serve for all computer-aided applications which require the representation of vegetation. The present L2L approach represents a step towards this goal, being able to generate synthetic samples with a relevant qualitative and quantitative resemblance to real leaves.
Why it matches plant phenotyping methods植物葉画像を生成し、葉形状・葉脈・表面色を再現する画像生成手法を開発・評価しており、植物フェノタイピング関連の画像解析ワークフローが中心である。
abstractwe implement Deep Learning (DL) techniques for the automatic generation of meaningful synthetic images of plant leaves
Reproduction assets foundThe paper explicitly states that the authors' code and data for the Leaf2Leaf generative leaf-image synthesis pipeline are publicly available on GitHub, which is a paper-specific, actionable asset.Code · publicData Availability: The code and data for reproducibility are available on GitHub ( https://github.com/AleBenfe/Leaf2Leaf ).Open asset ↗AleBenfe/Leaf2Leaflines:135-147Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Marssonina blotch infect the entire tree and black rot creates the possibility for fungicides to infect the neighborhood fruits thereby creating the entire fruit basket infected. Marssonina blotch and black rot are the most commonly occurring infections on fruits. Scab is discovered as the other category of fruit infection caused by a fungus infecting even leaves that cause cracks and immature stage of fruits and leaves. The preprocessing steps involve the process of segmentation, filtration and haar cascade defect training using the training data set consisting of 2500 defect records on apple, strawberry, grapefruit, cucumber, kiwi, lime, mango and guava. Deep learning and Stacking ensemble learning is used to diagnose the infections on fruits by using the CNN algorithm to create fine grained chunk visuals for disease prediction. The system is constructed to identify color of the fruit so that the system is able to predict the fruit image on all the possible colors for example on apple both red and green color apples are trained. The overall accuracy of prediction recorded by the proposed work is 97.8% which proves to hit the required efficiency of the diagnosis system to prevent the infection on fruits and increase the productivity and marketing strategy. Blotch prediction accuracy is observed as 97.5%, scab as 95.89 %and rot prediction accuracy is recorded to 98.2%.
Why it matches plant phenotyping methods果実画像から病害状態を分類・診断する画像解析手法が研究の中心であり、植物の病害表現型を直接推定しているため。
abstractDeep learning and Stacking ensemble learning is used to diagnose the infections on fruits by using the CNN algorithm to create fine grained chunk visuals for disease prediction.
The excessive application of nitrogen in cucumber cultivation may lead to nitrate accumulation in fruits with potential toxicity to humans. Harvested fruits of agricultural crops should be evaluated for residual nitrogen, phosphorus, and potassium (NPK) nutrient levels. This is necessary to avoid nutrient toxicity from the consumption of fresh produce with excessive nutrient levels. Electronic noses are instruments well-suited for the nondestructive detection of fruit and vegetable quality based on volatile organic compound (VOC) emissions. This proof-of-concept study was designed to test the efficacy of using an electronic nose with statistical regression models to indirectly predict excessive fertilizer application based on VOC emissions from cucumber fruits grown under controlled greenhouse conditions to simulate field conditions but eliminate most environmental variables affecting plant volatile emissions. To identify excess nitrogen in cucumber plants, five different levels of urea fertilizer application rates were tested on cucumbers (control without fertilizer, 100, 200, 300, and 400 kg/ha). Chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models to predict nitrogen (N), phosphorus (P), and potassium (K) levels in cucumber fruits following application of different fertilizer rates to greenhouse soils. The correlation coefficients for the MLR model (based on the optimal parameters of PCR and PLSR) were 0.905 and 0.905 for the calibration sets and 0.900 and 0.900 for the validation sets, respectively. The nitrogen prediction model for fruit nitrates was more accurate than other nutrient models. The proposed method could potentially be used to indirectly detect excessive use of fertilizers in cucumber field crops.
Why it matches plant phenotyping methodsキュウリ果実のVOCを電子鼻で取得し、回帰モデルにより果実中のNPK残留量を推定する測定・解析法が研究の中心であり、校正・検証も実施している。
abstractThis proof-of-concept study was designed to test the efficacy of using an electronic nose with statistical regression models to indirectly predict excessive fertilizer application based on VOC emissions from cucumber fruits
Plant diseases are the main factors affecting the agricultural production. At present, improving the efficiency of plant disease identification in natural scenarios is a crucial issue. Due to this significance, this study aims at providing an efficient detection method, which is applicable to disease detection in natural scenes. The proposed MTC-YOLOv5n method is based on the YOLOv5 model, which integrates the Coordinate Attention (CA) and Transformer in order to reduce invalid information interference in the background, and combines a Multi-scale training strategy (MS) and feature fusion network to improve the small object detection accuracy. MTC-YOLOv5n is trained and validated on a self-built cucumber disease dataset. The model size and FLOPs are respectively 4.7 MB and 6.1 G, achieving 84.9 % mAP and FPS up to 143. Compared with the advanced single-stage detection model, the experimental results show that MTC-YOLOv5n has higher detection accuracy and speed, smaller computation and model size. In addition, the proposed model is tested under the interference of strong noise conditions such as dense fog, drizzle and dark light, which shows that the model has strong robustness. Finally, the comprehensive experimental results demonstrate that MTC-YOLOv5n is lightweight, efficient and suitable for deployment to mobile terminals for disease detection in natural scenarios.
Why it matches plant phenotyping methodsキュウリの病徴を自然画像から検出するYOLOv5ベースの画像解析手法を開発し、自作データセットで精度・速度・頑健性を検証しており、植物フェノタイピング手法が中心である。
titleA multi-scale cucumber disease detection method in natural scenes based on YOLOv5
To explore the use of information technology in detecting crop diseases, a method based on hyperspectra-terahertz for detecting cucumber powdery mildew is proposed. Specifically, a method of effective hyperspectrum establishment, a method of spectral preprocessing, a method of selecting the feature wavelength, and a method of establishing discriminant models are studied. Firstly, the effective spectral information under visible light and near infrared is preprocessed by Savitzky-Golay (SG) smoothing, discrete wavelet transform, and move sliding window, which determine the optimal preprocessing method to be wavelet transform. Then stepwise discriminant analysis is used to select the feature wavelengths in the visible and near-infrared bands, forming the feature space. According to the features, a linear discriminant model is established for the wave bands, and the average recognition rate of cucumber powdery mildew is 93% in the whole wave band. The preprocessing method of terahertz data, the screening method of terahertz effective spectrum, the selection method of feature wavelength and the establishment method of classification model are studied. Python 3.8 is used to preprocess the terahertz raw data and establish the terahertz effective spectral data set for subsequent processing. Through iterative variable subset optimization - iterative retaining informative variables (IVSO-IRIV), the terahertz effective spectrum is screened twice to form the terahertz feature space. After that, the optimal regularization parameter and regularization solution methods are selected, and a sparse representation classification model is established. The accuracy of cucumber powdery mildew identification under the terahertz scale is 87.78%. The extraction and analysis methods of terahertz and hyperspectral feature images are studied, and more details of lesion samples are restored. Hence, the use of hyperspectral and terahertz technology can realize the detection of cucumber powdery mildew, which provides a basis for research on the hyperspectral and terahertz technology in detection of crop diseases.
Why it matches plant phenotyping methodsキュウリうどんこ病という植物の病徴を対象に、ハイパースペクトル・テラヘルツ計測、前処理、特徴波長選択、分類モデルを開発・評価しており、病害表現型の取得・判別方法が中心である。
abstracta method based on hyperspectra-terahertz for detecting cucumber powdery mildew is proposed.
In recent years, 3D imaging became an increasingly popular screening modality for high-throughput plant phenotyping. The 3D scans provide a rich source of information about architectural plant organization which cannot always be derived from multi-view projection 2D images. On the other hand, 3D scanning is associated with a principle inaccuracy by assessment of geometrically complex plant structures, for example, due the loss of geometrical information on reflective, shadowed, inclined and/or curved leaf surfaces. Here, we aim to quantitatively assess the impact of geometrical inaccuracies in 3D plant data on phenotypic descriptors of four different shoot architectures, including tomato, maize, cucumber, and arabidopsis. For this purpose, virtual laser scanning of synthetic models of these four plant species was used. This approach was applied to simulate different scenarios of 3D model perturbation, as well as the principle loss of geometrical information in shadowed plant regions. Our experimental results show that different plant traits exhibit different and, in general, plant type specific dependency on the level of geometrical perturbations. However, some phenotypic traits are tendentially more or less correlated with the degree of geometrical inaccuracies in assessing 3D plant architecture. In particular, integrative traits, such as plant area, volume, and physiologically important light absorption show stronger correlation with the effectively visible plant area than linear shoot traits, such as total plant height and width crossover different scenarios of geometrical perturbation. Our study addresses an important question of reliability and accuracy of 3D plant measurements and provides solution suggestions for consistent quantitative analysis and interpretation of imperfect data by combining measurement results with computational simulation of synthetic plant models.
Why it matches plant phenotyping methods3D植物形質測定の幾何的不正確さを仮想レーザースキャンで定量評価し、測定信頼性と精度を検証する研究であり、フェノタイピング手法が中心である。
abstractHere, we aim to quantitatively assess the impact of geometrical inaccuracies in 3D plant data on phenotypic descriptors of four different shoot architectures
The deep learning methods based on convolutional neural network (CNN) have been widely explored in dataset augmentation and recognition of plant leaf diseases. The recently developed transformer-based models such as Swin Transformer (SwinT) show competitive and even better performance on various visual benchmarks compared with CNN due to their inherent attention mechanism and ability to learn long-range dependency among pixels. In this paper, a backbone network based on improved SwinT was proposed and applied to the data augmentation and recognition of practical cucumber leaf diseases. Firstly, the patch partition of SwinT was improved by step-wise small patch embeddings for enhancing the ability of feature extraction without increasing the number of parameters. Secondly, the leaf extraction module composed of the proposed backbone network and Grad-CAM was integrated into the Generation Adversarial Network (GAN) to construct STA-GAN (SwinT-based and Attention-guided GAN), which generated diseased spots only in the leaf region of healthy images with complex background for augmenting the disease dataset. Finally, by means of transfer learning, the augmented datasets were used to train the recognition model of cucumber leaf diseases with the proposed backbone network. From the experimental results, it has been demonstrated that STA-GAN exhibited stronger ability to generate high-quality images than LeafGAN, even only approximated when LeafGAN consumed much more training images. Additionally, with STA-GAN, the disease recognition accuracies reached 98.97%, 96.81%, 94.85% and 90.01% when improved SwinT, original SwinT, EfficientNet-B5 and ResNet-101 were employed as the backbone of recognition model respectively, increasing by 2.17%, 3.62%, 2.13% and 11.23% compared with LeafGAN, revealing that the approaches based on improved SwinT could indeed help in boosting the performance of both data augmentation and recognition of practical cucumber leaf diseases. The proposed approach has the potential of dealing with the common challenge of insufficient data size and complex background in other similar plant science tasks.
Why it matches plant phenotyping methodsキュウリ葉の病斑を画像から生成・認識する深層学習手法を開発・評価しており、植物の病害状態の表現型推定が中心である。
abstracta backbone network based on improved SwinT was proposed and applied to the data augmentation and recognition of practical cucumber leaf diseases
Mechanistic models of canopy photosynthesis usually upscale leaf photosynthesis to crop level. A detailed prediction of canopy microclimate with accurate leaf morphological and physiological model parameters is the pre-requisite for accurate predictions. It is well established that certain leaf model parameters ( V cmax , J max ) of the frequently adopted Farquhar and Caemmerer photosynthesis model change with leaf age and light interception history. Previous approaches to predict V cmax and J max focused primarily on light interception, either by cumulative intercepted photosynthetic photon flux density (PPFD) or by closely related proxy variables such as leaf nitrogen content per leaf area. However, for plants with monopodial growth, such as vertically grown tomatoes or cucumber crops, in greenhouse production, there is a strong relationship between leaf age and light interception, complicating the experimental and mathematical separation of both effects. We propose a modeling framework that separates age and light intensity-related acclimation effects in a crop stand: Improved approximation of intra-leaf light absorption profiles with cumulative chlorophyll content ( Chl ) is the basis, while parameters are estimated via Gaussian process regression from total Chl , carotenoid content ( Car ), and leaf mass per area ( LMA ). The model approximates light absorption profiles within a leaf and links them to leaf capacity profiles of photosynthetic electron transport. Published datasets for Spinacia oleracea and Eucalyptus pauciflora were used to parameterize the relationship between light and capacity profiles and to set the curvature parameter of electron transport rate described by a non-rectangular hyperbola on Cucumis sativus . Using the modified capacity and light absorption profile functions, the new model was then able to predict light acclimation in a 2-month period of a fully grown tomato crop. An age-dependent lower limit of the electron transport capacity per unit Chl was essential in order to capture the decline of V cmax and J max over time and space of the investigated tomato crop. We detected that current leaf photosynthetic capacity in tomato is highly affected by intercepted light-sum of 3-5 previous days.
Why it matches plant phenotyping methods葉齢・光環境・葉内光吸収から光合成能力を推定する新しいモデリング枠組みを開発しており、植物の生理形質推定が研究の中心である。
abstractWe propose a modeling framework that separates age and light intensity-related acclimation effects in a crop stand
Wild species related to domesticated crops (crop wild relatives, or CWRs) represent a high level of genetic diversity that provides a practical gene pool for crop pre-breeding employed to address climate change and food demand challenges globally. Nevertheless, rapid identifying and visual tracking of alien chromosomes and sequences derived from CWRs have been a technical challenge for crop chromosome engineering. Here, a species-specific oligonucleotide (oligo) pool was developed by using the reference genome of Cucumis hystrix (HH, 2n = 2x = 24), a wild species carrying many favorable traits and interspecific compatibility with cultivated cucumber (C. sativus, CC, 2n = 2x = 14). These synthetic double-stranded oligo probes were applied to validate the assembly and characterize the chromosome architectures of C. hystrix, as well as to rapidly identify C. hystrix-chromosomes in diverse C. sativus-hystrix chromosome-engineered germplasms, including interspecific hybrid F1 (HC), synthetic allopolyploids (HHCC, CHC, and HCH) and alien additional lines (CC-H). Moreover, a ∼2Mb of C. hystrix-specific sequences, introduced into cultivated cucumber, were visualized by CWR-specific oligo-painting. These results demonstrate that the CWR-specific oligo-painting technique holds broad applicability for chromosome engineering of numerous crops, as it allows rapid identification of alien chromosomes, reliable detection of homoeologous recombination, and visual tracking of the introgression process. It is promising to achieve directed and high-precision crop pre-breeding combined with other breeding techniques, such as CRISPR/Cas9-mediated chromosome engineering.
Why it matches plant phenotyping methodsCWR特異的オリゴペインティングによる染色体構造・外来染色体・導入配列の可視化を開発し、複数の育種材料で検証している。染色体状態の画像取得・判定が研究の中心であり、単なる分子測定や生物学的実験の補助測定ではない。
titleRapid and visual monitoring of alien sequences using crop wild relatives specific oligo-painting
With the success of deep learning mechanisms, applications that automatically diagnose plant disease have been developed. When used with unknown test datasets, these systems are prone to overfitting, and their diagnostic performance degrades dramatically. From the attention techniques presented in this paper, a new image-to-image identification tool named LeafGAN has been developed. LeafGAN is designed to provide a wide range of healthy image modifications by emerging as a data multiplication tool to improve the efficiency of plant pathogenesis. The proposed method can only change significant portions from images with various backdrops in order to improve the diversity of training images. There are five different types of cucumber illness classification models. In order to reveal that, vanilla CycleGAN is used. Further it is ineffective in improving the data. Research using five types of cucumber disease classification reveals that the data multiplication with vanilla cyclone is ineffective and it is only improving generalization and increasing 0.7% based on disease identification performance. On the other hand, LeafGAN upgrades the diagnostic efficiency by 7.4%, and this research work firmly agrees that the image created by LeafGAN will be of higher quality and reliability when compared to the image created by Vanilla LeafGAN.
Why it matches plant phenotyping methods植物病害画像のデータ拡張手法LeafGANを開発・評価し、葉の病害状態の画像分類性能を改善することが中心であるため、植物フェノタイピング手法として含める。
abstracta new image-to-image identification tool named LeafGAN has been developed
The development of genetic and genomic resources for biological studies in cucumber has experienced an unprecedented boom in recent years. To investigate the function of putative meiotic genes and germplasm in breeding programs, an accurate cytogenetic characterization is required. Cytological methods and reference to investigate meiosis in cucumber are limited at present. Here we provide a set of cytological techniques that have been adapted for the study of meiosis in cucumber. The meiotic stages can be identified with high precision using hierarchical criteria from developing buds, undisturbed meiocytes, and freshly stained chromosomes. A meiotic cytological atlas of all stages is presented as a reference for identifying particular stages and for comparison of meiosis between normal and mutant plants. We performed a comparative analysis of the distribution of cytoplasmic organelles between cucumber and Arabidopsis, and we described a highly nonsynchronous condensation of chromosome parts during diplotene. A simplified fluorescence in situ hybridization (FISH) protocol, using robustly spread chromosomes, were developed. In addition, we designed a single oligonucleotide probe for 5S rDNA to use in karyotyping and monitoring of homologous chromosome pairing, which will make FISH analysis of 5S rDNA easier and more economical.
Why it matches plant phenotyping methodsキュウリの減数分裂段階・染色体構造・相同染色体対合を観察するための細胞学的手法とFISHプロトコルの開発が中心であり、植物の細胞遺伝学的状態を測定する方法論研究である。
abstractHere we provide a set of cytological techniques that have been adapted for the study of meiosis in cucumber.
Detection of morphological stress symptoms through 3D examination of plants might be a cost-efficient way to avoid yield losses and ensure product quality in agricultural and horticultural production. Although the 3D reconstruction of plants was intensively performed, the relationships between morphological and physiological plant responses to salinity stress need to be established. Therefore, cucumber plants were grown in a greenhouse in nutrient solutions under three salinity treatments: 0, 25, and 50 mM NaCl. To detect stress-induced changes in leaf transversal and longitudinal angles and dimensions, photographs were taken from plants for 3D reconstruction through photogrammetry. For assessment of physiological stress responses, invasive leaf measurements, including the determination of leaf osmotic potential, leaf relative water content, and the leaf dry to fresh weight ratio, were performed. The transversal and longitudinal leaf dimensions revealed statistically significant differences between stressed and control plants after 60 °Cd (day 3) for the leaves which appeared before stress imposition. Strong correlations were found between the transversal width and some investigated physiological traits. Morphological changes were shown as indicators of physiological responses of leaves under salinity stress.
Why it matches plant phenotyping methods3Dフォトグラメトリによる葉形状の取得・再構成を用いて、塩ストレスの早期兆候を定量化する手法が研究の中心であるため。
abstractDetection of morphological stress symptoms through 3D examination of plants might be a cost-efficient way to avoid yield losses
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 · UnverifiedCrossref · checked 15 Sept 2026
Published12 Feb 2022International Journal of Pattern Recognition and Artificial IntelligenceCited by 1 · OpenAlex ↗
With the development of modern agricultural facilities, crop diseases recognition, nutritional status and morphology achieved rapid growth. To avoid yield loss caused by the delay of disease detection, digital images that contain information with respect to crop growth, disease type and nutrition deficiency have been studied by some researchers. However, traditional image processing methods fail to extract typical disease features of crop images with ambiguous disease information. In this paper, a crop disease image recognition technique based on the salient region and hierarchical indexing was proposed. Improved Harris algorithm and maximum radius were used to calculate the widest salient region. In order to eliminate the effect of different salience distribution ranges between different features, a group of images in the cucumber disease image library were normalized. Experiment results indicate that the time complexity of each algorithm will go up as the size of the dataset increase. Especially when testing large datasets, nonhierarchical and nonclustering, hierarchical and nonclustering and hierarchical based on points all tend to raise the algorithm’s time complexity. Plant Village dataset and AI Challenger 2018 dataset were utilized to compare the recognition performances among the models based on machine learning, neural network, deep learning and our methods. The experiment results show that the method proposed in this paper is capable of recognizing local similar images effectively rather than global similar images, therefore, it has better recognition performance than the model learning methods in the early detection stage of crop disease.
Why it matches plant phenotyping methods作物病害画像から病害状態を認識する画像解析手法を提案・評価しており、植物病害フェノタイプの取得・抽出が中心的な貢献である。
abstractIn this paper, a crop disease image recognition technique based on the salient region and hierarchical indexing was proposed.
Accurately determining the nutritional status of plants can prevent many diseases caused by fertilizer disorders. Leaf analysis is one of the most used methods for this purpose. However, in order to get a more accurate result, disorders must be identified before symptoms appear. Therefore, this study aims to identify leaves with excessive nitrogen using one-dimensional convolutional neural networks (1D-CNN) on a dataset of spectral data using the Keras library. Seeds of cucumber were planted in several pots and, after growing the plants, they were divided into different classes of control (without excess nitrogen), N30% (excess application of nitrogen fertilizer by 30%), N60% (60% overdose), and N90% (90% overdose). Hyperspectral data of the samples in the 400–1100 nm range were captured using a hyperspectral camera. The actual amount of nitrogen for each leaf was measured using the Kjeldahl method. Since there were statistically significant differences between the classes, an individual prediction model was designed for each class based on the 1D-CNN algorithm. The main innovation of the present research resides in the application of separate prediction models for each class, and the design of the proposed 1D-CNN regression model. The results showed that the coefficient of determination and the mean squared error for the classes N30%, N60% and N90% were 0.962, 0.0005; 0.968, 0.0003; and 0.967, 0.0007, respectively. Therefore, the proposed method can be effectively used to detect over-application of nitrogen fertilizers in plants.
Why it matches plant phenotyping methods葉のハイパースペクトル画像から過剰窒素状態を推定するCNN手法を開発・評価しており、植物状態の取得・抽出が研究の中心である。
abstractthis study aims to identify leaves with excessive nitrogen using one-dimensional convolutional neural networks (1D-CNN) on a dataset of spectral data using the Keras library.
In the early stage of P deficiency in cucumbers, the P deficiency symptoms in leaves are similar to the symptoms in control leaves at the early stage of aging and are difficult to identify with naked eyes or computer image processing techniques. In order to realize the quick diagnosis of P deficiency in plants at the early stage, the NIR hyperspectral images of control leaves and P-deficient leaves were collected, and the feature information of the NIR hyperspectral images was extracted by PCA and ICA respectively. Through PCA and HCA verification, the IC1 component diagram of P-deficient leaves NIR hyperspectral image could effectively characterize the features of invisible water-stained plaques caused by early P-deficient leaves. Region of interest from IC1 was selected to extract spectral information for classification, and the diagnostic rate was remarkably improved. Finally, 240 leaves were diagnosed by using the BP-ANN model with a diagnostic rate of 97.5%. In addition, the experiment verified that it was possible to diagnose whether the plant was in the state of P deficiency 21 days in advance, and timely guidance of top dressing was of great significance to increase yield.
Why it matches plant phenotyping methodsキュウリ葉のNIRハイパースペクトル画像から、目視困難な早期リン欠乏状態を抽出・診断する画像解析手法を開発・検証しており、植物状態の取得方法が研究の中心である。
abstractthe feature information of the NIR hyperspectral images was extracted by PCA and ICA respectively
Research on the recognition and segmentation of vegetable diseases in simple environments based on deep learning has achieved a relative success. However, in complex environments, the image background often contains elements similar to the representation of leaves and disease spots, making it difficult for the recognition model to segment leaves and disease spots. Consequently, the segmentation precision is significantly reduced, which further affects the accuracy of disease severity classification. In response to this problem, while discussing and analyzing the advantages and disadvantages of DeepLabV3+ and U-Net, this study proposed a two-stage model that fuses DeepLabV3+ and U-Net for cucumber leaf disease severity classification (DUNet) in complex backgrounds. In the first stage, this model uses DeepLabV3+ to segment leaves from complex backgrounds. The images of leaves obtained after segmentation are used as the input for the second stage. In the second stage, U-Net is used to segment the diseased leaves to obtain disease spots. Finally, the ratio of the pixel area of disease spots over the pixel area of leaves is calculated so as to classify the disease severity. The experiment results show that the proposed model is able to segment leaves and disease spots from complex backgrounds in a step-by-step manner so as to complete disease severity classification. The leaf segmentation accuracy reached 93.27%, the Dice coefficient of disease spot segmentation reached 0.6914, and the average disease severity classification accuracy reached 92.85%. Compared with other models, the model proposed in this study has higher robustness, segmentation precision and classification accuracy, providing important ideas and methods for classifying the severity of cucumber leaf diseases in complex backgrounds.
Why it matches plant phenotyping methodsキュウリ葉の病斑を画像分割し、病斑面積比から病害重症度を推定する手法を開発・評価しており、植物表現型取得が中心である。
titleA cucumber leaf disease severity classification method based on the fusion of DeepLabV3+ and U-Net
In recent years, farmers have often mistakenly resorted to overuse of chemical fertilizers to increase crop yield. However, excessive consumption of fertilizers might lead to severe food poisoning. If nutritional deficiencies are detected early, it can help farmers to design better fertigation practices before the problem becomes unsolvable. The aim of this study is to predict the amount of nitrogen (N) content (mg l−1) in cucumber (Cucumis sativus L., var. Super Arshiya-F1) plant leaves using hyperspectral imaging (HSI) techniques and three different regression methods: a hybrid artificial neural networks-particle swarm optimization (ANN-PSO); partial least squares regression (PLSR); and unidimensional deep learning convolutional neural networks (CNN). Cucumber plant seeds were planted in 20 different pots. After growing the plants, pots were categorized and three levels of nitrogen overdose were applied to each category: 30%, 60% and 90% excesses, called N30%, N60%, N90%, respectively. HSI images of plant leaves were captured before and after the application of nitrogen excess. A prediction regression model was developed for each individual category. Results showed that mean regression coefficients (R) for ANN-PSO were inside 0.937–0.965, PLSR 0.975–0.997, and CNN 0.965–0.985 ranges, test set. We conclude that regression models have a remarkable ability to accurately predict the amount of nitrogen content in cucumber plants from hyperspectral leaf images in a non-destructive way, being PLSR slightly ahead of CNN and ANN-PSO methods.
Why it matches plant phenotyping methodsハイパースペクトル画像からキュウリ葉の窒素含量という植物生理形質を非破壊推定する回帰手法を開発・比較しており、表現型取得・抽出法が中心である。
abstractThe aim of this study is to predict the amount of nitrogen (N) content (mg l−1) in cucumber (Cucumis sativus L., var. Super Arshiya-F1) plant leaves using hyperspectral imaging (HSI) techniques and three different regression methods
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Plant scientists require high quality phenotypic datasets. Computer-vision based methods can improve the objectiveness and the accuracy of phenotypic measurements. In this paper, we focus on 3D point clouds for measuring plant architecture of cucumber plants, using spectral data and deep learning (DL). More specifically, the focus of this paper is on the segmentation of the point clouds, such that for each point it is known to which plant part (e.g. leaf or stem) it belongs. It was shown that the availability of spectral data can improve the segmentation, with the mean intersection-over-union rising from 0.90 to 0.95. Furthermore, we analysed the effect of uncertainty in the collection of ground truth data. For this purpose, we hand-labelled 264 point clouds of cucumber plants twice and show that the intra-observer variability between those two annotation sets can be as low as 0.49 for difficult classes, while it was 0.99 for the class with the least uncertainty. Adding the second set of hand-labelled data to the training of the network improved the segmentation performance slightly. Finally, we show the improved performance of a 4-class segmentation over an 8-class segmentation, emphasizing the need for a careful design of plant phenotyping experiments. The results presented in this paper contribute to further development of automated phenotyping methods for complex plant traits.
Why it matches plant phenotyping methodsキュウリの3D点群とスペクトルデータを用いた植物部位分割手法を開発・評価し、植物構造形質の自動フェノタイピングに直接貢献するため、方法が中心的である。
abstractIn this paper, we focus on 3D point clouds for measuring plant architecture of cucumber plants, using spectral data and deep learning (DL).
Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.
Why it matches plant phenotyping methods作物画像から開花・収量を推定するコンピュータビジョン／深層学習手法を中心に扱うレビューであり、植物表現型取得・推定手法のレビューとして収録対象。
titleRecognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review
The authors showed that a convenient, accurate and fast way of assessing the degree of influence of environmental factors on plants was needed to optimize photoculture. They emphasized the importance of non-destructive monitoring of crops physiological state of, for which they used phenomics technologies, for example, remote sensing using hyperspectral cameras. ( Research purpose ) To reveal the possibility of using hyperspectral imaging to determine the plant developmental stability. ( Materials and methods ) As a measure of the favorable impact of environmental factors on the growth and development of plants, their developmental stability was taken, numerically characterized by the fluctuating asymmetry value. The authors proposed to use vegetation indices determined from the leaf reflection spectra as a bilateral feature. The object of experimental research was juvenile cucumber plants. The studies were carried out in laboratory conditions. The spectral characteristics of cucumber leaves grown under different light quality of radiation were determined using a Specim IQ hyperspectral camera. Information on the spectral reflectances was extracted from the resulting data hypercube. As an example calculations were performed for Normalized Difference Vegetation Index. ( Results and discussion) The authors revealed differences in the productivity indicators of plants grown under different light quality. They revealed a significant frequency of occurrence of Normalized Difference Vegetation Index asymmetry in two halves of the cucumber leaf surface. The fluctuating nature of this asymmetry was confirmed. They found that with a light quality providing a higher productivity of plants, lower values of fluctuating asymmetry were observed, which indicate greater stability of plant development. ( Conclusions ) The authors proposed a method for determining the plant developmental stability using a hyperspectral camera. The method was based on the assessment of the fluctuating asymmetry of vegetation indices calculated for points on the leaf surface, characterized by the same location conditions relative to the border of its left and right halves. A preliminary assessment of the possibility of determining the developmental stability by the results of phenotyping using the example of cucumber plants showed the feasibility of the method and its practical applicability.
Why it matches plant phenotyping methods植物の発達安定性を、ハイパースペクトル画像から葉面の植生指数の変動非対称性として推定する方法を提案・予備評価しており、表現型取得手法が中心である。
abstractTo reveal the possibility of using hyperspectral imaging to determine the plant developmental stability.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Effective assessment of pathogen growth can facilitate screening for disease resistance, mapping of resistance loci, testing efficacy of control measures, or elucidation of fundamental host-pathogen interactions. Current methods are often limited by subjective assessments, inability to detect pathogen growth prior to appearance of symptoms, destructive sampling, or limited capacity for replication and quantitative analysis. In this work we sought to develop a real-time, in vivo , high-throughput assay that would allow for quantification of pathogen growth. To establish such a system, we worked with the broad host-range, highly destructive, soil-borne oomycete pathogen, Phytophthora capsici . We used an isolate expressing red fluorescence protein (RFP) to establish a microtiter plate, real-time assay to quantify pathogen growth in live tissue. The system was successfully used to monitor P. capsici growth in planta on cucumber ( Cucumis sativus ) fruit and pepper ( Capsicum annuum ) leaf samples in relation to different levels of host susceptibility. These results demonstrate usefulness of the method in different species and tissue types, allowing for highly replicated, quantitative time-course measurements of pathogen growth in vivo . Analyses of pathogen growth during initial stages of infection preceding symptom development show the importance of very early stages of infection in determining disease outcome, and provide insight into points of inhibition of pathogen growth in different resistance systems.
Why it matches plant phenotyping methods植物組織上の病原体増殖を蛍光でリアルタイム・定量測定する高スループット手法を開発し、複数の植物種・組織で検証している。病原体検出のみでなく、生体植物の感染進行・病害状態に関わる表現型取得が中心である。
abstractIn this work we sought to develop a real-time, in vivo , high-throughput assay that would allow for quantification of pathogen growth.
Economy of Bangladesh mostly depended on agriculture. As a small country, our population is over the limit. We are also a developing country also. For national GDP growth must be to increase the production. Every year a huge amount of agricultural production losses for the crop disease. As we know most of the farmers in our country are illiterate, have no proper knowledge about the disease, that's why they cannot manually detect the disease. I think if you properly detect the disease at the early stage we can solve the issue. We develop a model that can classify leaf disease. We focus on 5 major production crops in Bangladesh. Using computer vision technique our farmer will get the benefit. We use convolutional neural networks for classifying images. An algorithm cannot properly capture the features of existing data that's why we use a pre-trained feature extraction method that is MobileNetv2. MobileNetv2 is very useful for mobile devices. The research contains the proportions of validation accuracy of 90.38%. This approach resulted in the agriculture sector that will help a farmer to classify disease from harvest. The main goal of our model is to minimize the damage of suffering plants that can help to the growth of production. By solving this issue themselves farmers can also reduce cost. Our goal is that they can cure their crop at the right time. To achieve this goal we tend to think that we should develop a way to detect the leaf disease. We collect several kinds of cucumber leaves. And then any leave can be tasted by our model. By using our model we try to reduce the leaf disease.
Why it matches plant phenotyping methods葉画像から植物病害を分類するCNN手法を開発・検証しており、感染植物の状態推定が研究の中心であるため。
abstractWe develop a model that can classify leaf disease.
Identifying and characterizing the biochemical variation in plant tissues is an important task in many research fields. Small spectral differences of the plant cell wall that are caused by genetic or environmental influences may be superimposed by individual variation as well as by a microscopic heterogeneity in molecular composition and structure of different histological substructures. A set of 56 samples from Cucumis sativus (cucumber) plants, comprising a total of ~168,000 spectra from tissue sections of leaf, stem, and roots was investigated by Raman microspectroscopic mapping excited at 532 nm. A multivariate analysis was carried out in order to assess the variation of the spectra with respect to origin of the tissue, the histological (cell wall) substructures, and the possibility to discriminate the spectra obtained from different individuals that had been subjected to two different conditions during growth. Combining the results of principal component analysis (PCA) based classification with the original spatial information in the maps of 23 sections of leaf xylem, variation in cell wall composition is found for four different individuals that also includes a discrimination of tissue grown in the presence and absence of additional silicic acid in the irrigation water of the plants. The spectral data point to differences in a contribution by carotenoids, as well as by hydroxycinnamic acids to the spectra. The results give new insight into the chemical heterogeneity of plant tissues and may be useful for elucidating biochemical processes associated with biomineralization by vibrational spectroscopy.
Why it matches plant phenotyping methodsラマン顕微分光マッピングと多変量解析を用いて、植物組織の化学的不均一性や生育条件差を空間的・個体別に評価しており、植物の生理・化学的状態を取得する方法の適用が中心です。
titleMultivariate Raman mapping for phenotypic characterization in plant tissue sections.
CucumberLiDAR / point cloudLeafMorphology / geometry measurementSegmentationTrackingLeaf traits
Plant phenotyping deals with the metrological acquisition of plants in order to investigate the impact of environmental factors and a plant’s genotype on its appearance. Phenotyping methods that are used as standard in crop science are often invasive or even destructive. Due to the increase of automation within geodetic measurement systems and with the development of quasi-continuous measurement techniques, geodetic techniques are perfectly suitable for performing automated and non-invasive phenotyping and, hence, are an alternative to standard phenotyping methods. In this contribution, sequentially acquired point clouds of cucumber plants are used to determine the plants’ phenotypes in terms of their leaf areas. The focus of this contribution is on the spatio-temporal segmentation of the acquired point clouds, which automatically groups and tracks those sub point clouds that describe the same leaf. The application on example data sets reveals a successful segmentation of 93% of the leafs. Afterwards, the segmented leaves are approximated by means of B-spline surfaces, which provide the basis for the subsequent determination of the leaf areas. In order to validate the results, the determined leaf areas are compared to results obtained by means of standard methods used in crop science. The investigations reveal consistency of the results with maximal deviations in the determined leaf areas of up to 5%.
Why it matches plant phenotyping methodsキュウリの点群から葉を自動分割・追跡し、Bスプラインで葉面積を推定する手法を開発し、標準法との比較で検証しており、植物表現型取得が研究の中心である。
abstractThe focus of this contribution is on the spatio-temporal segmentation of the acquired point clouds, which automatically groups and tracks those sub point clouds that describe the same leaf.
Cucumber (Cucumis sativus L.) is a widely cultivated and economically profitable crop. However, Fusarium wilt disease can seriously affect cucumber yields, as it is difficult to prevent and eliminate. Therefore, a reliable method is needed for the rapid and early detection of Fusarium infection in cucumbers, which could be provided via the kinetic imaging of chlorophyll fluorescence (ChlF). In this study, ChlF imaging and kinetic parameters were utilized with gray and radial basis function models to monitor cucumber Fusarium wilt disease. The results indicate that the disease can be detected and predicted using this imaging technique before symptoms become visible.
Why it matches plant phenotyping methodsキュウリの感染状態をクロロフィル蛍光イメージングと定量解析で早期検出・予測する手法が研究の中心であり、植物病害状態の表現型計測に該当する。
abstracta reliable method is needed for the rapid and early detection of Fusarium infection in cucumbers, which could be provided via the kinetic imaging of chlorophyll fluorescence (ChlF).
Monitoring the growth of fruit vegetables is essential for the automation of cultivation management, and harvest. The objective of this study is to demonstrate that the current sensor technology can monitor the growth and yield of fruit vegetables such as tomato, cucumber, and paprika. We estimated leaf area, leaf area index (LAI), and plant height using coordinates of polygon vertices from plant and canopy surface models constructed using a three-dimensional (3D) scanner. A significant correlation was observed between the measured and estimated leaf area, LAI, and plant height (R 2 > 0.8, except for tomato LAI). The canopy structure of each fruit vegetable was predicted by integrating the estimated leaf area at each height of the canopy surface models. A linear relationship was observed between the measured total leaf area and the total dry weight of each fruit vegetable; thus, the dry weight of the plant can be predicted using the estimated leaf area. The fruit weights of tomato and paprika were estimated using the fruit solid model constructed by the fruit point cloud data extracted using the RGB value. A significant correlation was observed between the measured and estimated fruit weights (tomato: R 2 = 0.739, paprika: R 2 = 0.888). Therefore, it was possible to estimate the growth parameters (leaf area, plant height, canopy structure, and yield) of different fruit vegetables non-destructively using a 3D scanner.
Why it matches plant phenotyping methods3Dスキャナーと点群・3Dモデル解析を用いて、葉面積、LAI、草丈、樹冠構造、果実重量を非破壊推定し、実測値との相関で技術検証しているため、植物表現型取得法が中心である。
abstractWe estimated leaf area, leaf area index (LAI), and plant height using coordinates of polygon vertices from plant and canopy surface models constructed using a three-dimensional (3D) scanner.
The intelligent identification and classification of greenhouse plant diseases is an important research object in smart horticulture. In this study, our main task is to find an efficient method to solve the problem of disease similarity caused by two kinds of diseases occurring in the same leaf and the influence of external light. First, we obtain a cucumber leaf disease dataset in a naturally complex greenhouse background, which includes not only powdery mildew, downy mildew, healthy leaves, but also the combination of powdery mildew and downy mildew. Secondly, we use the current state-of-the-art method EfficientNet to construct a classification model for the above four types, Model accuracy is 97%, and prove that EfficientNet-B4 is the most suitable method for this study. Finally, we constructed a two-classification model of cucumber similar diseases by using EfficientNet-B4 improved with the most state-of-the-art optimizer Ranger, obtained unexpected accuracy (96%). The experimental results show that our improved method has significant effect on the classification of similar diseases of cucumber.
Why it matches plant phenotyping methodsキュウリ葉の画像から病害状態を分類する深層学習手法が研究の中心であり、植物の病害表現型を直接推定しているため。
abstractwe use the current state-of-the-art method EfficientNet to construct a classification model for the above four types
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Plant phenotyping is the task of measuring plant attributes mainly for agricultural purposes. We term length phenotyping the task of measuring the length of a plant part of interest. The recent rise of low cost RGB-D sensors and accurate deep artificial neural networks provides new opportunities for length phenotyping. We present a general technique for length phenotyping based on three stages: object detection, point of interest identification, and a 3D measurement phase. We address object detection and interest point identification by training network models for each task, and develop a robust de-projection procedure for the 3D measurement stage. We apply our method to three real world tasks: measuring the height of a banana tree, the length and width of banana leaves in potted plants, and the length of cucumbers fruits in field conditions. The three tasks were solved using the same pipeline with minor adaptations, indicating the method’s general potential. The method is stagewise analyzed and shown to be preferable to alternative algorithms, obtaining error of less than 10% deviation in all tasks. For leaves’ length and width, the measurements are shown to be useful for further phenotyping of plant treatment and mutant classification.
Why it matches plant phenotyping methods植物部位の長さをRGB-Dセンサー、物体検出、関心点検出、3D計測で推定する汎用フェノタイピング手法の開発・評価が中心である。
abstractWe present a general technique for length phenotyping based on three stages: object detection, point of interest identification, and a 3D measurement phase.
Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with sufficient training samples of each disease. However, collecting enough training samples is usually time consuming and labor-intensive. Moreover, influenced by the non-ideal measurement environment, samples may be corrupted by variables introduced by bad illumination and occlusions of adjacent leaves. Consequently, an extended collaborative representation (ECR)-based classification model is presented in this paper. Then, it is applied to cucumber leaf disease recognition, which constructs a pure spectral library consisting of several representative samples for each disease and designs a universal variation spectral library that deals with linear variables superimposed on samples. Thus, each query sample is encoded as a linear combination of atoms from these two spectral libraries and disease identity is determined by the disease of minimal reconstruction residuals. Experiments are conducted on spectral curves extracted from normal leaves and the disease lesions of leaves infected with cucumber anthracnose and brown spot. The diagnostic accuracy is higher than 94.7% and the average online diagnosis time is short, about 1 to 1.3 ms. The results indicate that the ECR-based classification model is feasible in the fast and accurate diagnosis of cucumber leaf diseases.
Why it matches plant phenotyping methodsキュウリ葉の病斑・病害状態を対象に、ハイパースペクトル画像から病害を認識するECR分類モデルを開発・評価しており、植物表現型（病害状態）の取得・判定法が中心である。
abstractConsequently, an extended collaborative representation (ECR)-based classification model is presented in this paper.
Obtaining high-quality phenotypic data that can be used to study the relationship between genotype, phenotype and environment is still labour-intensive. Digital plant phenotyping can assist in collecting these data by replacing human vision by computer vision. However, for complex traits, such as plant architecture, robust and generic digital phenotyping methods have not yet been developed. This study focusses on internode length in cucumber plants. A method for estimating internode length and internode development over time is proposed. The proposed method firstly applies a robust node-detection algorithm based on a deep convolutional neural network. In tests, the algorithm had a precision of 0.95 and a recall of 0.92. The nodes are detected in images from multiple viewpoints around the plant in order to deal with the complex and cluttered plant environment and to solve the occlusion of nodes by other plant parts. The nodes detected in the multiple viewpoint images are then clustered using affinity propagation. The predicted clusters had a homogeneity of 0.98 and a completeness of 0.99. Finally, a linear function is fitted, which allows to study internode development over time. The presented method was able to measure internode length in cucumber plants with a higher accuracy and a larger temporal resolution than other methods proposed in literature and without the time investment needed to obtain the measurements manually. The relative error of our complete method was 5.8%. The proposed method provides many opportunities for robust phenotyping of fruit-vegetable crops grown under greenhouse conditions.
Why it matches plant phenotyping methods深層学習とマルチビュー画像を用いてキュウリの節検出・追跡および節間長推定法を開発し、精度検証まで行っており、植物表現型取得が研究の中心である。
abstractA method for estimating internode length and internode development over time is proposed.
Aiming at the basic problems that plague crop growth in traditional agriculture, a method of identifying weeds using machine vision and applying chemical agents with selective variables was proposed. Collected visible light images of plant diseases, pre-processed the images, segmented the images using an instruction value composed of R, G, and B color components as a threshold, and wrote an algorithm to misjudge the background image after segmentation as a background. Pixels were used for information recovery; according to the analysis of the change in color characteristics after the occurrence of lesions, sample lesions were extracted using the two color features of G / R and G / B; the results of the damage degree of diseased leaves measured using image processing technology The analysis was performed and compared with the results of the plant disease degree determined by the paper card method in the traditional classification standard. The experiments show that the selected 7 characteristic parameters are used as the input of the neural network, and the number of types of cucumber leaf diseases that need to be identified is used as the output to build a BP neural network model. By adjusting various parameters in the BP neural network, the parameters with the best recognition effect are selected to train the network. The trained network is used to identify the plant disease image. As a result, the disease can be identified well, and the recognition rate is 93.5%.
Why it matches plant phenotyping methods画像処理とニューラルネットワークにより植物病害の病斑・被害度を推定する手法が研究の中心であり、従来の葉病害評価法との比較検証も行っているため。
abstractsample lesions were extracted using the two color features of G / R and G / B; the results of the damage degree of diseased leaves measured using image processing technology
Due to the imperfect development of the photosynthetic apparatus of the newborn leaves of the canopy, the photosynthesis ability is insufficient, and the photosynthesis intensity is not only related to the external environmental factors, but also significantly related to the internal mechanism characteristics of the leaves. Light suppression and even light destruction are likely to occur when there is too much external light. Therefore, focus on the newborn leaves of the canopy, the accurate construction of photosynthetic rate prediction model based on environmental factor analysis and fluorescence mechanism characteristic analysis has become a key problem to be solved in facility agriculture. According to the above problems, a photosynthetic rate prediction model of newborn leaves in canopy of cucumber was proposed. The multi-factorial experiment was designed to obtain the multi-slice large-sample data of photosynthetic and fluorescence of newborn leaves. The correlation analysis method was used to obtain the main environmental impact factors as model inputs, and core chlorophyll fluorescence parameters was used for auxiliary verification. The best modeling method PSO-BP neural network was used to construct the newborn leaf photosynthetic rate prediction model. The validation results show that the net photosynthetic rate under different environmental factors of cucumber canopy leaves can be accurately predicted. The coefficient of determination between the measured values and the predicted values of photosynthetic rate was 0.9947 and the root mean square error was 0.8787. Meanwhile, combined with the core fluorescence parameters to assist the verification, it was found that the fluorescence parameters can accurately characterize crop photosynthesis. Therefore, this study is of great significance for improving the precision of light environment regulation for new leaf of facility crops.
Why it matches plant phenotyping methodsキュウリ葉の光合成速度を環境要因と蛍光パラメータから予測するモデルを開発し、実測値で検証しており、植物生理形質の取得・推定手法が中心である。
abstracta photosynthetic rate prediction model of newborn leaves in canopy of cucumber was proposed
Chlorophyll fluorescence imaging (CFI) is useful for detecting physiological disorders or defects for green-skinned horticultural products, because defective and normal plant tissues would have different responses to ultraviolet (UV) or short-wavelength visible excitation. This study was intended to evaluate the effectiveness of a new CFI approach by using structured illumination coupled with a proposed automated method for vignetting correction of chlorophyll fluorescence images, for enhanced detection of chilling injury in cucumbers. A CFI system with UV-blue light as an excitation source under structured illumination was assembled. Spectral images over the spectral region of 660–800 nm in 5 nm increments were first acquired from chilling-treated cucumbers under uniform UV-blue illumination to determine appropriate wavebands for implementation of CFI under structured illumination. Further experiment was conducted on a larger group of chilling treated cucumbers to acquire chlorophyll fluorescence images under structured illumination for two wavebands centered at 675 nm and 750 nm. An automatic method for vignetting correction of fluorescence images was proposed by using a modified bi-dimensional empirical mode decomposition (BEMD) technique. Results showed that the chlorophyll fluorescence spectra of cucumbers were characterized by two emission peaks around the regions of 685–690 nm and 740–745 nm respectively. The proposed BEMD method was effective for vignetting correction of fluorescence images, which eliminates the need of using a physical fluorescence target for image correction. Moreover, compared to uniform illumination, structured illumination was found to provide significantly better fluorescence images in terms of the image sharpness and contrast between the normal and chilling-injury tissues, which were inductive to enhancing the detection of chilling injury in cucumbers.
Why it matches plant phenotyping methodsキュウリの低温障害という植物状態を対象に、構造化照明CFIと自動ビネット補正法を開発・評価しており、表現型取得手法が研究の中心である。
abstractThis study was intended to evaluate the effectiveness of a new CFI approach by using structured illumination coupled with a proposed automated method for vignetting correction of chlorophyll fluorescence images, for enhanced detection of chilling injury in cucumbers.
There are many kinds of crop diseases, which directly affect the yield and quality of crops and cause immeasurable losses.Using image processing and pattern recognition technology, it is simple and fast to identify crop diseases and provide necessary information for taking prevention measures in time.A crop disease recognition method is proposed based on two-dimensional discriminant locality preserving projections (2D-DLPP).2D-DLPP tries to find a mapping matrix to reduce the dimensionality of the original diseased leaf images, so that the intra-class samples in low-dimensional mapping subspace are closer to each other, while the inter-class samples are far from each other, which can improve the recognition rate of the algorithm.The experiments on the common cucumber disease leaf image dataset are carried on and compared with other plant disease recognition algorithms.The results show that the 2D-DLPP based method is effective and feasible for crop disease identification.
Why it matches plant phenotyping methodsキュウリの罹病葉画像から植物病害状態を認識する次元削減・分類手法を提案し、既存手法と比較評価しており、病害表現型の取得・判定が研究の中心である。
abstractA crop disease recognition method is proposed based on two-dimensional discriminant locality preserving projections (2D-DLPP).
Contamination of fresh cucumbers ( Cucumis sativus L.) with Escherichia coli O157:H7 can impact the health of consumers. Despite this, the pertinent mechanisms underlying E. coli O157:H7 colonization and internalization remain poorly documented. Herein we aimed to elucidate these mechanisms in cucumbers using an inverted fluorescence microscope and hyperspectral microscopy. We observed that E. coli O157:H7 primarily colonized around the stomata on cucumber epidermis without invading the internal tissues of intact cucumbers. Once the bacterial cells had infiltrated into the internal tissues, they colonized the cucumber placenta and vascular bundles (xylem vessels, in particular), and also migrated along the xylem vessels. Moreover, the movement rate of E. coli O157:H7 from the stalk to the flower bud was faster than that from the flower bud to the stalk. We then used hyperspectral microscope imaging to categorize the infiltrated and uninfiltrated areas with high accuracy using the spectral angle mapper (SAM) classification method, which confirmed the results obtained upon using the inverted fluorescence microscope. We believe that our results are pivotal for developing science-based food safety practices, interventions for controlling E. coli O157:H7 internalization, and new methods for detecting E. coli O157:H7-plant interactions.
Why it matches plant phenotyping methodsキュウリ組織への細菌侵入状態を、蛍光顕微鏡とハイパースペクトル画像解析で分類・検証しており、感染植物の状態取得手法が主要な貢献に含まれる。
abstractWe then used hyperspectral microscope imaging to categorize the infiltrated and uninfiltrated areas with high accuracy using the spectral angle mapper (SAM) classification method
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Published27 Sept 2019Current Medical Imaging Formerly Current Medical Imaging ReviewsCited by 11 · OpenAlex ↗
Background: Agriculture is one of the most essential industry that fullfills people’s need and also plays an important role in economic evolution of the nation. However, there is a gap between the agriculture sector and the technological industry and the agriculture plants are mostly affected by diseases, such as the bacterial, fungus and viral diseases that lead to loss in crop yield. The affected parts of the plants need to be identified at the beginning stage to eliminate the huge loss in productivity. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Conclusion: The result of the clustering algorithm achieved high accuracy, sensitivity, and specificity. The feature extraction is applied after the clustering process which produces minimum error rate.
Why it matches plant phenotyping methods植物葉の病害状態を画像から抽出・識別する画像処理手法が研究の中心であり、植物フェノタイピング手法に該当する。
abstractthe proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods.
Non-destructive plant growth parameters measurement is an important concern in automatic-seedling transplanting. Recently, several image-based monitoring approaches have been proposed and potentially developed for several agricultural applications. The presented study proposed and developed a RealSense-based machine vision system for the close-shot seedling-lump integrated monitoring. The strategy was based on the close-shot depth information. Further, the point cloud clustering and suitable algorithms were applied to obtain the segmentation of 3D seedling models. In addition, the data processing pipeline was developed to assess the different morphological parameter of 4 different seedling varieties. The experiments were carried out with 4 different seedling varieties (pepper, tomato, cucumber, and lettuce) and trained under different light conditions (light and dark). Moreover, analysis results showed that there was not significantly different (p < 0.05) found towards light and dark environments due to close-shot near-infrared detection. However, the results revealed that the stem diameter relationship between RealSense and the manual method was found for R2 = 0.68 cucumber, R2 = 0.54 tomato, R2 = 0.35 pepper, and R2 = 0.58 lettuce seedlings. Whereas, the seedling height relationship between RealSense and the manual method was found higher than R2 = 0.99, 0.99, 0.99, and 0.99 for pepper, tomato, cucumber, and lettuce, respectively. Based on the experiment results, it was concluded that the RGB-D integrated monitoring system with the purposed method could be practiced for nursery seedlings most promisingly without high labour requirements in terms of ease of use. The system revealed a good sturdiness and relevance for plant growth monitoring. Additionally, it has the perspective for future practical value to real-time vision servo operations for transplanting robots.
Why it matches plant phenotyping methodsRealSense深度カメラ、点群クラスタリング、処理パイプラインを開発し、苗の形態形質を非破壊測定して手動法と検証しているため、植物フェノタイピング手法が中心である。
abstractThe presented study proposed and developed a RealSense-based machine vision system for the close-shot seedling-lump integrated monitoring.
Optimization and control of the greenhouse light environment is key to increasing crop yield and quality. However, the light saturation point impacts the efficient use of light. Therefore, the dynamic acquisition of the light saturation point that is influenced by changes in temperature and CO 2 concentration is an important challenge for the development of greenhouse light environment control system. In view of this challenge, this paper describes a light environment optimization and control model based on a crop growth model for predicting cucumber photosynthesis. The photosynthetic rate values for different photosynthetic photon flux densities (PPFD), CO 2 concentration, and temperature conditions provided to cucumber seedlings were obtained by using an LI-6400XT portable photosynthesis system during multi-factorial experiments. Based on the measured data, photosynthetic rate predictions were determined. Next, a support vector machine(SVM) photosynthetic rate prediction model was used to obtain the light response curve under other temperatures and CO 2 conditions. The light saturation point was used to establish the light environment optimization and control model and to perform model validation. The slope of the fitting straight line comparing the measured and predicted light saturation point was 0.99, the intercept was 23.46 and the coefficient of determination was 0.98. The light control model was able to perform dynamic acquisition of the light saturation point and provide a theoretical basis for the efficient and accurate control of the greenhouse light environment.
Why it matches plant phenotyping methodsキュウリの光飽和点・光合成速度を動的に推定するSVMモデルと制御モデルを開発・検証しており、植物生理形質の取得・推定が中心的である。
abstracta support vector machine(SVM) photosynthetic rate prediction model was used to obtain the light response curve under other temperatures and CO 2 conditions.
Analysis of plant disease images can increase disease detection accuracy. Successful extraction of lesions can provide a good precondition for research on the feature analysis (texture, color and shape) and detection of occurrence area and severity for cucumber powdery mildew. This paper proposes a new Otsu algorithm based on three-dimensional histogram reconstruction and dimension reduction to segment powdery mildew from cucumber disease images. In total, 166 RGB images of cucumber powdery mildew with 1440 × 1080 pixels were obtained from the greenhouse nos. 1 and 2 using a high-speed dome camera. First, a new correction formula is proposed to correct anomalous points to the correct position (around the line connecting the origin with diagonal end). Second, we reduced the algorithm dimension to save time and spatial complexity and achieved the desired results. Finally, the optimal threshold was obtained by a Gaussian fitting iteration. Convergence analysis demonstrated that the method should be able to obtain a new threshold after each iteration. Experimental results showed an average false negative error rate of 0.10% and average false positive error rate of 1.27%. The average running time was 5.082 s. Taken together, these represent satisfactory results for rapid automatic identification of cucumber powdery mildew.
Why it matches plant phenotyping methodsキュウリうどんこ病の病斑を画像から抽出・識別する新規しきい値分割法を開発し、誤検出率と処理時間で評価しているため、植物病害表現型の取得手法が中心である。
abstractThis paper proposes a new Otsu algorithm based on three-dimensional histogram reconstruction and dimension reduction to segment powdery mildew from cucumber disease images.
Powdery mildew is a common disease in plants, and it is also one of the main diseases in the middle and final stages of cucumber ( Cucumis sativus ). Powdery mildew on plant leaves affects the photosynthesis, which may reduce the plant yield. Therefore, it is of great significance to automatically identify powdery mildew. Currently, most image-based models commonly regard the powdery mildew identification problem as a dichotomy case, yielding a true or false classification assertion. However, quantitative assessment of disease resistance traits plays an important role in the screening of breeders for plant varieties. Therefore, there is an urgent need to exploit the extent to which leaves are infected which can be obtained by the area of diseases regions. In order to tackle these challenges, we propose a semantic segmentation model based on convolutional neural networks (CNN) to segment the powdery mildew on cucumber leaf images at pixel level, achieving an average pixel accuracy of 96.08%, intersection over union of 72.11% and Dice accuracy of 83.45% on twenty test samples. This outperforms the existing segmentation methods, K-means, Random forest, and GBDT methods. In conclusion, the proposed model is capable of segmenting the powdery mildew on cucumber leaves at pixel level, which makes a valuable tool for cucumber breeders to assess the severity of powdery mildew.
Why it matches plant phenotyping methodsキュウリ葉のうどんこ病領域を画像から画素レベルで分割・定量し、病害重症度という植物状態を推定するCNN手法を開発・比較評価しており、フェノタイピング手法が中心である。
abstractwe propose a semantic segmentation model based on convolutional neural networks (CNN) to segment the powdery mildew on cucumber leaf images at pixel level
Three experiments were conducted to develop a bioassay method for assessing the bioavailability of prosulfocarb, pyroxasulfone and trifluralin in both crop residue and soil. In preliminary experiments, Italian ryegrass (Lolium multiflorum Lam.), cucumber (Cucumis sativus L.) and beetroot (Beta vulgaris L.) were tested as bioassay plant species for the three pre-emergent herbicides. Four growth parameters (shoot length, root length, fresh weight and dry weight) were measured for all plant species. Shoot-length inhibition was identified as the most responsive to the herbicide application rates. Italian ryegrass was the most sensitive species to all tested herbicides, whereas beetroot and cucumber had lower and similar sensitivity to shoot inhibition for the three herbicides. The bioassay species performed similarly in wheat and canola residues collected a few days after harvest. In bioassay calibration experiments, dose–response curves were developed for prosulfocarb, pyroxasulfone and trifluralin in a sandy loam soil typical of the grain belt of Western Australia and with wheat residue. The developed bioassay uses ryegrass shoot inhibition for relatively low suspected concentrations of herbicide, and cucumber shoot inhibition for higher rates. The bioassay was validated by spraying the three herbicides separately onto wheat residue and soil and comparing the concentrations derived from chemical analysis with those from the bioassay. All of the linear correlations between concentrations derived from chemical analyses and the bioassays were highly significant. These results indicate that the bioassay calibration curves are suitable for estimating herbicide concentrations in crop residue collected soon after harvest and a sandy-loam soil, low in organic matter.
Why it matches plant phenotyping methods除草剤濃度を推定するため、植物の生育阻害（特にシュート長）を測定するバイオアッセイを開発・較正・検証しており、表現型取得が研究の中心である。
abstractThree experiments were conducted to develop a bioassay method for assessing the bioavailability of prosulfocarb, pyroxasulfone and trifluralin in both crop residue and soil.
The plant parameters such as leaf area (LA) or leaf area index (LAI) play an important role in understanding photosynthesis, light interception, water and nutrient use and crop growth.A better understanding of the relationships between crop development and environment is thus important.Both LA and LAI can be measured directly by using digital meters (destructive methods) or indirectly using developed regression techniques (non-destructive methods).In the present study, non-destructive methods (regression equations) were developed for estimation of LA and LAI separately.The developed regression equations were used to estimate LA and LAI.Both LA and LAI predictions presented a high precision and accuracy.The developed non-destructive methods for estimation of LA and LAI were found functional for cucumber crop with a significant saving in energy and time.
Why it matches plant phenotyping methodsキュウリの葉面積・葉面積指数を非破壊的に推定する回帰法を開発し、精度・正確性を評価しており、植物形質取得法が研究の中心である。
abstractIn the present study, non-destructive methods (regression equations) were developed for estimation of LA and LAI separately.
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
Most effective nematicides for the control of root-knot nematodes are banned, which demands a better understanding of the plant-nematode interaction. Understanding how gene expression in the nematode-feeding sites relates to morphological features may assist a better characterization of the interaction. However, nematode-induced galls resulting from cell-proliferation and hypertrophy hinders such observation, which would require tissue sectioning or clearing. We demonstrate that a method based on the green auto-fluorescence produced by glutaraldehyde and the tissue-clearing properties of benzyl-alcohol/benzyl-benzoate preserves the structure of the nematode-feeding sites and the plant-nematode interface with unprecedented resolution quality. This allowed us to obtain detailed measurements of the giant cells’ area in an Arabidopsis line overexpressing CHITINASE-LIKE-1 (CTL1) from optical sections by confocal microscopy, assigning a role for CTL1 and adding essential data to the scarce information of the role of gene repression in giant cells. Furthermore, subcellular structures and features of the nematodes body and tissues from thick organs formed after different biotic interactions, i.e., galls, syncytia, and nodules, were clearly distinguished without embedding or sectioning in different plant species (Arabidopsis, cucumber or Medicago). The combination of this method with molecular studies will be valuable for a better understanding of the plant-biotic interactions.
Why it matches plant phenotyping methods根こぶ線虫摂食部位の構造を共焦点画像から高解像度に取得し、巨大細胞面積を測定する植物フェノタイピング法の開発が中心である。
titleA Phenotyping Method of Giant Cells from Root-Knot Nematode Feeding Sites by Confocal Microscopy Highlights a Role for CHITINASE-LIKE 1 in Arabidopsis
Reproduction assets foundThe paper describes a confocal-microscopy phenotyping method for nematode-induced giant cells. The only paper-specific public asset referenced is the authors' supplementary material (hosted at MDPI), which per the text contains Table S1 (gene filtering results) and Videos S6–S9 of the confocal optical sections used forSupplement · public(PEII-2014-020-P to Carmen Fenoll). Javier Cabrera is supported by a Cytema-Santander contract from Universidad de Castilla-La Mancha. Christian Hermans is a research associate from Fonds de la Recherche Scientifique—National Fund for Scientific Research (Belgium).
Supplementary Materials
Supplementary materials can be found at http://www.mdpi.com/1422-0067/19/2/429/s1 and www.mdpi.com/1422-0067/19/2/429/s2 .
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Author Contributions
Javier Cabrera, Rocio Olmo, Virginia Ruiz-Ferrer, and Christian Hermans conceived and designed the experiments; Javier Cabrera, Rocio Olmo, Virginia Ruiz-Ferrer, Christian Hermans, and Isabel Open asset ↗lines:51-66Plant phenotyping relevance match · UnverifiedCrossref · checked 10 Sept 2026
Raman microspectra combine information on chemical composition of plant tissues with spatial information. The contributions from the building blocks of the cell walls in the Raman spectra of plant tissues can vary in the microscopic sub-structures of the tissue. Here, we discuss the analysis of 55 Raman maps of root, stem, and leaf tissues of Cucumis sativus, using different spectral contributions from cellulose and lignin in both univariate and multivariate imaging methods. Imaging based on hierarchical cluster analysis (HCA) and principal component analysis (PCA) indicates different substructures in the xylem cell walls of the different tissues. Using specific signals from the cell wall spectra, analysis of the whole set of different tissue sections based on the Raman images reveals differences in xylem tissue morphology. Due to the specifics of excitation of the Raman spectra in the visible wavelength range (532 nm), which is, e.g., in resonance with carotenoid species, effects of photobleaching and the possibility of exploiting depletion difference spectra for molecular characterization in Raman imaging of plants are discussed. The reported results provide both, specific information on the molecular composition of cucumber tissue Raman spectra, and general directions for future imaging studies in plant tissues.
Why it matches plant phenotyping methodsラマンイメージングを用いて植物組織の細胞壁組成と木部形態を抽出・比較しており、画像取得・解析手法が研究の中心であるため。
abstractImaging based on hierarchical cluster analysis (HCA) and principal component analysis (PCA) indicates different substructures in the xylem cell walls of the different tissues.
Abstract. Disease spot segmentation from crop leaf images is a key prerequisite for disease early warning and diagnosis. To improve the accuracy and stability of disease spot segmentation, an adaptive segmentation method for crop disease images based on K-means clustering is proposed. The approach is based on three stages. First, the excess green feature and the a* component of the CIE (L*a*b*) color space were combined to adaptively learn the initial cluster centers. Second, iterative color clustering of two clusters was conducted using the squared Euclidian distance as the similarity distance. Finally, the distance of a* components between two clusters as the clustering criterion function was used to correct the clustering results. To verify the effectiveness of the proposed method, segmentation experiments were performed on images of three kinds of cucumber diseases and one kind of soybean disease. The results of the experiments were compared with the results obtained using a fixed threshold method, the Otsu method, the traditional K-means clustering method, and the Renyi entropy method, which showed that our adaptive segmentation method was accurate and robust for segmentation of crop disease images. Keywords: Adaptive, CIE L*a*b*, Disease spot, Image segmentation, K-means clustering.
Why it matches plant phenotyping methods植物病斑を画像から抽出する適応的セグメンテーション手法の開発・比較検証が中心であり、病害状態の表現型取得に該当する。
abstractan adaptive segmentation method for crop disease images based on K-means clustering is proposed
For both field and greenhouse crops, it is challenging to evaluate their growth information on a large area over a long time. In this work, we developed a chlorophyll fluorescence imaging-based system for crop population growth information detection. Modular design was used to make the system provide high-intensity uniform illumination. This system can perform modulated chlorophyll fluorescence induction kinetics measurement and chlorophyll fluorescence parameter imaging over a large area of up to 45 cm×34 cm. The system can provide different lighting intensity by modulating the duty cycle of its control signal. Results of continuous monitoring of cucumbers in nitrogen deficiency show the system can reduce the judge error of crop physiological status and improve monitoring efficiency. Meanwhile, the system is promising in high throughput application scenarios.
Why it matches plant phenotyping methods大面積・長期間の作物生育および生理状態を取得する蛍光イメージングシステムを開発しており、植物表現型の取得方法が研究の中心である。
abstractwe developed a chlorophyll fluorescence imaging-based system for crop population growth information detection.
This paper presents a novel image processing method using color information and region growing for segmenting greenhouse vegetable foliar disease spots images captured under real field conditions. Disease images captured under real field conditions are suffering from uneven illumination and complicated background, which is a big challenge to achieve robust disease spots segmentation. A disease spots segmentation method consisting of two pipelined procedures is proposed in this paper. Firstly a comprehensive color feature and its detection method are presented. The comprehensive color feature (CCF) consists of three color components, Excess Red Index (ExR), H component of HSV color space and b∗ component of L∗a∗b∗ color space, which implements powerful discrimination of disease spots and clutter background. Then an interactive region growing method based on the CCF map is used to achieve disease spots segmentation from clutter background. To evaluate the robustness and accuracy, the proposed segmentation method is assessed by cucumber downy mildew images. Results show that the proposed method can achieve accurate and robust segmentation under real field conditions.
Why it matches plant phenotyping methods植物病斑を画像から抽出するセグメンテーション手法の開発と評価が中心であり、植物病害状態の表現型取得に直接関係する。
abstractThis paper presents a novel image processing method using color information and region growing for segmenting greenhouse vegetable foliar disease spots images captured under real field conditions.
State observer is an essential component in computerized control loops for greenhouse-crop systems. However, the current accomplishments of observer modeling for greenhouse-crop systems mainly focus on mass/energy balance, ignoring physiological responses of crops. As a result, state observers for crop physiological responses are rarely developed, and control operations are typically made based on experience rather than actual crop requirements. In addition, existing observer models require a large number of parameters, leading to heavy computational load and poor application feasibility. To address these problems, we present a new state observer modeling strategy that takes both environmental information and crop physiological responses into consideration during the observer modeling process. Using greenhouse cucumber seedlings as an instance, we sample 10 physiological parameters of cucumber seedlings at different time point during the exponential growth stage, and employ them to build growth state observers together with 8 environmental parameters. Support vector machine (SVM) acts as the mathematical tool for observer modeling. Canonical correlation analysis (CCA) is used to select the dominant environmental and physiological parameters in the modeling process. With the dominant parameters, simplified observer models are built and tested. We conduct contrast experiments with different input parameter combinations on simplified and un-simplified observers. Experimental results indicate that physiological information can improve the prediction accuracies of the growth state observers. Furthermore, the simplified observer models can give equivalent or even better performance than the un-simplified ones, which verifies the feasibility of CCA. The current study can enable state observers to reflect crop requirements and make them feasible for applications with simplified shapes, which is significant for developing intelligent greenhouse control systems for modern greenhouse production.
Why it matches plant phenotyping methodsキュウリ苗の生理応答と成長状態を推定する観測モデルをSVMとCCAで開発・比較検証しており、植物状態の取得・推定手法が中心です。
abstractwe present a new state observer modeling strategy that takes both environmental information and crop physiological responses into consideration during the observer modeling process.
Research reported in this paper aims to improve the extraction of cucumber leaf spot disease under complex backgrounds. An improved fuzzy C-means (FCM) algorithm is proposed in this paper. First, three runs of the marked-watershed algorithm, based on HSI space, are applied to isolate the target leaf. Second, the distance between the pixel xj and the cluster center vi is defined as ‖xj2-vi2‖. Third, the pixel's neighborhood mean gray value, which constitutes a two-dimensional vector with grayscale information, is calculated as a sample point, rather than FCM grayscale. Finally, the neighborhood mean gray value and pixel gray value are weighted by matrix w. To evaluate the robustness and accuracy of the proposed segmentation method, tests were conducted for 129 cucumber disease images in vegetable disease database. Results show that average segmentation error was only 0.12%. The proposed method provides an effective and robust segmentation means for sorting and grading apples in cucumber disease diagnosis, and it can be easily adapted for other imaging-based agricultural applications.
Why it matches plant phenotyping methodsキュウリ葉斑病画像から病斑を抽出する画像セグメンテーション手法を開発し、129枚で精度・頑健性を評価しており、植物病害状態の取得方法が研究の中心である。
abstractAn improved fuzzy C-means (FCM) algorithm is proposed in this paper.
Most existing image-based crop disease recognition algorithms rely on extracting various kinds of features from leaf images of diseased plants. They have a common limitation as the features selected for discriminating leaf images are usually treated as equally important in the classification process. We propose a novel cucumber disease recognition approach which consists of three pipelined procedures: segmenting diseased leaf images by K-means clustering, extracting shape and color features from lesion information, and classifying diseased leaf images using sparse representation (SR). A major advantage of this approach is that the classification in the SR space is able to effectively reduce the computation cost and improve the recognition performance. We perform a comparison with four other feature extraction based methods using a leaf image dataset on cucumber diseases. The proposed approach is shown to be effective in recognizing seven major cucumber diseases with an overall recognition rate of 85.7%, higher than those of the other methods.
Why it matches plant phenotyping methodsキュウリ葉画像から病斑の形状・色特徴を抽出し、植物の病害状態を認識する画像解析手法を提案・比較評価しており、フェノタイピング手法が中心である。
abstractWe propose a novel cucumber disease recognition approach which consists of three pipelined procedures: segmenting diseased leaf images by K-means clustering, extracting shape and color features from lesion information, and classifying diseased leaf images using sparse representation (SR).
Expansins play an important role in cell wall metabolism and fruit softening. Determination of expansin activity is a challenging problem since it depends on measuring cell wall properties by using ad hoc extensometers, a fact that has strongly restricted its study. Then, the objective of the work was to adapt a methodology to measure cell wall creep and expansin activity using a commercial texture meter, equipped with miniature tensile grips and an ad hoc cuvette of easy construction.It was possible to measure hypocotyls acid growth and expansin activity in a reliable and reproducible way, using a commercial texture meter, common equipment found in laboratories of food science or postharvest technology. Expansin activity was detected in protein extracts from cucumber hypocotyls, tomato and strawberry fruits, and statistical differences in expansin activity were found in both fruit models at different ripening stages.The possibility of measuring expansin activity following this adapted protocol with a commercial texture meter could contribute to ease and increase the analysis of expansin in different systems, leading to a better understanding of the properties of these proteins under different experimental conditions.
Why it matches plant phenotyping methods市販テクスチャーアナライザーを用いて細胞壁クリープとエクスパンシン活性を測定する手法を適応・検証しており、植物の生理状態を取得する方法が中心である。
abstractthe objective of the work was to adapt a methodology to measure cell wall creep and expansin activity using a commercial texture meter
The occurrence of greenhouse vegetable diseases and its epidemic seriously affect the production and management of facility agriculture, which greatly reduce the economic benefits of facility agriculture. In order to achieve nondestructive and accurate prediction of greenhouse vegetable diseases, this paper taking cucumber downy mildew disease as the research object, constructed spectrum characteristic index by using chlorophyll fluorescence induced by laser and established the prediction model of greenhouse vegetable diseases. In this paper, the experiment used comparative analysis method. The healthy leaves of the crops were inoculated with the pathogen spores, the spectrum curves of four groups of test samples: healthy, 2 d inoculated, 6 d inoculated and the ones with obvious symptoms were collected; then qualitative analysis was given to the variation regulation of the fluorescence intensity with the leaf samples infected with the pathogen spores. The chlorophyll fluorescence spectrum index k1=F685/F512 and k2=F734/F512 were created by using the peak and valley values of different bands. According to the range of values, set k1=20 and k2=10 as the characteristic value to judge the sample with obvious symptoms or with no obvious symptoms, and the accuracy rate of the judgment was 96% and 94% respectively. Based on spectrum index created and the classification results of sample health status, we selected the spectrum index F685/F512, F685-F734, F715/F612 to determine the health status of the sample and selected spectrum index F685/F512, F734/F512, F685-F734, F715/F612 as the inputs of quantitative analysis model. Regarding classification accuracy of prediction set as the evaluation criteria, we compared three data modeling methods: discriminant analysis, BP neural network and support vector machine. The results showed that the forecasting ability can reach 91.38% when the support vector machine was used as the modeling method for predicting the downy mildew disease. Use the method with chlorophyll fluorescence induced by laser to construct spectrum index to study the prediction of plant diseases, which has a good classification and identification effect.
Why it matches plant phenotyping methodsレーザー誘起クロロフィル蛍光スペクトルからキュウリの病害状態を推定する指標と予測モデルを開発・評価しており、植物病害表現型の取得・抽出が中心的である。
abstractIn order to achieve nondestructive and accurate prediction of greenhouse vegetable diseases, this paper taking cucumber downy mildew disease as the research object, constructed spectrum characteristic index by using chlorophyll fluorescence induced by laser and established the prediction model of greenhouse vegetable diseases.
Sustainable N management of intensive vegetable crops requires accurate and timely on-farm assessment of crop N status. Proximal fluorescence-based sensors are promising tools for monitoring crop N status, by providing non-destructive optical measurements of N-sensitive indicator compounds such as chlorophyll and flavonols. The ability of the Multiplex® fluorescence sensor to determine crop N status was evaluated in two indeterminate cucumber crops grown in contrasting seasons (autumn and spring). Three fluorescence indices, leaf chlorophyll (SFR) and flavonols (FLAV) contents, and their ratio (Nitrogen Balance Index, NBI) were evaluated, and their consistency between the two crops compared. Actual crop N status was assessed by the Nitrogen Nutrition Index (NNI), calculated as the ratio between the actual and the critical crop N contents (i.e., the minimum N content for maximum growth). There were strong relationships between each of SFR, FLAV and NBI with crop NNI, for most weekly measurements made throughout the two crops. For the three indices, coefficients of determination (R2) were mostly 0.65–0.91 in the autumn crop, and 0.71–0.99 in the spring crop. SFR values were generally comparable between the two crops, which enabled the derivation of common relationships with NNI for individual phenological phases that applied to both cropping seasons. FLAV and NBI values were not comparable between the two crops; FLAV values were appreciably higher throughout the spring crop, which was attributed to the higher solar radiation. Consequently, phenological relationships of FLAV and NBI with NNI were established for each individual cropping season. Our findings suggested that SFR was the most consistent index between cropping seasons, and that NBI was the most sensitive index within each season. Regardless of the index and crops, all fluorescence indices were weakly related to crop NNI during the short vegetative phase while stronger relationships were found in the reproductive and harvest phases. This study also showed that the three fluorescence indices were sensitive to and able to distinguish deficient from optimal crop N status, but that they were insensitive to discriminate optimal from slightly excessive N status. Overall, this study demonstrated that fluorescence indices of chlorophyll content (SFR), flavonols content (FLAV) and nitrogen sufficiency (NBI) can be used as reliable indicators of crop N status in cucumber crops; however, there was variability in FLAV and NBI values between cropping seasons and a lack of sensitivity in the range of optimal to slightly excessive crop N status.
Why it matches plant phenotyping methodsMultiplex蛍光センサーによるキュウリの窒素状態推定を評価・比較し、NNIとの関係と季節間の一貫性を検証しているため、植物表現型取得法が中心である。
abstractThe ability of the Multiplex® fluorescence sensor to determine crop N status was evaluated in two indeterminate cucumber crops grown in contrasting seasons (autumn and spring).