To enhance crop performance, intercropping strategies leverage volatile organic compound (VOC)-driven interactions with companion plants that constitutively emit VOCs. Despite the agricultural importance, the mechanisms and kinetics of VOC-mediated sensory transduction in receiver plants eavesdropping on neighbouring non-kin emitters remain largely unknown due to a lack of appropriate non-destructive analytical tools. In this work, we employ multiplexed salicylic acid (SA) and H 2 O 2 nanosensors in Brassica rapa subsp. Chinensis (pak choy) plants to visualize, in real time, reactive oxygen species (ROS) and SA signal transduction following exposure to constitutively-released VOCs from neighbouring aromatic plants-namely sweet basil and spearmint. Unique emitter-specific temporal signatures of ROS and SA were observed in receiver pak choy: sweet basil VOCs induced concomitant generation of ROS and SA at 30 min, whereas spearmint VOCs triggered SA production at 30 min, followed by ROS accumulation. The temporal data enabled the formulation of a diffusion model that quantifies the VOC perception threshold that triggers the distinct early ROS and SA signalling. Transcriptomics analysis at 2 h revealed that both emitters evoke largely distinct changes in pak choy, likely stemming from variations in speed and sequence of the early signal transduction, leading to different phenotypic outcomes. Intercropping with sweet basil led to enhanced pak choy biomass, stress resilience and secondary metabolite accumulation, whereas spearmint as companions had a limited impact. Our study captures in real time, the VOC-induced rapid signalling in receiver plants and its ensuing effect on growth. These nanosensor-enabled findings represent an important advance in deciphering how emitter-specific volatile cues are integrated into plant responses, guiding rational selection of beneficial companion plants for improved yield and nutritional profiles in sustainable agriculture.
Why it matches plant phenotyping methods植物内のROSとSAシグナルをリアルタイム可視化する多重化ナノセンサーが研究の中心であり、植物の生理状態・応答を測定する方法として適用されている。
abstractdue to a lack of appropriate non-destructive analytical tools. In this work, we employ multiplexed salicylic acid (SA) and H 2 O 2 nanosensors in Brassica rapa subsp. Chinensis (pak choy) plants to visualize, in real time, reactive oxygen species (ROS) and SA signal transduction
Cauliflower emergence rate and seedling growth are key indicators of field conditions and varietal potential. Traditional manual surveys are unsuitable for continuous monitoring across multiple varieties. This study integrates UAV RGB imagery with the DualSlim-YOLO model to estimate cauliflower emergence rates and monitor seedling growth. Built on YOLOv11, the model incorporates a lightweight feature extraction structure and an optimized detection-scale configuration. It reduces computational complexity while maintaining detection accuracy, thereby improving the efficiency of cauliflower seedling detection. DualSlim-YOLO achieved P, R, F1-score, mAP@0.5, and mAP@0.5:0.95 of 95.35%, 96.75%, 96.05%, 98.55%, and 86.65%, respectively. The number of parameters was reduced by 38.61%, while the inference speed increased by 22.16%, demonstrating good lightweight performance. Based on this model, UAV images of 171 cauliflower varieties acquired at 7, 21, and 28 d after transplanting were used for seedling detection and emergence rate estimation. In addition, 18 time-series seedling phenotypic traits were extracted, enabling a comprehensive quantitative evaluation of emergence dynamics and early-growth performance across multiple cauliflower varieties. This method effectively screens cauliflower varieties for high emergence rates, rapid emergence, and excellent seedling growth performance. It provides technical support for high-throughput, nondestructive seedling phenotyping and early germplasm screening under field conditions.
Why it matches plant phenotyping methodsUAV画像と軽量YOLOモデルを用いて、カリフラワー苗の検出、出芽率推定、18種類の時系列表現型形質抽出を行う手法が中心であり、モデル性能も検証している。
abstractThis study integrates UAV RGB imagery with the DualSlim-YOLO model to estimate cauliflower emergence rates and monitor seedling growth.
Unmanned aerial vehicle (UAV)-based multispectral imaging enables flexible, non-destructive crop monitoring. Although UAV imagery offers much higher spatial resolution than satellite platforms, its effective spatial detail at typical operational flight altitudes can still be insufficient for plant-level interpretation and fine canopy structure, which can reduce vegetation-index reliability. Most super-resolution (SR) research targets RGB or satellite imagery and emphasizes perceptual or pixel-wise quality, leaving the spectral fidelity of reconstructed UAV multispectral imagery under-examined. This study benchmarked an SR evaluation framework for UAV-based five-band crop imagery (Blue, Green, Red, Red-edge, and near-infrared) using the open-source AI Hub cabbage dataset, with low-resolution inputs generated by controlled downsampling at ×2, ×3, and ×4. Nine methods (bicubic, SRCNN, EDSR, RCAN, SwinIR-based, ESRGAN-based, HAT-based, DAT-based, and DRCT-based SR) were compared under joint five-channel and band-wise reconstruction on 2170 test scenes using image-quality, spectral-angle, vegetation-index (NDVI, GNDVI, NDRE), band-wise, and efficiency metrics. EDSR and RCAN gave the most balanced performance. At ×4, band-wise reconstruction was strongest for per-band spatial fidelity, where EDSR reduced RMSE by 12.6%, and RCAN lowered near-infrared RMSE by about 21% relative to bicubic, whereas joint reconstruction with its spectral-angle and vegetation-index losses best preserved spectral relationships (spectral angle and vegetation-index errors). Learning-based gains were clearest at ×4. The recently proposed HAT-based, DAT-based, and DRCT-based attention models achieved the strongest pixel-wise RMSE and PSNR but did not surpass EDSR or RCAN on spectral angle or vegetation-index preservation under the equalized training budget. These results indicate that UAV multispectral SR should be assessed by spatial fidelity together with spectral consistency and agricultural index reliability.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像の超解像法を比較・ベンチマークし、スペクトル一貫性や植生指数の信頼性を評価する研究であり、植物キャノピー形質の画像取得・抽出基盤が中心である。
abstractThis study benchmarked an SR evaluation framework for UAV-based five-band crop imagery
Thermal imaging enables non-invasive assessment of canopy temperature, an essential indicator of plant stress, yet the lack of color cues and strong shadow interference make plant segmentation in thermal images difficult. Recent advances in foundation models have demonstrated improved performance and generalizability across applications, showing promise for domain-specific applications with limited annotated datasets such as plant segmentation in thermal images. This study investigates large multimodal models (LMMs) for thermal image segmentation in plant phenotyping. Building upon the SAM-CLIP framework, we design a unified pipeline spanning zero-shot inference, few-shot and low-shot fine-tuning, and active learning to maximize accuracy with minimal supervision. Evaluations on two thermal datasets, LadyBird Brassica and UGA Brassica, demonstrate robust performance after minimal adaptation across both datasets and superior performance compared with baselines, achieving mIoU D values of 97.54% on the LadyBird dataset and 76.94 % on the UGA dataset. We also release the resulting thermal segmentation annotations to support community benchmarking and reproducible research, highlighting the potential of LMMs to enable scalable, high-quality dataset construction for field phenotyping. The released datasets can be found at: https://cornell.box.com/s/dh69xf84464yrc1vlws92l1tflx7qa89
Why it matches plant phenotyping methods熱画像から植物を分割する手法を開発・評価し、植物フェノタイピング用データセットとアノテーションも公開しているため、フェノタイピング手法が中心的である。
abstractThis study investigates large multimodal models (LMMs) for thermal image segmentation in plant phenotyping.
Reproduction assets foundThe authors publicly released the paper-specific thermal segmentation annotations (20,538 LadyBird masks and 37,790 UGA masks) via a Cornell Box link stated in the abstract, results, and data availability statement. No author analysis code or trained model checkpoints are explicitly released; the mmsegmentation GitHub/Dataset · publicwe generated and publicly released segmentation annotations for the complete LadyBird and UGA thermal image datasets using the best-performing SAM-CLIP model. Specifically, the final model obtained through the multi-round training process was used to generate 20,538 masks for the LadyBird dataset and 37,790 masks for the UGA dataset. Details of the generated annotations are provided in Supplementary Fig. S1 , and both annotated datasets are publicly available at: https://cornell.box.com/s/dh69xf84464yrc1vlws92l1tflx7qa89Open asset ↗lines:220-232Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Conventional methods of plant distance and volume measurements are limited by low efficiency, limited spatial coverage, and high measurement error. LiDAR and RGB-D imaging offer cost-effective, precise, and non-destructive techniques for plant distance and volume measurements. This study aimed to measure cabbage height, volume, and distance using LiDAR and RGB-D imaging. The sensors were mounted on a 1.6 kW electric field scouting platform (EFSP) for data collection. Point cloud (PCD) data were collected using LiDAR, whereas data processing, visualization, and measurements were done using commercial software and open-source programming scripts. A total of 20 cabbage plants were analyzed. LiDAR data processing included data frame screening, outlier removal, denoising, voxelization, and generation of 3D PCD density maps. Depth image processing included importing raw data and metadata shaping using intrinsic camera parameters, visualization, extraction of depth points, and pixel-level measurements of distances and volume. RGB image processing involved image conversion, segmentation, normalization, binary masking, mask cleaning, region extraction of cabbages, separation of ROI and preparation of contours, Delaunay triangulation and convex hull preparation, ROI overlay, bounding box preparation, sharing boundary between two boxes, conversion to pixel distances, and for visualization, plant height, volume measurements, and center to center distance measurement for measuring the plant distance. LiDAR demonstrated higher measurement accuracy for cabbage plant height, circumferential volume (geometric canopy volume), and plant distance, followed by RGB-D imaging, while RGB imagery showed comparatively lower performance under the study field conditions. Overall, LiDAR and RGB-D imaging provided reliable and non-destructive approaches for cabbage geometric characterization under field conditions, although accurately capturing complex plant geometry remains challenging. Positive and negative values of bias represent the over- and under-estimated results, respectively. Future studies should include larger and more diverse plant datasets exhibiting diversified size, shape, and geometric structure to further improve the robustness and general applicability of the proposed sensing approaches.
Why it matches plant phenotyping methodsLiDAR、RGB、RGB-Dを用いてキャベツの高さ・体積・株間距離を取得し、処理手順と測定精度を比較評価する手法研究であり、植物表現型取得が中心である。
abstractThis study aimed to measure cabbage height, volume, and distance using LiDAR and RGB-D imaging.
Brassica juncea (Mustard) is one of the most economic seed vegetable crops of the world, playing a major role in the production of world edible oil and the agricultural economy. The third-largest producer is India, which had an area under cultivation of about 8.6 million hectares of Mustard in 2021 22, and annual revenue of over USD 5 billion in 2021 22. Nevertheless, the presence of diseases like Alternaria Leaf Spot, White Rust, Powdery Mildew, and Septoria Leaf Spot threatens yield and quality by up to an estimated 2070% loss every year, based on the severity of the disease, and thus economic losses are estimated at over USD 1.5 billion per year in India alone. Traditional diagnostic systems are based on a manual examination of an agronomist trained to look at the sample and make a judgment, which is time-consuming, subjective, and subject to human error. Current deep learning methods of automated disease detection, promising as they are, are prone to inaccuracies on complex disease patterns, poor uncertainty estimation that is essential in real-world implementation, and poor generalizability to different field conditions. In response to these drawbacks, Swin-BNN-RF, a hybrid framework that combines Swin Transformer as a hierarchical attention-based feature extractor, Bayesian Neural Network (BNN) with symmetrized posterior as a probabilistic learner, and a Random Forest (RF) as an ensemble classifier, is proposed in this study. The Swin Transformer also harnesses local and global spatial biases with its shifted window self-attention network, and it is able to extract better features on leaf images of high-resolution. The uncertainty estimates of the BNN component are trusted, and unambiguous predictions are highlighted to get the opinion of the human expert. Random Forest classifier uses the bagging and boosting ensemble methods to improve stability and the robustness of the classification. A large dataset was experimented with; it consisted of more than 10,000 samples per category of disease in four diseases. In the case of binary classification, the proposed model was 98.32% accurate, 98.52% precise, 98.70% recall, and 98.36% F1. On multi-class classification, it obtained 97.50, 97.82, 98.51, and 97.46 accuracy, precision, recall, and F1 score, respectively, which showed consistent performance in comparison with state-of-the-art models such as EfficientNet, MobileNet, and Residual Networks. The contribution of each component is verified by the Ablation studies and statistical analysis of significance (p
Why it matches plant phenotyping methodsマスタード葉の画像から病害状態を推定する深層学習フレームワークを開発・評価しており、植物病害表現型の取得・分類手法が中心である。
abstractSwin-BNN-RF, a hybrid framework that combines Swin Transformer as a hierarchical attention-based feature extractor, Bayesian Neural Network (BNN) with symmetrized posterior as a probabilistic learner, and a Random Forest (RF) as an ensemble classifier, is proposed in this study.
Abstract Leaf color is an important trait affecting vegetable quality, yield, and market value. However, traditional methods for leaf color assessment are often subjective or destructive, which limits accurate and high-throughput phenotyping. In this study, an unmanned aerial vehicle (UAV)–based multispectral imaging platform was used to collect phenotypic data from 214 Chinese cabbage inbred lines at the rosette stage. A multispectral UNet model was applied to segment individual plants, and a membership function was used to quantify leaf color on a continuous scale. Based on these high-throughput phenotypic data, a genome-wide association study was used to identify two candidate genes, BrEMB976 and BrGSH2, on chromosome A06. Subsequent virus-induced gene silencing analysis showed that silencing these genes altered leaf color. In addition, a deep learning-based genomic selection model, BrDeepGS, was developed for leaf color prediction, which achieved a Pearson correlation coefficient of 0.853. These results demonstrate the potential of integrating UAV-based high-throughput phenotyping, candidate gene analysis, and genomic prediction for leaf color evaluation and selection in Chinese cabbage breeding.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像、個体分割、葉色の連続量化による高スループット表現型取得が研究の中心であり、育種への応用も行っている。
abstractan unmanned aerial vehicle (UAV)–based multispectral imaging platform was used to collect phenotypic data from 214 Chinese cabbage inbred lines
Accurate, field-scale mapping of crop growth stages is critical for supply-sensitive vegetable production, where timely harvests require detailed phenological information. Consecutive growth stages often involve rapid and subtle morphological changes and are influenced by challenging open-field conditions, which frequently result in misclassification when stages are treated as independent, discrete categories. To address this issue, CropMap is proposed as a growth-stage mapping framework that integrates Hierarchical Semantic Segmentation Networks (HSSN) with multispectral unmanned aerial vehicles (UAVs) imagery. CropMap incorporates the structured biological progression of crop development into the learning objective through tree-based label constraints, allowing the model to recognize phenological continuity and reduce confusion between adjacent stages. The framework is evaluated on the publicly available National Information Society Agency of Korea (NIA) field crop growth-stage dataset, a large-scale, multi-institutional UAV dataset containing 337,665 multispectral patches across six hierarchically related growth stages of Chinese cabbage and radish, curated by the NIA. CropMap achieves a test-set mean Intersection over Union (mIoU) of 0.5382, representing a 5% relative improvement over the best-performing transformer baseline (SegFormer; mIoU = 0.5124). Performance varies across classes: background separation is strong (IoU = 0.9128) and the rosette stage is well distinguished (IoU = 0.6354), while the leaf expansion stage remains the primary challenge (IoU = 0.3541), reflecting the inherent difficulty of mapping this spectrally and morphologically transitional class. These findings indicate that hierarchy-aware learning reduces inter-stage confusion for most phenological classes, but transitional growth stages remain a significant limitation for field-scale deployment. The framework provides a foundation for stage-resolved crop monitoring to support harvest timing and supply forecasting in high-value vegetable systems.
Why it matches plant phenotyping methods作物の生育段階という植物状態を、マルチスペクトルUAV画像と階層型セマンティックセグメンテーションで推定する手法が研究の中心であり、公開データセット上で性能評価も行っている。
abstractCropMap is proposed as a growth-stage mapping framework that integrates Hierarchical Semantic Segmentation Networks (HSSN) with multispectral unmanned aerial vehicles (UAVs) imagery.
Broccoli is a globally significant vegetable, but climate change and soil salinization increasingly threaten its productivity. Precise seedling phenotyping is essential for selecting salt-tolerant germplasm, yet traditional manual methods are labor-intensive and error-prone. This study develops LBD-PointNet++, an optimized 3D point cloud semantic segmentation model for automated phenotypic parameter extraction of broccoli seedlings at the germination and early developmental phase under salt stress. High-fidelity 3D point clouds were reconstructed from a precision three-view imaging system using Structure from Motion (SfM) algorithms. LBD-PointNet++ introduces three core optimizations: (1) a Large Kernel Attention (LKA) mechanism using 3D sparse decomposition to capture long-range global dependencies; (2) a Dual Uncertainty and Shape-Adaptive Sampling (DUSAS) mechanism to preserve high-frequency features of fragile stems and margins; and (3) a joint Boundary-Aware Nested Contrastive and Adaptive Varifocal Joint Loss (BNCV-Loss) to effectively isolate overlapping leaves. Experimental results demonstrate superior performance, achieving an overall mean Intersection over Union (mIoU) of 88.07% across all three categories (Leaf, Stem, and Pot) and a Mean F1-score of 93.48%. Compared to state-of-the-art Transformer architectures like PTv3, LBD-PointNet++ achieves higher accuracy with less than 6% of the parameter volume and over twofold faster inference speed. Furthermore, dynamic monitoring across NaCl gradients (0-250 mmol/L) revealed a potential non-linear threshold effect, identifying 100 mmol/L as a preliminary phenotypic threshold under these conditions. Beyond this threshold, growth inhibition intensified rapidly; At 250 mmol/L, plant height decreased by 54.43% and the 3D entity volume shrank to approximately one-fifth of the control group. In summary, LBD-PointNet++ provides a high-efficiency solution for phenotypic identification and digital breeding of salt-tolerant Brassicaceae crops.
Why it matches plant phenotyping methods3D点群分割ネットワークと三視点SfM撮像を開発し、ブロッコリー幼植物の表現型形質抽出を中心的に評価しているため。
abstractThis study develops LBD-PointNet++, an optimized 3D point cloud semantic segmentation model for automated phenotypic parameter extraction of broccoli seedlings
Abstract Genomic selection has accelerated genetic gain in many breeding programs worldwide but genotype-by-environment-by-management (GxExM) hampers further progress for systems where these interactions are important and not well represented in the training data. Process-based crop growth models (CGMs), which encode physiological relationships between plants and their environments, can extrapolate to novel conditions but cannot directly leverage genomic information. Coupling these complementary approaches in the Crop Growth Model–Whole Genome Prediction (CGM-WGP) framework addresses both limitations, yet applications in horticultural crops remain scarce. In this study, we apply CGM-WGP to predict flowering time in broccoli ( Brassica oleracea var. italica ) and common bean ( Phaseolus vulgaris L.), two horticultural species with contrasting physiological responses to temperature and photoperiod. Genotype-specific thermal time requirements and photoperiod parameters were jointly estimated with genome-wide marker effects and predictions were compared against a Reaction Norm Genomic Best Linear Unbiased Prediction (RN-GBLUP) benchmark across four cross-validation scenarios of increasing predictive difficulty. RN-GBLUP achieved the highest accuracy under sparse-testing scenarios where training data covered all target environments, while CGM-WGP outperformed RN-GBLUP when predicting untested environments and untested genotype-environment combinations (broccoli: Pearson r = 0.66, RMSE = 9.4 days; bean: r = 0.86, RMSE = 5.2 days). These results demonstrate that CGM-WGP can be applied to horticultural crops using genome-wide markers alone, but without requiring prior identification of quantitative trait loci. CGM-WGP also provides a modular foundation that can be extended to predict the timing of other developmental transitions and output traits such as biomass and yield.
Why it matches plant phenotyping methods開花期という植物形質を対象に、CGM-WGPによる予測手法を適用し、RN-GBLUPとの交差検証で精度比較・評価しており、形質推定ワークフローが研究の中心である。
abstractCoupling these complementary approaches in the Crop Growth Model–Whole Genome Prediction (CGM-WGP) framework addresses both limitations
Non-heading Chinese cabbage is a cool-season crop, and high temperature has become a key factor limiting its quality and yield. Given that plant heat tolerance is a complex quantitative trait regulated by multiple genes, establishing a comprehensive evaluation system integrating multiple physiological and biochemical indicators is of great significance. In this study, 35 varieties of non-heading Chinese cabbage germplasm were used to investigate heat damage indices (HDI) and measure physiological and biochemical indicators under summer high-temperature stress, aiming to provide a basis for heat tolerance evaluation. Correlation analysis revealed significant correlations among the physiological and biochemical indicators, indicating information overlap. Principal component analysis (PCA) was subsequently employed to extract six independent composite indicators. A composite index of heat tolerance productivity, namely the Heat Tolerance Productivity Index (HTPI), was obtained through membership function analysis, and cluster analysis classified the tested germplasm into four heat tolerance levels. A regression equation for evaluating heat tolerance in non-heading Chinese cabbage was successfully established. Eleven key heat tolerance indicators were identified, and two highly heat-tolerant varieties, B21 and B32, with excellent comprehensive traits were selected. The comprehensive evaluation system established in this study not only provides an effective tool for high-throughput screening of heat-tolerant germplasm resources but also lays a solid foundation for subsequent genetic improvement and molecular breeding of heat-tolerant varieties. However, this study was conducted only at the seedling stage, and did not evaluate heat tolerance during the more sensitive reproductive stages (flowering and bolting).
Why it matches plant phenotyping methods複数の生理・生化学指標を統合し、PCA、メンバーシップ関数、回帰式による耐暑性評価システムを開発しており、植物表現型の抽出・スクリーニング手法が研究の中心である。
abstractestablishing a comprehensive evaluation system integrating multiple physiological and biochemical indicators is of great significance
Early identification of leaf-related infections in cauliflower is crucial for reducing crop damage and ensuring stable agricultural output. Traditional inspection techniques, which depend on human observation, are often inconsistent, labour intensive, and unsuitable for large farming environments. To overcome these challenges, this research introduces an intelligent hybrid framework combining Machine Learning (ML), Deep Learning (DL), and Transfer Learning (TL) for automated cauliflower leaf disease recognition. The proposed Cauliflower Leaf Disease Classification (CLDC) system utilizes a curated dataset categorized into eleven disease classes. Image preprocessing involves resizing to 64×64 pixels and normalization to enhance model performance. A novel Inception Residual Networkbased Convolutional Neural Network (IRN-CNN) is designed to extract high-level discriminative features using customized inception-residual modules. These deep features are further processed using Logistic Regression (LR) to improve classification accuracy and generalization. For performance benchmarking, conventional models such as Decision Tree Classifier (DTC), Artificial Neural Network (ANN), and standalone LR are also implemented. The system is integrated into a Tkinter-based Graphical User Interface (GUI), enabling functionalities such as dataset upload, preprocessing, training, evaluation, and real-time prediction. Batch image analysis with CSV export support enhances usability for large-scale applications. Additionally, an Explainable Artificial Intelligence (XAI) component powered by a generative AI API provides detailed insights, including disease severity, affected regions, and crop verification. A Telegram Bot interface further extends accessibility for mobile-based detection. Experimental findings confirm that the proposed IRN-CNN hybrid model delivers superior accuracy and reliability, making it a scalable solution for smart agriculture and precision farming systems.
Why it matches plant phenotyping methodsカリフラワー葉の病害状態を画像から分類・推定する手法の開発と性能比較が研究の中心であり、植物病害フェノタイピングに該当する。
abstractthis research introduces an intelligent hybrid framework combining Machine Learning (ML), Deep Learning (DL), and Transfer Learning (TL) for automated cauliflower leaf disease recognition.
A hybrid AI framework combining a spatial–spectral–temporal Transformer and unsupervised clustering was applied to five microgreen species—Pak Choi Cabbage, Tatsoi Mustard, Red Mizuna, Chinese Cabbage, and Arugula—grown for 3–4 weeks under water, nutrient, and combined stresses. Across five datasets collected within six months, the system achieved a Macro-F1 of 0.91 and a pre-symptomatic F1 of 0.88, enabling early detection before visible symptoms. Applications include NASA’s APH, Mars and Moon habitats, and terrestrial precision agriculture.
Why it matches plant phenotyping methods植物の水・養分ストレス状態をマルチモーダル画像から早期推定するAI手法が研究の中心であり、性能評価も示されている。
abstractA hybrid AI framework combining a spatial–spectral–temporal Transformer and unsupervised clustering was applied
The accurate quantification of glucoraphanin (GRA), a crucial health-promoting compound in broccoli, is vital for assessing its nutritional quality. However, traditional methods relying on destructive laboratory assays hinder rapid quality monitoring. To address this limitation, we developed a novel non-destructive, multimodal deep learning framework that integrates two phenotypic data modalities—image-based phenotypes from red-green-blue (RGB) leaf images and field-measured plant morphological traits—for accurate GRA estimation. Our proposed model, Parallel-Enhanced FasterNet (PE-FasterNet), incorporates two key innovations: a Gated Parallel Routing Attention (GPRA) mechanism for enhanced feature extraction, and a Phenotype-Guided Cross-Attention Feature Fusion (PG-CAFF) module for effective cross-modal fusion. Through rigorous evaluation, the model achieved a standard random-split test R 2 of 0.985 and a Leave-One-Group-Out (LOGO) cross-validation R 2 of 0.979, demonstrating highly accurate and generalized GRA predictions. This performance represents a substantial improvement over state-of-the-art convolutional neural network (CNN) and Vision Transformer models, affirming the architectural superiority of our approach. This study not only provides a robust tool for rapid, non-destructive prediction of GRA but also demonstrates a viable pathway toward data-driven crop quality management and precision breeding in broccoli.
Why it matches plant phenotyping methodsブロッコリー葉画像と形態形質からグルコラファニンを非破壊推定する深層学習法を開発・検証しており、表現型取得・抽出ワークフローが研究の中心である。
abstractwe developed a novel non-destructive, multimodal deep learning framework that integrates two phenotypic data modalities—image-based phenotypes from red-green-blue (RGB) leaf images and field-measured plant morphological traits—for accurate GRA estimation.
Climate change poses increasing challenges to Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production through unpredictable weather patterns that induce premature bolting and physiological disorders. Traditional breeding programs rely on labor-intensive visual assessment that cannot capture continuous developmental dynamics or precisely quantify stress responses across variable environments. This study validated and applied an automated high-throughput phenotyping system for evaluating seasonal adaptation in 134 Chinese cabbage genotypes across contrasting autumn (favorable) and spring (stressful) seasons in Taiwan. The system, based on a FieldScan gantry platform equipped with multispectral 3D scanners, operated autonomously 2-3 times daily, continuously monitoring morphological parameters (3D leaf area, digital biomass, plant height) and spectral indices (NDVI, PSRI) throughout the growth cycle. The system’s automated components -continuous data acquisition and real-time parameter extraction – generated approximately 100,000 data points from 63 morphological, spectral, and structural parameters during 6-week pre-harvest period. Subsequent quality and statistical analysis enabled objective genotype classification and breeding decisions. Automated measurements showed season-dependent associations with visual assessment scores (R² = 0.37-0.56 in autumn; R² = 0.73-0.80 in spring), with spring models substantially outperforming autumn models due to enhanced physiological differentiation under stress. Spring cultivation induced severe stress responses, evidenced by 71% increase in PSRI (0.12 vs. 0.07) and 26% increase in plant height, with bolting resistance emerging as the critical determinant of adaptation. A quantile-based multi-dimensional classification framework integrating seasonal composite scores and Euclidean distances stratified germplasm into actionable breeding categories: stable genotypes (3.7%), spring-specific types (0.7%), poor performers (13.4%), and intermediate materials (82.1%). Continuous temporal monitoring enabled early stress detection, with binned PSRI measurements predicting subsequent morphological development one week in advance (R² = 0.62). This integrated phenotyping framework provides efficient tools for accelerating climate-resilient breeding through objective genotype classification, early stress detection, and data-driven decision support, with potential adaptation to other vegetable crops and integration with IoT-based collaborative breeding network.
Why it matches plant phenotyping methods高スループット3D・マルチスペクトル表現型計測プラットフォームの検証と応用が研究の中心であり、形態・スペクトル形質の自動取得、抽出、予測性能、遺伝子型分類を評価している。
abstractThis study validated and applied an automated high-throughput phenotyping system for evaluating seasonal adaptation in 134 Chinese cabbage genotypes
The real-time quantitative estimation of herbaceous plant growth status holds significant potential for investigating fertilization effects, predicting growth curves, and enhancing crop yield. This study constructed a growth quantification model using an improved YOLOv5 architecture integrated with 3D point cloud processing, with pak choi as an exemplar crop. To improve the recognition accuracy while reducing the number of parameters, we employed a lightweight YOLOv5 model enhanced with Atrous Spatial Pyramid Pooling and Ghost convolution modules for individual pak choi plant localization and growth stage classification. We also developed a segmentation method based on the HSV color space to segment leaves. To estimate the total fresh weight of individual plants, we first calculated the leaf surface area by generating a triangular mesh from the corresponding leaf point clouds and predicted the chlorophyll content using a stacking ensemble model. Subsequently, to address the leaf occlusion issues, the leaf pixel ratio in the images, leaf surface area, and mean leaf chlorophyll content were collectively used as independent variables. Finally, a multiple linear regression model was developed to accurately estimate the total fresh weight of individual pak choi plants. Experimental results demonstrate that the modified YOLOv5 architecture achieves a 3.5% improvement in mAP@0.5 (reaching 96%) and a 4.66% increase in F1-score (attaining 90.26%), while significantly reducing the computational complexity compared to the baseline model. Statistical tests verified that the fitted equation could explain 79% of the variation in the total fresh weight, with an average relative error of 12.16%. This enables non-contact and accurate measurement of the pak choi growth status.
Why it matches plant phenotyping methodsYOLOv5、3D点群、葉面積・クロロフィル推定を統合し、個体の生体重という植物形質を非接触推定する手法が研究の中心である。
abstractThe real-time quantitative estimation of herbaceous plant growth status holds significant potential
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Efficient phenotyping monitoring of cauliflower is crucial for its breeding and production. However, traditional manual measurement methods are time-consuming and labor-intensive, and existing deep learning (DL) methods mostly focus on the seedling stage, lacking systematic research covering the entire growth period. In this study, RGB images of cauliflower from seedling to harvest were collected. Through systematic screening and evaluation of instance segmentation models, accurate segmentation of plants and curds was achieved, and plant canopy width, leaf area, and curd traits were automatically extracted to track their dynamic changes. Evaluation results showed that YOLO12s-seg was the optimal model. It can achieved a segmentation mask mAP 50 of 99.4% for plants and curds in sparsely planted images and showed an advantage in identifying partially occluded early curds beneath inner leaves. Traits such as plant canopy width and curd diameter automatically extracted from segmentation results were highly consistent with manual measurements (R 2 > 0.90). Furthermore, the Richards model and Sine model were used to accurately fit the growth dynamics of leaf area and curd area, respectively. Based on growth kinetics, curds were classified into three types: mature and compact type, peak-burst type, and steady-increase type. Cluster analysis of 47 germplasms based on high-throughput phenotyping data revealed four groups and their growth characteristics: comprehensively coordinated type, mid-maturity compact type, large high-yield type, and curd-dominant type. Integrating the above functions, a platform for cauliflower growth monitoring and phenotypic analysis was developed. It provided full-process support from automatic image processing to growth dynamic analysis. This work provides an effective automated solution for high-throughput phenotyping analysis and growth dynamic monitoring of cauliflower, and offers a referable analytical framework for crop growth pattern research and intelligent breeding decision-making.
Why it matches plant phenotyping methods植物のインスタンスセグメンテーションから葉面積・草冠幅・花蕾形質を自動抽出し、手動測定との技術検証と成長動態解析、統合プラットフォーム開発を行っており、フェノタイピング手法が研究の中心である。
abstractThrough systematic screening and evaluation of instance segmentation models, accurate segmentation of plants and curds was achieved, and plant canopy width, leaf area, and curd traits were automatically extracted to track their dynamic changes.
Crop diseases significantly reduce agricultural output and are a serious problem, especially in the parts of the world where diagnostic experts are not readily available. Deep learning has recently shown us that it is possible for a computer to identify plant diseases directly from images of the leaves. Nevertheless, to make such solutions available on the web or mobile devices one has to really think about how heavy the calculations will be, how easy the user interface should be, and also the limit on the data used. Here is a paper on a web-based applied deep learning system for disease detection in multiple crops. The system detects disease in eight crops Apple, Banana, Grape, Mango, Cauliflower, Tomato, Potato, and Corn with each crop having several disease classes and healthy samples. Three transfer-learning-based CNN architectures MobileNetV3, EfficientNetB4, and ResNet50 were compared for classification performance on the public datasets collected from PlantVillage, Kaggle, and Mendeley. Considering class-wise accuracy, prediction time, and deployment scenarios, MobileNetV3 was picked as the main model to be integrated into the system. To compensate for the differences in image quality often found in pictures taken by users, an optional super-resolution preprocessing step with Real-ESRGAN is added and quantitatively assessed. Disease prediction with spectral activation maps (Grad-CAM) enhances the model's interpretability by highlighting image areas where the disease is detected. The resulting model is embedded in a multilingual Progressive Web Application (PWA). The platform enables users to submit their crop images and receive predicted disease names and treatment options, which are generated by a Large Language Model (LLM) using structured disease metadata. The research acknowledges dataset bias and limitations in extrapolating from curated datasets to the general real-world setting although it reports very good performance of the method on the test sets. In summary, the system proposed here is intended as a practical digital agriculture decision-support tool that demonstrates deployment feasibility and raises a few issues for future validation at the field level and improvement.
Why it matches plant phenotyping methods葉画像から植物病害を推定するCNN手法の比較・前処理評価・実装を中心とした研究であり、植物の病害状態を直接評価するため、植物フェノタイピング手法として中心的です。
abstractDeep learning has recently shown us that it is possible for a computer to identify plant diseases directly from images of the leaves.
Carbon monoxide (CO) functions as a critical signaling molecule in both mammalian inflammation and plant stress responses. However, existing techniques face challenges in real-time monitoring of CO dynamics across biological kingdoms. Here we developed Z2CO, a xanthene-based red-emitting fluorescent probe constructed on a Pd(0)-triggered Tsuji-Trost allylic cleavage mechanism. Upon CO recognition, Z2CO generates a distinct turn-on fluorescence signal at 625 nm within 10 min. The probe exhibits favorable properties including an 80 nm Stokes shift, low detection limit (0.193 μM, 3σ/k criterion), excellent water solubility, and minimal cytotoxicity, making it suitable for complex biological applications. Using Z2CO, we successfully visualized endogenous CO generation in pulmonary tissues of lipopolysaccharide-induced bacterial pneumonia mice and quantitatively evaluated anti-inflammatory drug efficacy. Furthermore, we extended Z2CO to plant systems, achieving real-time monitoring of CO dynamics in cadmium-stressed edible sprouts and brassica rapa. These investigations provide direct evidence for CO involvement in heavy metal-triggered signal transduction networks. Collectively, Z2CO constitutes a versatile tool for elucidating CO-mediated physiological and pathological processes across animal and plant systems.
Why it matches plant phenotyping methods植物内在CO動態をリアルタイム可視化・定量する蛍光プローブを開発し、植物のストレス応答という生理状態の測定に実質的に適用しているため、測定法が中心である。
abstractHere we developed Z2CO, a xanthene-based red-emitting fluorescent probe
Climate change threatens global Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production, a cool-season crop essential for Asian markets. With optimal growth at 18-20°C and severe disruption above 25°C, developing heat-resilient varieties is critical. This study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes. Seedlings were subjected to heat stress (setpoint 40/35°C day/night; measured 35.7/31.5°C day/night air temperature) or controls (setpoint 25/20°C day/night; measured 25.0/17.7°C day/night air temperature) for 14 days, with continuous non-destructive monitoring of 14 morphological and spectral parameters using PlantEye F600 multispectral 3D scanner. Principal component analysis of temporal phenotyping data explained 62-68% of variance, enabling quantitative assessment of phenotypic stability through Euclidean distance measurements in PC space. Temporal analysis revealed crop-specific response patterns with maximum treatment separation at 3 days after treatment (DAT) (ΔC=3.27), reflecting Chinese cabbage’s rapid heat sensitivity as a cool-season crop, followed by progressive acclimation by 14 DAT (ΔC=1.41). Early responses (3-5 DAT) were dominated by morphological parameters, transitioning to physiological adjustments (10-14 DAT) characterized by spectral indices. Under heat stress, plants prioritized evaporative cooling through increased transpiration (four-fold increase) over carbon assimilation. A critical finding was the disproportionately greater reduction in root biomass relative to shoot biomass under to heat stress, with root biomass declining 38-47% versus 20% in shoots. Strong correlations (r>0.8) between 3D imaging parameters and destructive biomass measurements validated the non-destructive approach’s reliability. Notably, image-based root surface area analysis correlated strongly with actual root biomass (R 2 =0.698, p<0.001), enabling practical assessment of root area without conventional destructive processing. Based on integration of phenotypic stability (Euclidean distances in PC space) and biomass production under heat stress, this approach identified four distinct heat tolerance strategies: stable-productive genotypes (ideal breeding targets combining phenotypic stability with high heat-stress biomass production), stable-conservative genotypes (phenotypic stability with lower production), plastic-productive genotypes (substantial phenotypic changes yet high biomass production), and plastic-sensitive genotypes (phenotypically unstable and poor biomass production). This validated framework accelerates heat-tolerant Chinese cabbage breeding through efficient high-throughput phenotyping, enabling targeted genotype selection for diverse production environments facing climate warming.
Why it matches plant phenotyping methods3Dマルチスペクトルスキャナによる非破壊・時系列表現型取得と、その解析・検証が研究の中心であり、熱ストレス下の形態・生理形質を定量化する実質的なハイスループット表現型解析研究である。
abstractThis study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes.
Reproduction assets foundThe paper states its collected phenotyping data are available in the supplementary material hosted with the article (open access under CC BY-NC-ND), making the paper-specific phenotype dataset publicly actionable via the article DOI. The analysis code, however, is only available from the corresponding author uponReasonDataset · publichrough field phenotyping.) between RDA and the World Vegetable Center (WorldVeg)” and by the long-term strategic donors to the WorldVeg: Taiwan, the United States, Australia, the United Kingdom, Germany, Thailand, South Korea, Philippines, and Japan.
Footnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100221 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article.
Multimedia component 1
Data availability
The data collected and used in this study are available in the supplementary material. The code used for analysis can be obtained from the corresponding author upon reasonable request.
ReferenceOpen asset ↗lines:486-514Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Accurate plant organ segmentation is essential for high-throughput phenotyping and ideotype selection. However, current methods struggle with plants of complex morphology, particularly small organ categories with sparse point distributions. In addition, severe leaf adhesion in dense canopies often hinders reliable leaf instance segmentation using conventional clustering methods. To address these challenges, we propose a dual-path fusion network (DPFuseNet) for semantic segmentation and a hierarchical multi-scale spectral clustering algorithm (HMSC) for instance segmentation of plant point clouds. DPFuseNet introduces three innovations: a high-frequency information embedding strategy, a dual-path feature extraction module integrating CNN and Transformer branches, and a cross-attention–based dual-granularity feature fusion block. Evaluated on tomato, cabbage, and soybean datasets, DPFuseNet achieved superior performance over state-of-the-art baselines such as Stratified Transformer and Point Transformer v3, reaching average precision, recall, F1-score, and IoU of 96.51%, 96.27%, 96.38%, and 93.32%, respectively. Compared with the current leading single-branch model Point Transformer v3, DPFuseNet improves these metrics by 1.19%, 1.20%, 1.21%, and 2.05%, and by 0.89%, 1.14%, 1.02%, and 1.70% over the dual-branch model PVDST. For instance segmentation, the proposed HMSC algorithm, combined with region growing, achieved mPrec 89.65%, mRec 78.70%, mCov 76.88%, and mWCov 85.11% on multi-stage tomato, cabbage, and soybean datasets, consistently outperforming conventional spectral clustering. Overall, the proposed framework demonstrates robustness and efficiency in both semantic and instance segmentation, offering a novel pathway for advancing plant point cloud analysis and smart agriculture.
Why it matches plant phenotyping methods植物点群から器官の意味・個体分割を行う深層学習およびクラスタリング手法の開発と評価が研究の中心であり、植物表現型解析への直接的な応用を示している。
abstractAccurate plant organ segmentation is essential for high-throughput phenotyping and ideotype selection.
Hyperspectral imaging (HSI) systems offer rich spectral information for precision agriculture applications such as yield forecasting, but their high cost and complexity limit widespread adoption. The relationship spectral data complexity and prediction accuracy in non-destructive crop monitoring remains unclear, challenging the assumption that more complex spectral data inherently yields better predictions. We developed a workflow integrating UAV-based HSI (150 bands) with deep learning to non-destructively estimate fresh weight of cabbage heads (n = 680). A two-dimensional convolutional neural network (2D-CNN) was deployed to identify the single most predictive wavelength through systematic feature extraction. Performance was benchmarked against 20 conventional multi-band vegetation indices (VIs). The CNN identified a single spectral band at 565.63 nm as the optimal predictor. A predictive model using only this single-band input achieved a coefficient of determination (R²) of 0.49 on an independent test set, with a root mean square error (RMSE) of 0.99 kg and a mean absolute error (MAE) of 0.84 kg. This performance substantially surpassed the best-performing conventional multi-band VI (MCARI2, R² = 0.34), representing a 44% improvement in explained variance using single-band data input. This AI-driven approach autonomously distill HSI complexity into a single optimal wavelength that outperforms established multi-band indices. The findings provide a methodological framework for designing simplified, cost-effective spectral sensors for precision agriculture, potentially improving accessibility and scalability of crop monitoring technologies.
Why it matches plant phenotyping methodsキャベツ結球の生体重という植物形質を、UAVハイパースペクトル画像とCNNで非破壊推定するワークフローを開発し、独立テストと既存指標との比較で検証しており、形質取得手法が中心である。
abstractWe developed a workflow integrating UAV-based HSI (150 bands) with deep learning to non-destructively estimate fresh weight of cabbage heads (n = 680).
Monitoring the growth dynamics in field-grown cabbage is critically important for ensuring stable vegetable production and advancing precision agricultural management. However, conventional two-dimensional (2D) image-based monitoring approaches are limited to planar projection information and lack representations of spatial structural characteristics, rendering them inadequate for supporting high-precision, full-cycle phenotypic monitoring of cabbage under open-field conditions. In this study, a high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques. Building on this dataset, an adaptive point cloud segmentation network designed for the whole-cycle growth monitoring was proposed, incorporating a Head Refinement Module (HRM), a Leaf Instance Segmentation Module (LISM), and Cross Module Interaction (CMI) to address leaf adhesion and head boundary delineation. Experimental results demonstrated that the proposed method consistently outperformed state-of-the-art models in both semantic and instance segmentation tasks. For semantic segmentation, the mean Intersection over Union (mIoU) reached 0.767, with a point classification accuracy of 94.8%. The model comprises 54.25 million parameters and achieves an average response time of 0.76 s. For instance segmentation, the Average Precision (AP) improved by 2.3% for cabbage heads and 3.8% for leaves, while the Average Recall (AR) increased by 6.9%. Growth parameters, including plant height and canopy spread, extracted from the segmentation results showed strong agreement with ground-truth measurements, with correlation of coefficients (R 2 ) exceeding 0.9 for plant height, canopy length, and canopy width. Leveraging these multidimensional phenotypic descriptors, the temporal dynamics of cabbage growth throughout the entire growth cycle were systematically characterized. Overall, this study enables dynamic monitoring of cabbage phenotypes across the full growth cycle, providing a novel technical pathway for extending 3D phenotyping from controlled environments to open-field applications and offering important support for precise crop monitoring and the development of digital twin agriculture.
Why it matches plant phenotyping methods3D点群データセット、セグメンテーションネットワーク、形質抽出を開発・検証し、圃場キャベツの草高や冠幅を定量化する植物フェノタイピング手法が研究の中心である。
abstracta high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques.
Reproduction assets foundThe paper's authors publicly release their improved OneFormer3D point cloud segmentation code on GitHub; the cabbage 3D point cloud dataset is only available upon request.Code · publicThe code is available at https://github.com/PandaDalin/improve_oneformer3d. The data of this study are available from the corresponding author upon request.Open asset ↗PandaDalin/improve_oneformer3dhtml-lines:449-475Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
ABSTRACT Smart agriculture based on the use of Artificial Intelligence for crop disease detection to ensure food security. Fungal disease is one of the major causes that affects the quality of the vegetables. Convolutional neural networks (CNNs) and vision transformers (ViTs) enable the detection of crop diseases at an early stage, allowing farmers to take preventive measures and minimize further losses. The proposed method introduces an ensemble of Custom CNN to understand multilevel local features and Pretrained ViT to capture global dependencies as well as contextual information from the dataset. An interactive cross attention module (ICAM) facilitates bidirectional information exchange between CNN and transformer token representations, while an attention guided gated fusion (AGGF) mechanism adaptively combines complementary features. The Indian Crop Visual Disease Dataset (ICVDD‐5) has been developed in a real field for the proposed work with the help of domain experts. The dataset contains 880 diverse images depicting both healthy and diseased specimens of five vegetable crops. The crops selected for this research initiative include Brinjal, Cabbage, Chili, Okra, and Tomato. These five crops are examined for about 21 distinct disease classes. Comprehensive ablation studies are conducted to prove the contributions of each architectural component, including CNN‐only, ICAM‐disabled, and AGGF‐disabled configurations. Experimental results demonstrate that the proposed ICAG‐Net achieves a test accuracy of approximately 70%–73% with improved macro‐F1 score compared to baseline CNN models under identical training settings. The novelty of this work lies in an extensible solution for real world crop disease diagnosis systems and offers insights into hybrid CNN–transformer architectures for small scale agricultural datasets.
Why it matches plant phenotyping methodsCNN・Transformerによる植物病害症状の画像検出手法を開発し、実圃場画像データセットの構築、アブレーション、ベースライン比較で性能検証しているため、植物フェノタイピング手法が中心である。
abstractThe proposed method introduces an ensemble of Custom CNN to understand multilevel local features and Pretrained ViT to capture global dependencies as well as contextual information from the dataset.
In this study, we addressed agricultural labor shortages by developing a smart farming sensor module that integrated low-cost environmental sensors with a multipoint soil moisture sensor to predict broccoli growth, plant height ( PH ), and leaf count (L n ). Multivariable regression confirmed that integrated solar radiation ( S ) was the most dominant factor, although broccoli growth involved a complex interplay of solar radiation, optimal temperature, humidity, and soil moisture. More importantly, the analysis revealed that the middle layer soil moisture (u m ) exhibited the strongest positive contribution to PH . This finding indicated that water availability in the main root zone was essential for vertical growth and highlighted the indispensability of multipoint sensing over conventional single-depth measurements to accurately model the intricate relationship between soil moisture and crop development. Moving forward, we aim to leverage the superiority of multipoint data to construct a sophisticated growth prediction model, thereby contributing to the optimization of irrigation and temperature management in smart farming systems.
Why it matches plant phenotyping methods環境・土壌水分センサーを統合した測定モジュールを開発し、植物高や葉数などの作物形質を予測する手法が研究の中心である。
abstractdeveloping a smart farming sensor module that integrated low-cost environmental sensors with a multipoint soil moisture sensor to predict broccoli growth, plant height ( PH ), and leaf count (L n ).
Accurate monitoring of crop nitrogen status is essential to optimize fertilization management and reduce nitrate losses in intensive horticultural systems. This study aimed to calibrate crop monitoring tools based on ion-selective electrodes and remote sensing indices for nitrogen status assessment in horticultural crops.Field experiments were conducted during the 2025 growing season on broccoli and watermelon grown under Mediterranean conditions and subjected to different nitrogen fertilization levels. Crop nitrogen status was assessed using complementary approaches. Multispectral satellite imagery and UAV-based hyperspectral data were used to calculate vegetation indices related to chlorophyll and nitrogen status, including NDRE, GNDVI, TCARI and OSAVI. These indices were calibrated against leaf nitrogen concentration and nitrate content determined by conventional laboratory analyses. In parallel, xylem sap was extracted from leaves and analyzed using ion-selective electrodes to determine nitrate concentration.Strong relationships were observed between nitrogen supply, spectral indices and nitrate concentration in xylem sap, enabling the development of calibration models for real-time crop nitrogen monitoring. The integration of proximal sensing with remote sensing improved the robustness of nitrogen diagnostics across crops and growth stages.These results highlight the potential of combining ion-selective electrodes and remote sensing tools as decision-support systems for optimized nitrogen management.
Why it matches plant phenotyping methods植物の窒素状態を対象に、イオン選択電極・衛星/UAVリモートセンシングの校正モデルを開発・検証しており、表現型取得手法が研究の中心である。
abstractThis study aimed to calibrate crop monitoring tools based on ion-selective electrodes and remote sensing indices for nitrogen status assessment in horticultural crops.
The integration of electronic system into agricultural production can significantly enhance its efficiency and scalability. However, most of the current research focuses on the data acquisition and automated control. The development of expert-level, interpretable decision-making systems remains a challenge, primarily due to the prohibitive requirement for extensive domain-specific labeled data. In this manuscript, a novel agentic framework integrated with Large Language Models is proposed and demonstrated, using seedling assessment as a case study. The framework achieves high predictive accuracy, strong interpretability, and fast-adaption ability, offering a distinct advantage over methods that demand large labeled datasets. An agentic orchestration framework integrated with the Analytic Hierarchy Process and the reasoning ability of the Large Language Models is constructed to automatically derive the raw assessment rating. Based on a score calibration system using few-shot learning with three different types of lettuce, Butterhead, Grand Rapids, and Ramosa Hort, the final rating score can be derived with good prediction accuracy based on a small dataset (less than 20 labelled data). Additionally, three supplementary plant species (Sprout, Ball Brassica, and Rapa Brassica) are used to demonstrate the framework’s rapid adaptation capability. A field experiment guided by the agentic framework is conducted to prove that this seedling assessment system can be applied to help increase yield by more than 20 %. Our framework presents an important attempt towards an intelligent agricultural system that is capable to achieve expert-level and data-efficient decision making, thereby helping to bridge the critical gap between artificial intelligence research and practical agricultural application.
Why it matches plant phenotyping methodsLLMを用いた解釈可能なエージェント型フレームワークを開発し、苗の評価スコアという植物状態の推定・抽出に適用しているため、表現型取得・評価手法が研究の中心です。
titleFew-shot and interpretable agentic framework based on large language models for data-efficient plant phenotyping
This data article describes a curated RGB-Depth image dataset captured using an Intel RealSense D435 stereo depth camera mounted on an autonomous mobile platform during field deployments at commercial baby broccoli farms in Victoria, Australia. The dataset comprises 1759 paired RGB images (640 × 480 pixels) and corresponding 16-bit depth frames acquired under both daytime (natural sunlight) and night-time (LED illumination) conditions, designed to support research in agricultural computer vision and robotic harvesting. Images were selected from 39,765 raw acquisitions through a reproducible Python curation pipeline applying quality filtering (blur detection, brightness thresholds, corruption detection), perceptual hash-based duplicate removal, and manual review. The final dataset includes 924 daytime and 835 night-time image pairs containing baby broccoli plants at various growth stages. The dataset provides RGB camera intrinsic parameters and pixel-aligned depth maps to enable 3D point cloud reconstruction. Potential applications include developing deep learning models for crop detection and segmentation, validating depth-based size estimation methods, and benchmarking illumination-robust vision systems. All data and curation code are publicly available under a CC BY 4.0 license.
Why it matches plant phenotyping methodsRGB-Depth画像データセットの構築と再現可能なキュレーションを中心とし、作物検出に加えてサイズ推定という植物形質の評価・ベンチマークに利用できるため。
titleA field-acquired RGB-Depth image dataset for computer vision-based baby broccoli detection and size estimation under varying illumination conditions.
Reproduction assets foundThe paper is a data article describing a public Mendeley Data repository containing the authors' field-acquired RGB-D baby broccoli image dataset (1759 image pairs, ground truth diameter annotations, camera intrinsics, and curation/annotation code), directly reproducing the paper's phenotyping measurements and analysisDataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/px5p6zdk6k.3
Direct URL to data: https://data.mendeley.com/datasets/px5p6zdk6k/3Open asset ↗Mendeley Data · 10.17632/px5p6zdk6k.3html-lines:95-155Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Kale (Brassica oleracea var. acephala) is a high value leafy vegetable with an extensive domestication history and germplasm diversity, making it an ideal target for genetic improvement. To meet growing food security needs particularly with controlled environment agriculture (CEA) systems, specialized breeding strategies are required. The goal of this study was to survey the phenotypic architecture of a global kale germplasm collection under commercial CEA conditions. This study establishes a phenotypic baseline and serves as a hypothesis generating resource for future genetic and physiological studies in kale and other leafy vegetables grown under CEA. RESULTS: A total of 203 kale accessions were phenotyped for 113 quantitative traits using high-throughput phenotyping methods. Significant differentiation was observed across all traits, with coefficient of variation ranging from 2.5% to 180.7%, confirming broad genetic variability among accessions. Trait correlation networks and hierarchical clustering grouped phenotypes into seven biologically corresponding modules including leaf, stem and root morphology, plant architecture, hyperspectral indices, and seedling growth. These modules highlight coordinated phenotypic patterns among traits. Integrative yield analyses combining partial least squares variable importance in projection with differential trait analysis identified 28 phenotypes most strongly associated with total aboveground fresh weight, a robust proxy for CEA vegetative yield. Principal component analysis further distilled these traits into three orthogonal components explaining 87.1% of total yield variation. These components represented modules related to plant organ size, canopy structure, and density, emphasizing their biological contribution to harvestable biomass. CONCLUSIONS: This study generates a foundational phenomics resource and comprehensive dissection of kale’s yield architecture under CEA conditions. The composition of traits identified constitutes a targeted set of breeding traits to be further validated for improved leafy vegetable yield. By integrating large-scale germplasm resources with phenomics, this work establishes the utility of a high-throughput phenotypic analysis for further leafy crop research and improvement.
Why it matches plant phenotyping methods大規模なハイスループット植物表現型解析を中核とし、113形質の取得、統合解析、再利用可能なフェノミクス資源の構築を行っているため。
abstractA total of 203 kale accessions were phenotyped for 113 quantitative traits using high-throughput phenotyping methods.
Accurate measurement of plant height in leafy vegetables is challenging due to their short stature, high planting density, and severe canopy occlusion during later growth stages. These factors often limit the reliability of single-plant monitoring across the full growth cycle in open-field environments. To address this, we propose a multi-temporal point cloud alignment method for accurate plant height measurement, focusing on Choy Sum (Brassica rapa var. parachinensis). The method estimates plant height by calculating the vertical distance between the canopy and the ground. Multi-temporal point cloud maps are reconstructed using an enhanced Oriented FAST and Rotated BRIEF–Simultaneous Localization and Mapping (ORB-SLAM3) algorithm. A fixed checkerboard calibration board, leveled using a spirit level, ensures proper vertical alignment of the Z-axis and unifies coordinate systems across growth stages. Ground and plant points are separated using the Excess Green (ExG) index. During early growth stages, when the soil is minimally occluded, ground point clouds are extracted and used to construct a high-precision reference ground model through Cloth Simulation Filtering (CSF) and Kriging interpolation, compensating for canopy occlusion and noise. In later growth stages, plant point cloud data are spatially aligned with this reconstructed ground surface. Individual plants are identified using an improved Euclidean clustering algorithm, and consistent measurement regions are defined. Within each region, a ground plane is fitted using the Random Sample Consensus (RANSAC) algorithm to ensure alignment with the X–Y plane. Plant height is then determined by the elevation difference between the canopy and the interpolated ground surface. Experimental results show mean absolute errors (MAEs) of 7.19 mm and 18.45 mm for early and late growth stages, respectively, with coefficients of determination (R2) exceeding 0.85. These findings demonstrate that the proposed method provides reliable and continuous plant height monitoring across the full growth cycle, offering a robust solution for high-throughput phenotyping of leafy vegetables in field environments.
Why it matches plant phenotyping methods葉菜類の草丈を取得するための点群位置合わせ・地面復元・個体抽出・高さ推定手法を開発し、誤差と決定係数で検証している。植物フェノタイピング手法が研究の中心である。
abstractTo address this, we propose a multi-temporal point cloud alignment method for accurate plant height measurement
As an important leafy vegetable, pakchoi ( Brassica chinensis L.) frequently suffers from pests and diseases in field environments. These symptoms are often localized on specific leaf regions, resulting in substantial losses in yield and quality. To achieve efficient and accurate detection of pakchoi pests and diseases, this study proposes an improved lightweight object detection model, termed YOLOv8n-DBW, based on the YOLOv8n framework. First, the original C2f module in the backbone network is replaced with a novel C2f-PE module, which integrates Partial Convolution (PConv) and an Efficient Multi-Scale Attention (EMA) mechanism to enhance high-level semantic feature extraction and multi-scale information fusion. Second, a Weighted Bidirectional Feature Pyramid Network (BiFPN) is introduced into the neck network to strengthen multi-scale feature fusion while improving model generalization and lightweight performance. Finally, the original CIoU loss in the regression branch is replaced with the Wise-IoU (Weighted Interpolation of Sequential Evidence for Intersection over Union) bounding box loss function, which improves bounding box regression accuracy and significantly enhances the detection of small and irregular pest and disease targets. Experimental results on a field-collected pakchoi pest and disease dataset demonstrate that the proposed YOLOv8n-DBW model reduces the number of parameters and model size by 33.3% and 31.8%, respectively, while improving precision and mean average precision (mAP) by 5.0% and 7.5% compared with the baseline YOLOv8n model. Overall, the proposed method outperforms several mainstream object detection algorithms and provides an efficient and accurate solution for real-time pakchoi pest and disease detection, showing strong potential for deployment on embedded systems and mobile devices.
Why it matches plant phenotyping methods圃場画像からパクチョイの病害症状を検出する軽量画像解析モデルを開発・評価しており、植物の病害状態の取得が研究の中心である。
titleAn improved YOLOv8n model for in-field detection of pests and diseases in pakchoi.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Generating accurate and visually realistic 3D models of plants from single-view images is crucial yet remains challenging due to plants' intricate geometry and frequent occlusions. This capability matters because it supplements current plant datasets and enables non-destructive, high-throughput phenotyping for crop breeding and precision agriculture. More broadly, 3D reconstruction is particularly important because plant morphology is inherently three-dimensional, while 2D representations miss occluded leaves, branching geometry, and volumetric traits. However, plants present unique challenges compared to common rigid objects, and most current generative methods have not been systematically tested in this domain, leaving a gap in understanding their reliability for realistic plant reconstruction. This study systematically evaluates six advanced generative techniques-Hunyuan3D 2.0, Trellis (Structured 3D Latents), One2345++, InstantMesh, Direct3D and Unique3D-using the existing PlantDreamer dataset. Specifically, this research reconstructs mesh models from images of Bean plants and quantitatively assesses each method's performance against ground-truth models using Chamfer Distance, Normal Consistency, F-Score, PSNR, LPIPS, and CLIP Score. The paper also presents qualitative results of Kale and Mint plants. The results indicate that Hunyuan3D 2.0 achieves superior performance overall, suggesting its effectiveness in capturing complex plant structures. This work provides valuable insights into strengths and limitations of contemporary 3D generative approaches, guiding future improvements in realistic plant digitisation.
Why it matches plant phenotyping methods植物画像からの3D再構成手法を体系的に比較・定量評価し、植物形態の非破壊・高スループット表現型解析への利用可能性を検証しており、方法が研究の中心です。
abstractThis study systematically evaluates six advanced generative techniques-Hunyuan3D 2.0, Trellis (Structured 3D Latents), One2345++, InstantMesh, Direct3D and Unique3D-using the existing PlantDreamer dataset.
Precision agriculture progressively relies on remote sensing (RS) technologies to enhance crop classification and monitoring. Among various RS platforms, spectroradiometer offers the highest spectral precision, making them essential for validating the accuracy and performance of other RS methods. Each crop exhibits a unique spectral signature that corresponds to its biophysical characteristics. This spectral information plays a crucial role in accurately classifying crop types and assessing their health status, including water and nutrient availability. Specifically, evaluating crop chlorophyll content enables effective nitrogen management and yield optimization. This study focuses on collecting spectral data using a spectroradiometer (350-1050 nm) at a height of 30 cm above the crop canopy from eight crops, i.e., rice, finger millet, cotton, sunflower, sweet corn, broccoli, cauliflower, and brinjal, classifying the collected data, and measuring chlorophyll content using a Soil Plant Analysis Development (SPAD) meter and predicting the same using key spectral bands and machine learning (ML) techniques. Six supervised ML algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP) were employed for crop classification. The feature selection process revealed that the spectral range of 710-750 nm is the most significant for crop classification. The MLP model achieved the highest accuracy of 97% during training, 93% in testing, and 85% during validation stage, outperforming other ML classifiers. For chlorophyll content prediction, the RF demonstrated the best performance, with coefficient of determination values of 0.92 for training and 0.72 for testing stage. The ML-based framework, developed in this study, can be applied to various RS platforms, including satellites and unmanned aerial vehicles (UAVs), for crop classification and prediction of chlorophyll content. The developed modelling framework would assist government agencies and policymakers in identifying crop types accurately, enhancing agricultural planning, and optimizing resource allocation to support sustainable on-farm practices.
Why it matches plant phenotyping methods分光反射センシングと機械学習により作物のクロロフィル含量という植物形質を推定する枠組みが研究の中心であり、モデル性能の検証も行っているため。
abstractpredicting the same using key spectral bands and machine learning (ML) techniques
Brassica vegetablesLettuceRadishMicroscopyMultimodalRootVisualization / data management
Microfibers (MFs), primarily originating from sewage sludge and laundry effluents, are the most prevalent form of microplastics (MPs) in agricultural soils. While their ecological effects have been explored, the visualization, crop-level accumulation, and potential transport mechanisms of MFs within soil-plant systems remain poorly understood. This study combines 1,3,6,8-pyrene tetrasulfonic acid (PTSA) fluorescent staining with a sequential multimodal microscopy workflow to effectively track the distribution, adsorption, accumulation, and uptake of MFs under realistic soil cultivation conditions. Three edible vegetables-lettuce, Chinese cabbage, and cherry radish-were used to evaluate species-specific response patterns. The results revealed clear differences in MF interactions across species: lettuce exhibited strong MF adsorption on root surfaces and subsequent penetration via crack-entry and apoplastic pathways without entering cells. In contrast, Chinese cabbage and cherry radish showed limited MF adsorption and no uptake. These patterns were associated with root permeability and antioxidative capacities, indicating that plant functional traits play a critical role in determining the transport capacity of MPs. Beyond introducing a novel method for MF visualization in complex terrestrial matrices, this study provides new insights into the risks posed by MFs to soil-plant systems. The findings also highlight potential threats to food safety and underscore the need to establish plant-specific thresholds and pollution mitigation strategies to support sustainable agriculture and protect public health.
Why it matches plant phenotyping methods植物体内のマイクロファイバー分布・吸着・蓄積・取り込みを可視化する新規蛍光染色・マルチモーダル顕微鏡ワークフローが研究の中心であり、植物状態の測定法として該当する。
abstractThis study combines 1,3,6,8-pyrene tetrasulfonic acid (PTSA) fluorescent staining with a sequential multimodal microscopy workflow to effectively track the distribution, adsorption, accumulation, and uptake of MFs under realistic soil cultivation conditions.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Abstract Generating accurate and visually realistic 3D models of plants from single-view images is crucial yet remains challenging due to plants' intricate geometry and frequent occlusions. This capability matters because it supplements current plant datasets and enables non-destructive, high-throughput phenotyping for crop breeding and precision agriculture. More broadly, 3D reconstruction is particularly important because plant morphology is inherently three-dimensional, while 2D representations miss occluded leaves, branching geometry, and volumetric traits. However, plants present unique challenges compared to common rigid objects, and most current generative methods have not been systematically tested in this domain, leaving a gap in understanding their reliability for realistic plant reconstruction. This study systematically evaluates six advanced generative techniques—Hunyuan3D 2.0, Trellis (Structured 3D Latents), One2345++, InstantMesh, Direct3D and Unique3D—using the existing PlantDreamer dataset. Specifically, this research reconstructs mesh models from images of Bean plants and quantitatively assesses each method’s performance against ground-truth scans using Chamfer Distance, Normal Consistency, F-Score, PSNR, LPIPS, and CLIP Score. The paper also presents qualitative results of Kale and Mint plants. The results indicate that Hunyuan3D 2.0 achieves superior performance overall, suggesting its effectiveness in capturing complex plant structures. This work provides valuable insights into strengths and limitations of contemporary 3D generative approaches, guiding future improvements in realistic plant digitisation.
Why it matches plant phenotyping methods植物画像からの3D再構成手法を体系的に比較・定量評価しており、植物形態の取得と高スループット表現型解析への応用が中心である。
abstractThis study systematically evaluates six advanced generative techniques
Traditionally, destructive analysis of the internal chemical components of plants is necessary to assess their overall health. This study proposes a novel approach to non-destructively estimate and classify organic and inorganic components associated with the general health of kimchi cabbage by integrating spectral imaging and system dynamic modeling techniques. Existing vegetation indices rely on constant values, which limits their ability to classify plants with similar measured constant values but different internal component contents. This problem is addressed by systematically approaching the vegetation indices and extracting and using intrinsic steady-state value and response-velocity parameters. It uses the principle that healthy plant pixel data have low-pass filter characteristics, and reflectance data from stressed or damaged plant areas exhibit high-pass filter characteristics. The proposed dynamic model identifies the relationship between red and near-infrared wavelength reflectances as time series data, and this framework can transform constant-based vegetation indices into dynamic system-based models. The mechanistic model improved accuracy by 33 % for Lutein, 10.5 % for beta-carotene, 8 % for Chlorophyll-a, and 15.8 % for Chlorophyll-b compared to the existing method. In addition, differences in calcium content between treatment groups, which are difficult to resolve using traditional vegetation indices, were identified. The dynamic model provides a solution for simultaneous, non-destructive analysis of pigments and calcium components, which traditionally relied on destructive testing. This scalable and efficient technology has great potential to bridge the gap between precision agriculture and conventional agriculture and contribute to sustainable agricultural realization.
Why it matches plant phenotyping methodsスペクトルイメージングと動的モデルを用いて、植物の健康状態や色素・カルシウム含量を非破壊推定する手法の開発が中心であり、植物フェノタイピング手法に該当する。
abstractThis study proposes a novel approach to non-destructively estimate and classify organic and inorganic components associated with the general health of kimchi cabbage by integrating spectral imaging and system dynamic modeling techniques.
The vertical projection leaf area (VPA) of cabbage (Brassica oleracea var. capitata) is an important phenotypic trait that is closely related to total leaf area, biomass, and head weight (yield). Developing a high-throughput method to measure individual VPAs of cabbage can assist in monitoring growth conditions in the field and improve the prediction of crop growth. High-throughput phenotyping using drones that can extract phenotypic crop traits from aerial images is widely used. Here, an R program was developed to detect and segment cabbages in images and calculate each cabbage’s VPA. The detection error and precision of the method were evaluated by using orthoimages collected from cabbage fields of NARO, Kannondai, Tsukuba, Ibaraki, Japan. The average detection error was 3.93%–5.40% and the average detection accuracy was 93.03%–96.35%. The accuracy of the VPA generated by the method was sufficient to be used in a cabbage growth prediction model and has the potential to predict yields of individual cabbages.
Why it matches plant phenotyping methodsキャベツの航空画像から個体を検出・セグメンテーションし、垂直投影葉面積という表現型形質を算出するRプログラムを開発・評価しており、表現型取得手法が研究の中心である。
abstractHere, an R program was developed to detect and segment cabbages in images and calculate each cabbage’s VPA.
Various fusion methods of optical satellite images have been proposed for monitoring heterogeneous farmlands requiring high spatial and temporal resolution. In this study, a three-meter normalized difference vegetation index (NDVI) was generated by applying the spatiotemporal fusion (STF) method to simultaneously generate a full-length normalized difference vegetation index time series (SSFIT) and enhanced spatial and temporal adaptive reflectance fusion method (ESTARFM) to the NDVI of Sentinel-2 (S2) and PlanetScope (PS), using images from 2019 to 2021 of rice paddy and heterogeneous cabbage fields in Korea. Before fusion, S2 was processed with the maximum NDVI composite (MNC) and the spatiotemporal gap-filling technique to minimize cloud effects. The fused NDVI image had a spatial resolution similar to PS, enabling more accurate monitoring of small and heterogeneous fields. In particular, the SSFIT technique showed higher accuracy than ESTARFM, with a root mean square error of less than 0.16 and correlation of more than 0.8 compared to the PS NDVI. Additionally, SSFIT takes four seconds to process data in the field area, while ESTARFM requires a relatively long processing time of five minutes. In some images where ESTARFM was applied, outliers originating from S2 were still present, and heterogeneous NDVI distributions were also observed. This spatiotemporal fusion (STF) technique can be used to produce high-resolution NDVI images for any date during the rainy season required for time-series analysis.
Why it matches plant phenotyping methods衛星画像の時空間融合により作物圃場の高解像度NDVI時系列を生成し、精度と処理時間を比較検証しており、植物状態の取得手法が研究の中心である。
abstractIn this study, a three-meter normalized difference vegetation index (NDVI) was generated by applying the spatiotemporal fusion (STF) method
Here, we used Raman spectroscopy to characterize the effects of chitin treatment and fungal inoculations on Arabidopsis thaliana and Brassica vegetables. Chitin, a recognized fungal pathogen-associated molecular pattern (PAMP), elicited a dose dependent positive Elicitor Response Index (ERI) in wild-type Arabidopsis. Mutant plants lacking chitin receptors ( cerk1 and lyk4/5 ) displayed minimal ERI, whereas fls2 mutant deficient in the bacterial-specific flg22 receptor was hyper-responsive. These results confirm critical role of chitin receptors in activating downstream pathways and highlighting distinct responses in two separate pattern-triggered immunity (PTI) systems. Inoculations of Colletotrichum higginsianum and Alternaria brassicicola induced significant changes in Infection Response Index (IRI) values, with the former giving positive IRI at 12-48 hours post-inoculation whereas the latter exhibited a transient negative IRI before transitioning to positive values. Notably, Raman shifts could predict fungal infection before the appearance of visible symptoms, establishing Raman shifts as a potential early diagnostic marker. Comparative analyses of infected Brassica vegetables revealed varied sensitivity to fungal pathogens and a correlation between symptom severity and IRI values. Furthermore, randomized controlled trials validated the reliability of Raman technology for early, pre-symptomatic detection of fungal infections, achieving an accuracy rate of 76.2% in Arabidopsis and 72.5% in Pak-Choy ( Brassica rapa chinensis ). Principal component analysis differentiated Raman spectral features associated with fungal and bacterial infections, emphasizing their unique profiles and reinforcing the utility of Raman spectroscopy for early detection of pathogen-related plant stress. Our work supports the application of non-invasive diagnostic techniques in agricultural practices, enabling timely intervention against crop diseases.
Why it matches plant phenotyping methodsラマン分光法を用いて植物の真菌感染を症状出現前に検出し、精度を検証している。感染状態という植物表現型の取得が研究の中心である。
abstractRaman shifts could predict fungal infection before the appearance of visible symptoms, establishing Raman shifts as a potential early diagnostic marker.
Brassica vegetablesGreenhouseLeafPhysiological trait estimationLeaf traitsWater status / transpiration
This study evaluates how predicted leaf area index (LAI) affects evapotranspiration (ET) model performance and uncertainty in greenhouse Pak Choi cultivation. Five ET models (Penman-Monteith, Stanghellini, Fynn, Shin, and Baille) were compared using both measured and Convolutional Neural Network-Predicted LAI data. Greenhouse environment experiments from June to August 2021 provided validation data under controlled conditions. LAI was estimated using image analysis with high accuracy (R² = 0.9986, RMSE = 0.0547 m²·m⁻²). Sensitivity analysis revealed that ET models were most responsive to radiation and LAI variations, with lower sensitivity to air temperature and relative humidity. Among physical models, the Fynn model demonstrated superior performance based on ET prediction accuracy (R² > 0.87), while the Shin model excelled among simplified approaches (R² > 0.92). Uncertainty propagation analysis revealed that the Stanghellini model exhibited the highest sensitivity to LAI estimation errors (12.55 W·m⁻² error when LAI error = 1.0 m²·m⁻²), whereas the Penman–Monteith model showed minimal sensitivity. Model performance remained consistent when using predicted versus measured LAI (R² > 0.99 for all models), indicating the robustness of image-based LAI estimation for ET modelling. This research provides quantitative insights into model selection and uncertainty assessment for precision irrigation management in protected cultivation systems, with particular applicability to leafy vegetable crops under controlled greenhouse conditions.
Why it matches plant phenotyping methodsCNN画像解析による植物のLAI推定を高精度に検証し、推定LAIの誤差・頑健性をETモデル比較で評価しており、植物形質取得法が技術的に中心的である。
abstractLAI was estimated using image analysis with high accuracy (R² = 0.9986, RMSE = 0.0547 m²·m⁻²).
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Although Brassica rapa (B. rapa) is vital in agricultural production and vulnerable to the pathogen Plasmodiophora, the intracellular water–nutrient metabolism and immunoregulation of Plasmodiophora infection in B. rapa leaves remain unclear. This study aimed to analyze the responsive mechanisms of Plasmodiophora-infected B. rapa using rapid detection technology. Six soil groups planted with Yangtze No. 5 B. rapa were inoculated with varying Plasmodiophora concentrations (from 0 to 10 × 109 spores/mL). The results showed that at the highest infection concentration (PWB5, 10 × 109 spores/mL) of B. rapa leaves, the plant electrophysiological parameters showed the intracellular water-holding capacity (IWHC), the intracellular water use efficiency (IWUE), and the intracellular water translocation rate (IWTR) declined by 41.99–68.86%. The unit for translocation of nutrients (UNF) increased by 52.83%, whereas the nutrient translocation rate (NTR), the nutrient translocation capacity (NTC), the nutrient active translocation (NAT) value, and the nutrient active translocation capacity (NAC) decreased by 52.40–77.68%. The cellular energy metabolism decreased with worsening Plasmodiophora infection, in which the units for cellular energy metabolism (∆GE) and cellular energy metabolism (∆G) of the leaves decreased by 44.21% and 78.14% in PWB5, respectively. Typically, based on distribution of B-type dielectric substance transfer percentage (BPn), we found PWB4 (8 × 109 spores/mL) was the maximal immune response concentration, as evidenced by a maximal BPnR (B-type dielectric substance transfer percentage based on resistance), with increasing lignin and cork deposition to enhance immunity, and a minimum BPnXc (B-type dielectric substance transfer percentage based on capacitive reactance), with a decreasing quantity of surface proteins in the B. rapa leaves. This study suggests plant electrophysiological parameters could characterize intracellular water–nutrient metabolism and immunoregulation of B. rapa leaves under various Plasmodiophora infection concentrations, offering a dynamic detection method for agricultural disease management.
Why it matches plant phenotyping methods植物電気生理パラメータを用いて感染葉の水分・栄養代謝、エネルギー状態、免疫応答を動的に評価する手法が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
abstractThis study suggests plant electrophysiological parameters could characterize intracellular water–nutrient metabolism and immunoregulation of B. rapa leaves under various Plasmodiophora infection concentrations, offering a dynamic detection method for agricultural disease management.
Raman spectroscopy enables non-destructive detection of nitrates and other nitrogen-related biochemical markers, including chlorophyll and polyphenols, with unparalleled specificity and sensitivity. Integrating Raman spectroscopy with proximal optical sensors, such as Dualex (Dx) and Multiplex (Mx), offers a transformative approach to precision nitrogen management in broccoli seedlings, complementing their ability to rapidly estimate nitrogen balance indices and key vegetation compounds. The integration demonstrated strong correlations between Raman spectral bands, optical indices, and biochemical parameters across varying nitrogen levels, enhancing the precision of nitrogen status assessment, resulting in a robust, scalable, and information-rich system. By combining molecular-level detail with practical field applications, this hybrid strategy represents a significant advancement in sustainable agriculture. Future research will explore the applicability of this integrated methodology to other plant species.
Why it matches plant phenotyping methodsRaman分光と光学センサーを統合し、ブロッコリー幼植物の窒素状態や関連指標を非破壊推定する測定システムが研究の中心であるため、植物フェノタイピング手法として採択。
abstractIntegrating Raman spectroscopy with proximal optical sensors, such as Dualex (Dx) and Multiplex (Mx), offers a transformative approach to precision nitrogen management in broccoli seedlings
In controlled environment agriculture (CEA), accurate yield forecasting remains challenging due to reliance on environmental sensor data, which fails to capture plants’ dynamic morphological responses to growth conditions. This study bridges the gap by establishing a vision-based framework to forecast plant growth dynamics over prediction windows of 2, 4, and 8 days using automated phenotyping and time-series modelling. A plant phenotype monitoring framework was implemented using commercially available cameras and off-the-shelf deep learning-based models (YOLO). The robustness of the YOLO and time-series models was rigorously evaluated under a range of treatment conditions, including a control, salt stress levels at 3, 6, and 9 ppt, and different root architectures (single-root and split-root) in hydroponic greenhouse trials conducted over two growing seasons. Top-view images of the plants were collected using GoPro and Raspberry Pi cameras, and different YOLOv8 instance segmentation model variants were trained on four image datasets to extraction of morphological traits such as area, major, and minor axes. Results indicated that YOLOv8 generalized well, achieving mAP50 for bounding boxes and masks in the range of 0.897 – 0.952 and 0.896 – 0.947, respectively. Model-derived morphological parameters effectively captured growth differences across salt levels and root architectures, with split-root plants showed resiliency under salt stress compared to single-root. Comparisons between physical measurements and image-derived parameters such as major and minor axes yielded high R² values of 0.85 and 0.92 for single-root systems, and 0.90 and 0.84 for split root systems. Additionally, the area parameter obtained from images showed an R² of 0.882 when compared with plant fresh weight. ARIMA model used to forecast the plant area parameters over 2-, 4-, and 8-days windows and evaluated using MAPE. Notably, the 2-day forecasts for single-root plants under 9 ppt salt stress yielded the lowest MAPE values (3.99 in the fall and 1.70 in the spring), although 8-day forecasts at higher salt concentrations exhibited generally larger errors. For split-root plants, the 4 days forecast under 3 ppt salt stress produced a MAPE of 7.13 in the fall, while in the spring, the 8 days forecast at 9 ppt achieved a MAPE of 2.08. The forecasted area values demonstrated R² values of 0.623, 0.671, and 0.75 for the 2-, 4-, and 8-day forecast windows respectively when compared with fresh weight, indicating that the area parameter is a reliable predictor of yield. These findings confirm that morphological changes capture environmental influences and can be reliably forecasted, introducing a scalable, data-driven method to predict yield in CEA while helping growers optimize resource usage and reduce productivity risks.
Why it matches plant phenotyping methods画像ベースの植物表現型取得と時系列予測を中心に、形態形質の抽出、モデル性能評価、物理測定との検証を行っているため、方法論文として適格。
abstractThis study bridges the gap by establishing a vision-based framework to forecast plant growth dynamics over prediction windows of 2, 4, and 8 days using automated phenotyping and time-series modelling.
Broccoli's pigments enhance its nutritional value by affecting color and antioxidant properties. Traditional methods like high-performance liquid chromatography (HPLC) and spectrophotometry are accurate but destructive, labor-intensive, and unsuitable for high-throughput screening. This study constructed non-destructive models based on near-infrared spectroscopy (NIRS) technology to predict pigment compounds in broccoli. The optimal models for total chlorophyll (Chl), Chl a, and Chl b were established with the use of SNV / 2nd derivative / PLS, which yielded an R 2 of 0.992, RMSEC of 0.478 mg g -1 DW, and RPD of 6.476. For carotenoids (CAR), the SNV / 1st derivative / PLS model provided the best results, with an R 2 of 0.976, RMSEC of 0.098 mg g -1 DW, and RPD of 4.455. However, the ACN model based on SNV / 1st derivative / PLS exhibited relative lower accuracy, with an R 2 of 0.790, RMSEC of 1.777 units g -1 DW, RPD of 1.267, suggesting the necessity for preliminary analysis. This study fills a critical gap in NIRS applications for plant pigment analysis, presenting a rapid, non-destructive, and high-throughput approach for quality assessment and breeding selection.
Why it matches plant phenotyping methodsブロッコリーの色素という植物形質をNIRSで非破壊・高スループット推定するモデルを構築・評価しており、表現型取得法が研究の中心である。
abstractThis study constructed non-destructive models based on near-infrared spectroscopy (NIRS) technology to predict pigment compounds in broccoli.
Broccoli's pigments enhance its nutritional value by affecting color and antioxidant properties. Traditional methods like high-performance liquid chromatography (HPLC) and spectrophotometry are accurate but destructive, labor-intensive, and unsuitable for high-throughput screening. This study constructed non-destructive models based on near-infrared spectroscopy (NIRS) technology to predict pigment compounds in broccoli. The optimal models for total chlorophyll (Chl), Chl a, and Chl b were established with the use of SNV / 2nd derivative / PLS, which yielded an R² of 0.992, RMSEC of 0.478 mg g⁻¹ DW, and RPD of 6.476. For carotenoids (CAR), the SNV / 1st derivative / PLS model provided the best results, with an R² of 0.976, RMSEC of 0.098 mg g⁻¹ DW, and RPD of 4.455. However, the ACN model based on SNV / 1st derivative / PLS exhibited relative lower accuracy, with an R² of 0.790, RMSEC of 1.777 units g⁻¹ DW, RPD of 1.267, suggesting the necessity for preliminary analysis. This study fills a critical gap in NIRS applications for plant pigment analysis, presenting a rapid, non-destructive, and high-throughput approach for quality assessment and breeding selection.
Why it matches plant phenotyping methodsブロッコリーの色素という植物形質を、近赤外分光法と定量モデルで非破壊・高スループット推定する手法を構築・評価しており、フェノタイピング手法が中心である。
abstractThis study constructed non-destructive models based on near-infrared spectroscopy (NIRS) technology to predict pigment compounds in broccoli.
Cauliflower is among the more well-known vegetables there are. Consumed all around the globe due to it being rich in nutrients such as vitamins, antioxidants, and for being high in fibre. These are nutritional qualities that help with digestion, immune-system, and minimizing inflammation. It is a common issue among farmers to have to deal with various diseases in cauliflower leaves that are difficult to diagnose in their early stages. These diseases have a tendency to propagate in a really swift pace throughout entire fields worth of crops. This in-turn causes heavy losses in the harvest, and makes it much more tedious and resource-intensive to protect the crops. As a result, farmers get more likely to use high amounts of pesticides and harmful chemicals to streamline the process of getting a more reliable yield on their crops. This is not only costly, but it is also harmful both to the quality of crops and to the well-being of the environment. In this publication, we are introducing a dataset containing a considerable number of images of cauliflower leaves. This is intended to drive development on this topic at a faster pace than it is now, and to help enhance disease monitoring, diagnosis, and precautionary techniques. We collected our dataset images between November 2024 and January 2025. In this dataset, cauliflower leaves were categorized into three classes: Healthy, Insect Holes, and Black Rot, each reflecting a specific condition that impacts plant health at different stages. This dataset consists of 2,661 images. The pictures were captured at different locations in Bangladesh, under different weather conditions, dates, temperatures, and with different devices. To enhance the data quality, we used several steps to process the dataset, making sure it would reflect real-world conditions and be ready for training. The images were resized to a standard size of 3000 × 3000 pixels, brightness was adjusted to make the images more easily discernible, and we removed duplicates and poor-quality images. These actions helped ensure the dataset was in the best possible shape for effective model training. This dataset will be highly effective for agricultural research, precision agriculture, and effective management of diseases. It should help develop highly accurate machine learning models for early detection of Cauliflower leaf diseases. The dataset is employed to train deep learning models to support automated monitoring and smart decision-making in precision agriculture. This data set also has immense potential for real-time and practical use. It can be utilized to develop applications like mobile apps or automated systems where farmers can easily identify diseases at early stages and take immediate action, without the requirement of expert on-site knowledge. This data set can also be utilized with smart farming equipment like drones and sensors to track big fields in real time.
Why it matches plant phenotyping methodsカリフラワー葉の健康状態・病害状態を画像で分類するデータセット自体が研究の中心であり、植物病害表現型の取得・解析基盤に該当します。
abstractIn this publication, we are introducing a dataset containing a considerable number of images of cauliflower leaves.
Reproduction assets foundThe paper's core asset is its own cauliflower leaf disease image dataset (2,661 images, three classes), publicly deposited on Mendeley Data with DOI 10.17632/x995snz7p3.1 and a direct URL matching an allowed URL.Dataset · publiced from the following geographic locations:
1. Zailla, Singair, Manikganj
Latitude : 23°47′46.11"N Longitude : 90°13′15.73"E
2. Dattapara, Ashulia, Savar, Dhaka
Latitude : 23°52′26.3"N Longitude : 90°19′06.3"E
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/x995snz7p3.1
Direct URL to data: https://data.mendeley.com/datasets/x995snz7p3/1
The dataset is publicly available and can be accessed via the provided Mendeley Data repository link.
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This dataset holds high-resolution images of diseased cauliflower leaves infected with multiple diseases, which provide a wealth of material for the development and vaOpen asset ↗Mendeley Data · 10.17632/x995snz7p3.1lines:35-107Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 14 Sept 2026
With the increasing demand for precision agriculture, efficient phenotypic analysis is crucial for crop breeding and productivity enhancement. This study presents an economically efficient and high-throughput phenotypic analysis framework for Chinese cabbage ( Brassica rapa L. subsp. pekinensis ), combining low-cost multispectral imaging drones with deep learning technologies. During the seedling stage, we achieved a mAP of 97.0 % and an F1-score of 93.3 %, representing a 12.5 % improvement over baseline models, enabling precise localization of individual plants. At the rosette stage, we employed a multispectral super-resolution generative adversarial network (MSRGAN) to enhance image quality, achieving a canopy segmentation accuracy of 97.99 %, with improvements of 0.83 % in mIoU and 0.63 % in FWIoU compared to baseline models. From the segmentation results, we extracted 23 key phenotypic parameters (e.g., NDVI, EVI, RGB, RE, NIR), which facilitated quantification of leaf color (range: 0–100). These parameters provided support for the prediction of SPAD (R 2 = 0.64) and N content (R 2 = 0.59) across the entire growth period. During the heading stage, we addressed the limitations of 2D imaging for complex 3D structures by achieving 85.01 % mIoU and 92.06 % accuracy in point cloud segmentation, a 10.3 % improvement over existing approaches. Combined with an optimized clustering analysis algorithm, we achieved precise segmentation of individual plants, extracting 33 morphological parameters (e.g., length, width, height) and quantitatively assessing head expansion degree (range: 0–100). The framework demonstrates that this integrated approach, as a practical alternative to traditional field-based methods, could improve the accuracy and efficiency of phenotypic trait extraction for crop monitoring and breeding.
Why it matches plant phenotyping methodsドローンのマルチスペクトル画像、深層学習、点群処理を統合し、植物の形態・色・栄養関連形質を抽出する高スループット表現型解析フレームワークが研究の中心である。
abstractThis study presents an economically efficient and high-throughput phenotypic analysis framework for Chinese cabbage ( Brassica rapa L. subsp. pekinensis ), combining low-cost multispectral imaging drones with deep learning technologies.
Global soil salinization presents an increasing threat to vegetable productivity and agricultural yields. The Chinese cabbage (Brassica rapa subsp. pekinensis) is a vital vegetable crop in China and across many regions in Asia. However, its quality and yield are highly susceptible to salt stress. Consequently, developing an efficient and accurate evaluation system for screening salt-tolerant Chinese cabbage varieties is essential. This study proposes a dual-input data fusion model for salt tolerance evaluation. The first input consists of one-dimensional sequence data derived from the region of interest texture features and spectral data, processed using convolutional neural network (CNN) and long short-term memory. The second input comprises two-dimensional multispectral images, analyzed through a CNN and ResNet network, optimized using fine-tuning and pruning techniques. These inputs were independently processed in a dual-branch network, with their outputs fused in a fully connected layer to deliver a comprehensive assessment of salt tolerance. A comparative analysis with a photosynthetic phenotype imaging system revealed the superior information richness and accuracy of the proposed model. Validation of the salt tolerance classification achieved an accuracy of 95.00% on the Day 5 following salt stress, with only four varieties misclassified, underscoring the efficiency and effectiveness of the model in early screening. On Day 9 following salt stress, all salt-sensitive varieties were fully identified. Using this model, we identified seven salt-tolerant, seven salt-neutral, and five salt-sensitive Chinese cabbage varieties. Integrating one-dimensional and two-dimensional data enabled the extraction of key plant parameters, such as chlorophyll content, growth status, and leaf structure. This approach provides an effective tool for evaluating salt tolerance in Chinese cabbage and other vegetables, facilitating breeding efforts and mitigating the effects of soil salinization on global crop yield.
Why it matches plant phenotyping methods植物のマルチスペクトル画像と画像由来特徴を用いて塩耐性を評価するデータ融合・深層学習システムを開発し、既存の表現型イメージングシステムと比較検証しているため、表現型取得・抽出法が中心である。
abstractThis study proposes a dual-input data fusion model for salt tolerance evaluation.
Brassica vegetablesRootMorphology / geometry measurementSegmentationRoot system architecture
Background As an important economic crop, the growth status of the root system of cabbage directly affects its overall health and yield. To monitor the root growth status of cabbage seedlings during their growth period, this study proposes a new network architecture called Swin-Unet++. This architecture integrates the Swin-Transformer module and residual networks and uses attention mechanisms to replace traditional convolution operations for feature extraction. It also adopts the residual concept to fuse contextual information from different levels, addressing the issue of insufficient feature extraction for the thin and mesh-like roots of cabbage seedlings. Results Compared with other backbone high-precision semantic segmentation networks, SwinUnet + + achieves superior segmentation results. The results show that the accuracy of Swin-Unet + + in root system segmentation tasks reached as high as 98.19%, with a model parameter of 60 M and an average response time of 29.5 ms. Compared with the classic Unet network, the mIoU increased by 1.08%, verifying that the Swin-Transformer and residual networks can accurately extract the fine-grained features of roots. Furthermore, when images after different semantic segmentations are compared to locate the root position through contours, Swin-Unet + + has the best positioning effect. On the basis of the root pixels obtained from semantic segmentation, the calculated maximum root length, extension width, and root thickness are compared with actual measurements. The resulting goodness of fit R² values are 94.82%, 94.43%, and 86.45%, respectively. Verifying the effectiveness of this network in extracting the phenotypic traits of cabbage seedling roots. Conclusions The Swin-Unet + + framework developed in this study provides a new technique for the monitoring and analysis of cabbage root systems, ultimately leading to the development of an automated analysis platform that offers technical support for intelligent agriculture and efficient planting practices.
Why it matches plant phenotyping methodsキャベツ幼苗根の画像セグメンテーションと根形質推定のための新規ネットワークを開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractthis study proposes a new network architecture called Swin-Unet++.
The increased adoption of controlled environment agriculture (CEA) and soilless growing systems (SGS) offers new opportunities to advance the year-round production of high-quality specialty crops through the development and implementation of precision agriculture solutions. Traditionally, crop monitoring in CEA soilless systems is a critical time-consuming task requiring specialized personnel. Nevertheless, traditional crop monitoring methods do not allow frequent data collection to capture the plant growth dynamics throughout the crop cycle. Automated crop monitoring systems may allow continuous monitoring of the crop with frequent data collection and a more efficient and informed management of the crop. In this study we developed an integrated Internet of Things (IoT) and computer vision system tailored for CEA-SGS, enabling continuous monitoring and analysis of plant growth throughout the crop cycle. The core innovation of this research is the implementation of a recursive image segmentation model that processes sequential image data to accurately track temporal changes in plant growth. The vision system developed is supported by an IoT framework designed to capture high-resolution imagery at predetermined temporal frequencies. Tested on bok choy grown in an NFT (nutrient film technique) SGS, the integrated system developed successfully segmented individual plants and tracked leaf coverage area throughout their growth cycle. The quantitative analysis of Intersection over Union (IoU) scores among the segmentation approaches showed that the recursive model began with an IoU score of 0.99 during the early growth stages and maintained a robust performance, achieving a score of 0.90 at later stages. These findings demonstrate that the recursive segmentation approach significantly enhanced the precision of the bok choy crop monitoring. The outcome of this study facilitates the potential development of decision support systems for the efficient management and optimization of crops in soilless CEA systems.
Why it matches plant phenotyping methods植物の成長状態（葉面積）を画像から抽出する再帰的セグメンテーション手法とIoT画像取得システムを開発・評価しており、表現型取得法が研究の中心です。
abstractIn this study we developed an integrated Internet of Things (IoT) and computer vision system tailored for CEA-SGS, enabling continuous monitoring and analysis of plant growth throughout the crop cycle.
The accurate quantification of plant types can provide a scientific basis for crop variety improvement, whereas efficient automatic classification methods greatly enhance crop management and breeding efficiency. For leafy crops such as Chinese cabbage, differences in the plant type directly affect their growth and yield. However, in current agricultural production, the classification of Chinese cabbage plant types largely depends on manual observation and lacks scientific and unified standards. Therefore, it is crucial to develop a method that can quickly and accurately quantify and classify plant types. This study has proposed a method for the rapid and accurate quantification and classification of Chinese cabbage plant types based on point-cloud data processing and the deep learning algorithm PointNet++. First, we quantified the traits related to plant type based on the growth characteristics of Chinese cabbage. K-medoids clustering analysis was then used for the unsupervised classification of the data, and specific quantification of Chinese cabbage plant types was performed based on the classification results. Finally, we combined 1024 feature vectors with 10 custom dimensionless features and used the optimized PointNet++ model for supervised learning to achieve the automatic classification of Chinese cabbage plant types. The experimental results showed that this method had an accuracy of up to 92.4% in classifying the Chinese cabbage plant types, with an average recall of 92.5% and an average F1 score of 92.3%.
Why it matches plant phenotyping methods点群データからハクサイの草型形質を定量化し、PointNet++で自動分類する手法の開発・評価が研究の中心であるため。
abstractThis study has proposed a method for the rapid and accurate quantification and classification of Chinese cabbage plant types based on point-cloud data processing and the deep learning algorithm PointNet++.
Selective harvesting robots for broccoli face significant challenges in field operations, where occlusions by leaves and stems, varying maturity stages and lighting interferences greatly affect performance. Addressing the need for a robust network capable of maturity recognition and localisation under various occlusion conditions for spherical crops, OccluInst-a single-stage instance segmentation network based on RGB-D and CNN-Transformer architecture was proposed. The solution is to make full use of visible information and crop characteristics. This model builds a dual-branch cross-modal calibration framework to generate instance-aware kernels and segmentation mask features. The proposed Attention Weight Interactive Fusion Module (AWIF) enhances the fusion efficiency of multi-scale RGB and depth features in complex scenarios, while the designed Adaptive Fusion Ratio Module (AFR) filters out noisy depth data and extracts valuable information to achieve feature alignment. Additionally, the developed Material Awareness Module (MA) highlights critical areas, improving feature extraction for irregular, multi-scale targets. The improved circular boundary anchor box accurately localises broccoli under various levels of occlusion. Ablation studies confirm the effectiveness of each module. OccluInst can swiftly and accurately identify the maturity categories and coordinates of broccoli under different occlusion levels. It achieves a mAP₅₀ of 86.2% and mAR of 83.5%, with an average centre point deviation of 3.68 pixels on images with a resolution of 848×480, and a detection speed of 51.4 frames per second, providing a robust visual foundation for selective harvesting robots.
Why it matches plant phenotyping methodsRGB-D画像からブロッコリーの成熟カテゴリーという植物状態を推定するセグメンテーション手法を開発し、遮蔽条件下で性能評価している。単なる収穫対象の位置検出にとどまらず、成熟度推定が中心的である。
abstractOccluInst can swiftly and accurately identify the maturity categories and coordinates of broccoli under different occlusion levels.
Cabbage plants are a commodity needed by the community and an export commodity that must have good quality and be worth selling. There are approaches to create detection systems, namely rule-based and image-based. The use of images allows the system to be reorganized by training data, resulting in a flexible system. The image will be detected by the model and then predict the cabbage plant disease. The data used is image data, namely Alternaria Spots, Healthy, Black Root, and White Rust. Implementation This research tests the YOLO model in making a detection system with the highest precision-confidence result for all labels is 78,5%. While in confusion-matrix testing, the highest result is 0.67 in White Rust disease. This indicates that the YOLO model can identify diseases in cabbage plants based on data that has been trained with great results.
Why it matches plant phenotyping methodsキャベツの病害状態を画像からYOLOで推定する検出システムの実装と性能評価が研究の中心であり、植物病害フェノタイピング手法に該当する。
titleImplementation of YOLO in Cabbage Plant Disease Detection for Smart and Sustainable Agriculture
Verticillium wilt greatly hampers Chinese cabbage growth, causing significant yield limitations. Rapid and accurate detection of Verticillium wilt in the Chinese cabbage (Brassica rapa L. ssp. pekinensis) can provide significant agronomic benefits. Here, we propose a detection model, DSConv-GAN, which is based on images acquired by an unmanned aerial vehicle (UAV). Based on YOLOv8, with the addition of the dynamic snake convolution (DSConv) module and the improved loss function maximum possible distance intersection-over-union (MPDIoU), we acquired enhanced complex structures and global characteristics in Chinese cabbage images under different growth conditions. To reduce the difficulty of acquiring diseased Chinese cabbage data, a cycle-consistent generative adversarial network (CycleGAN) was used to simulate and generate images of the Verticillium wilt characteristics for multiple fields. The detection of lightly infected plants achieved precision, recall, mean average precision (mAP), and F1-score of 81.3, 86.6, 87.7, and 83.9%, respectively. DSConv-GAN outperforms other models in terms of precision, detection speed, robustness, and generalization. The model is combined with software to improve the practicability of the proposed method. Our results demonstrate DSConv-GAN to be an effective intelligent farming tool that provides early, rapid, and accurate detection of Chinese cabbage Verticillium wilt in complex growing environments.
Why it matches plant phenotyping methodsUAV画像から中国白菜の萎黄病状態を推定する検出モデルを開発・評価しており、植物病徴の取得・抽出手法が研究の中心である。
abstractHere, we propose a detection model, DSConv-GAN, which is based on images acquired by an unmanned aerial vehicle (UAV).
Reproduction assets foundThe paper's authors publicly release their analysis/detection code (DSConv-GAN model and monitoring software) via a GitHub repository, while the underlying UAV image dataset is only available upon request.Code · publicData will be made available upon request. The code could be downloaded from https://github.com/919449869coder/disease-detection-DSConv.git .Open asset ↗919449869coder/disease-detection-DSConvlines:151-175Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
var. capitata L.) quantification cultivated under different types of mulching, using aerial images captured by RPAS (Remotely Piloted Aircraft System). Design/methodology/approach: The cabbage plantation used for the study was established under a completely randomized block design with different types of mulch as treatments: black plastic, white plastic, straw, and bare soil. Manual plant counts and automated estimates were performed using two agricultural artificial intelligence platforms (Platforms A and B). The relationship was evaluated using linear regression correlation (R²), and the following indicators were subsequently used: estimation accuracy (Ps), estimation error percentage (Es), mean absolute error (MAE), and root mean square error (RMSE). Results: Platform A showed a correlation coefficient range of R²=0.41 to 0.91. Platform B obtained R² values ranging from 0.77 to 0.88. Platform A exhibited the highest estimation accuracy (Ps) with 98.3% and an estimation error (Es) of -1.7% for straw mulch, with a mean absolute error (MAE) of 2.0% and a root mean square error (RMSE) of 1 for bare soil. Both platforms showed underestimations in the number of detected plants, ranging from -6.7% to -1.7%. Limitations on study/implications: The use of RPAS was limited by atmospheric conditions such as wind and rain. Findings/conclusions: The effectiveness of counting cabbage plants using RPAS was validated.
Why it matches plant phenotyping methodsRPAS画像とAIプラットフォームによるキャベツ個体数の自動推定を開発・評価し、手動計数との相関や誤差で検証しているため、植物表現型取得手法が中心である。
abstractManual plant counts and automated estimates were performed using two agricultural artificial intelligence platforms (Platforms A and B).
In-season crop growth and yield prediction at high spatial resolution are essential for informing decision-making for precise crop management, logistics and market planning in horticultural crop production. This research aimed to establish a plant-level cabbage yield prediction system by assimilating the leaf area index (LAI) estimated from UAV imagery and a segmentation model into a crop simulation model, the WOrld FOod STudies (WOFOST). The data assimilation approach was applied for one cultivar in five fields and for another cultivar in three fields to assess the yield prediction accuracy and robustness. The results showed that the root mean square error (RMSE) in the prediction of cabbage yield ranged from 1,314 to 2,532 kg ha–¹ (15.8–30.9% of the relative RMSE). Parameter optimisation via data assimilation revealed that the reduction factor in the gross assimilation rate was consistently attributed to a primary yield-limiting factor. This research further explored the effect of reducing the number of LAI observations on the data assimilation performance. The RMSE of yield was only 107 kg ha–¹ higher in the four LAI observations obtained from the early to mid-growing season than for the nine LAI observations over the entire growing season for cultivar ‘TCA 422’. These results highlighted the great possibility of assimilating UAV-derived LAI data into crop simulation models for plant-level cabbage yield prediction even with LAI observations only in the early and mid-growing seasons.
Why it matches plant phenotyping methodsUAV画像からセグメンテーションモデルでLAIを推定し、作物モデルへ同化して植物レベルのキャベツ収量を予測するシステムを構築・精度検証しており、表現型取得・推定手法が研究の中心である。
abstractThis research aimed to establish a plant-level cabbage yield prediction system by assimilating the leaf area index (LAI) estimated from UAV imagery and a segmentation model into a crop simulation model, the WOrld FOod STudies (WOFOST).
Hyperspectral imaging has proven to be a reliable technique for estimating dry matter, a common variable when considering the quality of the fresh produce. However, developing models capable of generalising across different crops is challenging. In this study, several pipelines were explored towards achieving a robust and accurate generic regression model were evaluated and the development of Automatic Relevance Determination (ARD) and Partial Least Squares (PLS) algorithms for fruit and vegetable dry matter estimation. The models were built using a VIS-NIR dataset that includes both fruit and vegetables, namely, apples, broccoli and leek (n = 779). The PLS regression model obtained Root Mean Square on Prediction (RMSEP) = 0.0137, outperforming ARD regression (RMSEP = 0.0140) on a 10x5-fold cross-validation protocol. The evaluated preprocessing techniques affect the two regression algorithms differently, with the best results achieved when the pipeline was used without feature extraction. Overall, the pipeline using either ARD or PLS regression shows strong performance and generalisation for Visible-Near Infrared (VIS-NIR)-based dry matter estimation across diverse fruits and vegetables.
Why it matches plant phenotyping methodsVIS-NIRハイパースペクトル画像から果実・野菜の乾物含量を推定するパイプラインと回帰モデルを比較・検証しており、植物形質取得が研究の中心である。
titleEvaluation of a hyperspectral image pipeline toward building a generalisation capable crop dry matter content prediction model
Studies on the phenotypic traits and their associations in Chinese cabbage lack precise and objective digital evaluation metrics. Traditional assessment methods often rely on subjective evaluations and experience, compromising accuracy and reliability. This study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology, with the aim of enhancing the precision, reliability, and standardization of the comprehensive phenotypic traits of Chinese cabbage. By using multi-view image sequences and structure-from-motion algorithms, 3D point clouds of 50 plants from each of the 17 Chinese cabbage varieties were reconstructed. Color-based region growing and 3D convex hull techniques were employed to measure 30 agronomic traits. Comparisons between 3D point cloud-based measurements of the plant spread, plant height, leaf area, and leaf ball volume and traditional methods yielded R2 values greater than 0.97, with root mean square errors of 1.27 cm, 1.16 cm, 839.77 cm3, and 59.15 cm2, respectively. Based on the plant spread and plant height, a linear regression prediction of Chinese cabbage weights was conducted, yielding an R2 value of 0.76. Integrated optimization algorithms were used to test the parameters, reducing the measurement time from 55 min when using traditional methods to 3.2 min. Furthermore, in-depth analyses including variation, correlation, principal component analysis, and clustering analyses were conducted. Variation analysis revealed significant trait variability, with correlation analysis indicating 21 pairs of traits with highly significant positive correlations and 2 pairs with highly significant negative correlations. The top six principal components accounted for 90% of the total variance. Using the elbow method, k-means clustering determined that the optimal number of clusters was four, thus classifying the 17 cabbage varieties into four distinct groups. This study provides new theoretical and methodological insights for exploring phenotypic trait associations in Chinese cabbage and facilitates the breeding and identification of high-quality varieties. Compared with traditional methods, this system provides significant advantages in terms of accuracy, speed, and comprehensiveness, with its low cost and ease of use making it an ideal replacement for manual methods, being particularly suited for large-scale monitoring and high-throughput phenotyping.
Why it matches plant phenotyping methods中国白菜の表現型を3D点群から抽出する測定法を開発し、従来法との精度比較・検証および高速化を行っており、植物表現型測定が研究の中心である。
abstractThis study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology
Reproduction assets foundThe paper's phenotyping analysis code is explicitly deposited on a public GitHub repository with an authors' URL. The phenotype/trait measurement data themselves are only available upon request, so they do not qualify as a public asset.Code · publicapproach significantly streamlines the process, saving time and
enhancing efficiency by automating tasks which previously required extensive manual ef-
fort, thereby ensuring a more systematic and reliable method of phenotypic information
detection. The code used in this study can be accessed at the following GitHub repository:
https://github.com/chongchong123123/code (accessed on 18 October 2024).
2.4. Accuracy Analysis of Agronomic Parameter Measurements
In the course of agronomic trait measurement research, we utilized point cloud tech-
nology to measure key agronomic traits, including the plant height, plant spread, various
leaf dimensions (leaf length and leaf width), the width and thicOpen asset ↗chongchong123123/codepdf-raw-page:8 lines:1-62Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
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:1-40Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 8 Sept 2026
Accurately detecting the maturity and 3D position of flowering Chinese cabbage ( Brassica rapa var. chinensis) in natural environments is vital for autonomous robot harvesting in unstructured farms. The challenge lies in dense planting, small flower buds, similar colors and occlusions. This study proposes a YOLOv8-Improved network integrated with the ByteTrack tracking algorithm to achieve multi-object detection and 3D positioning of flowering Chinese cabbage plants in fields. In this study, C2F-MLCA is created by adding a lightweight Mixed Local Channel Attention (MLCA) with spatial awareness capability to the C2F module of YOLOv8, which improves the extraction of spatial feature information in the backbone network. In addition, a P2 detection layer is added to the neck network, and BiFPN is used instead of PAN to enhance multi-scale feature fusion and small target detection. Wise-IoU in combination with Inner-IoU is adopted as a new loss function to optimize the network for different quality samples and different size bounding boxes. Lastly, ByteTrack is integrated for video tracking, and RGB-D camera depth data are used to estimate cabbage positions. The experimental results show that YOLOv8-Improve achieves a precision ( P ) of 86.5% and a recall ( R ) of 86.0% in detecting the maturity of flowering Chinese cabbage. Among them, mAP50 and mAP75 reach 91.8% and 61.6%, respectively, representing an improvement of 2.9% and 4.7% over the original network. Additionally, the number of parameters is reduced by 25.43%. In summary, the improved YOLOv8 algorithm demonstrates high robustness and real-time detection performance, thereby providing strong technical support for automated harvesting management.
Why it matches plant phenotyping methods開花中国白菜の成熟度という植物状態を画像から検出・推定する改良YOLOv8と3D位置推定手法が研究の中心であり、収穫対象の単なる位置検出を超える。
abstractThis study proposes a YOLOv8-Improved network integrated with the ByteTrack tracking algorithm to achieve multi-object detection and 3D positioning of flowering Chinese cabbage plants in fields.
Bangladesh's agricultural landscape is significantly influenced by vegetable cultivation, which substantially enhances nutrition, the economy, and food security in the nation. Millions of people rely on vegetable production for their daily sustenance, generating considerable income for numerous farmers. However, leaf diseases frequently compromise the yield and quality of vegetable crops. Plant diseases are a common impediment to global agricultural productivity, adversely affecting crop quality and yield, leading to substantial economic losses for farmers. Early detection of plant leaf diseases is crucial for improving cultivation and vegetable production. Common diseases such as Bacterial Spot, Mosaic Virus, and Downy Mildew often reduce vegetable plant cultivation and severely impact vegetable production and the food economy. Consequently, many farmers in Bangladesh struggle to identify the specific diseases, incurring significant losses. This dataset contains 12,643 images of widely grown crops in Bangladesh, facilitating the identification of unhealthy leaves compared to healthy ones. The dataset includes images of vegetable leaves such as Bitter Gourd (2223 images), Bottle Gourd (1803 images), Eggplants (2944 images), Cauliflowers (1598 images), Cucumbers (1626 images), and Tomatoes (2449 images). Each vegetable class encompasses several common diseases that affect cultivation. By identifying early leaf diseases, this dataset will be invaluable for farmers and agricultural researchers alike.
Why it matches plant phenotyping methods植物葉の画像から健康状態と病徴を識別するデータセットを提供しており、植物の病害状態を観測する再利用可能な画像ベースのフェノタイピング資源が中心です。
abstractThis dataset contains 12,643 images of widely grown crops in Bangladesh, facilitating the identification of unhealthy leaves compared to healthy ones.
Reproduction assets foundThe paper is a Data in Brief article describing a public smartphone image dataset of vegetable leaf diseases hosted on Mendeley Data, with an explicit direct URL matching an allowed URL.Dataset · publicRepository name: Mendeley Data
Data identification number: DOI: 10.17632/n67gctmjyj.3
Direct URL to data: https://data.mendeley.com/datasets/n67gctmjyj/3Open asset ↗Mendeley Data · 10.17632/n67gctmjyj.3lines:1-48Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Introduction Accurate and rapid identification of cabbage posture is crucial for minimizing damage to cabbage heads during mechanical harvesting. However, due to the structural complexity of cabbages, current methods encounter challenges in detecting and segmenting the heads and roots. Therefore, exploring efficient cabbage posture prediction methods is of great significance. Methods This study introduces YOLOv5-POS, an innovative cabbage posture prediction approach. Building on the YOLOv5s backbone, this method enhances detection and segmentation capabilities for cabbage heads and roots by incorporating C-RepGFPN to replace the traditional Neck layer, optimizing feature extraction and upsampling strategies, and refining the C-Seg segmentation head. Additionally, a cabbage root growth prediction model based on Bézier curves is proposed, using the geometric moment method for key point identification and the anti-gravity stem-seeking principle to determine root-head junctions. It performs precision root growth curve fitting and prediction, effectively overcoming the challenge posed by the outer leaves completely enclosing the cabbage root stem. Results and discussion YOLOv5-POS was tested on a multi-variety cabbage dataset, achieving an F1 score of 98.8% for head and root detection, with an instance segmentation accuracy of 93.5%. The posture recognition model demonstrated an average absolute error of 1.38° and an average relative error of 2.32%, while the root growth prediction model reached an accuracy of 98%. Cabbage posture recognition was completed within 28 milliseconds, enabling real-time harvesting. The enhanced model effectively addresses the challenges of cabbage segmentation and posture prediction, providing a highly accurate and efficient solution for automated harvesting, minimizing crop damage, and improving operational efficiency.
Why it matches plant phenotyping methodsキャベツの頭部・根の検出／セグメンテーションと姿勢角・根の成長曲線を画像から推定する手法を開発し、精度と処理時間を評価しており、植物形質取得が中心である。
abstractThis study introduces YOLOv5-POS, an innovative cabbage posture prediction approach.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
Cauliflower cultivation is subject to high-quality control criteria during sales, which underlines the importance of accurate harvest timing. Using time series data for plant phenotyping can provide insights into the dynamic development of cauliflower and allow more accurate predictions of when the crop is ready for harvest than single-time observations. However, data acquisition on a daily or weekly basis is resource-intensive, making selection of acquisition days highly important. We investigate which data acquisition days and development stages positively affect the model accuracy to get insights into prediction-relevant observation days and aid future data acquisition planning. We analyze harvest-readiness using the cauliflower image time series of the GrowliFlower dataset. We use an adjusted ResNet18 classification model, including positional encoding of the data acquisition dates to add implicit information about development. The explainable machine learning approach GroupSHAP analyzes time points' contributions. Time points with the lowest mean absolute contribution are excluded from the time series to determine their effect on model accuracy. Using image time series rather than single time points, we achieve an increase in accuracy of 4%. GroupSHAP allows the selection of time points that positively affect the model accuracy. By using seven selected time points instead of all 11 ones, the accuracy improves by an additional 4%, resulting in an overall accuracy of 89.3%. The selection of time points may therefore lead to a reduction in data collection in the future.
Why it matches plant phenotyping methodsカリフラワー画像時系列を用いた収穫適期という植物状態の推定手法を開発・評価し、データ取得時点の選択とモデル精度を検証しているため、フェノタイピング手法が中心である。
abstractUsing time series data for plant phenotyping can provide insights into the dynamic development of cauliflower and allow more accurate predictions of when the crop is ready for harvest than single-time observations.
Reproduction assets foundThe paper analyzes the GrowliFlower cauliflower UAV image time series dataset and provides a public data availability link to the dataset metadata on phenoroam.phenorob.de. No author analysis code or trained model checkpoint is explicitly deposited.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found at: https://phenoroam.phenorob.de/geonetwork/srv/eng/catalog.search#/metadata/cb328232-31f5-4b84-a929-8e1ee551d66a .Open asset ↗phenoroam.phenorob.de · cb328232-31f5-4b84-a929-8e1ee551d66alines:386-397Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, a Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using aerial image datasets, which then facilitated the training of the Grounded Segment Anything Model framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study’s findings utilized a multi-modal monitoring approach to highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. The results indicate consistent and comparable growth patterns between aerial images and ortho-mosaics, with significant periods of rapid expansion and minor fluctuations over time. The results also indicated a correlation between the time and method of observation which paves a future possibility of integration of such techniques aimed at increasing the accuracy in crop growth monitoring based on automatically derived temporal crop row segmentation masks.
Why it matches plant phenotyping methods航空画像・オルソモザイクから作物列を自動セグメンテーションし、時系列の生育・成長を推定する画像解析パイプラインが研究の中心であり、植物形質の取得手法として適格。
abstractThis study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops
Reproduction assets foundThe paper's Data Availability Statement points to the authors' public Mendeley Data repository (GobhiSet, DOI 10.17632/dcjjcwc5dh.4), which contains the raw, manually, and automatically annotated RGB aerial images and ortho-mosaics of cauliflower used for the YOLOv8x-seg and Grounded SAM training and growth analysis inDataset · publicon of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: No new data was created. However, the data that were used to perform
this research can be found in the article published at https://doi.org/10.1016/j.dib.2024.110506 and
available in the repository DOI: 10.17632/dcjjcwc5dh.4 (https://data.mendeley.com/drafts/dcjjcwc5dh).Conflicts of Interest: The authors declare no conflicts of interest.
References
1. Di, L.; Ustundag, B. Crop Growth Modeling and Yield Forecasting. In Agro-Geoinformatics; Springer: Cham, Switzerland, 2021.
[CrossRef]
2. Mithen, S.; Jenkins, E.; Jamjoum, K.; Nuimat, S.; Nortcliff, S.; Finlayson, B. Experimental crop growingOpen asset ↗data.mendeley.com · 10.17632/dcjjcwc5dh.4pdf-raw-page:17 lines:1-52Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using aerial image datasets, which then facilitated the training of the Grounded Segment Anything Model framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study’s findings utilized a multi-modal monitoring approach to highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. Results indicate consistent and comparable growth patterns between aerial images and ortho-mosaics, with significant periods of rapid expansion and minor fluctuations over time. The results also indicated a correlation between the both the time and method of observation which paves a future possibility of integration of such techniques aimed at increasing the accuracy in crop growth monitoring based on automatically derived temporal crop row segmentation masks.
Why it matches plant phenotyping methods航空画像とオルソモザイクから作物列を自動セグメンテーションし、時系列の生育を推定する画像解析手法が研究の中心であるため。
abstractThis study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops
Crop models are instrumental in simulating resource utilization in agriculture, yet their complexity necessitates extensive calibration, which can impact the accuracy of yield predictions. Machine learning shows promise for enhancing yield estimations but relies on vast amounts of training data. This study aims to improve the pakchoi yield prediction accuracy of simulation models. We developed a stacking ensemble learning model that integrates three base models—EU-Rotate_N, Random Forest Regression and Support Vector Regression—with a Multi-layer Perceptron as the meta-model for the pakchoi dry matter yield prediction. To enhance the training dataset and bolster machine learning performance, we employed the EU-Rotate_N model to simulate daily dry matter yields for unsampled data. The test results revealed that the stacking model outperformed each base model. The stacking model achieved an R² value of 0.834, which was approximately 0.1 higher than that of the EU-Rotate_N model. The RMSE and MAE were 0.283 t/ha and 0.196 t/ha, respectively, both approximately 0.6 t/ha lower than those of the EU-Rotate_N model. The performance of the stacking model, developed with the expanded dataset, showed a significant improvement over the model based on the original dataset.
Why it matches plant phenotyping methodsパクチョイの乾物収量という植物形質を推定するスタッキング予測モデルを開発し、複数モデルとの性能比較・検証を行っており、形質推定手法が研究の中心である。
abstractWe developed a stacking ensemble learning model that integrates three base models—EU-Rotate_N, Random Forest Regression and Support Vector Regression—with a Multi-layer Perceptron as the meta-model for the pakchoi dry matter yield prediction.
Accurate and timely prediction of Napa cabbage (Brissica rapa subsp. Perkinensis) fresh weight is crucial for optimizing harvest timing, crop management, and supply chain logistics, contributing to food security and price stabilization. Traditional manual sampling methods are labor-intensive and imprecise. This study addresses this challenge by developing a comprehensive (artificial intelligence) AI-powered model for predicting Napa cabbage fresh weight using unmanned aerial vehicle (UAV)-based multi-sensor data. High-resolution RGB, multispectral, and thermal infrared (TIR) imagery were collected over a Napa cabbage field throughout the 2020 growing season. Various vegetation indices, crop features (vegetation fraction, crop height model), and water stress indi-cators (CWSI) were extracted from the imagery. Three AI algorithms—deep neural network (DNN), support vector machine (SVM), and random forest (RF)—were trained and evaluated, with the DNN model consistently outperforming the others. The DNN model achieved the highest accuracy (R² = 0.86 for training, 0.82 for testing; root mean square error (RMSE) = 0.432 kg for training, 0.465 kg for testing) during the mid-to-late rosette growth stage (DAP 35-42), highlighting this period as crucial for fresh weight estimation due to stable leaf area and well-developed canopy structure. The model tended to underestimate the weight of Napa cabbages exceeding 5 kg, potentially due to limited samples and saturation effects of vegetation indices. However, the overall error rate was less than 5%, demonstrating the feasibility and effectiveness of this approach. Spatial analysis revealed that the model accurately captured the variability in Napa cabbage growth across different soil types and irrigation conditions, particularly reflecting the positive impact of drip irrigation on the sandy loam plot. Bias analysis indicated the DNN model's tendency to overestimate smaller Napa cabbages (2 kg), suggesting areas for future refinement. This study demonstrates the potential of UAV-based multi-sensor data and AI algorithms for accurate and non-invasive prediction of Napa cabbage fresh weight. The developed DNN model offers a promising tool for optimizing harvest timing, improving crop management practices, and en-hancing supply chain efficiency. Future research should focus on refining the model for specific weight ranges and diverse environmental conditions, as well as extending its application to other crops, to further advance precision agriculture and contribute to sustainable food production.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル・熱画像からキャベツの生体重を推定する手法を開発・評価しており、植物形質取得が研究の中心です。
abstractThis study addresses this challenge by developing a comprehensive (artificial intelligence) AI-powered model for predicting Napa cabbage fresh weight using unmanned aerial vehicle (UAV)-based multi-sensor data.
This study introduces a novel method for early prediction of Kimchi cabbage (Brassica rapa subsp. pekinensis (Lour.) Hanelt) height, utilizing drone imagery and a long short-term memory (LSTM) model. The research was conducted on a testbed at the National Institute of Agricultural Sciences (NAS) in South Korea, encompassing two distinct soil types (loam and sandy loam) to investigate their impact on growth. High-resolution drone images were captured throughout the growing season to generate a canopy height model (CHM) for estimating plant height at various stages. Missing height data were interpolated using a logistic growth curve, and an LSTM model was trained on this data to predict the final height of Kimchi cabbage at harvest. Three LSTM models were developed using time-series data collected at 29, 36, and 44 days after planting (DAP). The model trained on data from DAP 44 demonstrated the highest accuracy with a coefficient of determination (R²) of 0.83, a mean absolute error (MAE) of 2.48 cm, and a root mean square error (RMSE) of 3.26 cm, outperforming models trained on earlier data. Color-coded maps were generated to visualize the spatial distribution of predicted Kimchi cabbage heights, revealing variations in growth patterns across the testbed and confirming the model's potential for site-specific management. Considering the trade-off between accuracy and prediction timing, the model trained on DAP 36 data (MAE = 2.77 cm) was deemed optimal for informing cultivation management decisions. This research demonstrates the feasibility and effectiveness of integrating drone imagery, logistic growth curves, and LSTM models for early and accurate prediction of Kimchi cabbage height. The proposed technology enables data-driven decision-making for farmers, facilitating timely interventions based on predicted growth patterns. This could lead to improved crop yields, resource optimization, and a more sustainable agricultural future. Future research will focus on refining the model's accuracy and exploring its applicability to other crops, further expanding the potential of precision agriculture technologies.
Why it matches plant phenotyping methodsドローン画像からキャベツの草高を推定し、補間とLSTMによる早期予測を開発・評価しており、植物表現型の取得・推定手法が研究の中心である。
abstractThis study introduces a novel method for early prediction of Kimchi cabbage (Brassica rapa subsp. pekinensis (Lour.) Hanelt) height, utilizing drone imagery and a long short-term memory (LSTM) model.
Real-time monitoring of seedling emergence is vital for vegetable crop management and yield estimation. Traditionally, crop seedling emergence monitoring relies on low-efficient and time-consuming manual counting. To address this issue, this research proposed an efficient, fast, and real-time cabbage seedling counting method (combining the improved YOLOv8n, tracking algorithm, and image processing) to accurately track cabbage seedlings in the field and implement counting with an unmanned aerial vehicle (UAV). The improved YOLOv8n replaced the C2f Block in the YOLO backbone with a Swin-conv block and incorporated ParNet attention modules in both the backbone and neck parts. This enhancement enables the YOLOv8n to surpass the base model's performance, achieving a mAP50–95 of 90.3 %, representing a 14.5 % improvement. The experiments demonstrated the superior capabilities of the counting method in terms of speed and accuracy. In field experiments, the proposed Tracking algorithms-Swin-conv blocks-ParNet attention-YOLOv8n (TSP-yolo) counting method demonstrated consistent and reliable accuracy in counting cabbage seedlings while demanding only one-seventh of the time needed compared to the manual counting method. In summary, based on TSP-yolo and implemented through an UAV, the developed seedling emergence counting method demonstrated an excellent capability of counting cabbage seedlings, resulting in significant savings in human resources for crop management.
Why it matches plant phenotyping methodsUAV画像と改良YOLOを用いてキャベツ苗の出芽数を自動推定する手法を開発・評価しており、植物状態の取得方法が研究の中心である。
abstractthis research proposed an efficient, fast, and real-time cabbage seedling counting method (combining the improved YOLOv8n, tracking algorithm, and image processing)
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Remote sensing has been increasingly used in precision agriculture. Buoyed by the developments in the miniaturization of sensors and platforms, contemporary remote sensing offers data at resolutions finer enough to respond to within-farm variations. LiDAR point cloud, offers features amenable to modelling structural parameters of crops. Early prediction of crop growth parameters helps farmers and other stakeholders dynamically manage farming activities. The objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops. LiDAR point clouds were acquired using a terrestrial laser scanner on five dates during the growth cycles of tomato, eggplant and cabbage on the experimental research farms of the University of Agricultural Sciences, Bengaluru, India. We implemented a hybrid deep learning framework combining distinct features of long-term short memory (LSTM) and Gated Recurrent Unit (GRU) for the predictions of plant height and crown area. The predictions are validated with reference ground truth measurements. These predictions were validated against ground truth measurements. The findings demonstrate that plant-level structural parameters can be predicted well ahead of crop growth stages with around 80% accuracy. Notably, the LSTM and the GRU models exhibited limitations in capturing variations in structural parameters. Conversely, the hybrid model offered significantly improved predictions, particularly for crown area, with error rates for height prediction ranging from 5 to 12%, with deviations exhibiting a more balanced distribution between overestimation and underestimation This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications. However, the prediction quality is relatively low at the advanced growth stage, closer to the harvest. In contrast, the prediction quality is stable across the three different crops. The results indicate the presence of a robust relationship between the features of the LiDAR point cloud and the auto-feature map of the deep learning methods adapted for plant-level crop structural characterization. This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications.
Why it matches plant phenotyping methodsLiDAR点群と深層学習を用いて、植物体レベルの草丈・樹冠面積を推定する手法を開発・検証しており、表現型取得・抽出が研究の中心です。
abstractThe objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops.
BACKGROUND: Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. While image-based models provide more flexibility for crop growth modeling than process-based models, there is still a significant research gap in the comprehensive integration of various growth-influencing conditions. Further exploration and investigation are needed to address this gap. METHODS: We present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained. The image generation model is a conditional Wasserstein generative adversarial network (CWGAN). In the generator of this model, conditional batch normalization (CBN) is used to integrate conditions of different types along with the input image. This allows the model to generate time-varying artificial images dependent on multiple influencing factors. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits and comparing them with those of non-artificial (real) reference images. In addition, image quality is evaluated using multi-scale structural similarity (MS-SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID). During inference, the framework allows image generation for any combination of conditions used in training; we call this generation data-driven crop growth simulation. RESULTS: Experiments are performed on three datasets of different complexity. These datasets include the laboratory plant Arabidopsis thaliana (Arabidopsis) and crops grown under real field conditions, namely cauliflower (GrowliFlower) and crop mixtures consisting of faba bean and spring wheat (MixedCrop). In all cases, the framework allows realistic, sharp image generations with a slight loss of quality from short-term to long-term predictions. For MixedCrop grown under varying treatments (different cultivars, sowing densities), the results show that adding these treatment information increases the generation quality and phenotyping accuracy measured by the estimated biomass. Simulations of varying growth-influencing conditions performed with the trained framework provide valuable insights into how such factors relate to crop appearances, which is particularly useful in complex, less explored crop mixture systems. Further results show that adding process-based simulated biomass as a condition increases the accuracy of the derived phenotypic traits from the predicted images. This demonstrates the potential of our framework to serve as an interface between a data-driven and a process-based crop growth model. CONCLUSION: The realistic generation and simulation of future plant appearances is adequately feasible by multi-conditional CWGAN. The presented framework complements process-based models and overcomes their limitations, such as the reliance on assumptions and the low exact field-localization specificity, by realistic visualizations of the spatial crop development that directly lead to a high explainability of the model predictions.
Why it matches plant phenotyping methods植物画像を生成し、そこから植物個体別形質を推定する二段階の画像ベース表現型解析フレームワークを開発・評価しており、表現型取得・推定手法が研究の中心である。
abstractWe present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained.
Reproduction assets foundThe paper's authors explicitly state that source code and links to the phenotyping datasets (Arabidopsis, GrowliFlower, MixedCrop) are publicly available in their GitHub repository, which implements the multi-conditional CWGAN crop growth simulation and growth estimation framework.Code · publicSource code and links to the datasets are publicly available at https://github.com/luked12/crop-growth-cgan .Open asset ↗luked12/crop-growth-cganlines:216-253Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Abstract The study aimed to develop a measurement apparatus for in vivo chlorophyll-a (Chl-a) fluorescence decay measurements of plants by means of time correlated single photon counting. In this approach, sub-nanosecond laser pulses with a repetition rate of 10 MHz are applied to excite the sample, followed by the analysis of arrival times of the emitted fluorescence photons. Photon statistics are generated by iteratively fitting the sum of two exponential functions. The tool was tested on both plastid and in vivo leaf samples of Savoy cabbage ( Brassica oleracea var. sabauda) with 3–4 subsequent leaves giving a complete sample coverage starting from the outermost. The Chl-a fluorescence lifetime exhibited a gradual increase in both the isolated plastid suspensions and the in vivo leaf samples towards the innermost leaf layers explained by an increase of natural absence of light (etiolation syndrome). Furthermore, cadmium stress and iron deficiency were investigated on treated sugar beet ( Beta vulgaris ) samples in vivo using TCSPS measurements. The reduced fluorescence quenching resulted in an increased fluorescence lifetime. Finally, a long-term (10 week) testing of the setup was carried out on Chl-retaining resurrection Haberlea rhodopensis plants protecting themselves by an elevated non-photochemical quenching yielding a decrease of fluorescence lifetime during their desiccation.
Why it matches plant phenotyping methods植物の生体クロロフィル蛍光寿命を測定する装置を開発し、複数の植物試料・ストレス条件・長期試験で検証しており、植物生理状態の取得法が研究の中心である。
abstractThe study aimed to develop a measurement apparatus for in vivo chlorophyll-a (Chl-a) fluorescence decay measurements of plants by means of time correlated single photon counting.
This research introduces an extensive dataset of unprocessed aerial RGB images and orthomosaics of Brassica oleracea crops, captured via a DJI Phantom 4. The dataset, publicly accessible, comprises 244 raw RGB images, acquired over six distinct dates in October and November of 2020 as well as 6 orthomosaics from an experimental farm located in Portici, Italy. The images, uniformly distributed across crop spaces, have undergone both manual and automatic annotations, to facilitate the detection, segmentation, and growth modelling of crops. Manual annotations were performed using bounding boxes via the Visual Geometry Group Image Annotator (VIA) and exported in the Common Objects in Context (COCO) segmentation format. The automated annotations were generated using a framework of Grounding DINO + Segment Anything Model (SAM) facilitated by YOLOv8x-seg pretrained weights obtained after training manually annotated images dated 8 October, 21 October, and 29 October 2020. The automated annotations were archived in Pascal Visual Object Classes (PASCAL VOC) format. Seven classes, designated as Row 1 through Row 7, have been identified for crop labelling. Additional attributes such as individual crop ID and the repetitiveness of individual crop specimens are delineated in the Comma Separated Values (CSV) version of the manual annotation. This dataset not only furnishes annotation information but also assists in the refinement of various machine learning models, thereby contributing significantly to the field of smart agriculture. The transparency and reproducibility of the processes are ensured by making the utilized codes accessible. This research marks a significant stride in leveraging technology for vision-based crop growth monitoring.
Why it matches plant phenotyping methods作物の生育モニタリングを目的としたRGB画像・オルソモザイクの公開データセットで、手動／自動アノテーションと成長モデリングを中心的に扱っているため、植物フェノタイピング手法・データセットに該当する。
abstractThis research introduces an extensive dataset of unprocessed aerial RGB images and orthomosaics of Brassica oleracea crops
Reproduction assets foundThe paper's own GobhiSet dataset (raw RGB images, orthomosaics, manual/automatic annotations, binary masks) and Python analysis scripts are publicly deposited on Mendeley Data with an explicit direct URL and DOI.Dataset · public0137
Longitude: 14; 20; 47.7701
Data post-processing and storage location: Department of Engineering, University of Campania ‘Luigi Vanvitelli,’ Aversa, Italy
Coordinates: 40.96846317808221, 14.208207168044456
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/dcjjcwc5dh.3
Direct URL to data: https://data.mendeley.com/datasets/dcjjcwc5dh/3
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Value of the Data
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This dataset is a collection of multi-date aerial imagery of the Brassica oleracea var. Botrytis crop [ 1 ]. The images were acquired between the first and seventh weeks after sowing the cauliflower, with the intention of observing its growth over this period. The images were annotated with two typOpen asset ↗Mendeley Data · 10.17632/dcjjcwc5dh.3lines:50-75Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
β-Galactosidase (β-gal), an enzyme related to cell wall degradation, plays an important role in regulating cell wall metabolism and reconstruction. However, activatable fluorescence probes for the detection and imaging of β-gal fluctuations in plants have been less exploited. Herein, we report an activatable fluorescent probe based on intramolecular charge transfer (ICT), benzothiazole coumarin-bearing β-galactoside (BC-βgal), to achieve distinct in situ imaging of β-gal in plant cells. It exhibits high sensitivity and selectivity to β-gal with a fast response (8 min). BC-βgal can be used to efficiently detect the alternations of intracellular β-gal levels in cabbage root cells with considerable imaging integrity and imaging contrast. Significantly, BC-βgal can assess β-gal activity in cabbage roots under heavy metal stress (Cd 2+ , Cu 2+ , and Pb 2+ ), revealing that β-gal activity is negatively correlated with the severity of heavy metal stress. Our work thus facilitates the study of β-gal biological mechanisms.
Why it matches plant phenotyping methods植物細胞内のβ-ガラクトシダーゼ活性を可視化・定量する蛍光プローブを開発し、重金属ストレス下の根で実証しており、植物状態の取得法が中心である。
abstractHerein, we report an activatable fluorescent probe based on intramolecular charge transfer (ICT), benzothiazole coumarin-bearing β-galactoside (BC-βgal), to achieve distinct in situ imaging of β-gal in plant cells.
Three-dimensional reconstruction plays a crucial role in quantifying crop phenotypes and exploring crop physiological structures. This paper presents a phenotyping platform designed for the 3D reconstruction of complex plants, utilizing multi-view images and introducing a joint evaluation criterion for both the reconstruction algorithm and the platform. Initially, a device composed of Raspberry Pi camera, SSH protocol, USB-TTL, motorized turntable, and shadow booth is built for automated image acquisition and transmission. Then, a dataset containing carex cabbage and kale is created and trained based on U2-net to achieve precise image segmentation. After that, an improved structure from motion algorithm, named IVOP & AKAZE-SFM, and multi-view stereo algorithm are utilized for the fine-scale reconstruction of plants. Next, by combing color filtering and Euclidean clustering, a denoising algorithm is proposed to obtain clean point clouds of plants. Finally, a method for calibrating the size of the plant point cloud based on priori condition is proposed to solve the problem of point cloud deformation in reconstruction. The evaluation of image segmentation model resulted in a precision of 0.91, a recall of 0.972, an IOU of 0.943, and a maxFβ of 0.099. The proposed IVOP&AKAZE-SFM is assessed against mainstream algorithms, the results show that our algorithm has the minimum average track length, minimum average reprojection error and generated the most points. The correlation coefficient (R2) between the extracted traits and measured phenotype, such as plant height and plant width, are 0.999 and 1.000, while the root means squared errors (RMSE) are 0.298 cm and 0.338 cm. Consequently, the platform offers a cost-effective, automated, and integrated solution for fine-scale plant 3D reconstruction.
Why it matches plant phenotyping methods植物のマルチビュー画像から3D形状を再構成し、草丈・株幅などの表現型を抽出するプラットフォームの開発、評価、検証が中心であるため。
abstractThis paper presents a phenotyping platform designed for the 3D reconstruction of complex plants, utilizing multi-view images and introducing a joint evaluation criterion for both the reconstruction algorithm and the platform.
Although exclusion measures (e.g., air filters, biosecurity practices) can be employed to prevent occurrence of pest outbreaks, indoors vegetable farms in Singapore are still susceptible to various arthropod pests. Due to strong interest from the industry to pursue pesticide-free production, indoors pest management is often focused on early detection for timely containment and eradication, implying the importance of robust and vigorous pest monitoring programs. In recent years, application of machine vision technologies, especially hyperspectral imaging (HSI), has been studied for their capacity to early detect pest infestation. However, there is a lack of studies conducted in actual indoor environments and on multiple arthropod pests. Thus, this study aimed to non-destructively collect hyperspectral data of bok choy Brassica rapa subspecies chinensis which were healthy or infested with either mustard aphids Lipaphis erisymi, vegetable thrips Echinothrips americanus or two-spotted spider mites Tetranichus urticae in indoor environment to build deep neural network (DNN) classification model for early detection. Based on HSI data of control and infested plants collected daily over a period of two weeks, we found that point percentage change (PPC) values associated with leaf reflectance in 420–440 nm, 500–520 nm, 620–637 nm, 720–800 nm, and 850 nm were sensitive to infestation by the mentioned arthropod pests. Deep Neural Network (DNN) classification models trained on collected HSI data were found to outperform Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) classification models. DNN models achieved 92.8 ± 0.4 % overall classification accuracy across all days. As early as two days after infestation, DNN models could achieved classification precision values of 96.4 %, 96.9 %, 93.9 % and 100 % for control plants and plants infested with either aphids, spider mites or thrips respectively. These results highlight the feasibility of multiclass early detection of different arthropod pests and the potential of HSI system coupled with DNN classification as an autonomous plant health monitoring tool in indoor crop production.
Why it matches plant phenotyping methods植物の感染・害虫被害状態をHSIで取得し、DNNで分類する手法が研究の中心であり、植物ヘルス状態の非破壊フェノタイピングに該当する。
abstractbuild deep neural network (DNN) classification model for early detection
Single-plant growth monitoring aids precision agricultural decision-making to reduce the costs related to pesticides, fertilizers, and labor. This study integrated visible/multi-spectral UAV imagery with two deep learning methods, object detection and semantic segmentation, to obtain a visualized map that could assist in precise field monitoring and management for broccoli cultivation. For plant detection, feature extraction was conducted using multiscale dilated convolution, which enabled the effective detection of broccoli in images taken under different photographic conditions and resolutions. Two crops of broccoli (cultivar: Broccoli No. 42) were planted in 2020 at Taichung Agricultural Research and Extension Station, in which the first crop was treated as the training data. The detection of individual broccoli plants was processed using a feature extraction architecture of the AlexNet-Like backend at the SSD frontend, where the input scale of the detector complies with the original SSD architecture. For the model test on the second crop, the recall and precision were 98.58% and 99.73%, respectively, after histogram matching based on the first crop images. Moreover, the proposed approach was applied to a real farming field to verify its robustness across different conditions, and achieved a recall of 61.13% using dilated convolution. This study also generated a visualized growth map on a single-plant basis, which allows operators to detect growth situations, such as uneven irrigation or fertilization and necrosis and apoptosis, to greatly enhance the viability of precision agriculture in the calculation of unit yield and intragroup differences for a regime. The proposed approach can be used to determine the optimal amount of fertilization and observe the size of broccoli heads to determine the optimal harvest time. Expectedly, the method may also be applied to the monitoring and management of other crops to improve the efficiency and reduce the labor demand for precision agriculture.
Why it matches plant phenotyping methodsUAV画像と深層学習による単株検出・成長マップ生成を開発し、精度検証と圃場での頑健性評価を行っており、植物表現型の取得・抽出手法が研究の中心である。
abstractThis study integrated visible/multi-spectral UAV imagery with two deep learning methods, object detection and semantic segmentation, to obtain a visualized map that could assist in precise field monitoring and management for broccoli cultivation.
Object detection technology plays a crucial role in crop growth monitoring within smart agriculture. However, data labeling is a costly process necessary for constructing a large-scale dataset, which is essential to prevent overfitting in deep learning-based object detection models. Semi-Supervised Object Detection (SSOD) presents a cost-effective solution to reduce labeling and model training expenses; nevertheless, existing SSOD algorithms fall short in addressing the specific challenges posed by detection tasks in Brassica Chinensis growth monitoring. Specifically, the two-stage object detector cannot be well-suited for scenes characterized by severe occlusion and complex backgrounds. The Non-Maximum Suppression (NMS) may filter out numerous true positives in scenarios with severe occlusion. Moreover, the label assignment enlarges the negative effects of the noise introduced by teacher model’s prediction, resulting in potential divergence. To tackle these challenges, we propose an end-to-end SSOD method based on Detection Transformer (DETR), which streamlines the post-processing without NMS and adopts a more advanced bipartite matching assignment strategy. These modifications tailor the semi-supervised training method to better align with the unique characteristics of detection tasks in Brassica Chinensis growth monitoring. Furthermore, two key techniques: low threshold filtering and decoupled optimization, are introduced to address class-imbalance and multi-task optimization conflict in the tasks, respectively. In the end, we conduct experiments using two self-constructed Brassica Chinensis image datasets to validate the effectiveness of the proposed method, which demonstrates state-of-the-art (SOTA) performance in both tasks. For plant detection, the proposed method achieves an mAP of 74.1 using only 5 % of the total data volume (18 images). In the wormhole detection task, the method achieves an AP50 of 73.7 using 5 % of the total data volume (73 images). These impressive results meet the requirements for practical applications in Brassica Chinensis growth monitoring.
Why it matches plant phenotyping methodsBrassica chinensisの生育画像から植物および食害（wormhole）を検出する半教師あり画像解析法を開発・検証しており、植物状態の取得方法が研究の中心である。
abstractwe propose an end-to-end SSOD method based on Detection Transformer (DETR)
The demand for more sustainable farming is driving interest in alternative cropping systems, such as strip intercropping. In such systems, two or more crops are grown simultaneously on the same field, offering advantages such as increased biocontrol of weed, pest, diseases, and increasing productivity in resource-limited ecosystems. However, with strip intercropping, complexity increases and quantitative data to study the competition between plants are still limited due to the current manual process of acquiring data. While individual-plant data would facilitate this study, the manual acquisition is not feasible for large-scale experiments. Alternatively, unmanned aerial vehicles (UAV) equipped with high-resolution camera can cover large areas and estimate individual-plant growth from RGB imagery using automated image-processing methods. This study investigated its applicability to monitor the plant-height development of individual cabbage plants in space and time with sufficient accuracy and to identify the potential differences between strip intercropping treatments. Using RGB imagery and structure-from-motion analysis, a digital surface model (DSM) was created. Individual plant-height was calculated from the DSM by estimating the height of the vegetation and the height of the soil. Comparing the height estimations with ground-truth height measurements showed an overall root mean square error (RMSE) of 4.67 cm, which is in the same range as the 4cm standard deviation between measurements of multiple observers. The UAV-based height estimation of individual plants was used not only to compare the development in a strip intercropping field to that in a monoculture but also to compare with various treatments in the strip intercropping system. The results show that the plants grew faster in intercropping conditions than in monocropping conditions, with a subtle difference between treatments. Our results illustrate that with a UAV-based imaging approach we can go beyond current experimental practice and collect vast amounts of data on individual plants with high spatial and temporal resolution with an accuracy similar to that of manual measurements.
Why it matches plant phenotyping methodsUAV画像とSfM/DSMを用いて個体別の植物高を自動推定し、地上測定との精度検証まで行っており、フェノタイピング手法の開発・検証と実質的な適用が中心です。
abstractestimate individual-plant growth from RGB imagery using automated image-processing methods
Downy mildew caused by Hyaloperonospora brassicae is a severe disease in Brassica oleracea that significantly reduces crop yield and marketability. This study aims to evaluate different vegetation indices to assess different downy mildew infection levels in the Brassica variety Mildis using hyperspectral data. Artificial inoculation using H. brassicae sporangia suspension was conducted to induce different levels of downy mildew disease. Spectral measurements, spanning 350 nm to 1050 nm, were conducted on the leaves using an environmentally controlled setup, and the reflectance data were acquired and processed. The Successive Projections Algorithm (SPA) and signal sensitivity calculation were used to extract the most informative wavelengths that could be used to develop downy mildew indices (DMI). A total of 37 existing vegetation indices and three proposed DMIs were evaluated to indicate downy mildew (DM) infection levels. The results showed that the classification using a support vector machine achieved accuracies of 71.3%, 80.7%, and 85.3% for distinguishing healthy leaves from DM1 (early infection), DM2 (progressed infection), and DM3 (severe infection) leaves using the proposed downy mildew index. The proposed new downy mildew index potentially enables the development of an automated DM monitoring system and resistance profiling in Brassica breeding lines.
Why it matches plant phenotyping methodsハイパースペクトル反射データからアブラナ科植物のべと病感染レベルを推定する指標を開発・評価しており、病害表現型の取得・抽出手法が研究の中心である。
abstractThis study aims to evaluate different vegetation indices to assess different downy mildew infection levels in the Brassica variety Mildis using hyperspectral data.
Multispectral imaging, combined with stoichiometric values, was used to construct a prediction model to measure changes in dietary fiber (DF) content in Chinese cabbage leaves across different growth periods. Based on all the spectral bands (365-970 nm) and characteristic spectral bands (430, 880, 590, 490, 690 nm), eight quantitative prediction models were established using four machine learning algorithms, namely random forest (RF), backpropagation neural network, radial basis function, and multiple linear regression. Finally, a quantitative prediction model of RF learning algorithm is constructed based on all spectral bands, which has good prediction accuracy and model robustness, prediction performance with R 2 of 0.9023, root mean square error (RMSE) of 2.7182 g/100 g, residual predictive deviation (RPD) of 3.1220 > 3.0. In summary, this model efficiently detects changes in DF content across different growth periods of Chinese cabbage, which offers technical support for vegetable sorting and grading in the field.
Why it matches plant phenotyping methods白菜葉の食物繊維含量という植物器官形質を、マルチスペクトル画像と機械学習で非破壊推定する予測モデルを構築・評価しており、形質取得手法が中心である。
abstractMultispectral imaging, combined with stoichiometric values, was used to construct a prediction model to measure changes in dietary fiber (DF) content in Chinese cabbage leaves across different growth periods.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Brassica vegetablesLeafPhysiological trait estimationGrowth / time-series analysisVisualization / data managementGrowth / development / phenologyStress response / tolerancePlant / canopy temperature
Real-time in situ monitoring of plant physiology is essential for establishing a phenotyping platform for precision agriculture. A key enabler for this monitoring is a device that can be noninvasively attached to plants and transduce their physiological status into digital data. Here, we report an all-organic transparent plant e-skin by micropatterning poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on polydimethylsiloxane (PDMS) substrate. This plant e-skin is optically and mechanically invisible to plants with no observable adverse effects to plant health. We demonstrate the capabilities of our plant e-skins as strain and temperature sensors, with the application to Brassica rapa leaves for collecting corresponding parameters under normal and abiotic stress conditions. Strains imposed on the leaf surface during growth as well as diurnal fluctuation of surface temperature were captured. We further present a digital-twin interface to visualize real-time plant surface environment, providing an intuitive and vivid platform for plant phenotyping.
Why it matches plant phenotyping methods植物に非侵襲的に装着する有機e-skinセンサーを開発し、葉のひずみと表面温度を取得して表現型解析プラットフォームとして実証しているため、方法が中心的である。
abstractHere, we report an all-organic transparent plant e-skin by micropatterning poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on polydimethylsiloxane (PDMS) substrate.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Traditional single-point measurements fail to capture dynamic chemical responses of plants, which are complex, nonequilibrium biological systems. We report TETRIS ( t ime-resolved e lectrochemical t echnology for plant r oot environment i n s itu chemical sensing), a real-time chemical phenotyping system for continuously monitoring chemical signals in the often-neglected plant root environment. TETRIS consisted of low-cost, highly scalable screen-printed electrochemical sensors for monitoring concentrations of salt, pH, and H 2 O 2 in the root environment of whole plants, where multiplexing allowed for parallel sensing operation. TETRIS was used to measure ion uptake in tomato, kale, and rice and detected differences between nutrient and heavy metal ion uptake. Modulation of ion uptake with ion channel blocker LaCl 3 was monitored by TETRIS and machine learning used to predict ion uptake. TETRIS has the potential to overcome the urgent “bottleneck” in high-throughput screening in producing high-yielding plant varieties with improved resistance against stress.
Why it matches plant phenotyping methods植物の根圏における化学シグナルを連続測定するセンサー型フェノタイピングシステムを開発し、イオン吸収の測定と機械学習による予測まで扱っており、取得手法が研究の中心です。
abstractWe report TETRIS ( t ime-resolved e lectrochemical t echnology for plant r oot environment i n s itu chemical sensing), a real-time chemical phenotyping system for continuously monitoring chemical signals in the often-neglected plant root environment.
In the dataset presented in this article, samples belonging to one of the following crops, apple, broccoli, leek, and mushroom, were measured by hyperspectral cameras in the visible/near-infrared spectral domain (430-900 nm). The dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models. In particular, this dataset focuses on estimating dry matter content across various crops by a single model in a non-destructive way using hyperspectral measurements. This dataset contains extracted mean reflectance spectra for each sample (n=1028) and their respective dry matter content (%).
Why it matches plant phenotyping methods複数作物の果実・器官について、ハイパースペクトル画像から乾物含量を非破壊推定するデータセットを構築しており、形質取得・推定手法と再利用可能なベンチマークが研究の中心である。
abstractThe dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models.
Reproduction assets foundThe paper is a data descriptor for the SpectroFood hyperspectral dataset; all five Zenodo deposits (meta-dataset plus per-crop hyperspectral image data) are public, paper-specific phenotype/trait datasets with direct URLs in the Specifications Table.Dataset · publicce), Rc: corrected hyperspectral image.
Data source location
Data are stored at Agricultural University of Athens (AUA) premises. Iera Odos 75, 11855 Athens, Greece, Department of Horticultural Engineering
Data accessibility
Repository name:Zenodo
Table data
Data identification number: 10.5281/zenodo.8362947
Direct URL to data: https://zenodo.org/record/8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https:/Open asset ↗Zenodo · 10.5281/zenodo.8362947lines:1-65Dataset · publicOdos 75, 11855 Athens, Greece, Department of Horticultural Engineering
Data accessibility
Repository name:Zenodo
Table data
Data identification number: 10.5281/zenodo.8362947
Direct URL to data: https://zenodo.org/record/8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302Open asset ↗Zenodo · 10.5281/zenodo.10301753lines:1-65Dataset · publicdo
Table data
Data identification number: 10.5281/zenodo.8362947
Direct URL to data: https://zenodo.org/record/8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302386
1.
Value of the Data
•
Spectra were acquired using calibrated hyperspectral imaging systems under the sameOpen asset ↗Zenodo · 10.5281/zenodo.10302438lines:1-65Dataset · public8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302386
1.
Value of the Data
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Spectra were acquired using calibrated hyperspectral imaging systems under the same controlled conditions for four crops with high variation of dry matter values amongst the same crop and acrossOpen asset ↗Zenodo · 10.5281/zenodo.10302426lines:1-65Dataset · publicps://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302386
1.
Value of the Data
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Spectra were acquired using calibrated hyperspectral imaging systems under the same controlled conditions for four crops with high variation of dry matter values amongst the same crop and across all four.
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The dry matter content of the four crops is the common variable when considering the quality of theOpen asset ↗Zenodo · 10.5281/zenodo.10302386lines:1-65Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Jan 2024Computers and Electronics in Agriculture.
It is challenging to accurately and rapidly extract crops based on the ultra-high spatial resolution images of uncrewed aerial vehicle (UAV). Object-based image analysis (OBIA) was regarded as an effective technique for high-spatial-resolution image classification because of its ability to achieve high accuracy by integrating multi-dimensional features. In recent years, deep learning (DL) techniques, with their ability to automatically learn image features from a large number of images, have shown great potential for crop monitoring. However, a systematic comparison of these two mainstream methods for monitoring the crop phenotype has not been conducted. Therefore, this study compares the performance of two advanced methods, DL and OBIA, in individual cabbage plant detection tasks. The results show that the Mask R-CNN deep learning model outperforms the object-based image analysis-multilevel distance transform watershed segmentation (OBIA-MDTWS) method in crop extraction and counting, with an overall mean F1-Score, accuracy of 2.70, 4.15 percentage points higher, respectively. Moreover, the Mask R-CNN deep learning model has higher computing efficiency, which is 3.74 times higher than the OBIA-MDTWS model. In summary, this study shows that the Mask R-CNN deep learning model performs better in vegetable extraction and quantity estimation, providing technical support for subsequent field nursery management and fine planting.
Why it matches plant phenotyping methods個体キャベツの検出・計数という植物形態・個体数形質の抽出について、Mask R-CNNとOBIAを比較し、精度と計算効率を評価する手法検証が中心である。
abstracta systematic comparison of these two mainstream methods for monitoring the crop phenotype has not been conducted
An empirical growth-response model (GRM) that can accurately predict leafy vegetable (e.g., kailan) shoot fresh weight, in terms of photosynthetic photon flux density (PPFD) and certain cultivation duration counted from sowing, in an environment-controlled vertical farm, was developed. This GRM was constructed as the product of three independent models including light-time-biomass response model (LTBRM), dry-weight-based shoot/seedling ratio (DSSR) and shoot fresh/dry weight ratio (SFDR), which were established separately, through using various mathematical models to fit the experimental growth data and selecting the optimal ones, respectively. The robustness verification, and the validation tests on GRM proved that this model is qualified for precisely forecasting kailan shoot fresh weight at the seedling stage. The framework built in this study can be introduced as a universal modeling approach in indoor farming to (i) quickly assess seedling productivity throughout the farm once PPFD distribution and duration are known, (ii) cooperate with artificial intelligence technology for growth prediction, (iii) confirm the transplantation date according to designated transplanting criteria to minimize electric energy loss, and (iv) increase final productivity by 29.41% and profit by 12.99% in vertical farms using an appropriate strategy (taking kailan as an estimated example). GRM could thus act as an excellent auxiliary tool for monitoring plant growth; it can also be a strategy to boost vegetable production in resource-dependent regions to handle unexpected food supply chain disruptions.
Why it matches plant phenotyping methods植物のシュート生重量という明示的形質を予測する成長応答モデルを開発し、頑健性検証と妥当性検証を実施しているため、単なる生産実験ではなく計算的な表現型推定手法が中心である。
abstractAn empirical growth-response model (GRM) that can accurately predict leafy vegetable (e.g., kailan) shoot fresh weight
Timely detection of pest infestation in agricultural crops plays a pivotal role in the planning and execution of pest management interventions. In this study, a ground measured electromagnetic spectrum through hyperspectral sensing (400–2500 nm) was conducted in healthy and aphid-infested mustard crops in different regions of the Bharatpur district of Rajasthan state, India. The ground measured hyperspectral reflectance and its derivatives during the mustard aphid infestation period were used to identify the sensitive spectral regions in the electromagnetic spectrum concerning Aphid Infestation Severity Grade (AISG) to discriminate Lipaphis-infested mustard crops from the healthy ones. Further Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were utilized to identify specific spectral bands to differentiate the healthy from aphid-infested crops. The spectral regions of 493–497 nm (blue), 509–515 nm (green), 690–714 nm (red), 717–721 nm (red edge), and 752–756 nm (NIR) showed high correlation with AISG for reflectance, first and second order derivatives. Further analysis of the spectra using PCA and PLSR indicated that spectral bands of 679 nm, 746 nm, and 979 nm had high sensitivity for discriminating aphid-infested crops from the healthy ones. Average reflectance and various spectral indices such as ratio spectral index (RSI), difference spectral index (DSI), and normalized difference spectral index (NDSI) of identified spectral regions and absolute reflectance of identified specific spectral bands were used for predicting AISG. Several regression models, including PCR and PLSR, were examined to predict the AISG. PLSR was found to better predict infestation grade with RMSE of 0.66 and r2 0.71. Our outcomes counseled that hyperspectral reflectance data have the ability to detect aphid-infested severity in mustard.
Why it matches plant phenotyping methodsハイパースペクトル計測と回帰モデルにより、植物上のアブラムシ被害重症度（AISG）を推定する手法を開発・評価しており、植物状態の取得・抽出が中心である。
abstractThe ground measured hyperspectral reflectance and its derivatives during the mustard aphid infestation period were used to identify the sensitive spectral regions in the electromagnetic spectrum concerning Aphid Infestation Severity Grade (AISG)
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Traditional methods for assessing plant health often lack the necessary attributes for continuous and non-destructive monitoring. In this pilot study, we present a novel technique utilizing a customized fiber optic probe based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) with a contact force control unit for non-invasive and continuous plant health monitoring. We also developed a normalized difference mid-infrared reflectance index through statistical analysis of spectral features, enabling differentiation of drought and age conditions in plants. Our research aims to characterize phytochemicals and plant endogenous status optically, addressing the need for improved analytical measurement methods for in situ plant health assessment. The probe configuration was optimized with a triple-loop tip and a 3 N contact force, allowing sensitive measurements while minimizing leaf damage. By combining polycrystalline and chalcogenide fiber probes, a comprehensive wavenumber range analysis (4000–900 cm −1 ) was achieved. Results revealed significant variations in phytochemical composition among plant species, for example, red spinach with the highest polyphenolic content and green kale with the highest lignin content. Petioles displayed higher lignin and cellulose absorbance values compared to veins. The technique effectively monitored drought stress on potted green bok choy plants in situ, facilitating the quantification of changes in water content, antioxidant activity, lignin, and cellulose levels. This research represents the first demonstration of the potential of fiber optic ATR-FTIR probes for non-invasive and rapid plant health measurements, providing insights into plant health and advancements in quantitative monitoring for indoor farming practices, bioanalytical chemistry, and environmental sciences.
Why it matches plant phenotyping methods植物の健康状態・乾燥ストレス・水分や生化学的状態を非侵襲的に定量する光ファイバーATR-FTIRプローブと指標を開発・適用しており、植物フェノタイピング手法が中心である。
abstractwe present a novel technique utilizing a customized fiber optic probe based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) with a contact force control unit for non-invasive and continuous plant health monitoring.
The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R 2 ) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R 2 greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest.
Why it matches plant phenotyping methodsUAV画像から個体特徴を自動抽出し、収穫重量という植物形質を予測する手法が研究の中心であるため。
abstractwe propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight.
Reproduction assets foundThe paper's analysis code is publicly available on GitHub with explicit availability language. The UAV imagery (RGB/multispectral orthomosaics and point cloud data) is only available upon reasonable request from the corresponding author, so it does not qualify as a public asset.Code · publicAll code associated with the current study is available at: https://github.com/anaguilarar/CC_Weight_Prediction .Open asset ↗anaguilarar/CC_Weight_Predictionlines:155-233Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 15 Sept 2026
The utilization of 3-dimensional point cloud technology for non-invasive measurement of plant phenotypic parameters can furnish important data for plant breeding, agricultural production, and diverse research applications. Nevertheless, the utilization of depth sensors and other tools for capturing plant point clouds often results in missing and incomplete data due to the limitations of 2.5D imaging features and leaf occlusion. This drawback obstructed the accurate extraction of phenotypic parameters. Hence, this study presented a solution for incomplete flowering Chinese Cabbage point clouds using Point Fractal Network-based techniques. The study performed experiments on flowering Chinese Cabbage by constructing a point cloud dataset of their leaves and training the network. The findings demonstrated that our network is stable and robust, as it can effectively complete diverse leaf point cloud morphologies, missing ratios, and multi-missing scenarios. A novel framework is presented for 3D plant reconstruction using a single-view RGB-D (Red, Green, Blue and Depth) image. This method leveraged deep learning to complete localized incomplete leaf point clouds acquired by RGB-D cameras under occlusion conditions. Additionally, the extracted leaf area parameters, based on triangular mesh, were compared with the measured values. The outcomes revealed that prior to the point cloud completion, the R 2 value of the flowering Chinese Cabbage's estimated leaf area (in comparison to the standard reference value) was 0.9162. The root mean square error (RMSE) was 15.88 cm 2 , and the average relative error was 22.11%. However, post-completion, the estimated value of leaf area witnessed a significant improvement, with an R 2 of 0.9637, an RMSE of 6.79 cm 2 , and average relative error of 8.82%. The accuracy of estimating the phenotypic parameters has been enhanced significantly, enabling efficient retrieval of such parameters. This development offers a fresh perspective for non-destructive identification of plant phenotypes.
Why it matches plant phenotyping methods深層学習による葉の点群補完と3D再構成を開発し、葉面積推定を比較検証しており、植物表現型取得法が研究の中心である。
abstractThis method leveraged deep learning to complete localized incomplete leaf point clouds acquired by RGB-D cameras under occlusion conditions.
Kimchi cabbage (Brassica rapa pekinensis), one of the main agricultural products in Korea, is susceptible to downy mildew disease infections. Infected plants develop yellow spots (chlorosis) on the upper (adaxial) side of the infected leaf, undermining cabbage production and quality. An early detection method to recognize and treat the disease is crucial to prevent downy mildew and lessen its physical effects on plants. Hyperspectral imaging can capture data from a broad spectrum, which can be utilized to detect disease occurrence before any visible symptoms appear. Combining a hyperspectral camera with an unmanned aerial vehicle (UAV) can provide a non-destructive, field-scale disease detection system. In this study, three-dimensional (3D) convolutional neural network (CNN) models were used to simultaneously account for the spectral and spatial features of the disease to enable automatic disease detection. Using a 3D-residual network (ResNet) CNN with four residual blocks, each followed by a rectified linear unit activation function and a max-pooling layer, helped achieve an overall accuracy of 0.876 and a diseased class accuracy of 0.873. Disease severity was estimated by grouping nearby diseased leaves using the density-based spatial clustering of applications with noise clustering algorithm to achieve a 27.07 % relative error or a 1.08 level difference from the actual.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と葉セグメンテーション、3D-ResNetを用いて、植物病害の検出と病勢（severity）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。
abstractAn early detection method to recognize and treat the disease is crucial to prevent downy mildew and lessen its physical effects on plants.
Agriculture plays a pivotal role in the economies of developing countries by providing livelihoods, sustenance, and employment opportunities in rural areas. However, crop diseases pose a significant threat to both farmers' incomes and food security. Furthermore, these diseases also show adverse effects on human health by causing various illnesses. Till date, only a limited number of studies have been conducted to identify and classify diseased cauliflower plants but they also face certain challenges such as insufficient disease surveillance mechanisms, the lack of comprehensive datasets that are properly labelled as well as are of high quality, and the considerable computational resources that are necessary for conducting thorough analysis. In view of the aforementioned challenges, the primary objective of this manuscript is to tackle these significant concerns and enhance understanding regarding the significance of cauliflower disease identification and detection in rural agriculture through the use of advanced deep transfer learning techniques. The work is conducted on the four classes of cauliflower diseases i.e. Bacterial spot rot, Black rot, Downy Mildew, and No disease which are taken from VegNet dataset. Ten deep transfer learning models such as EfficientNetB0, Xception, EfficientNetB1, MobileNetV2, EfficientNetB2, DenseNet201, EfficientNetB3, InceptionResNetV2, EfficientNetB4, and ResNet152V2, are trained and examined on the basis of root mean square error, recall, precision, F1-score, accuracy, and loss. Remarkably, EfficientNetB1 achieved the highest validation accuracy (99.90%), lowest loss (0.16), and root mean square error (0.40) during experimentation. It has been observed that our research highlights the critical role of advanced CNN models in automating cauliflower disease detection and classification and such models can lead to robust applications for cauliflower disease management in agriculture, ultimately benefiting both farmers and consumers.
Why it matches plant phenotyping methodsカリフラワー植物の病害状態を画像ベースの深層学習で検出・分類する方法が研究の中心であり、植物病害表現型の取得・推定に該当する。
abstractthe primary objective of this manuscript is to tackle these significant concerns and enhance understanding regarding the significance of cauliflower disease identification and detection
The development of Pakcoy cultivation holds good prospects, as seen from the demand for vegetable commodities in Indonesia. Its cultivation is consistently rising in terms of volume and value of vegetable imports. However, the cultivation process encounters multiple issues caused by pests and diseases. In addition, the volatile climate in Indonesia has resulted in uninterrupted pest development and the potential decline of Pakcoy’s productivity. Therefore, the detection system for pests and diseases in the Pakcoy plant is called upon to accurately and quickly assist farmers in determining the right treatment, thereby reducing economic losses and producing abundant quality crops. A web-based application with several well-known Convolutional Neural Network (CNN) were incorporated, such as MobileNetV2, GoogLeNet, and ResNet101. A total of 1,226 images were used for training, validating, and testing the dataset to address the problem in this study. The dataset consisted of several plant conditions with leaf miners, cabbage butterflies, powdery mildew disease, healthy plants, and multiple data labels for pests and diseases presented in the individual image. The results show that the MobileNetV2 provides a minimum loss compared to GoogLeNet and ResNet-101 with scores of 0.076, 0.239, and 0.209, respectively. Since the MobileNetV2 architecture provides a good model, the model was carried out to be integrated and tested with the web-based application. The testing accuracy rate reached 98% from the total dataset of 70 testing images. In this direction, MobileNetV2 can be a viable method to be integrated with web-based applications for classifying an image as the basis for decision-making.
Why it matches plant phenotyping methods植物画像から病害・害虫による状態を分類するCNNとWebアプリケーションを開発・評価しており、植物の病害状態推定が中心的な方法論的貢献である。
abstractA web-based application with several well-known Convolutional Neural Network (CNN) were incorporated, such as MobileNetV2, GoogLeNet, and ResNet101.
An accurate assessment of vegetable yield is essential for agricultural production and management. One approach to estimate yield with remote sensing is via vegetation indices, which are selected in a statistical and empirical approach, rather than a mechanistic way. This study aimed to estimate the dry matter of Choy Sum by both a causality-guided intercepted radiation-based model and a spectral reflectance-based model and compare their performance. Moreover, the effect of nitrogen (N) rates on the radiation use efficiency ( RUE ) of Choy Sum was also evaluated. A 2-year field experiment was conducted with different N rate treatments (0 kg/ha, 25 kg/ha, 50 kg/ha, 100 kg/ha, 150 kg/ha, and 200 kg/ha). At different growth stages, canopy spectra, photosynthetic active radiation, and canopy coverage were measured by RapidScan CS-45, light quantum sensor, and camera, respectively. The results reveal that exponential models best match the connection between dry matter and vegetation indices, with coefficients of determination ( R 2 ) all below 0.80 for normalized difference red edge (NDRE), normalized difference vegetation index (NDVI), red edge ratio vegetation index (RERVI), and ratio vegetation index (RVI). In contrast, accumulated intercepted photosynthetic active radiation ( Aipar ) showed a significant linear correlation with the dry matter of Choy Sum, with root mean square error ( RMSE ) of 9.4 and R 2 values of 0.82, implying that the Aipar -based estimation model performed better than that of spectral-based ones. Moreover, the RUE of Choy Sum was significantly affected by the N rate, with 100 kg N/ha, 150 kg N/ha, and 200 kg N/ha having the highest RUE values. The study demonstrated the potential of Aipar -based models for precisely estimating the dry matter yield of vegetable crops and understanding the effect of N application on dry matter accumulation of Choy Sum.
Why it matches plant phenotyping methods植生指数および積算遮断光合成有効放射からチョイサムの乾物収量を推定するモデルを開発・比較し、RMSEやR²で性能評価しており、植物形質取得手法が中心である。
abstractThis study aimed to estimate the dry matter of Choy Sum by both a causality-guided intercepted radiation-based model and a spectral reflectance-based model and compare their performance.
On-farm food loss (i.e., grade-out vegetables) is a difficult challenge in sustainable agricultural systems. The simplest method to reduce the number of grade-out vegetables is to monitor and predict the size of all individuals in the vegetable field and determine the optimal harvest date with the smallest grade-out number and highest profit, which is not cost-effective by conventional methods. Here, we developed a full pipeline to accurately estimate and predict every broccoli head size ( n > 3,000) automatically and nondestructively using drone remote sensing and image analysis. The individual sizes were fed to the temperature-based growth model and predicted the optimal harvesting date. Two years of field experiments revealed that our pipeline successfully estimated and predicted the head size of all broccolis with high accuracy. We also found that a deviation of only 1 to 2 days from the optimal date can considerably increase grade-out and reduce farmer's profits. This is an unequivocal demonstration of the utility of these approaches to economic crop optimization and minimization of food losses.
Why it matches plant phenotyping methodsドローンリモートセンシングと画像解析により、個々のブロッコリー頭部サイズを自動・非破壊推定するパイプラインを開発・検証しており、植物形質取得が中心的です。
abstractwe developed a full pipeline to accurately estimate and predict every broccoli head size ( n > 3,000) automatically and nondestructively using drone remote sensing and image analysis.
Reproduction assets foundThe authors' full phenotyping/analysis pipeline source code is publicly available on GitHub (UAVbroccoli). Original drone image data (224 GB for 2020, 72 GB for 2021) exist but are only available upon request via Google Drive. Generic tools (YOLOv5, BiSeNet, labelme, EasyIDP, scikit-image) are third-party libraries, soCode · publicurvey powered by ML/DL for sustainable agricultural development, there are some limitations to its use. First, our system is neither fully automated nor app-based; therefore, farmers without computer science backgrounds cannot use this system directly in their own fields. However, because the source code is open to the public ( https://github.com/UTokyo-FieldPhenomics-Lab/UAVbroccoli ), local agricultural institutes and agricultural companies are able to modify and use the system according to their target. This study is definitely not a one-stop solution, but is a pioneer in real agriculture applications. Second, unlike traditional manual methods with limited throughput, the proposed method Open asset ↗UTokyo-FieldPhenomics-Lab/UAVbroccolilines:291-292Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Sept 2023Computers and Electronics in Agriculture.
The trichome trait is one of the important phenotypes for variety classification and breeding improvement of Chinese cabbage (Brassica campestris L. syn. B. rapa). However, obtaining the number of trichomes per unit area on leaves is a time-consuming and laborious detection work, especially when hundreds of germplasm resources need to be evaluated. Therefore, this study constructed the first diverse Chinese cabbage trichome dataset called CCTD with10,955 RGB images and proposed a deep learning model for trichome detection called TRI-YOLOv8. By adding the RepVGG module in the Backbone, adding a new detection layer in the Neck and replacing the loss function with Normalized Gaussian Wasserstein Distance Loss, the detection performance of the model for small trichomes was effectively improved. At the same time, Ghost convolution was used to reduce memory consumption and speed up inference. The experimental results showed that TRI-YOLOv8 outperformed other classical detection models. AP₅₀ was as high as 94.4%, which was 3.8% higher than YOLOv8n. Furthermore, the number of trichomes per unit area was obtained by TRI-YOLOv8 and combined with genome-wide association study and selective sweep analysis, the candidate gene BraA03g029740.3.5C (STP7) was screened out. Overall, this study achieved the accurate detection and counting of trichomes, and provided a feasible plan for breeders to digitally analyze phenotypes, automatically identify and screen Chinese cabbage germplasm resources.
Why it matches plant phenotyping methods中国白菜葉のトライコーム数という植物形質を、RGB画像・三眼ステレオ顕微鏡・深層学習モデルで検出および計数する方法を開発し、データセットと性能評価も提示しており、表現型取得が研究の中心である。
abstractthis study constructed the first diverse Chinese cabbage trichome dataset called CCTD with10,955 RGB images and proposed a deep learning model for trichome detection called TRI-YOLOv8.
Agriculture plays a pivotal role in food security and food security is challenged by pests and pathogens. Due to these challenges, the yields and quality of agricultural production are reduced and, in response, restrictions in the trade of plant products are applied. Governments have collaborated to establish robust phytosanitary measures, promote disease surveillance, and invest in research and development to mitigate the impact on food security. Classic as well as modernized tools for disease diagnosis and pathogen surveillance do exist, but most of these are time-consuming, laborious, or are less sensitive. To that end, we propose the innovative application of a hybrid imaging approach through the combination of confocal fluorescence and optoacoustic imaging microscopy. This has allowed us to non-destructively detect the physiological changes that occur in plant tissues as a result of a pathogen-induced interaction well before visual symptoms occur. When broccoli leaves were artificially infected with Xanthomonas campestris pv. campestris ( Xcc ), eventually causing an economically important bacterial disease, the induced optical absorption alterations could be detected at very early stages of infection. Therefore, this innovative microscopy approach was positively utilized to detect the disease caused by a plant pathogen, showing that it can also be employed to detect quarantine pathogens such as Xylella fastidiosa .
Why it matches plant phenotyping methods植物組織の病原体誘導性の生理変化を、蛍光・光音響のハイブリッド顕微鏡で非破壊かつ早期に検出する方法が研究の中心であり、植物病害状態のフェノタイピングに該当する。
abstractwe propose the innovative application of a hybrid imaging approach through the combination of confocal fluorescence and optoacoustic imaging microscopy.
While point clouds hold promise for measuring the geometrical features of 3D objects, their application to plants remains problematic. Plants are three dimensional (3D) organisms whose morphology is complex, varies from one individual to another and changes over time. Objective measurement of attributes in 3D point cloud domain is increasingly attractive as techniques improve the accuracy and reduce computational time. Analysis of point cloud data, however, is not straightforward, due to its discrete nature, imaging noise and cluttered background. In this paper, we introduce a robust method for the direct analysis of plants of point cloud data. To this end, we generalise the random sample consensus (RANSAC) algorithm for the analysis of 3D point cloud data and then use it to model different plant organs. Since 3D point clouds are obtained from multi-view stereo images, they are often contaminated with a considerable level of noise, distortions and out-of-distribution points. Key to our approach is the use of the RANSAC algorithm on 3D point cloud, making our technique more robust to undesirable outliers. We tested our proposed method on Brassica and grapevine by comparing the estimated measurements extracted from the models with manual ones taken from the actual plants. Our proposed method achieved R2>0.90 for measured diameters of branches and stems in Brassica while it yielded R2>0.91 for the measured leaf angles of grapevine and branch angles of Brassica. In all cases, the approach produced stable performance under imaging noise and cluttered background while the conventional methods often failed to work.
Why it matches plant phenotyping methods3D点群から植物器官の形態形質を抽出するRANSAC手法を開発し、実測値との比較で検証しており、植物フェノタイピング手法が研究の中心である。
abstractwe generalise the random sample consensus (RANSAC) algorithm for the analysis of 3D point cloud data and then use it to model different plant organs.
Crop water stress index (CWSI) is a reliable, economic and non-destructive method of monitoring the onset of water stress for irrigation scheduling purposes. Its application, however, is limited due to the need of obtaining the baseline canopy temperatures. This study developed a self-organizing map (SOM) based model to predict the CWSI using microclimatic variables, namely air temperature, canopy temperature and relative humidity. The canopy temperature measurements were made from Indian mustard crop grown in a humid sub-tropical agro-climate during the 2017 and 2018 cropping seasons. Eight levels of irrigation treatments (I₁ – I₈) based on maximum allowable depletion of available soil water were considered in the study. The CWSI for treatments I₂ – I₇ was computed using the empirical approach based on the experimentally measured baseline canopy temperatures from treatments I₁ and I₈. The number of data points used was 1260 and 1350 for model training and testing, respectively. The developed SOM model was evaluated using the error indices Nash-Sutcliffe efficiency (NSE), bias error (BE), absolute error (AE), and coefficient of determination (R²). The SOM predicted CWSI presented a good agreement with the baseline computed CWSI values during model training (R² = 0.98, NSE = 0.97, AE = 0.018, BE = 0.0004) and testing (R² = 0.98, NSE = 0.98, AE = 0.018, BE = 0.002). Treatment specific analysis was conducted to evaluate the performance of SOM predicted CWSI for different irrigation levels. Results indicated that the presence of zero CWSI values in a significant proportion in the dataset impacted the model prediction performance at low CWSI (<0.1) values, with an R² of 0.71 during testing. Nonetheless, the model performed exceptionally well in predicting CWSI values between 0.1 and 0.6 (R² = 0.93–0.98, NSE = 0.92–0.98, AE = 0.013–0.015, BE = −0.002–0.004), which is the commonly observed CWSI range for irrigation scheduling in field crops. For better understanding, the developed SOM model was also analysed through the component planes, U-matrix, clusters and high-low bar planes in the cluster features.
Why it matches plant phenotyping methodsSOMモデルによる作物水ストレス指数（CWSI）の推定法を開発し、訓練・テストデータで性能評価している。植物の生理状態を定量化する手法が研究の中心である。
abstractThis study developed a self-organizing map (SOM) based model to predict the CWSI using microclimatic variables, namely air temperature, canopy temperature and relative humidity.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Plant phenotyping is essential for understanding and managing plant growth and development. 3D point clouds provide a better understanding of plant 3D structures. Point cloud segmentation is the basis for studying the 3D structure of plants through 3D point clouds, and accurate point cloud segmentation is crucial for extracting relevant phenotypic parameters. In this study, cabbage was used as an example, and a plant point cloud segmentation method combining deep learning algorithms and clustering algorithms was proposed. Specifically, a cabbage point cloud dataset was constructed using a 3D scanning platform. The ASAP attention module was incorporated into the PointNet++ model, resulting in the improved ASAP-PointNet model. Superior semantic segmentation performance on the cabbage point cloud dataset was demonstrated by this model. The workflow of the DBSCAN algorithm was also optimized, which exhibited enhanced performance in organ-level plant point cloud segmentation experiments. Subsequently, five phenotypic features were extracted. The experimental results revealed that an accuracy of 0.95 and an intersection over union (IoU) of 0.86 for semantic segmentation were achieved by the ASAP-PointNet model. The correlation coefficients between the four phenotype parameters (plant height, leaf length, leaf width, and leaf area) and their corresponding measured values were 0.96, 0.91, 0.95, and 0.94, respectively. An automated data analysis, from plant 3D point clouds to phenotypic parameters, is enabled by the proposed method, which serves as a valuable reference for plant phenotype research.
Why it matches plant phenotyping methods3D点群の分割・解析手法を開発し、植物器官から複数の表現型形質を自動抽出・検証しており、フェノタイピング手法が研究の中心である。
abstracta plant point cloud segmentation method combining deep learning algorithms and clustering algorithms was proposed
Purple Chinese cabbage (PCC) has become a new breeding trend due to its attractive color and high nutritional quality since it contains abundant anthocyanidins. With the aim of rapid evaluation of PCC anthocyanidins contents and screening of breeding materials, a fast quantitative detection method for anthocyanidins in PCC was established using Near Infrared Spectroscopy (NIR). The PCC samples were scanned by NIR, and the spectral data combined with the chemometric results of anthocyanidins contents obtained by high-performance liquid chromatography were processed to establish the prediction models. The content of cyanidin varied from 93.5 mg/kg to 12,802.4 mg/kg in PCC, while the other anthocyanidins were much lower. The developed NIR prediction models on the basis of partial least square regression with the preprocessing of no-scattering mode and the first-order derivative showed the best prediction performance: for cyanidin, the external correlation coefficient (RSQ) and standard error of cross-validation (SECV) of the calibration set were 0.965 and 693.004, respectively; for total anthocyanidins, the RSQ and SECV of the calibration set were 0.966 and 685.994, respectively. The established models were effective, and this NIR method, with the advantages of timesaving and convenience, could be applied in purple vegetable breeding practice.
Why it matches plant phenotyping methods紫キャベツのアントシアニン含量という植物形質を、NIR分光とケモメトリクスで迅速推定する方法を開発・検証しており、表現型取得法が研究の中心である。
abstracta fast quantitative detection method for anthocyanidins in PCC was established using Near Infrared Spectroscopy (NIR).
Reproduction assets foundThe paper's supplementary material (Table S1) contains the paper-specific HPLC-measured anthocyanidin contents for the 106 purple Chinese cabbage samples used to build the NIR prediction models, and is publicly downloadable from MDPI. No author analysis code, spectral files, or trained model files are explicitly sharedSupplement · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/foods12091922/s1 , Table S1: Contents of anthocyanidins in purple Chinese cabbage analyzed by high-performance liquid chromatography (mg/kg).
Click here for additional data file.
Author Contributions
Conceptualization, D.-S.Z. and H.-J.H.; software, G.-M.L.; validation, Y.-Q.W. and L.-P.H.; formal analysis, G.-M.L. and X.-Z.Z.; invOpen asset ↗lines:56-122Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
It is challenging to accurately and rapidly extract crops based on the ultra-high spatial resolution images of uncrewed aerial vehicle (UAV). Object-based image analysis (OBIA) was regarded as an effective technique for high-spatial-resolution image classification because of its ability to achieve high accuracy by integrating multi-dimensional features. In recent years, deep learning (DL) techniques, with their ability to automatically learn image features from a large number of images, have shown great potential for crop monitoring. However, a systematic comparison of these two mainstream methods for monitoring the crop phenotype has not been conducted. Therefore, this study compares the performance of two advanced methods, DL and OBIA, in individual cabbage plant detection tasks. The results show that the Mask R-CNN deep learning model outperforms the object-based image analysis-multilevel distance transform watershed segmentation (OBIA-MDTWS) method in crop extraction and counting, with an overall mean F1-Score, accuracy of 2.70, 4.15 percentage points higher, respectively. Moreover, the Mask R-CNN deep learning model has higher computing efficiency, which is 3.74 times higher than the OBIA-MDTWS model. In summary, this study shows that the Mask R-CNN deep learning model performs better in vegetable extraction and quantity estimation, providing technical support for subsequent field nursery management and fine planting.
Why it matches plant phenotyping methods個体キャベツの画像検出・抽出・計数手法を比較し、性能と計算効率を評価しており、植物フェノタイピング手法が研究の中心である。
abstractHowever, a systematic comparison of these two mainstream methods for monitoring the crop phenotype has not been conducted.
Single-plant growth monitoring aids precision agricultural decision-making to reduce the costs related to pesticides, fertilizers, and labor. This study integrated visible/multi-spectral UAV imagery with two deep learning methods, object detection and semantic segmentation, to obtain a visualized map that could assist in precise field monitoring and management for broccoli cultivation. For plant detection, feature extraction was conducted using multiscale dilated convolution, which enabled the effective detection of broccoli in images taken under different photographic conditions and resolutions. Two crops of broccoli (cultivar: Broccoli No. 42) were planted in 2020 at Taichung Agricultural Research and Extension Station, in which the first crop was treated as the training data. The detection of individual broccoli plants was processed using a feature extraction architecture of the AlexNet-Like backend at the SSD frontend, where the input scale of the detector complies with the original SSD architecture. For the model test on the second crop, the recall and precision were 98.58% and 99.73%, respectively, after histogram matching based on the first crop images. Moreover, the proposed approach was applied to a real farming field to verify its robustness across different conditions, and achieved a recall of 61.13% using dilated convolution. This study also generated a visualized growth map on a single-plant basis, which allows operators to detect growth situations, such as uneven irrigation or fertilization and necrosis and apoptosis, to greatly enhance the viability of precision agriculture in the calculation of unit yield and intragroup differences for a regime. The proposed approach can be used to determine the optimal amount of fertilization and observe the size of broccoli heads to determine the optimal harvest time. Expectedly, the method may also be applied to the monitoring and management of other crops to improve the efficiency and reduce the labor demand for precision agriculture.
Why it matches plant phenotyping methodsUAV画像と深層学習によって個体検出・成長状態・ブロッコリー頭部サイズを推定する手法を開発し、精度検証と実圃場での頑健性評価を行っており、植物表現型取得が中心である。
abstractThis study integrated visible/multi-spectral UAV imagery with two deep learning methods, object detection and semantic segmentation, to obtain a visualized map that could assist in precise field monitoring and management for broccoli cultivation.
Plants are non-equilibrium systems consisting of time-dependent biological processes. Phenotyping of chemical responses, however, is typically performed using plant tissues, which behave differently to whole plants, in one-off measurements. Single point measurements cannot capture the information rich time-resolved changes in chemical signals in plants associated with nutrient uptake, immunity or growth. In this work, we report a high-throughput, modular, real-time chemical phenotyping platform for continuous monitoring of chemical signals in the often-neglected root environment of whole plants: TETRIS ( T ime-resolved E lectrochemical T echnology for plant R oot I n-situ chemical S ensing). TETRIS consists of screen-printed electrochemical sensors for monitoring concentrations of salt, pH and H 2 O 2 in the root environment of whole plants. TETRIS can detect time-sensitive chemical signals and be operated in parallel through multiplexing to elucidate the overall chemical behavior of living plants. Using TETRIS, we determined the rates of uptake of a range of ions (including nutrients and heavy metals) in Brassica oleracea acephala. We also modulated ion uptake using the ion channel blocker LaCl 3 , which we could monitor using TETRIS. We developed a machine learning model to predict the rates of uptake of salts, both harmful and beneficial, demonstrating that TETRIS can be used for rapid mapping of ion uptake for new plant varieties. TETRIS has the potential to overcome the urgent “bottleneck” in high-throughput screening in producing high yielding plant varieties with improved resistance against stress.
Why it matches plant phenotyping methods植物全体の根圏における化学シグナルを連続測定する高スループット表現型解析プラットフォームを開発し、イオン吸収速度の推定と機械学習による予測まで行っており、フェノタイピング手法が研究の中心である。
abstractwe report a high-throughput, modular, real-time chemical phenotyping platform for continuous monitoring of chemical signals in the often-neglected root environment of whole plants
Oxidative stress is closely related to the crop health status under stress conditions. H 2 O 2 is an important signaling molecule in plants under stress. Therefore, monitoring H 2 O 2 fluctuations is of great significance when risk-assessing oxidative stress. However, few fluorescent probes have been reported for the in situ tracking of H 2 O 2 fluctuations in crops. Herein, we designed a "turn-on" NIR fluorescent probe (DRP-B) to detect and in situ-image H 2 O 2 in living cells and crops. DRP-B exhibited good detection performance for H 2 O 2 and could image endogenous H 2 O 2 in living cells. More importantly, it could semi-quantitatively visualize H 2 O 2 in cabbage roots under abiotic stress. Visualization of H 2 O 2 in cabbage roots revealed H 2 O 2 upregulation in response to adverse environments (metals, flood, and drought). This study provides a new method for risk-assessing oxidative stress in plants under abiotic stress and is expected to provide guidance for the development of new antioxidant defense strategies to enhance plant resistance and crop productivity.
Why it matches plant phenotyping methods植物の酸化ストレス状態をH2O2のin situ蛍光イメージングで可視化する新規プローブを開発しており、植物状態の取得法が研究の中心である。
abstractwe designed a "turn-on" NIR fluorescent probe (DRP-B) to detect and in situ-image H 2 O 2 in living cells and crops.
Chinese cabbage (Brassica rapa L. ssp. pekinensis), a leafy vegetable, exhibits a range of leaf colors, with the dark green varieties being favored by consumers. Manual visual identification of Chinese cabbage leaf color phenotypes is subjective and it is difficult to distinguish between subtle differences in leaf color, posing challenges for precision breeding. In this study, we constructed a partial least squares discriminant analysis (PLS-DA) leaf color identification model and compared four classification methods for leaf color, namely red, green, and blue (RGB) channels, hue, saturation, and lightness (HSL) color space, multi-spectrum and data-fusion. The PLS-DA supervised leaf color phenotype identification model based on data fusion can improve the recognition rate by 1%−13% compared to a single spectral model. To further validate the model, we conducted a bulked segregant analysis (BSA) of a mixed pool of a Chinese cabbage F2 population (F2-449) using whole-genome sequencing. The candidate locus related to dark green leaf color was reduced by 9.76 Mb compared to the manual visual inspection which provides convenience for the localization of candidate genes. Therefore, the development of a precise phenotypic identification system for Chinese cabbage that can distinguish subtle leaf color differences using high-throughput phenotype analysis technology is of great significance and agricultural practical value for the mining of high-throughput genomic data.
Why it matches plant phenotyping methodsマルチスペクトル画像とPLS-DAを用いて、白菜の葉色という植物表現型を高スループットかつ高精度に識別する手法を開発・検証しており、表現型取得・抽出法が研究の中心である。
abstractwe constructed a partial least squares discriminant analysis (PLS-DA) leaf color identification model and compared four classification methods for leaf color
Fruit volume and leaf area are important indicators to draw conclusions about the growth condition of the plant. However, the current methods of manual measuring morphological plant properties, such as fruit volume and leaf area, are time consuming and mainly destructive. In this research, an image-based approach for the non-destructive determination of fruit volume and for the total leaf area over three growth stages for cabbage ( brassica oleracea ) is presented. For this purpose, a mask-region-based convolutional neural network (Mask R-CNN) based on a Resnet-101 backbone was trained to segment the cabbage fruit from the leaves and assign it to the corresponding plant. Combining the segmentation results with depth information through a structure-from-motion approach, the leaf length of single leaves, as well as the fruit volume of individual plants, can be calculated. The results indicated that even with a single RGB camera, the developed methods provided a mean accuracy of fruit volume of 87% and a mean accuracy of total leaf area of 90.9%, over three growth stages on an individual plant level.
Why it matches plant phenotyping methods画像分割と深度情報を組み合わせ、キャベツ個体の葉面積・果実体積を非破壊推定する手法を開発・精度評価しており、植物表現型取得が研究の中心です。
abstractan image-based approach for the non-destructive determination of fruit volume and for the total leaf area over three growth stages for cabbage
The phenotypic parameters of crop plants can be evaluated accurately and quickly using an unmanned aerial vehicle (UAV) equipped with imaging equipment. In this study, hundreds of images of Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) germplasm resources were collected with a low-cost UAV system and used to estimate cabbage width, length, and relative chlorophyll content (soil plant analysis development [SPAD] value). The super-resolution generative adversarial network (SRGAN) was used to improve the resolution of the original image, and the semantic segmentation network Unity Networking (UNet) was used to process images for the segmentation of each individual Chinese cabbage. Finally, the actual length and width were calculated on the basis of the pixel value of the individual cabbage and the ground sampling distance. The SPAD value of Chinese cabbage was also analyzed on the basis of an RGB image of a single cabbage after background removal. After comparison of various models, the model in which visible images were enhanced with SRGAN showed the best performance. With the validation set and the UNet model, the segmentation accuracy was 94.43%. For Chinese cabbage dimensions, the model was better at estimating length than width. The R 2 of the visible-band model with images enhanced using SRGAN was greater than 0.84. For SPAD prediction, the R 2 of the model with images enhanced with SRGAN was greater than 0.78. The root mean square errors of the 3 semantic segmentation network models were all less than 2.18. The results showed that the width, length, and SPAD value of Chinese cabbage predicted using UAV imaging were comparable to those obtained from manual measurements in the field. Overall, this research demonstrates not only that UAVs are useful for acquiring quantitative phenotypic data on Chinese cabbage but also that a regression model can provide reliable SPAD predictions. This approach offers a reliable and convenient phenotyping tool for the investigation of Chinese cabbage breeding traits.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像、SRGAN、UNetを用いて個体ごとの形態とSPADを推定し、手測定と検証したフェノタイピング手法が研究の中心である。
abstractThe super-resolution generative adversarial network (SRGAN) was used to improve the resolution of the original image, and the semantic segmentation network Unity Networking (UNet) was used to process images for the segmentation of each individual Chinese cabbage.
Cadmium (Cd) is a toxic element that can accumulate in edible plant tissues and negatively impact human health. Traditional Cd quantification methods are time-consuming, expensive, and generate a lot of toxic waste, slowing development of methods to reduce uptake. The objective of this study was to determine whether hyperspectral imaging (HSI) and machine learning (ML) can be used to predict Cd concentrations in plants using kale ( Brassica oleracea ) and basil ( Ocimum basilicum ) as model crops. The experiments were conducted in an automated phenotyping facility where all environmental conditions except soil Cd concentration were kept constant. Cd concentrations were determined at harvest using traditional methods and used to train the ML models with data collected from the imaging sensor. Visible/near infrared (VNIR) images were also collected at harvest and processed to calculate reflectance at 473 bands between 400 to 998 nm. All reflectance spectra were subject to the feature selection algorithm ReliefF and Principal Component Analysis (PCA) to generate data and provide input to evaluate three ML classification models: artificial neural network (ANN), ensemble learning (EL), and support vector machine (SVM). Plants were categorized according to Cd concentrations higher or lower than the safety threshold of 0.2 mg kg -1 Cd. Wavelengths with the highest ranks for Cd detection were between 519 and 574, and 692 and 732 nm, indicating that Cd content likely altered the plants' chlorophyll content and altered leaf internal structure. All models were able to sort the plants into groups, though the model with the best F1 score was the ANN for the validation subset that utilized reflectance from all wavelengths. This study demonstrates that HSI and ML are promising technologies for the fast and precise diagnosis of Cd in leafy green plants, though additional studies are needed to adapt this approach for more complex field environments.
Why it matches plant phenotyping methods葉菜のCd濃度をHSI画像と機械学習から推定する手法が研究の中心であり、植物の化学的状態を非破壊的に評価する方法としてモデル比較・検証も行っている。
abstractThe objective of this study was to determine whether hyperspectral imaging (HSI) and machine learning (ML) can be used to predict Cd concentrations in plants
Premise With modern advances in genetic sequencing technology, plant phenotyping has become a substantial bottleneck in crop improvement programs. Traditionally, researchers have manually measured phenotypic traits to help determine genotype-phenotype relationships, but manual measurements can be time consuming and expensive. Recently, automated phenotyping systems have increased the spatial and temporal density of measurements, but most of these systems are extremely expensive and require specialized expertise. In the present paper, we develop and validate a low-cost, scalable, high-throughput phenotyping (HTP) system for automating the measurement of foliar area and greenness. Methods During a greenhouse experiment on the effects of abiotic stress on Brassica rapa , we collected images of hundreds of plants every hour for over a month with a system that cost approximately US$1000. Results In comparison with manually acquired images, this HTP system was able to produce similar estimates of foliar area and greenness, developmental trends, and treatment effects. Foliar area was correlated between the two image sets, but greenness was not. Discussion These findings highlight the potential of HTP systems built from low-cost hardware and freely available software. Future work can use this system to investigate genotype-environment interactions and the genetic loci underlying morphological changes resulting from abiotic stress.
Why it matches plant phenotyping methods低コストの画像ベース高スループット表現型計測システムを開発・検証し、葉面積と緑色度を自動推定することが研究の中心であるため。
abstractwe develop and validate a low-cost, scalable, high-throughput phenotyping (HTP) system for automating the measurement of foliar area and greenness.
Reproduction assets foundThe paper's data availability statement deposits both the phenotyping data (images/measurements) and the analysis scripts on Zenodo with explicit public DOIs, making both paper-specific assets directly actionable.Dataset · publicAll of the data and analysis scripts have been deposited to Zenodo (data: https://doi.org/10.5281/zenodo.5725224 ; scripts: https://doi.org/10.5281/zenodo.6366716 ).Open asset ↗Zenodo · 10.5281/zenodo.5725224lines:188-243Code · publicAll of the data and analysis scripts have been deposited to Zenodo (data: https://doi.org/10.5281/zenodo.5725224 ; scripts: https://doi.org/10.5281/zenodo.6366716 ).Open asset ↗Zenodo · 10.5281/zenodo.6366716lines:188-243Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published24 Nov 20222022 IEEE 19th India Council International Conference (INDICON)Cited by 14 · OpenAlex ↗
The image processing technique is a method useful in agricultural processes for enhancing accuracy and uniformity of processes in farming while decreasing farmers’ manual observation. Leaf disease detection using deep learning applications can be helpful for farmers to analyze the affected leaves at an early stage which will in turn aid in the agricultural process. In this paper, we have used Convolution Neural Network (CNN), a deep learning algorithm mainly used for analyzing visual imagery, for the detection of various crop leaf diseases. The CNN-based model will help in differentiating between diseased and healthy leaves, which will improve farmers’ harvest quality. The main objective of the paper is to create a new dataset that contains three plant leaves that are cauliflower, tomato, and mango, and then compare the accuracy using various CNN models which are generally used for unstructured datasets i.e., images. Also analyzing the results on the basis of different experimental configurations such as choice of deep learning architecture, choice of dataset type, and Choice of training-testing set distribution. Results achieved from these experiments display the performance and precision of the model best fit for disease detection of plants.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定するCNN手法を開発・比較し、新規データセットも作成しているため、病害表現型の取得・推定が中心です。
abstractLeaf disease detection using deep learning applications can be helpful for farmers to analyze the affected leaves at an early stage
Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting. The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors. This paper proposes a novel monitoring approach that comprises high-throughput imaging sensor measurements and their automatic analysis to predict future plant growth. Our approach’s core is a novel machine learning-based generative growth model based on conditional generative adversarial networks, which is able to predict the future appearance of individual plants. In experiments with RGB time series images of laboratory-grown Arabidopsis thaliana and field-grown cauliflower plants, we show that our approach produces realistic, reliable, and reasonable images of future growth stages. The automatic interpretation of the generated images through neural network-based instance segmentation allows the derivation of various phenotypic traits that describe plant growth.
Why it matches plant phenotyping methods高スループット画像計測、将来生長の生成モデル、画像解析による形質導出が研究の中心であり、植物フェノタイピング手法の開発に該当する。
abstractThis paper proposes a novel monitoring approach that comprises high-throughput imaging sensor measurements and their automatic analysis to predict future plant growth.
Brassica vegetablesField / plotRootMorphology / geometry measurementRoot system architecture
Urban agriculture has been broadly acknowledged for its potential to reduce carbon emissions, increase food security, and improve economic growth in some of the most vulnerable communities in the United States. Collard ( B.oleracea var. viridis ) is a diploid leafy green, grown on urban farms and community gardens across the country, including the St. Louis Metro region. Beyond their nutritional importance, collards provide urban and commercial agronomic systems with a plethora of important ecosystem services. They scavenge nitrogen and available resources, suppress weeds, and act as a biofumigant to control soil-borne pests and pathogens. Recently, The Heirloom Collard Project characterized the above-ground growth habits of 18 landrace collard varieties across 250 organic gardens and farms. Little work has been published to investigate collard root system architecture, which influences both quality traits and ecosystem services that contribute to sustainable crop production. The objectives of this research are to 1) quantify root spatial and temporal diversity across 18 landrace collard varieties, and 2) evaluate the relationship between root phenotype and urban farmer crowd-sourced data for key traits such as germination rate, disease resistance, vigor, yield, flavor, and winter hardiness. This work will lead to the development of a participatory framework for urban farmers and chefs to select varieties with improved root architecture based on regional needs.
Why it matches plant phenotyping methods根系アーキテクチャという植物形質をデジタルに定量化・解析することが研究の中心であり、単なる生物学的実験の補助測定ではないため。
titleDigital quantification and characterization of root architectural diversity across collard landrace varieties
Accurate canopy mapping and head-volume estimation of large areas of broccoli is an important prerequisite for precision farming since it provides important phenotypic traits associated with field management, environmental control, and yield prediction. Currently, the detection and characterization of broccoli mostly rely on ground surveys and human interpretation, which is often time- and labor-intensive. Recent developments based on unmanned aerial vehicle (UAV) remote sensing offer low cost, timely, and flexible data acquisition, thereby providing a potential alternative technique to enhance in situ field surveys. The combination of UAV data and deep learning has led to a series of breakthroughs in rapid and automated collection of simultaneous multisensor and multimodal plant phenotyping data. However, their application for monitoring broccoli remains problematic when faced with the significant spatial scale involved and the variety of vegetation species. To address this problem, we propose herein a fast and reliable semi-automatic workflow based on deep learning to process UAV RGB imagery and LiDAR point clouds and thereby remotely detect and characterize broccoli canopy and heads. First, we explore the use of TransUNet to differentiate canopy and non-canopy regions in RGB images at the individual-plant scale. The results demonstrate that TransUNet consistently achieves the highest accuracy (average returned Precision, Recall, F1 score, and IoU of 0.917, 0.864, 0.901, and 0.895, respectively) compared with three CNN-based and two shallow learning-based approaches. In addition, TransUNet performs best in terms of robustness against variations in training samples. Subsequently, to estimate the volume of broccoli heads, a point cloud transformer (PCT) network is developed for point cloud segmentation. Improving upon the results of three existing methods PointNet, PointNet++, and K-means that were applied to the same datasets, the best-performing PCT produced a precision of 0.914, an overall recall of 0.899, an overall F1 score of 0.901, and an overall IoU of 0.879. A regression analysis indicates that the PCT estimates had R2 = 0.875, RMSE = 18.62, and rRMSE = 3.64 %, which is also superior to the results from other comparison approaches. Collectively, the wide application of such technology would facilitate applied research in plant phenotyping and precision agro-ecological applications and field management.
Why it matches plant phenotyping methodsUAV画像とLiDAR点群からブロッコリーの canopy と頭部体積を抽出する深層学習ワークフローを開発・比較検証しており、植物形質取得法が研究の中心である。
abstractwe propose herein a fast and reliable semi-automatic workflow based on deep learning to process UAV RGB imagery and LiDAR point clouds and thereby remotely detect and characterize broccoli canopy and heads.
High-resolution remote sensing data has expanded the scope, precision, and scale of remote sensing applications in agriculture. Availability of spatial information at actionable field units is vital for using remote sensing data in agriculture. Crop discrimination and biophysical characterisation sensitive to nutrient levels have not been addressed at the patch level. This work investigates the synergetic application of high-resolution satellite imagery and terrestrial LiDAR point cloud for object-level discrimination and biophysical characterisation of a few crops at different nitrogen (N) levels. To this end, cabbage, eggplant, and tomato at three levels of N were grown on the experimental fields of the University of Agricultural Sciences, Bengaluru, India, in 2017. Fusing the multispectral imagery (WorldView-III) and LiDAR point cloud (terrestrial laser scanner) at the feature level, object-level supervised classification and estimation of two critical biophysical parameters (crown area and biomass) were performed using the support vector machine (SVM) and Random Forests (RF) algorithms with reference to different N levels. Results suggest discrimination of vegetable crops with high accuracy (92%), about 20% higher than the individual sensors, from the fused imagery sensitive to N levels. The quality of retrievals indicates a contrasting pattern wherein the accuracy of the crown area is high with the LiDAR point cloud at various N levels. For the biomass, there is no perceptible differentiation of N levels within a crop. The accuracy of crop classification with reference to N levels is similar from both RF and SVM algorithms. However, RF algorithm offered substantially higher classification results when the N status is ignored. In contrast, the quality of biophysical modelling is very high and is similar from both the algorithms. Weather conditions and sub-field level environment-induced variations in the crop growth likely are the factors responsible for the reduced sensitivity of remote sensing data to crop N levels at the patch level.
Why it matches plant phenotyping methodsマルチスペクトル画像とLiDARを融合し、作物の冠面積とバイオマスという植物形質を推定する手法が研究の中心であり、センサー融合と推定精度を評価している。
abstractFusing the multispectral imagery (WorldView-III) and LiDAR point cloud (terrestrial laser scanner) at the feature level, object-level supervised classification and estimation of two critical biophysical parameters (crown area and biomass) were performed
Precise and site-specific nitrogen (N) fertilizer management of vegetables is essential to improve the N use efficiency considering temporal and spatial fertility variations among fields, while the current N fertilizer recommendation methods are proved to be time- and labor-consuming. To establish a site-specific N topdressing algorithm for bok choy ( Brassica rapa subsp. chinensis ), using a hand-held GreenSeeker canopy sensor, we conducted field experiments in the years 2014, 2017, and 2020. Two planting densities, viz, high (123,000 plants ha -1 ) in Year I and low (57,000 plants ha -1 ) in Year II, whereas, combined densities in Year III were used to evaluate the effect of five N application rates (0, 45, 109, 157, and 205 kg N ha -1 ). A robust relationship was observed between the sensor-based normalized difference vegetation index (NDVI), the ratio vegetation index (RVI), and the yield potential without topdressing (YP 0 ) at the rosette stage, and 81-84% of the variability at high density and 76-79% of that at low density could be explained. By combining the densities and years, the R 2 value increased to 0.90. Additionally, the rosette stage was identified as the earliest stage for reliably predicting the response index at harvest (RI Harvest ), based on the response index derived from NDVI (RI NDVI ) and RVI (RI RVI ), with R 2 values of 0.59-0.67 at high density and 0.53-0.65 at low density. When using the combined results, the RI RVI performed 6.12% better than the RI NDVI , and 52% of the variability could be explained. This study demonstrates the good potential of establishing a sensor-based N topdressing algorithm for bok choy, which could contribute to the sustainable development of vegetable production.
Why it matches plant phenotyping methods携帯型キャノピーセンサーのNDVI/RVIから収量ポテンシャルと施肥応答を推定するアルゴリズムを開発・検証しており、植物形質推定手法が研究の中心です。
abstractTo establish a site-specific N topdressing algorithm for bok choy ( Brassica rapa subsp. chinensis ), using a hand-held GreenSeeker canopy sensor, we conducted field experiments in the years 2014, 2017, and 2020.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2 ), the empirical exponential model was used to determine the relationship between YP 0 and the sensor-based vegetation indices (NDVI and RVI) for bok choy across growth stages ( Table 3 ).Open asset ↗lines:391-483Plant phenotyping relevance match · UnverifiedCrossref · checked 8 Sept 2026
The estimation accuracy of plant dry matter by spectra- or remote sensing-based methods tends to decline when canopy coverage approaches closure; this is known as the saturation problem. This study aimed to enhance the estimation accuracy of plant dry matter and subsequently use the critical nitrogen dilution curve (CNDC) to diagnose N in Choy Sum by analyzing the combined information of canopy imaging and plant height. A three-year experiment with different N levels (0, 25, 50, 100, 150, and 200 kg∙ha−1) was conducted on Choy Sum. Variables of canopy coverage (CC) and plant height were used to build the dry matter and N estimation model. The results showed that the yields of N0 and N25 were significantly lower than those of high-N treatments (N50, N100, N150, and N200) for all three years. The variables of CC × Height had a significant linear relationship with dry matter, with R2 values above 0.87. The good performance of the CC × Height-based model implied that the saturation problem of dry matter prediction was well-addressed. By contrast, the relationship between dry matter and CC was best fitted by an exponential function. CNDC models built based on CC × Height information could satisfactorily differentiate groups of N deficiency and N abundance treatments, implying their feasibility in diagnosing N status. N application rates of 50–100 kgN/ha are recommended as optimal for a good yield of Choy Sum production in the study region.
Why it matches plant phenotyping methodsキャノピー画像と草丈を統合して乾物量・窒素状態を推定する手法を構築し、飽和問題への対処と精度評価を行っており、表現型取得・推定が研究の中心である。
abstractThis study aimed to enhance the estimation accuracy of plant dry matter and subsequently use the critical nitrogen dilution curve (CNDC) to diagnose N in Choy Sum by analyzing the combined information of canopy imaging and plant height.
Brassica vegetablesChlorophyll fluorescenceMicroscopyCell / cellular structureRootVisualization / data management
Infection of Brassica crops by the soilborne protist Plasmodiophora brassicae leads to gall formation on the underground organs. The formation of galls requires cellular reprogramming and changes in the metabolism of the infected plant. This is necessary to establish a pathogen-oriented physiological sink toward which the host nutrients are redirected. For a complete understanding of this particular plant-pathogen interaction and the mechanisms by which host growth and development are subverted and repatterned, it is essential to track and observe the internal changes accompanying gall formation with cellular resolution. Methods combining fluorescent stains and fluorescent proteins are often employed to study anatomical and physiological responses in plants. Unfortunately, the large size of galls and their low transparency act as major hurdles in performing whole-mount observations under the microscope. Moreover, low transparency limits the employment of fluorescence microscopy to study clubroot disease progression and gall formation. This article presents an optimized method for fixing and clearing galls to facilitate epifluorescence and confocal microscopy for inspecting P. brassicae-infected galls. A tissue-clearing protocol for rapid optical clearing was used followed by vibratome sectioning to detect anatomical changes and localize gene expression with promoter fusions and reporter lines tagged with fluorescent proteins. This method will prove useful for studying cellular and physiological responses in other pathogen-triggered structures in plants, such as nematode-induced syncytia and root knots, as well as leaf galls and deformations caused by insects.
Why it matches plant phenotyping methods感染ゴールの解剖学的変化や生理応答を可視化するための組織透明化・蛍光顕微鏡法を最適化しており、植物病態の表現型取得が中心である。
abstractThis article presents an optimized method for fixing and clearing galls to facilitate epifluorescence and confocal microscopy for inspecting P. brassicae-infected galls.
Cauliflower, a winter seasoned vegetable that originated in the Mediterranean region and arrived in Europe at the end of the 15th century, takes the lead in production among all vegetables. It's high in fiber and can keep us hydrated, and have medicinal properties like the chemical glucosinolates, which may help prevent cancer. If proper care is not given to the plants, several significant diseases can affect the plants, reducing production, quantity, and quality. Plant disease monitoring by hand is extremely tough because it demands a great deal of effort and time. Early detection of the diseases allows the agriculture sector to grow cauliflower more efficiently. In this scenario, an insightful and scientific dataset can be a lifesaver for researchers looking to analyze and observe different diseases in cauliflower development patterns. So, in this work, we present a well-organized and technically valuable dataset "VegNet' to effectively recognize conditions in cauliflower plants and fruits. Healthy and disease-affected cauliflower head and leaves by black rot,downy mildew, and bacterial spot rot are included in our suggested dataset. The images were taken manually from December 20th to January 15th, when the flowers were fully blown, and most of the diseases were observed clearly. It is a well-organized dataset to develop and validate machine learning-based automated cauliflower disease detection algorithms. The dataset is hosted by the Institute - National Institute of Textile Engineering and Research (NITER),the Department of Computer Science and Engineering and is available at the link following: https://data.mendeley.com/datasets/t5sssfgn2v/3.
Why it matches plant phenotyping methodsカリフラワーの病害状態を画像で識別するためのデータセットを構築・提供しており、植物の病害表現型取得が研究の中心である。
abstractwe present a well-organized and technically valuable dataset "VegNet' to effectively recognize conditions in cauliflower plants and fruits.
Reproduction assets foundThe paper is a data descriptor for VegNet, a public dataset of cauliflower disease images hosted on Mendeley Data with an explicit permanent identifier and direct link, directly reproducing the paper's plant image measurements.Dataset · publicrly. It is a well-organized dataset to develop and validate machine learning-based automated cauliflower disease detection algorithms. The dataset is hosted by the Institute – National Institute of Textile Engineering and Research (NITER),the Department of Computer Science and Engineering and is available at the link following: https://data.mendeley.com/datasets/t5sssfgn2v/3 .
Keywords: Computer vision system, Agriculture, Feature extraction, Machine learning
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2022 May 5; Revised 2022 Jun 8; Accepted 2022 Jun 21; Collection date 2022 Aug.
Specifications Table
SOpen asset ↗lines:1-47Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
A rapid diagnosis of black rot in brassicas, a devastating disease caused by Xanthomonas campestris pv. campestris (Xcc), would be desirable to avoid significant crop yield losses. The main aim of this work was to develop a method of detection of Xcc infection on broccoli leaves. Such method is based on the use of imaging sensors that capture information about the optical properties of leaves and provide data that can be implemented on machine learning algorithms capable of learning patterns. Based on this knowledge, the algorithms are able to classify plants into categories (healthy and infected). To ensure the robustness of the detection method upon future alterations in climate conditions, the response of broccoli plants to Xcc infection was analyzed under a range of growing environments, taking current climate conditions as reference. Two projections for years 2081-2100 were selected, according to the Assessment Report of Intergovernmental Panel on Climate Change. Thus, the response of broccoli plants to Xcc infection and climate conditions has been monitored using leaf temperature and five conventional vegetation indices (VIs) derived from hyperspectral reflectance. In addition, three novel VIs, named diseased broccoli indices (DBI 1 -DBI 3 ), were defined based on the spectral reflectance signature of broccoli leaves upon Xcc infection. Finally, the nine parameters were implemented on several classifying algorithms. The detection method offering the best performance of classification was a multilayer perceptron-based artificial neural network. This model identified infected plants with accuracies of 88.1, 76.9, and 83.3%, depending on the growing conditions. In this model, the three Vis described in this work proved to be very informative parameters for the disease detection. To our best knowledge, this is the first time that future climate conditions have been taken into account to develop a robust detection model using classifying algorithms.
Why it matches plant phenotyping methodsブロッコリー葉の感染状態を画像センサー、分光反射、葉温度、植生指数、機械学習で推定する検出法を開発・評価しており、植物病害状態のフェノタイピングが中心である。
abstractThe main aim of this work was to develop a method of detection of Xcc infection on broccoli leaves.
Ultraviolet-B (UV-B, 280-315 nm) radiation has been known as an elicitor to enhance bioactive compound contents in plants. However, unpredictable yield is an obstacle to the application of UV-B radiation to controlled environments such as plant factories. A typical three-dimensional (3D) plant structure causes uneven UV-B exposure with leaf position and age-dependent sensitivity to UV-B radiation. The purpose of this study was to develop a model for predicting phenolic accumulation in kale ( Brassica oleracea L. var. acephala ) according to UV-B radiation interception and growth stage. The plants grown under a plant factory module were exposed to UV-B radiation from UV-B light-emitting diodes with a peak at 310 nm for 6 or 12 h at 23, 30, and 38 days after transplanting. The spatial distribution of UV-B radiation interception in the plants was quantified using ray-tracing simulation with a 3D-scanned plant model. Total phenolic content (TPC), total flavonoid content (TFC), total anthocyanin content (TAC), UV-B absorbing pigment content (UAPC), and the antioxidant capacity were significantly higher in UV-B-exposed leaves. Daily UV-B energy absorbed by leaves and developmental age was used to develop stepwise multiple linear regression models for the TPC, TFC, TAC, and UAPC at each growth stage. The newly developed models accurately predicted the TPC, TFC, TAC, and UAPC in individual leaves with R 2 > 0.78 and normalized root mean squared errors of approximately 30% in test data, across the three growth stages. The UV-B energy yields for TPC, TFC, and TAC were the highest in the intermediate leaves, while those for UAPC were the highest in young leaves at the last stage. To the best of our knowledge, this study proposed the first statistical models for estimating UV-B-induced phenolic contents in plant structure. These results provided the fundamental data and models required for the optimization process. This approach can save the experimental time and cost required to optimize the control of UV-B radiation.
Why it matches plant phenotyping methods3DスキャンとレイトレーシングによるUV-B吸収量の推定、およびフェノール含量予測モデルの開発が研究の中心であり、植物形質の計測・推定手法に該当する。
abstractThe spatial distribution of UV-B radiation interception in the plants was quantified using ray-tracing simulation with a 3D-scanned plant model.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1
Relative growth rate and expansion rate of leaf groups, and the assigned leaf order in kale plants at 23, 30, and 38 DAT.Open asset ↗lines:612-691Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
The development of a new method to accurately and non-destructively identify the quality of vegetables cultivated under different light treatments is urgent because traditional methods of quality identification are time-consuming, costly and destructively. A method based on hyperspectral imaging technology combined with machine learning was developed in this paper to rapidly identify the quality of kale cultivated under different light treatments in a plant factory. UV-A supplementation in different photoperiods was used to regulate the quality of kale by improving the contents of moisture, photosynthetic pigments and the phytochemicals accumulation, and the quality grades were first established based on these indicators obtained by the traditional method. Then, a non-destructive quality identification method was presented by constructing an identification model based on the hyperspectral images of kale leaves and machine learning. It was revealed that the accuracy of the identification model based on linear discriminant analysis reached a high value of 95% by employing different feature selection methods to optimize the model. The differences of model accuracy were also investigated when the reflectance spectra extracted from different regions of interest (ROIs) such as whole leaf, mesophyll and leaf veins were used for modeling. It was shown that the quality identification models had higher accuracy when the mesophyll was used as ROI than others ROIs, indicating that the selection of ROI-mesophyll was an efficient way to improve the accuracy of the model. These results demonstrate that the proposed method combining hyperspectral imaging technology and machine learning can be used to rapidly and accurately identify the quality of vegetables cultivated under different light treatments.
Why it matches plant phenotyping methodsケール葉の品質状態を非破壊推定するハイパースペクトル画像と機械学習の手法開発・精度評価が中心であり、植物フェノタイピング手法に該当する。
abstractA method based on hyperspectral imaging technology combined with machine learning was developed in this paper to rapidly identify the quality of kale
Background Unmanned aerial vehicle (UAV)-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. Results In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated as "cataloging" based on comprehensible computer vision methods. We evaluate the workflow on 2 real-world datasets. One dataset is recorded for observation of Cercospora leaf spot-a fungal disease-in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliflower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers a large-scale spatiotemporal image dataset that in turn can be applied to train further machine learning models including various data layers. Conclusion The presented approach improves analysis and interpretation of UAV data in agriculture significantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning-based recognition techniques. Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.
Why it matches plant phenotyping methodsUAV画像から個体を時空間的に同定・個別化し、植物画像データセットを抽出するコンピュータビジョン手法が研究の中心であり、精度検証も行っている。
abstractwe present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs
Reproduction assets foundThe paper's authors publicly released their plant cataloging workflow code on GitHub and deposited a supporting subset of the sugar beet UAV image data with code snapshots in GigaDB (10.5524/102225). The GitHub repository URL is in the allowed list; the GigaDB DOI is not, so only the code asset is listed with an exact-Code · publicponding data. By automatizing the plant cataloging and providing a data framework, our work helps to exploit the full potential of UAV imaging in agricultural contexts.
Availability of Source Code
The source code of our workflow is available in the following repository:
Project name: Plant Cataloging Workflow
GitHub repository: https://github.com/mrcgndr/plant_cataloging_workflow
RRID: SCR_022276
Operating system(s): Platform independent (with conda), Linux (with Docker)
Programming language: Python (3.9 or higher)
License: Apache License 2.0
Data Availability
A subset of the sugar beet data is available in order to run the workflow and reproduce our results. The data have been uploaded to tOpen asset ↗https://github.com/mrcgndr/plant_cataloging_workflowlines:172-190Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Jun 2022Computers and Electronics in Agriculture.
In the literature of hyperspectral remote sensing to assess nutrient status in crops, there is a general lack of studies conducted under greenhouse conditions. This may be attributed to technical issues associated with inconsistent lighting conditions during daytime data acquisitions due to shadows and spectral scattering inside greenhouse structures. In this proof-of-concept study, we developed a novel night-based hyperspectral remote sensing system with attached halogen lighting to study leaf reflectance of bok choy [Brassica rapa L. var Chinensis] and spinach [Spinacia oleracea L. ‘Correnta”] grown under high, medium and low fertilization regimes. The study objectives were to: 1) identify spectral regions in which average leaf reflectance values could be used accurately to characterize crop responses to overall fertilizer regimes, and 2) characterize consistency across crops of associations between crop leaf reflectance and levels of individual macronutrient elements. Our findings were: 1) leaf reflectance could be used to differentiate low versus medium/high fertilization regimes with 75% (bok choy) and 80% (spinach) accuracy, and 2) the following spectral regions: 700–709 nm, 780–787 nm and 817–821 nm were associated with N, K, Mg and Ca levels in bok choy and spinach. Based on comprehensive sensitivity analysis, we demonstrated that classification accuracy was highly similar when 50–80% of the crop reflectance data were used as training data, indicating robustness of the proposed linear discriminant classification models. We believe the proposed sensitivity analysis has broad relevance as a method to thoroughly examine the robustness of reflectance-based algorithms that are used to classify agricultural products.
Why it matches plant phenotyping methods夜間ハイパースペクトル撮像システムを開発し、葉の反射率から施肥応答・栄養状態を推定する方法と分類モデルの頑健性を評価しており、植物フェノタイピング手法が中心である。
abstractwe developed a novel night-based hyperspectral remote sensing system with attached halogen lighting
This study develops an automated crop growth detection APP, with the functionality to access the cadastral data for the target field, that was to be used for a satellite-imagery-based field survey. A total of 735 ground-truth records of the cabbage cultivation areas in Yunlin were collected via the implemented APP in order to train a deep learning model to make accurate predictions of the growth stages of the cabbage from 0 to 70 days. A regression analysis was performed by the gradient boosting decision tree (GBDT) technique. The model was trained on multitemporal multispectral satellite images, which were retrieved from the ground-truth data. The experimental results show that the mean average error of the predictions is 8.17 days, and that 75% of the predictions have errors less than 11 days. Moreover, the GBDT algorithm was also adopted for the classification analysis. After planting, the cabbage growth stages can be divided into the cupping, early heading, and mature stages. For each stage, the prediction capture rate is 0.73, 0.51, and 0.74, respectively. If the days of growth of the cabbages are partitioned into two groups, the prediction capture rate for 0–40 days is 0.83, and that for 40–70 days is 0.76. Therefore, by applying appropriate data mining techniques, together with multitemporal multispectral satellite images, the proposed method can predict the growth stages of the cabbage automatically, which can assist the governmental agriculture department to make cabbage yield predictions when creating precautionary measures to deal with the imbalance between production and sales when needed.
Why it matches plant phenotyping methods衛星画像と機械学習によりキャベツの生育段階という植物状態を自動推定する手法を開発・評価しており、フェノタイピング手法が中心的である。
abstractThis study develops an automated crop growth detection APP
The contents of photosynthetic pigment, which directly affect the growth of crops, could be evaluated with spectral and multispectral imaging technologies in an accurate and rapid way. For commodity germplasm resources on the market, the estimation of photosynthetic pigments and soil and plant analyzer development (SPAD) value was accomplished using the two techniques combined with machine learning. The spectrometer used in this study employed 781 bands from 320 nm to 1100 nm, and a multispectral imaging camera was used to acquire images in visible and near-infrared. Convolutional neural network (CNN), multiple linear regression (MLR) and generalized linear model (GLM) were used to establish the machine learning models, which established by preprocessed spectral data or 4-channel multispectral images. For estimating photosynthetic pigments (chlorophyll a, chlorophyll b, total chlorophyll and carotenoids), the GLM model established by spectral data was the optimal among all the models. For the SPAD optimal estimation model, the GLM model established by the spectral data and CNN model established by the multispectral images were fair. The R² and RMSE of the CNN model validation set in estimating SPAD were 0.87 and 2.31, respectively. The R² and RMSE of the GLM model validation set in estimating SPAD were 0.88 and 2.39, respectively. By combining two techniques with different machine learning methods, a comprehensive analysis of photosynthetic pigments and SPAD was accomplished in this paper.
Why it matches plant phenotyping methods分光・マルチスペクトル画像と機械学習により、白菜の光合成色素およびSPADという植物形質を推定する手法を構築・検証しており、表現型取得・推定法が研究の中心である。
abstractthe estimation of photosynthetic pigments and soil and plant analyzer development (SPAD) value was accomplished using the two techniques combined with machine learning.
BACKGROUND: Cabbage white butterflies (Pieris spp.) can be severe pests of Brassica crops such as Chinese cabbage, Pak choi (Brassica rapa) or cabbages (B. oleracea). Eggs of Pieris spp. can induce a hypersensitive response-like (HR-like) cell death which reduces egg survival in the wild black mustard (B. nigra). Unravelling the genetic basis of this egg-killing trait in Brassica crops could improve crop resistance to herbivory, reducing major crop losses and pesticides use. Here we investigated the genetic architecture of a HR-like cell death induced by P. brassicae eggs in B. rapa. RESULTS: A germplasm screening of 56 B. rapa accessions, representing the genetic and geographical diversity of a B. rapa core collection, showed phenotypic variation for cell death. An image-based phenotyping protocol was developed to accurately measure size of HR-like cell death and was then used to identify two accessions that consistently showed weak (R-o-18) or strong cell death response (L58). Screening of 160 RILs derived from these two accessions resulted in three novel QTLs for Pieris brassicae-induced cell death on chromosomes A02 (Pbc1), A03 (Pbc2), and A06 (Pbc3). The three QTLs Pbc1-3 contain cell surface receptors, intracellular receptors and other genes involved in plant immunity processes, such as ROS accumulation and cell death formation. Synteny analysis with A. thaliana suggested that Pbc1 and Pbc2 are novel QTLs associated with this trait, while Pbc3 also contains an ortholog of LecRK-I.1, a gene of A. thaliana previously associated with cell death induced by a P. brassicae egg extract. CONCLUSIONS: This study provides the first genomic regions associated with the Pieris egg-induced HR-like cell death in a Brassica crop species. It is a step closer towards unravelling the genetic basis of an egg-killing crop resistance trait, paving the way for breeders to further fine-map and validate candidate genes.
Why it matches plant phenotyping methodsHR様細胞死のサイズを定量する画像ベース表現型解析プロトコルを開発し、遺伝解析に実質的に使用しているため、植物フェノタイピング手法が中心的です。
abstractAn image-based phenotyping protocol was developed to accurately measure size of HR-like cell death
Reproduction assets foundThe authors state that datasets and scripts used for data analysis (including phenotypic data of the germplasm screening and RIL QTL experiments) are publicly available in a Zenodo repository, which is a paper-specific, publicly actionable asset.Dataset · publicDatasets and scripts used for data analysis are also available in a Zenodo repository ( https://doi.org/10.5281/zenodo.6014948 ).Open asset ↗Zenodo · 10.5281/zenodo.6014948lines:176-273Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 8 Sept 2026
Agriculture practices in monocropping need to become more sustainable and one of the ways to achieve this is to reintroduce intercropping. However, quantitative data to evaluate plant growth in intercropping systems are still lacking. Unmanned aerial vehicles (UAV) have the potential to become a state-of-the-art technique for the automatic estimation of plant growth. Individual plant height is an important trait attribute for field investigation as it can be used to derive information on crop growth throughout the growing season. This study aimed to investigate the applicability of UAV-based RGB imagery combined with the structure from motion (SfM) method for estimating the individual plants height of cabbage, pumpkin, barley, and wheat in an intercropping field during a complete growing season under varying conditions. Additionally, the effect of different percentiles and buffer sizes on the relationship between UAV-estimated plant height and ground truth plant height was examined. A crop height model (CHM) was calculated as the difference between the digital surface model (DSM) and the digital terrain model (DTM). The results showed that the overall correlation coefficient (R2) values of UAV-estimated and ground truth individual plant heights for cabbage, pumpkin, barley, and wheat were 0.86, 0.94, 0.36, and 0.49, respectively, with overall root mean square error (RMSE) values of 6.75 cm, 6.99 cm, 14.16 cm, and 22.04 cm, respectively. More detailed analysis was performed up to the individual plant level. This study suggests that UAV imagery can provide a reliable and automatic assessment of individual plant heights for cabbage and pumpkin plants in intercropping but cannot be considered yet as an alternative approach for barley and wheat.
Why it matches plant phenotyping methodsUAV画像とSfMによる個体植物高の自動推定を評価・検証しており、植物形質の取得手法が研究の中心です。
abstractThis study aimed to investigate the applicability of UAV-based RGB imagery combined with the structure from motion (SfM) method for estimating the individual plants height
Plants synthesize phytochelatins to chelate in vivo toxic heavy metal ions and produce nontoxic complexes for tolerating the stress. Detection of the complexes would simplify the identification of high phytoremediation cultivars, as well as assessment of plant food for safe consumption. Thus, a confocal Raman spectroscopy combined with density functional theory and deep learning was used for characterizing phytochelatin2 (PC 2 ), and Cd-PC 2 mixtures. Results showed the PC 2 chelate Cd 2+ in a 2:1 ratio to produce Cd(PC 2 ) 2 ; Cd-S bonds of the Cd(PC 2 ) 2 have signature Raman vibrations at 305 and 610 cm -1 which are the most distinctive spectral signatures for varieties of Cd-PCs complexes. The PC 2 was used as a natural probe to stabilize the chemical status of Cd, and to enrich and magnify Raman signature of the trace Cd for deep learning models which enabled condition of the Cd(PC 2 ) 2 in pak choi leaf to be visualized, quantified, and classified by directly using raw spectra of the leaf. This study provides a general protocol by using Raman information for structure analysis and non-invasive detection of heavy metal-PCs complexes in plants and provides a novel idea for simplifying identification of high phytoremediation cultivars, as well as assessment of heavy metal related food safeties.
Why it matches plant phenotyping methods植物葉内のCd-PC複合体をラマン分光と深層学習で非侵襲的に可視化・定量・分類する測定プロトコルが研究の中心であり、植物の重金属ストレス状態を直接推定するため、植物フェノタイピング手法に該当します。
abstracta confocal Raman spectroscopy combined with density functional theory and deep learning was used for characterizing phytochelatin2 (PC 2 ), and Cd-PC 2 mixtures.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
Abstract Background Cabbage white butterflies (Pieris spp.) can be severe pests of Brassica crops such as Chinese cabbage, Pak choi (Brassica rapa) or cabbages (B. oleracea). Eggs of Pieris spp. can induce a hypersensitive response-like (HR-like) cell death which reduces egg survival in the wild black mustard (B. nigra). Unravelling the genetic basis of this egg-killing trait in Brassica crops could improve crop resistance to herbivory, reducing major crop losses and pesticides use. Here we investigated the genetic architecture of a HR-like cell death induced by P. brassicae eggs in B. rapa. Results A germplasm screening of B. rapa 56 accessions, representing the genetic and geographical diversity of a B. rapa core collection, showed phenotypic variation for cell death. An image-based phenotyping protocol was developed to accurately measure size of HR-like cell death and was then used to identify two accessions that consistently showed weak (R-o-18) or strong cell death response (L58). Screening of 160 RILs derived from these two accessions resulted in three novel QTLs for Pieris brassicae-induced cell death on chromosomes A02 (Pbc1), A03 (Pbc2), and A06 (Pbc3). The three QTLs Pbc1-3 contain cell surface receptors, intracellular receptors and other genes involved in plant immunity processes, such as ROS accumulation and cell death formation. Synteny analysis with A. thaliana suggested that Pbc1 and Pbc2 are novel QTLs associated with this trait, while Pbc3 contains also LecRK-I.1, a gene of A. thaliana previously associated with cell death induced by a P. brassicae egg extract. Conclusions This study provides the first genomic regions associated with the Pieris egg-induced HR-like cell death in a Brassica crop species. It is a step closer towards unravelling the genetic basis of an egg-killing crop resistance trait, paving the way for breeders to further fine-map and validate candidate genes.
Why it matches plant phenotyping methodsHR様細胞死の大きさを画像で正確に測定するフェノタイピングプロトコルを開発し、遺伝資源・RIL集団の評価に実質的に使用しているため、植物表現型取得法が中心的役割を持つ。
abstractAn image-based phenotyping protocol was developed to accurately measure size of HR-like cell death
Cadmium (Cd) is a toxic metal that can accumulate in soils and negatively impact crop as well as human health. Amendments like biochar have potential to address these challenges by reducing Cd bioavailability in soil, though reliance on post-harvest wet chemical methods to quantify Cd uptake have slowed efforts to identify the most effective amendments. Hyperspectral imaging (HSI) is a novel technology that could overcome this limitation by quantifying symptoms of Cd stress while plants are still growing. The goals of this study were to: 1) determine whether HSI can detect Cd stress in two distinct leafy green crops, 2) quantify whether a locally sourced biochar derived from hardwoods can reduce Cd stress and uptake in these crops, and 3) identify vegetative indices (VIs) that best quantify changes in plant stress responses. Experiments were conducted in a tightly controlled automated phenotyping facility that allowed all environmental factors to be kept constant except Cd concentration (0, 5 10 and 15 mg kg -1 ). Symptoms of Cd stress were stronger in basil (Ocimum basilicum) than kale (Brassica oleracea), and were easier to detect using HSI. Several VIs detected Cd stress in basil, but only the anthocyanin reflectance index (ARI) detected all levels of Cd stress in both crop species. The biochar amendment did reduce Cd uptake, especially at low Cd concentrations in kale which took up more Cd than basil. Again, the ARI index was the most effective in quantifying changes in plant stress mediated by the biochar. These results indicate that the biochar evaluated in this study has potential to reduce Cd bioavailability in soil, and HSI could be further developed to identify rates that can best achieve this benefit. The technology also may be helping in elucidating mechanisms mediating how biochar can influence plant growth and stress responses.
Why it matches plant phenotyping methodsHSIを用いて生育中の植物のCdストレス症状を定量し、植生指数を比較・検証することが研究の中心であり、植物フェノタイピング手法の技術評価に該当する。
abstractHyperspectral imaging (HSI) is a novel technology that could overcome this limitation by quantifying symptoms of Cd stress while plants are still growing.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
We have developed a rapid Raman spectroscopy-based method for the detection and quantification of early innate immunity responses in Arabidopsis and Choy Sum plants. Arabidopsis plants challenged with flg22 and elf18 elicitors could be differentiated from mock-treated plants by their Raman spectral fingerprints. From the difference Raman spectrum and the value of p at each Raman shift, we derived the Elicitor Response Index (ERI) as a quantitative measure of the response whereby a higher ERI value indicates a more significant elicitor-induced immune response. Among various Raman spectral bands contributing toward the ERI value, the most significant changes were observed in those associated with carotenoids and proteins. To validate these results, we investigated several characterized Arabidopsis pattern-triggered immunity (PTI) mutants. Compared to wild type (WT), positive regulatory mutants had ERI values close to zero, whereas negative regulatory mutants at early time points had higher ERI values. Similar to elicitor treatments, we derived an analogous Infection Response Index (IRI) as a quantitative measure to detect the early PTI response in Arabidopsis and Choy Sum plants infected with bacterial pathogens. The Raman spectral bands contributing toward a high IRI value were largely identical to the ERI Raman spectral bands. Raman spectroscopy is a convenient tool for rapid screening for Arabidopsis PTI mutants and may be suitable for the noninvasive and early diagnosis of pathogen-infected crop plants.
Why it matches plant phenotyping methods植物の免疫応答をRamanスペクトルから定量化する指標を開発し、変異体・病原体感染で検証した中心的なフェノタイピング手法研究。
abstractWe have developed a rapid Raman spectroscopy-based method for the detection and quantification of early innate immunity responses in Arabidopsis and Choy Sum plants.
Brassica vegetablesLaboratory / benchtopRootMorphology / geometry measurementRoot system architecture
Long-duration space missions will need to rely on the use of plants in bio-regenerative life support systems (BLSSs) because these systems can produce fresh food and oxygen, reduce carbon dioxide levels, recycle metabolic waste, and purify water. In this scenario, the need for new experiments on the effects of altered gravity conditions on plant biological processes is increasing, and significant efforts should be devoted to new ideas aimed at increasing the scientific output and lowering the experimental costs. Here, we report the design of an easy-to-produce and inexpensive device conceived to analyze the effect of interaction between gravity and light on root tropisms. Each unit consisted of a polystyrene multi-slot rack with light-emitting diodes (LEDs), capable of holding Petri dishes and assembled with a particular filter-paper folding. The device was successfully used for the ROOTROPS (for root tropisms) experiment performed in the Large Diameter Centrifuge (LDC) and Random Positioning Machine (RPM) at ESA's European Space Research and Technology centre (ESTEC). During the experiments, four light treatments and six gravity conditions were factorially combined to study their effects on root orientation of Brassica oleracea seedlings. Light treatments (red, blue, and white) and a dark condition were tested under four hypergravity levels (20 g, 15 g, 10 g, 5 g), a 1 g control, and a simulated microgravity (RPM) condition. Results of validation tests showed that after 24 h, the assembled system remained unaltered, no slipping or displacement of seedlings occurred at any hypergravity treatment or on the RPM, and seedlings exhibited robust growth. Overall, the device was effective and reliable in achieving scientific goals, suggesting that it can be used for ground-based research on phototropism-gravitropism interactions. Moreover, the concepts developed can be further expanded for use in future spaceflight experiments with plants.
Why it matches plant phenotyping methods根の向きを測定するための安価な装置を設計し、異なる重力・光条件下での安定性と信頼性を検証しており、植物表現型取得系が研究の中心です。
abstractHere, we report the design of an easy-to-produce and inexpensive device conceived to analyze the effect of interaction between gravity and light on root tropisms.
Phytochelatins are plants' small metal-binding peptides which chelate internal heavy metals to form nontoxic complexes. Detecting the complexes in plants would simplify identification of cultivars with both high tolerance and enrichment capabilities for heavy metals which represent phytoextraction performance. Thus, a terahertz spectroscopy combined with density functional theory, chemometrics and circular dichroism was used for characterization of phytochelatin2 (PC 2 ), Cd-PC 2 mixture standards, and pak choi (Brassica chinensis) leaves as a plant model. Results showed PC 2 chelates Cd 2+ in a 2:1 ratio to form Cd(PC 2 ) 2 complex; Cd connected to thoils of PC 2 and changed β-turn and random coil of PC 2 peptide chain to β-Sheet which presented as terahertz vibrations of PC 2 around 1.03 and 1.71 THz being suppressed; the best models for detecting the complex in pak choi were obtained by partial least squares regression modeling combined with successive projections algorithm selection; the models used PC 2 as a natural probe for visualizing and quantifying chelated Cd in pak choi leaf and achieved a limit of detection up to 1.151 ppm. This study suggested that terahertz information of the heavy metal-PCs complexes is qualified for representing a simpler alternative to classical index for evaluating phytoextraction performance of plant; it provided a general protocol for structure analysis and detection of heavy metal-PCs complexes in plant by terahertz absorbance.
Why it matches plant phenotyping methods植物中のカドミウム-フィトケラチン複合体をテラヘルツ分光と回帰モデルで検出・定量し、植物の重金属蓄積・ファイトエクストラクション性能を評価する測定プロトコルを開発しているため、方法が中心的です。
abstracta terahertz spectroscopy combined with density functional theory, chemometrics and circular dichroism was used for characterization of phytochelatin2 (PC 2 ), Cd-PC 2 mixture standards, and pak choi (Brassica chinensis) leaves as a plant model.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
Dietary supplements of anthocyanin-rich vegetables have been known to increase potential health benefits for humans. The optimization of environmental conditions to increase the level of anthocyanin accumulations in vegetables during the cultivation periods is particularly important in terms of the improvement of agricultural values in the indoor farm using artificial light and climate controlling systems. This study reports on the measurement of variations in anthocyanin accumulations in leaf tissues of four different cultivars in Brassica rapa var. chinensis (bok choy) grown under the different environmental conditions of the indoor farm using hyperspectral imaging. Anthocyanin accumulations estimated by hyperspectral imaging were compared with the measured anthocyanin accumulation obtained by destructive analysis. Between hyperspectral imaging and destructive analysis values, no significant differences in anthocyanin accumulation were observed across four bok choy cultivars grown under the anthocyanin stimulation environmental condition, whereas the estimated anthocyanin accumulations displayed cultivar-dependent significant differences, suggesting that hyperspectral imaging can be employed to measure variations in anthocyanin accumulations of different bok choy cultivars. Increased accumulation of anthocyanin under the stimulation condition for anthocyanin accumulation was observed in “purple magic” and “red stem” by both hyperspectral imaging and destructive analysis. In the different growth stages, no significant differences in anthocyanin accumulation were found in each cultivar by both hyperspectral imaging and destructive analysis. These results suggest that hyperspectral imaging can provide comparable analytic capability with destructive analysis to measure variations in anthocyanin accumulation that occurred under the different light and temperature conditions of the indoor farm. Leaf image analysis measuring the percentage of purple color area in the total leaf area displayed successful classification of anthocyanin accumulation in four bok choy cultivars in comparison to hyperspectral imaging and destructive analysis, but it also showed limitation to reflect the level of color saturation caused by anthocyanin accumulation under different environmental conditions in “red stem,” “white stem,” and “green stem.” Finally, our hyperspectral imaging system was modified to be applied onto the high-throughput plant phenotyping system, and its test to analyze the variation of anthocyanin accumulation in four cultivars showed comparable results with the result of the destructive analysis.
Why it matches plant phenotyping methods植物のアントシアニン蓄積という形質をハイパースペクトル画像から推定し、破壊分析と比較検証している。さらに高スループット表現型解析システムへの改変・適用も行っており、方法が中心的である。
abstractAnthocyanin accumulations estimated by hyperspectral imaging were compared with the measured anthocyanin accumulation obtained by destructive analysis.
The growth and the harvestability of a broccoli crop is monitored by the size of the broccoli head. This size estimation is currently done by humans, and this is inconsistent and expensive. The goal of our work was to develop a software algorithm that can estimate the size of field-grown broccoli heads based on RGB-Depth (RGB-D) images. For the algorithm to be successful, the problem of occlusion must be solved, which is the partial visibility of the broccoli head due to overlapping leaves. This partial visibility causes sizing errors. In this research, we studied the use of deep-learning algorithms to deal with occlusions. We specifically applied the Occlusion Region-based Convolutional Neural Network (ORCNN) that segmented both the visible and the amodal region of the broccoli head (which is the visible and the occluded region combined). We hypothesised that ORCNN, with its amodal segmentation, can improve the size estimation of occluded broccoli heads. The ORCNN sizing method was compared with a Mask R–CNN sizing method that only used the visible broccoli region to estimate the size. The sizing performance of both methods was evaluated on a test set of 487 broccoli images with systematic levels of leaf occlusion. With a mean sizing error of 6.4 mm, ORCNN outperformed Mask R–CNN, which had a mean sizing error of 10.7 mm. Furthermore, ORCNN had a significantly lower absolute sizing error on 161 heavily occluded broccoli heads with an occlusion rate between 50% and 90%. Our software and data set are available on https://git.wur.nl/blok012/sizecnn.
Why it matches plant phenotyping methodsRGB-D画像と深層学習によりブロッコリー頭部サイズを推定する手法を開発し、Mask R-CNNと比較検証しているため、植物形質取得法が研究の中心です。
abstractThe goal of our work was to develop a software algorithm that can estimate the size of field-grown broccoli heads based on RGB-Depth (RGB-D) images.
During the past few years, milder autumn and winter seasons have caused severe problems to cauliflower harvest of Brittany region in France, mainly due to curd deformation. Consequently, cauliflower breeders are working on breeding new varieties that are more robust to climate change to stabilize the quality of cauliflower production. The aim of this study was to identify at which stage of the curd formation, significant difference can be detected between healthy and stressed cauliflower. A non-invasive classification based on Magnetic Resonance Imaging (MRI) images for cauliflower phenotyping was proposed. Plants exposed to vernalization stress were sampled at different times around primary meristem stage, then both MRI imaged and apex dissected. A work flow was developped to extract features from MRI images. A classification on phenotype was learned by LDA, QDA, PLSDA and CNN binary classification between two groups: healthy and stressed cauliflower. Promising F1 score and MCC up to 95% were achieved. Curd deformation is the main cause for cauliflower’s later physiological disorders when reaching maturity. Therefore, the cauliflowers with deformation could be removed at the earliest, e.g., screening for plant breeding. At the same time, the healthy cauliflowers are not destroyed and continue their life cycle.
Why it matches plant phenotyping methodsMRI画像からカリフラワーの健全・ストレス状態を分類する非侵襲的表現型解析ワークフローを開発し、複数の分類器で性能評価しており、表現型取得・抽出法が中心である。
abstractA non-invasive classification based on Magnetic Resonance Imaging (MRI) images for cauliflower phenotyping was proposed.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Estimation of biophysical parameters at various crop growth stages is vital for precision agricultural crop production. Spatial delineation of crops’ responses to various levels of nutrients helps optimise resources and reduce nutrient leaching. This paper explores the potential of 3D terrestrial laser scanning (TLS) for the estimation of plant height, crown area, and biomass of vegetable crops at various N levels. Experimental setup of growing three vegetable crops: tomato (Solanumlycopersicum L.), eggplant (Solanummelongena L.) and cabbage (Brassica oleracea L.) with three levels of N fertilization was laid out at the University of Agricultural Sciences, Bengaluru, India in 2017. LiDAR point clouds using a terrestrial laser scanner were collected at different growth stages. A methodology which included, among other processing steps, adaptive spatial filtering, canopy height modelling, watershed segmentation, and support vector regression has been adapted for the estimation of plant height, crown area, and biomass. Validation with ground measurements show high prediction accuracies for plant height (lowest coefficient of determination (R²), 0.96; highest symmetric mean absolute percentage error (SMAPE) of 3.18), and crown area (lowest R², 0.82; highest SMAPE, 8.82) for all the three crops across growth stages. The combined use of plant height and the crown area has enabled accurate and consistent estimation of biomass (lowest R², 0.92; highest SMAPE, 7.53) throughout the growing season. However, the mapping of a specific range of biomass to a specific N level is ambiguous due to wider variations in the crop growth due to rainfall, and wind interferences.
Why it matches plant phenotyping methodsTLSと点群処理・回帰を用いて植物形質（草丈、冠面積、バイオマス）を推定し、地上計測で精度検証しており、フェノタイピング手法が研究の中心である。
abstractThis paper explores the potential of 3D terrestrial laser scanning (TLS) for the estimation of plant height, crown area, and biomass of vegetable crops at various N levels.
Image-based yield detection in agriculture could raiseharvest efficiency and cultivation performance of farms. Following this goal, this research focuses on improving instance segmentation of field crops under varying environmental conditions. Five data sets of cabbage plants were recorded under varying lighting outdoor conditions. The images were acquired using a commercial mono camera. Additionally, depth information was generated out of the image stream with Structure-from-Motion (SfM). A Mask R-CNN was used to detect and segment the cabbage heads. The influence of depth information and different colour space representations were analysed. The results showed that depth combined with colour information leads to a segmentation accuracy increase of 7.1%. By describing colour information by colour spaces using light and saturation information combined with depth information, additional segmentation improvements of 16.5% could be reached. The CIELAB colour space combined with a depth information layer showed the best results achieving a mean average precision of 75.
Why it matches plant phenotyping methodsキャベツ頭部の画像セグメンテーションを対象に、深度情報と色空間の組合せを比較・改良し、精度を評価しているため、植物器官の取得・推定手法が中心である。
abstractthis research focuses on improving instance segmentation of field crops under varying environmental conditions
The purposes are to monitor the nitrogen utilization efficiency of crops and intelligently evaluate the absorption of nutrients by crops during the production process. The research object is Chinese cabbage. The Chinese cabbage population with different agricultural parameters is constructed through different densities and nitrogen fertilizer application rates based on digital image processing technology, and an estimation NC (Nitrogen Content) model is established. The population is classified through the K-Means Clustering algorithm using the feature extraction method, and the Chinese cabbage population quality BPNN (Backpropagation Neural Network) model is constructed. The nonlinear mapping relationship between different agricultural parameters and population quality, and the contribution rate of each indicator, are studied. The nitrogen utilization of Chinese cabbage is monitored effectively. Results demonstrate that the proposed NC estimation model has correlation coefficients above 0.70 in different growth stages. This model can accurately estimate the NC of the Chinese cabbage population. The results of the Chinese cabbage population quality BPNN model show that the population planting density based on the seedling number is reasonable. The constructed population quality evaluation model has a high R2 value and a comparatively low RMSE (Root Mean Square Error) value for the quality evaluation of Chinese cabbage in different periods, showing that it applies to evaluate the population quality of Chinese cabbage in different growth stages. The constructed nitrogen utilization model and quality evaluation model can monitor the nutrient utilization of crops in different growth stages, ascertain the agricultural characteristics of other yield groups in different growth stages, and clarify the performance of agricultural parameters in different growth stages. The above results can provide some ideas for crop growth intelligent detection.
Why it matches plant phenotyping methodsデジタル画像処理と特徴抽出、K-Means、BPNNを用いて白菜集団の窒素含量および群体品質を推定・評価するモデルを構築しており、植物形質の取得・推定手法が研究の中心である。
abstractbased on digital image processing technology, and an estimation NC (Nitrogen Content) model is established
The spectral reflectance technique for the quantification of the functional components was applied in different studies for different crops, but related research on kale leaves is limited. This study was conducted to estimate the glucosinolate and anthocyanin components of kale leaves cultivated in a plant factory based on diffuse reflectance spectroscopy through regression methods. Kale was grown in a plant factory under different treatments. After specific periods of transplantation, leaf samples were collected, and reflectance spectra were measured immediately from nine different points on each leaf. The same leaf samples were freeze-dried and stored for analysis of the functional components. Regression procedures, such as principal component regression (PCR), partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR), were applied to relate the functional components with the spectral data. In the laboratory analysis, progoitrin and glucobrassicin, as well as cyanidin and malvidin, were found to be dominating components in glucosinolates and anthocyanins, respectively. From the overall analysis, the SMLR model showed better performance, and the identified wavelengths for estimating the glucosinolates and anthocyanins were in the early near-infrared (NIR) region. Specifically, reflectance at 742, 761, 787, 796, 805, 833, 855, 932, 947, and 1000 nm showed a strong correlation.
Why it matches plant phenotyping methodsケール葉の成分量を拡散反射分光と回帰モデルから推定する測定・推定手法が研究の中心であり、植物葉の化学的形質を非破壊推定する方法開発に該当する。
abstractThis study was conducted to estimate the glucosinolate and anthocyanin components of kale leaves cultivated in a plant factory based on diffuse reflectance spectroscopy through regression methods.
Brassica oleracea is an important crop species that at early growth stages may exhibit failure of the apical growing point, an abnormality called “blindness”. The occurrence of blindness is promoted by exposure to low temperatures during imbibition and germination, but the causes of sensitivity to such conditions are unknown. We combined three analytical seed technology instruments to explore seed physical properties that are highly correlated with quality parameters and might be used directly for grading or sorting seed lots into subpopulations varying in potential susceptibility to blindness. For image analysis, we used the VideometerLab instrument, which can scan 19 wavelengths from ultraviolet to infrared and utilize that information in any combination to potentially identify unique criteria related to seed quality. The iXeed CF Analyzer was utilized to obtain chlorophyll fluorescence values for individual seeds. Chlorophyll contents of many seeds can be used as an indicator of seed maturity, a major contributor to seed quality. Finally, oxygen consumption measurements of individual seeds as obtained with the Q2 instrument are highly correlated with their performance under a wide variety of conditions. Six Brassica seed lots differed in their susceptibility to induction of blindness or loss of viability due to 48 h hydrated incubation at 1.5 ∘C. Analysis of physical and respiratory parameters identified some measurements that were highly correlated with the occurrence of blindness. Higher chlorophyll content, as detected by the CF-Mobile and certain wavelengths in the Videometer, was associated with greater occurrence of blindness or death following the induction treatment, suggesting that more immature seeds may be susceptible to blindness. Further research is required, but methods to detect and sort such seeds based on physical characteristics appear to be feasible.
Why it matches plant phenotyping methods種子の画像・蛍光・呼吸測定を組み合わせ、物理特性から発芽品質やblindness感受性を評価・選別する方法が研究の中心であり、単なる生物学的結果測定ではない。
abstractWe combined three analytical seed technology instruments to explore seed physical properties that are highly correlated with quality parameters and might be used directly for grading or sorting seed lots into subpopulations varying in potential susceptibility to blindness.
Reproduction assets foundThe paper's individual-seed phenotyping measurements (chlorophyll fluorescence, multispectral imaging, Q2 respiration, plant blindness scores) are consolidated in Supplemental Table S1 (Seed parameters database) and related supplements, publicly hosted on the MDPI article site. No author analysis code was deposited; CRDataset · publicSupplementary Materials: The following are available at https://www.mdpi.com/2077-0472/11/3
/220/s1, Table S1: Seed parameters database, Table S2: Q2 parameters, Table S3: MFA EigenvaluesOpen asset ↗pdf-page:20 lines:1-58Plant phenotyping relevance match · UnverifiedCrossref · checked 9 Sept 2026
Ultraviolet-B (UV-B) acts as a regulatory stimulus, inducing the dose-dependent biosynthesis of phenolic compounds such as flavonoids at the leaf level. However, the heterogeneity of biosynthesis activation generated within a whole plant is not fully understood until now and cannot be interpreted without quantification of UV-B radiation interception. In this study, we analyzed the spatial UV-B radiation interception of kales (Brassica oleracea L. var. Acephala) grown under supplemental UV-B LED using ray-tracing simulation with 3-dimension-scanned models and leaf optical properties. The UV-B-induced phenolic compounds and flavonoids accumulated more, with higher UV-B interception and younger leaves. To distinguish the effects of UV-B energy and leaf developmental age, the contents were regressed separately and simultaneously. The effect of intercepted UV-B on flavonoid content was 4.9-fold that of leaf age, but the effects on phenolic compound biosynthesis were similar. This study confirmed the feasibility and relevance of UV-B radiation interception analysis and paves the way to explore the physical and physiological base determining the intraindividual distribution of phenolic compound in controlled environments.
Why it matches plant phenotyping methods3次元スキャン植物モデルとレイトレーシングにより、植物体内のUV-B放射遮蔽・分布を定量化する手法を中心に検証しており、植物構造に基づく生理状態の測定法として適格です。
abstractwe analyzed the spatial UV-B radiation interception of kales (Brassica oleracea L. var. Acephala) grown under supplemental UV-B LED using ray-tracing simulation with 3-dimension-scanned models and leaf optical properties.
The main purpose of this work is to thoroughly describe the implementation protocol of laser-induced breakdown spectroscopy (LIBS) method in the plant analysis. Numerous feasibility studies and recent progress in instrumentation and trends in chemical analysis make LIBS an established method in plant bioimaging. In this work, we present an easy and straightforward phytotoxicity case study with a focus on LIBS method. We intend to demonstrate in detail how to manipulate with plants after exposures and how to prepare them for analyses. Moreover, we aim to achieve 2D maps of spatial element distribution with a good resolution without any loss of sensitivity. The benefits of rapid, low-cost bioimaging are highlighted. In this study, cabbage (Brassica oleracea L.) was treated with an aqueous dispersion of photon-upconversion nanoparticles (NaYF 4 doped with Yb 3+ and Tm 3+ coated with carboxylated silica shell) in a hydroponic short-term toxicity test. After a 72-hour plant exposure, several macroscopic toxicity end-points were monitored. The translocation of Y, Yb, and Tm across the whole plant was set by employing LIBS with a lateral resolution 100 µm. The LIBS maps of rare-earth elements in B.oleracea plant grown with 50 μg/mL nanoparticle-treated and ion-treated exposures showed the root as the main storage, while the transfer via stem into leaves was minimal. On the contrary, the LIBS maps of plants exposed to the 500 μg/mL nanoparticle-treated and ion-treated uncover slightly different trends, nanoparticles as well as ions were transferred through the stem into leaves. However, the main storage organ was a root as well.
Why it matches plant phenotyping methods植物内元素分布を取得するLIBSバイオイメージングの実装プロトコルと空間マッピングを中心に扱っており、植物状態の計測手法として方法論的役割が明確です。
abstractThe main purpose of this work is to thoroughly describe the implementation protocol of laser-induced breakdown spectroscopy (LIBS) method in the plant analysis.
Vegetation monitoring can be used to detect CO 2 leakage in carbon capture and storage (CCS) sites because it can monitor a large area at a relatively low cost. However, a rapidly responsive, sensitive, and cost-effective plant parameters must be suggested for vegetation monitoring to be practically utilized as a CCS management strategy. To screen the proper plant parameters for leakage monitoring, a greenhouse experiment was conducted by exposing kale ( Brassica oleracea var. viridis), a sensitive plant, to 10%, 20%, and 40% soil CO 2 concentrations. Water and water with CO 2 stress treatments were also introduced to examine the parameters differentiating CO 2 stress from water stresses. We tested the hypothesis that chlorophyl fluorescence parameters would be early and sensitive indicator to detect CO 2 leakage. The results showed that the fluorescence parameters of effective quantum yield of photosystem II (Y(II)), detected the difference between CO 2 treatments and control earlier than any other parameters, such as chlorophyl content, hyperspectral vegetation indices, and biomass. For systematic comparison among many parameters, we proposed an indicator evaluation score (IES) method based on four categories: CO 2 specificity, early detection, field applicability, and cost. The IES results showed that fluorescence parameters (Y(II)) had the highest IES scores, and the parameters from spectral sensors (380-800 nm wavelength) had the second highest values. We suggest the IES system as a useful tool for evaluating new parameters in vegetation monitoring.
Why it matches plant phenotyping methods植物のCO2ストレスを検出する生理形質（光化学系IIの有効量子収率）の感度・早期性を他指標と比較し、評価指標IESも提案しており、表現型取得・評価法が中心です。
abstractWe tested the hypothesis that chlorophyl fluorescence parameters would be early and sensitive indicator to detect CO 2 leakage.
The correct fertilization of vegetable crops is commonly determined on the basis of soil and plant costly destructive analyses, demanding more sustainable non-invasive optical detection. Here, we tested the ability of the combined transmittance/fluorescence leaf clip Dualex device for determining the nitrogen (N) status of cabbage plants. Fully developed leaves from plants grown under different N rates of 0; 100; 200; 300 kg N ha -1 in 2018 and 2019 were measured in the field by the Dualex sensor twice a year in July and October. The chlorophyll (Chl) and nitrogen (nitrogen balance index, NBI) indices and the flavonols (Flav) index of the sensor were positively and negatively correlated to leaf nitrogen, respectively. Merging the two-years data, the NBI versus leaf N correlation was less point dispersed in October than July (R 2 = 0.76 and 0.64, respectively). NBI was also correlated to cabbage yield, better in July than October. Our results showed that the multiparametric Dualex device can be used as precision agriculture tool for the early prediction of plant N and cabbage yield with economic advantage for the growers and reduced environmental contamination due to nitrate leaching.
Why it matches plant phenotyping methodsDualex光学センサーによる葉の窒素状態・収量の非破壊推定性能を評価しており、植物表現型の取得・推定手法が研究の中心である。
abstractHere, we tested the ability of the combined transmittance/fluorescence leaf clip Dualex device for determining the nitrogen (N) status of cabbage plants.
Identifying the extracellular metabolites of microorganisms in fresh vegetables is industrially useful for assessing the quality of processed foods. Pectobacterium carotovorum subsp. carotovorum (PCC) is a plant pathogenic bacterium that causes soft rot disease in cabbages. This microbial species in plant tissues can emit specific volatile molecules with odors that are characteristic of the host cell tissues and PCC species. In this study, we used headspace solid-phase microextraction followed by gas chromatography coupled with mass spectrometry (HS-SPME-GC-MS) to identify volatile compounds (VCs) in PCC-inoculated cabbage at different storage temperatures. HS-SPME-GC-MS allowed for recognition of extracellular metabolites in PCC-infected cabbages by identifying specific volatile metabolic markers. We identified 4-ethyl-5-methylthiazole and 3-butenyl isothiocyanate as markers of fresh cabbages, whereas 2,3-butanediol and ethyl acetate were identified as markers of soft rot in PCC-infected cabbages. These analytical results demonstrate a suitable approach for establishing non-destructive plant pathogen-diagnosis techniques as alternatives to standard methods, within the framework of developing rapid and efficient analytical techniques for monitoring plant-borne bacterial pathogens. Moreover, our techniques could have promising applications in managing the freshness and quality control of cabbages.
Why it matches plant phenotyping methods感染キャベツの軟腐病状態を揮発性代謝マーカーから非破壊的に診断する分析手法が研究の中心であり、植物病害状態の表現型取得に該当する。
abstractHS-SPME-GC-MS allowed for recognition of extracellular metabolites in PCC-infected cabbages by identifying specific volatile metabolic markers.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Precision agriculture requires new technologies for rapid diagnosis of plant stresses, such as nutrient deficiency and drought, before the onset of visible symptoms and subsequent yield loss. Here, we demonstrate a portable Raman probe that clips around a leaf for rapid, in vivo spectral analysis of plant metabolites including carotenoids and nitrates. We use the leaf-clip Raman sensor for early diagnosis of nitrogen deficiency of the model plant Arabidopsis thaliana as well as two important vegetable crops, Pak Choi ( Brassica rapa chinensis ) and Choy Sum ( Brassica rapa var. parachinensis ) . In vivo measurements using the portable leaf-clip Raman sensor under full-light growth conditions were consistent with those obtained with a benchtop Raman spectrometer measurements on leaf-sections under laboratory conditions. The portable leaf-clip Raman sensor offers farmers and plant scientists a new precision agriculture tool for early diagnosis and real-time monitoring of plant stresses in field conditions.
Why it matches plant phenotyping methods葉クリップ型Ramanセンサーを開発し、植物の栄養欠乏・乾燥ストレスを早期診断する手法として検証しており、植物ストレス状態の取得が中心である。
abstractHere, we demonstrate a portable Raman probe that clips around a leaf for rapid, in vivo spectral analysis of plant metabolites including carotenoids and nitrates.
Background Shade avoidance syndrome (SAS) commonly occurs in plants experiencing vegetative shade, causing morphological and physiological changes that are detrimental to plant health and consequently crop yield. As the effects of SAS on plants are irreversible, early detection of SAS in plants is critical for sustainable agriculture. However, conventional methods to assess SAS are restricted to observing for morphological changes and checking the expression of shade-induced genes after homogenization of plant tissues, which makes it difficult to detect SAS early. Results Using the model plant Arabidopsis thaliana , we introduced the use of Raman spectroscopy to measure shade-induced changes of metabolites in vivo. Raman spectroscopy detected a decrease in carotenoid contents in leaf blades and petioles of plants with SAS, which were induced by low Red:Far-red light ratio or high density conditions. Moreover, by measuring the carotenoid Raman peaks, we were able to show that the reduction in carotenoid content under shade was mediated by phytochrome signaling. Carotenoid Raman peaks showed more remarkable response to SAS in petioles than leaf blades of plants, which greatly corresponded to their morphological response under shade or high plant density. Most importantly, carotenoid content decreased shortly after shade induction but before the occurrence of visible morphological changes. We demonstrated this finding to be similar in other plant species. Comprehensive testing of Brassica vegetables showed that carotenoid content decreased during SAS, in both shade and high density conditions. Likewise, carotenoid content responded quickly to shade, in a manner similar to Arabidopsis plants. Conclusions In various plant species tested in this study, quantification of carotenoid Raman peaks correlate to the severity of SAS. Moreover, short-term exposure to shade can induce the carotenoid Raman peaks to decrease. These findings highlight the carotenoid Raman peaks as a biomarker for early diagnosis of SAS in plants.
Why it matches plant phenotyping methodsRaman分光法で植物体内のカロテノイド変化を非破壊測定し、形態変化前の遮蔽回避症候群を定量・早期診断する方法を開発・検証しており、植物表現型取得が中心である。
abstractwe introduced the use of Raman spectroscopy to measure shade-induced changes of metabolites in vivo.
Remote sensing (RS) has been an effective tool to monitor agricultural production systems, but for vegetable crops, precision agriculture has received less interest to date. The objective of this study was to test the predictive performance of two types of RS data—crop height information derived from point clouds based on RGB UAV data, and reflectance information from terrestrial hyperspectral imagery—to predict fresh matter yield (FMY) for three vegetable crops (eggplant, tomato, and cabbage). The study was conducted in an experimental layout in Bengaluru, India, at five dates in summer 2017. The prediction accuracy varied strongly depending on the RS dataset used. For all crops, a good predictive performance with cross-validated prediction error
Why it matches plant phenotyping methodsRGB UAV 3Dデータと地上ハイパースペクトル画像を用いた作物高さ・反射情報から、野菜作物の生体重収量を推定し、予測性能を検証することが研究の中心であるため。
abstractThe objective of this study was to test the predictive performance of two types of RS data—crop height information derived from point clouds based on RGB UAV data, and reflectance information from terrestrial hyperspectral imagery—to predict fresh matter yield (FMY) for three vegetable crops (eggplant, tomato, and cabbage).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Background Glucobrassicin (GBS) and its hydrolysis product indole-3-carbinol are important nutritional constituents implicated in cancer chemoprevention. Dietary consumption of vegetables sources of GBS, such as cabbage and Brussels sprouts, is linked to tumor suppression, carcinogen excretion, and cancer-risk reduction. High-performance liquid-chromatography (HPLC) is the current standard GBS identification method, and quantification is based on UV-light absorption in comparison to known standards or via mass spectrometry. These analytical techniques require expensive equipment, trained laboratory personnel, hazardous chemicals, and they are labor intensive. A rapid, nondestructive, inexpensive quantification method is needed to accelerate the adoption of GBS-enhancing production systems. Such an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties. Near-infrared reflectance spectroscopy (NIRS) paired with partial least squares regression (PLSR) could be a useful tool to develop such a method. Results Here we demonstrate that GBS concentrations of freeze-dried tissue from a wide variety of cabbage and Brussels sprouts can be predicted using partial least squares regression from NIRS data generated from wavelengths between 950 and 1650 nm. Cross-validation models had R 2 =0.75 with RPD=2.3 for predicting µmol GBS·100g -1 fresh weight and R 2 =0.80 with RPD=2.4 for predicting µmol GBS·g -1 dry weight. Inspections of equation loadings suggest the molecular associations used in modeling may be due to first overtones from O-H stretching and/or N-H stretching of amines. Conclusions A calibration model suitable for screening GBS concentration of freeze-dried leaf tissue using NIRS-generated data paired with PLSR can be created for cabbage and Brussels sprouts. Optimal NIRS wavelength ranges for calibration remain an open question.
Why it matches plant phenotyping methodsNIRSとPLSRを用いて植物葉組織中のGBS濃度を非破壊・高スループットに推定する校正モデルを開発・検証しており、育種用フェノタイピング手法としての技術的貢献が中心である。
abstractSuch an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Background Glucobrassicin (GBS) and its hydrolysis product indole-3-carbinol are important nutritional constituents implicated in cancer chemoprevention. Dietary consumption of vegetables sources of GBS, such as cabbage and Brussels sprouts, is linked to tumor suppression, carcinogen excretion, and cancer-risk reduction. High-performance liquid-chromatography (HPLC) is the current standard GBS identification method, and quantification is based on UV-light absorption in comparison to known standards or via mass spectrometry. These analytical techniques require expensive equipment, trained laboratory personnel, hazardous chemicals, and they are labor intensive. A rapid, nondestructive, inexpensive quantification method is needed to accelerate the adoption of GBS-enhancing production systems. Such an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties. Near-infrared reflectance spectroscopy (NIRS) paired with partial least squares regression (PLSR) could be a useful tool to develop such a method. Results Here we demonstrate that GBS concentrations of freeze-dried tissue from a wide variety of cabbage and Brussels sprouts can be predicted using partial least squares regression from NIRS data generated from wavelengths between 950 and 1650 nm. Cross-validation models had R 2 = 0.75 with RPD = 2.3 for predicting µmol GBS·100 g −1 fresh weight and R 2 = 0.80 with RPD = 2.4 for predicting µmol GBS·g −1 dry weight. Inspections of equation loadings suggest the molecular associations used in modeling may be due to first overtones from O–H stretching and/or N–H stretching of amines. Conclusions A calibration model suitable for screening GBS concentration of freeze-dried leaf tissue using NIRS-generated data paired with PLSR can be created for cabbage and Brussels sprouts. Optimal NIRS wavelength ranges for calibration remain an open question.
Why it matches plant phenotyping methodsNIRSとPLSRによる葉組織中グルコブラシシン濃度の非破壊・高スループット推定法を開発し、交差検証している。育種用フェノタイピング手法としての位置づけも明示され、方法が研究の中心である。
abstractSuch an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties.
Abstract BackgroundGlucobrassicin (GBS) and its hydrolysis product indole-3-carbinol are important nutritional constituents implicated in cancer chemoprevention. Dietary consumption of vegetables sources of GBS, such as cabbage and Brussels sprouts, is linked to tumor suppression, carcinogen excretion, and cancer-risk reduction. High-performance liquid-chromatography (HPLC) is the current standard GBS identification method, and quantification is based on UV-light absorption in comparison to known standards or via mass spectrometry. These analytical techniques require expensive equipment, trained laboratory personnel, hazardous chemicals, and they are labor intensive. A rapid, nondestructive, inexpensive quantification method is needed to accelerate the adoption of GBS-enhancing production systems. Such an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties. Near-infrared reflectance spectroscopy (NIRS) paired with partial least squares regression (PLSR) could be a useful tool to develop such a method. ResultsHere we demonstrate that GBS concentrations of freeze-dried tissue from a wide variety of cabbage and Brussels sprouts can be predicted using partial least squares regression from NIRS data generated from wavelengths between 950 and 1650 nm. Cross-validation models had R2=0.75 with RPD=2.3 for predicting µmol GBS·100g-1 fresh weight and R2=0.80 with RPD=2.4 for predicting µmol GBS·g-1 dry weight. Inspections of equation loadings suggest the molecular associations used in modeling may be due to first overtones from O-H stretching and/or N-H stretching of amines. ConclusionsA calibration model suitable for screening GBS concentration of freeze-dried leaf tissue using NIRS-generated data paired with PLSR can be created for cabbage and Brussels sprouts. Optimal NIRS wavelength ranges for calibration remain an open question.
Why it matches plant phenotyping methodsキャベツ葉組織中のGBS濃度をNIRSとPLSRで非破壊・高速推定する校正モデルを開発・検証しており、育種用の表現型スクリーニング手法として中心的である。
abstractSuch an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties.
In the literature of hyperspectral remote sensing to assess nutrient status in crops, there is a general lack of studies conducted under greenhouse conditions. This may be attributed to technical issues associated with inconsistent lighting conditions during daytime data acquisitions due to shadows and spectral scattering inside greenhouse structures. In this proof-of-concept study, we developed a novel night-based hyperspectral remote sensing system with attached halogen lighting to study leaf reflectance of bok choy [Brassica rapa L. var Chinensis] and spinach [Spinacia oleracea L. ‘Correnta”] grown under high, medium and low fertilization regimes. The study objectives were to: 1) identify spectral regions in which average leaf reflectance values could be used accurately to characterize crop responses to overall fertilizer regimes, and 2) characterize consistency across crops of associations between crop leaf reflectance and levels of individual macronutrient elements. Our findings were: 1) leaf reflectance could be used to differentiate low versus medium/high fertilization regimes with 75% (bok choy) and 80% (spinach) accuracy, and 2) the following spectral regions: 700–709 nm, 780–787 nm and 817–821 nm were associated with N, K, Mg and Ca levels in bok choy and spinach. Based on comprehensive sensitivity analysis, we demonstrated that classification accuracy was highly similar when 50–80% of the crop reflectance data were used as training data, indicating robustness of the proposed linear discriminant classification models. We believe the proposed sensitivity analysis has broad relevance as a method to thoroughly examine the robustness of reflectance-based algorithms that are used to classify agricultural products.
Why it matches plant phenotyping methods夜間ハイパースペクトル撮像システムを開発し、葉反射率から施肥応答・栄養状態を推定する方法の精度と頑健性を評価しており、植物表現型取得法が中心である。
abstractwe developed a novel night-based hyperspectral remote sensing system with attached halogen lighting
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 9 Sept 2026
Achieving the non-contact and non-destructive observation of broccoli head is the key step to realize the acquisition of high-throughput phenotyping information of broccoli. However, the rapid segmentation and grading of broccoli head remains difficult in many parts of the world due to low equipment development level. In this paper, we combined an advanced computer vision technique with a deep learning architecture to allow the acquisition of real-time and accurate information about broccoli head. By constructing a private image dataset with 100s of broccoli-head images (acquired using a self-developed imaging system) under controlled conditions, a deep convolutional neural network named "Improved ResNet" was trained to extract the broccoli pixels from the background. Then, a yield estimation model was built based on the number of extracted pixels and the corresponding pixel weight value. Additionally, the Particle Swarm Optimization Algorithm (PSOA) and the Otsu method were applied to grade the quality of each broccoli head according to our new standard. The trained model achieved an Accuracy of 0.896 on the test set for broccoli head segmentation, demonstrating the feasibility of this approach. When testing the model on a set of images with different light intensities or with some noise, the model still achieved satisfactory results. Overall, our approach of training a deep learning model using low-cost imaging devices represents a means to improve broccoli breeding and vegetable trade.
Why it matches plant phenotyping methodsブロッコリー頭部の画像取得、セグメンテーション、収量推定、品質等級化を中核とする画像ベース表現型計測手法の開発・検証である。
abstractAchieving the non-contact and non-destructive observation of broccoli head is the key step to realize the acquisition of high-throughput phenotyping information of broccoli.
Abstract Background : Vegetables are one of the most important nitrate sources of human diary diet. Establishing fast and accurate in situ nitrate monitoring approaches that could be used in the plant growth process and vegetable markets is essential. Results: Incorporating the unique feature of N-O asymmetric stretch absorption in the mid-infrared region (1500-1200 cm -1 ), portable Fourier-transform infrared attenuated total reflectance (FTIR-ATR) spectroscopic instruments, along with the Euclidean distance-modified intelligent algorithm extreme learning machine (ED-ELM) model, were employed to evaluate the nitrate contents in leafy vegetables. A total of 1224 samples of four popular vegetables (Chinese cabbage, swamp cabbage, celery, and lettuce) were analyzed. The results indicated that the nitrate contents (mean values: Chinese cabbage: 7550 mg/kg; swamp cabbage: 4219 mg/kg; celery: 4164 mg/kg; lettuce: 4322 mg/kg) highly exceeded the World Health Organization (WHO))-specified maximum tolerance limits. The ED-ELM model showed a better performance with the root-mean-square-error of 799.7 mg/kg, the determination coefficients of 0.93, the ratio of performance to deviation of 2.22, the optimized calibration dataset number of 100, and the number of hidden neurons of 30. Conclusion: The results confirmed that FTIR-ATR, along with the suitable model algorithms, could be used as a potential rapid and accurate method to monitor the nitrate contents in the fields of agriculture and food safety.
Why it matches plant phenotyping methods生葉野菜の硝酸含量という植物形質を、携帯型FTIR-ATR分光と知的アルゴリズムで非破壊推定する方法を開発・評価しており、測定・抽出手法が研究の中心である。
abstractEstablishing fast and accurate in situ nitrate monitoring approaches that could be used in the plant growth process and vegetable markets is essential.
Plants with circadian rhythms which are synchronised to their surrounding environments have a well described fitness advantage. Optimising circadian rhythms to fit local conditions has the potential to affect crop productivity, plant health and resource use efficiency. A rising global population and changing climate mean that these factors are becoming increasingly important. Compared to our understanding of other variables affecting plant growth and yields, circadian variation in crop panels is a relatively untapped source of phenotypic variation. Delayed fluorescence (DF) is an intrinsic property of all photosynthetic material, oscillating with a ~24h period controlled by the circadian clock. It has an advantage over other circadian assays in that it requires no prior genetic modification and works well in monocot species which don’t display robust leaf-movement rhythms. In this project, DF imaging was applied as a tool for circadian phenotyping in wheat, Brassica and Arabidopsis. As part of the process, a high throughput, reliable assay was developed specific to detached leaves of either Brassica or wheat. Several factors which contribute to circadian trait variation were examined and demonstrate species- ... (continues)
Why it matches plant phenotyping methodsDFイメージングを用いた概日時計の植物表現型解析を適用し、Brassicaおよびコムギ葉向けの高スループット・高信頼性アッセイを開発しており、手法が研究の中心である。
abstractDF imaging was applied as a tool for circadian phenotyping in wheat, Brassica and Arabidopsis.
Abstract. Multitemporal drone surveys are a perfect tool to determine various geometric and spectral crop parameters for rapid phenotyping in field trials. Depending on the geometric resolution and the size of the crop, information at leaf level or canopy level can be obtained. The focus of this paper is to demonstrate which geometric properties can be automatically derived from high resolution drone imagery during the vegetation period. For this research approx. 1920 cauliflower with a large genetic variety were planted and monitored by five different drone surveys at an altitude of 20 m, using a high resolution 36 Mpix. RGB-camera. In order to minimize intensive radiometric calibration, BRDF effects and eliminate shade, flights were carried out at overcast skies. After photogrammetric image processing, detailed crop height models (CHM) were computed. 10 distinct crop parameters were derived from a combination of the orthophotos, the CHM and additional information. According to the phenological phase a specific set of parameters was developed for every flight. For instance, the position of the individual plants is computed right after the first flight. For the flight prior to harvesting, an algorithm for the head diameter and the curvature of the cauliflower heads was developed. Geometric parameters are generally better suited for automation, because they require less specific ground truth or reference information, than spectrally derived biophysical parameters.
Why it matches plant phenotyping methodsドローン画像と写真測量から個体別の草高、位置、花球径、曲率などの植物形質を自動抽出する手法が研究の中心であり、圃場フェノタイピング手法に該当する。
abstractThe focus of this paper is to demonstrate which geometric properties can be automatically derived from high resolution drone imagery during the vegetation period.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 9 Sept 2026
Monitoring plant nitrogen (N) in a timely way and accurately is critical for precision fertilization. The imaging technology based on visible light is relatively inexpensive and ubiquitous, and open-source analysis tools have proliferated. In this study, texture- and geometry-related phenotyping combined with color properties were investigated for their potential use in evaluating N in pakchoi (Brassica campestris ssp. chinensis L.). Potted pakchoi treated with four levels of N were cultivated in a greenhouse. Their top-view images were acquired using a camera at six growth stages. The corresponding plant N concentration was determined destructively. The quantitative relationships between the nitrogen nutrition index (NNI) and the image-based phenotyping features were established using the following algorithms: random forest (RF), support vector regression (SVR), and neural network (NN). The results showed the full model based on the color, texture, and geometry-related features outperforms the model based on only the color-related feature in predicting the NNI. The RF full model exhibited the most robust performance in both the seedling and harvest stages, reaching prediction accuracies of 0.823 and 0.943, respectively. The high prediction accuracy of the model allows for a low-cost, non-destructive monitoring of N in the field of precision crop management.
Why it matches plant phenotyping methods画像から植物の窒素栄養状態を推定するフェノタイピング手法の開発・評価が研究の中心であり、画像特徴量と機械学習モデルを比較している。
abstracttexture- and geometry-related phenotyping combined with color properties were investigated for their potential use in evaluating N in pakchoi
Illumination in the natural environment is uncontrollable, and the field background is complex and changeable which all leads to the poor quality of broccoli seedling images. The colors of weeds and broccoli seedlings are close, especially under weedy conditions. The factors above have a large influence on the stability, velocity and accuracy of broccoli seedling recognition based on traditional 2D image processing technologies. The broccoli seedlings are higher than the soil background and weeds in height due to the growth advantage of transplanted crops. A method of broccoli seedling recognition in natural environments based on Binocular Stereo Vision and a Gaussian Mixture Model is proposed in this paper. Firstly, binocular images of broccoli seedlings were obtained by an integrated, portable and low-cost binocular camera. Then left and right images were rectified, and a disparity map of the rectified images was obtained by the Semi-Global Matching (SGM) algorithm. The original 3D dense point cloud was reconstructed using the disparity map and left camera internal parameters. To reduce the operation time, a non-uniform grid sample method was used for the sparse point cloud. After that, the Gaussian Mixture Model (GMM) cluster was exploited and the broccoli seedling points were recognized from the sparse point cloud. An outlier filtering algorithm based on k-nearest neighbors (KNN) was applied to remove the discrete points along with the recognized broccoli seedling points. Finally, an ideal point cloud of broccoli seedlings can be obtained, and the broccoli seedlings recognized. The experimental results show that the Semi-Global Matching (SGM) algorithm can meet the matching requirements of broccoli images in the natural environment, and the average operation time of SGM is 138 ms. The SGM algorithm is superior to the Sum of Absolute Differences (SAD) algorithm and Sum of Squared Differences (SSD) algorithms. The recognition results of Gaussian Mixture Model (GMM) outperforms K-means and Fuzzy c-means with the average running time of 51 ms. To process a pair of images with the resolution of 640×480, the total running time of the proposed method is 578 ms, and the correct recognition rate is 97.98% of 247 pairs of images. The average value of sensitivity is 85.91%. The average percentage of the theoretical envelope box volume to the measured envelope box volume is 95.66%. The method can provide a low-cost, real-time and high-accuracy solution for crop recognition in natural environment.
Why it matches plant phenotyping methods双目立体视觉与GMMによるブロッコリー苗の認識・点群抽出法を開発し、複数手法との性能比較と精度評価を行っており、植物表現型取得が中心である。
abstractThe experimental results show that the Semi-Global Matching (SGM) algorithm can meet the matching requirements of broccoli images in the natural environment
Why it matches plant phenotyping methods植物葉の機能性成分を非破壊分光で推定するセンサー・解析手法の開発が中心であり、単なる生物学的実験の測定ではない。
abstractThe present study aimed to investigate the potential of a non‐destructive diffuse reflectance spectroscopy technique for estimating functional components (i.e. glucosinolates, amino acids, sugars and carotenoids) in the leaves of Chinese cabbage grown in a plant factory.
Isolates of Hyaloperonospora brassicae inoculated onto cotyledons of 28 diverse Brassicaceae genotypes, 13 from Brassica napus, two from B. juncea, five from B. oleracea, two from Eruca vesicaria, and one each from B. nigra, B. carinata, B. rapa, Crambe abyssinica, Raphanus sativus and R. raphanistrum, showed significant effects (P ≤ 0.001) of isolate, host and their interaction. Host responses ranged from no visible symptom or a hypersensitive response, to systemic spread and abundant pathogen sporulation. Isolates were generally most virulent on their host of origin. Using an octal classification, six host genotypes were identified as suitable host differentials to characterize pathotypes of H. brassicae and distinguished eight distinct pathotypes. There were fewer, but more virulent, pathotypes in 2015–2016 isolates than 2006–2008 pathogen populations, probably explaining the increase in severity of canola downy mildew over the past decade. Phylogenetic relationships determined across 20 H. brassicae isolates collected in 2006–2008 and 88 isolates collected in 2015–2016 showed seven distinct clades, with 70% of 2006–2008 isolates distributed within clade I (bootstrap value (BVs) of 100%) and the remaining 30% in clade V (BVs 83.3%). This is the first study to define phylogenetic relationships of H. brassicae isolates in Australia, setting a benchmark for understanding current and future genetic shifts within pathogen populations; it is also the first to use octal classification to characterize pathotypes of H. brassicae, providing a novel basis for standardizing phenotypic characterization and monitoring of pathotypes on B. napus and some crucifer species in Australia.
Why it matches plant phenotyping methods宿主基因型上的症状反応を用いた病原菌病型の八進分類法を開発・適用し、病害表現型の標準化とモニタリングを主題としているため、単なる病害実験ではない。
abstractUsing an octal classification, six host genotypes were identified as suitable host differentials to characterize pathotypes of H. brassicae and distinguished eight distinct pathotypes.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Why it matches plant phenotyping methods中国白菜葉の機能性成分を、非破壊の拡散反射分光法とPLS回帰で推定する手法を中心に検討しており、植物器官の化学的形質取得・センサー設計に関する方法研究である。
abstractThe present study aimed to investigate the potential of a non‐destructive diffuse reflectance spectroscopy technique for estimating functional components (i.e. glucosinolates, amino acids, sugars and carotenoids) in the leaves of Chinese cabbage grown in a plant factory.
3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).
Why it matches plant phenotyping methodsUAV画像の3D点群から作物高・バイオマスを推定する手法を開発・評価し、成長段階の影響と精度を検証しているため、植物表現型取得が研究の中心である。
abstract3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height
Conventional crop-monitoring methods are time-consuming and labor-intensive, necessitating new techniques to provide faster measurements and higher sampling intensity. This study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties. Chinese cabbage seedlings and white radish seeds were planted at 7–10-day intervals to provide a wide range of growth rates. Remotely sensed digital imagery data were collected for test fields at approximately one-week intervals using a UAV platform equipped with an RGB digital camera flying at 2 m/s at 20 m above ground. Radiometric calibrations for the RGB band sensors were performed on every UAV flight using standard calibration panels to minimize the effect of ever-changing light conditions on the RGB images. Vegetation fractions (VFs) of crops in each region of interest from the mosaicked ortho-images were calculated as the ratio of pixels classified as crops segmented using the Otsu threshold method and a vegetation index of excess green (ExG). Plant heights (PHs) were estimated using the structure from motion (SfM) algorithm to create 3D surface models from crop canopy data. Multiple linear regression equations consisting of three predictor variables (VF, PH, and VF × PH) and four different response variables (fresh weight, leaf length, leaf width, and leaf count) provided good fits with coefficients of determination (R2) ranging from 0.66 to 0.90. The validation results using a dataset of crop growth obtained in a different year also showed strong linear relationships (R2 > 0.76) between the developed regression models and standard methods, confirming that the models make it possible to use UAV-RGB images for quantifying spatial and temporal variability in biophysical properties of Chinese cabbage and white radish over the growing season.
Why it matches plant phenotyping methodsUAV-RGB画像、Otsu/ExGセグメンテーション、SfMによる草高推定と回帰モデルを開発・検証し、作物の生体形質を定量化しているため、フェノタイピング手法が中心である。
abstractThis study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties.
Brassica vegetablesField / plotGreenhousePhysiological trait estimationGrowth / development / phenology
The development of cauliflower ( Brassica oleracea var. botrytis ) is highly dependent on temperature due to vernalization requirements, which often causes delay and unevenness in maturity during months with warm temperatures. Integrating quantitative genetic analyses with phenology modeling was suggested to accelerate breeding strategies toward wide-adaptation cauliflower. The present study aims at establishing a genome-based model simulating the development of doubled haploid (DH) cauliflower lines to predict time to curd induction of DH lines not used for model parameterization and test hybrids derived from the bi-parental cross. Leaf appearance rate and the relation between temperature and thermal time to curd induction were examined in greenhouse trials on 180 DH lines at seven temperatures. Quantitative trait loci (QTL) analyses carried out on model parameters revealed ten QTL for leaf appearance rate (LAR), five for the slope and two for the intercept of linear temperature-response functions. Results of the QTL-based phenology model were compared to a genomic selection (GS) model. Model validation was carried out on data comprising four field trials with 72 independent DH lines, 160 hybrids derived from the parameterization set, and 34 hybrids derived from independent lines of the population. The QTL model resulted in a moderately accurate prediction of time to curd induction ( R 2 = 0.42-0.51) while the GS model generated slightly better results ( R 2 = 0.52-0.61). Predictions of time to curd induction of test hybrids from independent DH lines were less precise with R 2 = 0.40 for the QTL and R 2 = 0.48 for the GS model. Implementation of juvenile-to-adult phase transition is proposed for model improvement.
Why it matches plant phenotyping methodsカリフラワーの発達時期という植物形質を予測するQTL・ゲノム選抜モデルを構築し、独立系統・圃場試験で検証しており、予測手法が研究の中心である。
abstractThe present study aims at establishing a genome-based model simulating the development of doubled haploid (DH) cauliflower lines to predict time to curd induction
Efficient and precise yield prediction is critical to optimize cabbage yields and guide fertilizer application. A two-year field experiment was conducted to establish a yield prediction model for cabbage by using the Greenseeker hand-held optical sensor. Two cabbage cultivars (Jianbao and Pingbao) were used and Jianbao cultivar was grown for 2 consecutive seasons but Pingbao was only grown in the second season. Four chemical nitrogen application rates were implemented: 0, 80, 140, and 200 kg·N·ha -1 . Normalized difference vegetation index (NDVI) was collected 20, 50, 70, 80, 90, 100, 110, 120, 130, and 140 days after transplanting (DAT). Pearson correlation analysis and regression analysis were performed to identify the relationship between the NDVI measurements and harvested yields of cabbage. NDVI measurements obtained at 110 DAT were significantly correlated to yield and explained 87-89% and 75-82% of the cabbage yield variation of Jianbao cultivar over the two-year experiment and 77-81% of the yield variability of Pingbao cultivar. Adjusting the yield prediction models with CGDD (cumulative growing degree days) could make remarkable improvement to the accuracy of the prediction model and increase the determination coefficient to 0.82, while the modification with DFP (days from transplanting when GDD > 0) values did not. The integrated exponential yield prediction equation was better than linear or quadratic functions and could accurately make in-season estimation of cabbage yields with different cultivars between years.
Why it matches plant phenotyping methods携帯型光学センサーによるNDVIからキャベツ収量を推定するモデルを開発・評価しており、植物形質の取得・推定手法が研究の中心である。
titleIn-Season Yield Prediction of Cabbage with a Hand-Held Active Canopy Sensor.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 10 Sept 2026
Abstract Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.
Why it matches plant phenotyping methods植物フェノタイピングと機械学習を統合した水分状態識別法を開発・検証しており、フェノタイプ抽出とモデル性能評価が研究の中心である。
abstractThis study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
Harnessing natural variation in photosynthetic capacity is a promising route toward yield increases, but physiological phenotyping is still too laborious for large-scale genetic screens. Here, we evaluate the potential of leaf reflectance spectroscopy to predict parameters of photosynthetic capacity in Brassica oleracea and Zea mays, a C 3 and a C 4 crop, respectively. To this end, we systematically evaluated properties of reflectance spectra and found that they are surprisingly similar over a wide range of species. We assessed the performance of a wide range of machine learning methods and selected recursive feature elimination on untransformed spectra followed by partial least squares regression as the preferred algorithm that yielded the highest predictive power. Learning curves of this algorithm suggest optimal species-specific sample sizes. Using the Brassica relative Moricandia, we evaluated the model transferability between species and found that cross-species performance cannot be predicted from phylogenetic proximity. The final intra-species models predict crop photosynthetic capacity with high accuracy. Based on the estimated model accuracy, we simulated the use of the models in selective breeding experiments, and showed that high-throughput photosynthetic phenotyping using our method has the potential to greatly improve breeding success. Our results indicate that leaf reflectance phenotyping is an efficient method for improving crop photosynthetic capacity.
Why it matches plant phenotyping methods葉反射スペクトルと機械学習により光合成能力を推定する高スループット植物フェノタイピング手法を評価・最適化しており、方法が中心的です。
abstractwe systematically evaluated properties of reflectance spectra
This study describes the development of near-infrared spectroscopy (NIRS) calibration to determine individual and total glucosinolates (GSLs) content of 12 new-bred open-pollinating genotypes of broccoli (Brassica oleracea convar. botrytis var. italica). Six individual GSLs were identified using high-performance-liquid chromatography (HPLC). The NIRS calibration was established based on modified partial least squares regression with reference values of HPLC. The calibration was analyzed using coefficient of determination in prediction (R 2 ) and ratio of preference of determination (RPD). Large variation occurred in the calibrations, R 2 and RPD due to the variability of the samples. Derived calibrations for total-GSLs, aliphatic-GSLs, glucoraphanin and 4-methoxyglucobrassicin were quantitative with a high accuracy (RPD=1.36, 1.65, 1.63, 1.11) while, for indole-GSLs, glucosinigrin, glucoiberin, glucobrassicin and 1-methoxyglucobrassicin were more qualitative (RPD=0.95, 0.62, 0.67, 0.81, 0.56). Overall, the results indicated NIRS has a good potential to determine different GSLs in a large sample pool of broccoli quantitatively and qualitatively.
Why it matches plant phenotyping methodsブロッコリーの植物体由来GSL含量という化学的形質を対象に、NIRS校正法を開発し、HPLC参照値および予測性能指標で検証しているため、形質取得法が中心である。
abstractThis study describes the development of near-infrared spectroscopy (NIRS) calibration to determine individual and total glucosinolates (GSLs) content of 12 new-bred open-pollinating genotypes of broccoli
The aim of this study was to develop a diagrammatic scale to evaluate black rot (Xanthomonas campestris pv. compestris) severity on kale (Brassica oleraceae var. acephala) leaves. The diagrammatic scale was developed and validated with eight levels of severity, ranging from 0.19 to 48.8%. More than 95% of the leaves collected from the field showed severity levels ranging from 0.1 to 21%, and 5% of the leaves showed severities higher than 22%. The validation of the scale was performed by 10 inexperienced evaluators, and the data were analysed with two methods: linear regression and Lin's statistics. Without the scale, most evaluators overestimated disease severity, whereas the use of the scale resulted in increased precision, accuracy, repeatability, and reproducibility of the estimates according to both validation methods. In conclusion, the proposed diagrammatic scale proved to be useful for assessments of black rot severity in kale leaves. The scale may be of interest to researches performing studies on epidemiology or breeding for resistance.
Why it matches plant phenotyping methodsケール葉の病害重症度という植物状態を評価する図解スケールを開発し、精度・正確性・再現性を検証した、中心的な植物フェノタイピング手法研究。
abstractThe aim of this study was to develop a diagrammatic scale to evaluate black rot (Xanthomonas campestris pv. compestris) severity on kale (Brassica oleraceae var. acephala) leaves.
Due to the importance of glucosinolates and their hydrolysis products in human nutrition and plant defense, optimizing the content of these compounds is a frequent breeding objective for Brassica crops. Toward this goal, we investigated the feasibility of using models built from relative transcript abundance data for the prediction of glucosinolate and hydrolysis product concentrations in broccoli. We report that predictive models explaining at least 50% of the variation for a number of glucosinolates and their hydrolysis products can be built for prediction within the same season, but prediction accuracy decreased when using models built from one season's data for prediction of an opposing season. This method of phytochemical profile prediction could potentially allow for lower phytochemical phenotyping costs and larger breeding populations. This, in turn, could improve selection efficiency for phase II induction potential, a type of chemopreventive bioactivity, by allowing for the quick and relatively cheap content estimation of phytochemicals known to influence the trait.
Why it matches plant phenotyping methods相対転写量からブロッコリーのグルコシノレート濃度を推定する予測モデルを開発し、季節間で精度を検証している。植物化学形質の低コストなフェノタイピング手法が中心である。
abstractwe investigated the feasibility of using models built from relative transcript abundance data for the prediction of glucosinolate and hydrolysis product concentrations in broccoli.
Brassica vegetablesRaman / spectroscopyLeafPhysiological trait estimationVisualization / data management
Understanding nitrogen (N) status in the leaves of Chinese cabbage (Brassica rapa subsp. chinensis) is of significance to both vegetable growth and quality control. Fourier transform infrared photoacoustic spectroscopy was used to perform rapid qualification of N distribution in leaves; a partial least squares algorithm was used to develop a model for prediction of the N content; and N distribution in individual leaves was mapped on the basis of interpolation analysis, which was found to be variable. A reasonable N input level (13 mmol L -1 N) showed the largest variance of the N content, benefiting N redistribution and use efficiency, but variance decreased at the old stage. Moreover, the pattern of N distribution within a leaf was irregular even among the replications performed for each treatment, and sunlight was found to be the dominant factor as a result of leaves receiving variable light intensities.
Why it matches plant phenotyping methods葉内窒素分布という植物の生理状態を、FTIR光音響分光法、PLS予測モデル、補間によるマッピングで取得・推定する手法が研究の中心であり、単なる栽培試験の routine 測定ではない。
titleTwo-Dimensional Visualization of Nitrogen Distribution in Leaves of Chinese Cabbage (Brassica rapa subsp. chinensis) by the Fourier Transform Infrared Photoacoustic Spectroscopy Technique.
Clubroot disease caused by Plasmodiophora brassicae is one of the most serious diseases in Brassica crops worldwide. In this study, the pathotypes of 12 Korean P. brassicae field isolates were determined using various Chinese cabbage including 22 commercial cultivars from Korea, China, and Japan, and 15 inbred lines. All P. brassicae isolates exhibited the typical clubroot disease on non-clubroot resistant cultivar, indicating that the isolates were highly pathogenic. According to the reactions on the Williams' hosts, the 12 field isolates were initially classified into five races. However, when these isolates were inoculated onto clubroot-resistant (CR) cultivars of Chinese cabbage, several isolates led to different disease responses even though the isolates have been assigned to the same race by the Williams' host responses. Based on the pathogenicity results, the 12 field isolates were reclassified into four different groups: pathotype 1 (GN1, GN2, GS, JS, and HS), 2 (DJ and KS), 3 (HN1, PC, and YC), and 4 (HN2 and SS). In addition, the CR cultivars from Korea, China, and Japan exhibited distinguishable disease responses to the P. brassicae isolates, suggesting that the 22 cultivars used in this study, including the non-CR cultivars, are classified into four different host groups based on their disease resistance. Combining these findings, the four differential hosts of Chinese cabbage and four pathotype groups of P. brassicae might provide an efficient screening system for resistant cultivars and a new foundation of breeding strategies for CR Chinese cabbage.
Why it matches plant phenotyping methodsアブラナの根こぶ病応答を用いた病害表現型による病原型分類と、抵抗性品種スクリーニング系の構築が研究の中心であり、単なる病害測定ではない。
abstractBased on the pathogenicity results, the 12 field isolates were reclassified into four different groups