Plant phenotyping methods.

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

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

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

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

Continuous monitoring of deficit irrigation in avocado across two contrasting rainfall years using sensor networks, telemetry, and machine learning →

Water scarcity and increasingly irregular rainfall threaten avocado production in Mediterranean regions, yet the long term physiological responses of mature trees to sustained deficit irrigation remain poorly understood. We conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping in a mature avocado orchard subjected to three irrigation regimes. The two study years differed markedly in rainfall, providing a unique opportunity to evaluate how environmental conditions modulate tree responses to water limitation. Trees under severe deficit irrigation showed depletion of water in deeper soil layers and a flattened physiological profile, with near-zero diel variation in leaf thickness and trunk water potential, indicating minimal transpiration and decoupling of tree water status from environmental demand. Drone telemetry via NDVI detected stress during fruit growth and maturation, but not during flowering or the new summer leaf flush, revealing greater drought sensitivity at later maturation stages. Although canopy area did not differ among irrigation treatments, canopy surface roughness increased significantly under deficit irrigation, thereby identifying a novel structural indicator of drought stress. Despite large physiological differences among treatments, fruit number remained stable, while fruit weight decreased significantly under severe deficit irrigation, particularly in the wetter year, suggesting that annual rainfall modulates the trade-off between fruit retention and fruit growth. This study provides the first continuous, multi-scale characterization of avocado performance under sustained deficit irrigation in Mediterranean conditions. By integrating plant-based sensors, remote sensing, and artificial intelligence, we reveal previously undescribed stress dynamics and identify new indicators for precision irrigation management in fruit crops.

Why it matches plant phenotyping methods継続的な植物センサー、ドローン画像、樹冠構造解析、果実表現型計測を統合し、NDVIや樹冠表面粗さなどのストレス指標を抽出する方法が研究の主要部分であるため。

abstractWe conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping

From occlusion to 3D: amodal completion-assisted single-view wheat reconstruction →

Occlusion is a major factor limiting accurate three-dimensional (3D) wheat phenotyping. In natural growth conditions, overlapping spikes, leaves, and stems often make only partial target regions visible in single-view images, hindering complete and reliable 3D reconstruction. To address this problem, this study proposes an amodal completion-assisted, sequential framework for single-view 3D reconstruction of occluded wheat. The framework first uses visible prompts to recover the complete appearance and structural cues of occluded targets, and then feeds the completed images into single-view 3D reconstruction models to generate complete 3D structures. We construct the MMWO (Multi-view Multi-instance Wheat Occlusion) dataset from MMW, which is captured under controlled indoor scenarios, by synthesizing diverse occlusion samples through organ-level cutouts, random geometric transformations, and region-constrained pasting, with annotations including visible masks, occlusion masks, and complete target images. Six representative reconstruction methods, including Direct3D, Real3D, SF3D, Spar3D, TRELLIS.2, and Hunyuan3D, are systematically evaluated. Hunyuan3D achieves the best geometric performance, with the lowest mean CD- \(L_1\) and CD- \(L_2\) values of 0.1286 and 0.0536, and the highest mean F-score of 0.5668. SF3D achieves the best rendering quality in terms of PSNR, SSIM, and LPIPS. In addition, Pix2Gestalt completion reduces the estimation errors of spike length, width, and area from 9.31%, 10.89%, and 32.23% to 4.64%, 9.70%, and 9.45%, respectively. These results demonstrate that amodal completion can effectively alleviate occlusion-induced information loss and provide more complete structural priors for single-view 3D wheat reconstruction. This study offers a feasible solution for 3D wheat phenotyping under occlusion and provides a systematic reference for applying 3D generative models to agricultural phenotyping.

Why it matches plant phenotyping methods遮蔽下の単一画像から小麦器官を3D再構成し、形質推定精度を改善する手法を開発・比較検証しており、植物フェノタイピング手法が中心である。

abstractTo address this problem, this study proposes an amodal completion-assisted, sequential framework for single-view 3D reconstruction of occluded wheat.

A UAV-based sparse-view 3DGS framework for greenhouse strawberry reconstruction →

UAV-based multi-view reconstruction is an important approach for high-precision, non-destructive 3D crop phenotyping. However, in greenhouse environments, UAV image acquisition is often restricted to sparse viewpoints because of UAV-induced airflow disturbances and the structural complexity of the greenhouse, which severely hinders accurate 3D phenotyping. To address this challenge, this study develops a task-driven phenotyping framework for constrained UAV viewpoints in greenhouse environments, integrating a vision-triggered flight planning strategy with an improved sparse-view 3DGS pipeline, termed SparseBerry-3DGS, for multi-view image acquisition and 3D phenotyping of greenhouse strawberries in GNSS-denied environments. Specifically, 3D Gaussian Splatting (3DGS) is improved by incorporating flow-guided initialization, depth supervision, and an adaptive pruning strategy, which effectively alleviate geometric collapse and floating artifacts under sparse-view conditions. Furthermore, sequential semantic masks generated by SAM2 are utilized to guide the segmentation of strawberry point clouds, thereby reducing background interference and segmentation errors. Experimental results show that the vision-triggered flight strategy enables stable capture of 16 surrounding images for each target fruit. Under sparse-view conditions, SparseBerry-3DGS improves reconstruction stability, with the average peak signal-to-noise ratio (PSNR) reaching 18.25 dB, corresponding to an 18% improvement. The SAM2-based segmentation module achieves high accuracy, with the mean intersection over union (mIoU) above 0.95. Geometric evaluation based on strawberry longitudinal diameter yielded an of 0.88, supporting the accuracy of fruit-scale geometric reconstruction. For weight estimation, five-fold cross-validation yielded an of 0.90 and an RMSE of 3.62 g, showing better predictive performance than models based on 2D projected area and standard 3DGS point clouds. This study provides a new approach for high-throughput, non-invasive digital crop phenotyping in greenhouse horticulture.

Why it matches plant phenotyping methods温室イチゴの3D形状再構成と重量推定を目的に、制約視点UAV撮影、SparseBerry-3DGS再構成、点群セグメンテーションを統合した表現型取得手法を開発・検証しており、方法が研究の中心である。

abstractthis study develops a task-driven phenotyping framework for constrained UAV viewpoints in greenhouse environments

AI-enabled Low-Cost 3D Maize Ear Morphometry Platform at Breeding Scale →

Maize ear geometry (length, width, curvature, and volume) is closely tied to yield and grain-filling outcomes, but existing high-throughput phenotyping pipelines remain constrained by the cost, labor, and specialized hardware they require. We developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear from a single 20-second video captured with a consumer-grade DSLR on a motorized turntable under uniform LED illumination. Camera poses from a multi-seed COLMAP procedure initialize a Neural Radiance Field (NeRF), and a cylindrical holder of known diameter, visible in every frame, provides automatic metric scaling with downstream geometric quality control. Applied to 300 ears spanning a diverse maize inbred panel, 250 (83.3%) passed automated processing and quality control. Skeleton length agreed with manual caliper measurements across all 250 ears (R^2 = 0.964, RMSE = 4.68 mm), and convex-hull volume agreed with water-displacement volume on a 15-ear subset spanning the full size range (R^2 = 0.982, RMSE = 5.26 mL). Residual length error grew with ear curvature, whereas bounding-box height, which records the same straight-line chord as calipers, showed no such trend; the discrepancy therefore originates in the measurement definition, since calipers record the chord while skeleton length traces the geodesic arc. The capture hardware costs approximately 607 USD, and operator involvement fell from roughly five minutes to one minute per ear, with all downstream processing running unattended. The platform provides a foundation for breeding-scale 3-D ear phenotyping.

Why it matches plant phenotyping methodsトウモロコシ雌穂の3D形態形質を抽出する低コスト画像計測パイプラインを開発し、手動測定および体積測定で技術検証しているため、方法が研究の中心である。

abstractWe developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear

Image‐based and biochemical multimodal phenotyping for explainable classification of chia ( Salvia hispanica L.) genotypes →

Abstract BACKGROUND This study developed an explainable machine learning framework integrating morphological, color, and biochemical characteristics for classifying chia ( Salvia hispanica L.) genotypes. A dataset was assembled from 1200 seed images spanning four genotypes, from which 17 morphological and color features were extracted. These were complemented by six sample‐level biochemical traits – crude protein, fat, ash, fiber, carbohydrate, and total sugar – obtained from the corresponding experimental‐unit seed sample, resulting in a total of 23 variables in the integrated dataset. The dataset was evaluated comparatively with 10 machine learning algorithms under repeated 10‐fold cross‐validation, with all preprocessing confined to each training fold to avoid data leakage. RESULTS The highest performance was obtained with XGBoost, reaching 86.99% accuracy, a Matthews correlation coefficient of 0.820, a receiver operating characteristic (ROC) area of 0.975, and a precision–recall curve (PRC) area of 0.933; Simple Logistic followed closely at 86.85% accuracy, with comparable ROC and PRC areas (0.974 and 0.933). Significant differences among the algorithms were confirmed by the Friedman test ( P = 2.47 × 10 −120 ), with post hoc comparisons placing XGBoost and Simple Logistic within the same top‐performing group. Protein, fiber, ash, and fat were the most influential biochemical traits, while hue and saturation among color parameters and shape index and geometric mean diameter among morphological features also contributed appreciably. The G1 genotype, which showed comparatively high protein (27.62%) and fiber (40.62%) contents, was the most consistently distinguished class, with XGBoost and Simple Logistic achieving F‐measures of 0.954 and 0.955, respectively, whereas greater phenotypic overlap between G2 and G3 resulted in more frequent mutual misclassifications. CONCLUSION These findings indicate that multimodal phenotyping, coupled with explainable machine learning, offers a practical and biologically interpretable decision‐support approach for chia genotype classification. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Why it matches plant phenotyping methods画像から種子の形態・色形質を抽出し、生化学形質と統合したマルチモーダル表現型解析・機械学習分類法の開発と比較評価が研究の中心であるため。

abstractThis study developed an explainable machine learning framework integrating morphological, color, and biochemical characteristics for classifying chia ( Salvia hispanica L.) genotypes.

A Vision-Based Deep Learning Framework enables High-Accuracy Prediction of Geng Rice Eating Quality and Facilitates QTL Mapping. →

Northeast China's Geng rice (Oryza sativa subsp. japonica) dominates the high-value rice markets in China due to its superior eating quality. However, current evaluation methods rely on either labor-intensive, subjective sensory protocols or low-accuracy, calibration-heavy near-infrared spectroscopy (NIRS), constraining breeding for high eating quality and market development. Here, we report a vision-based deep learning framework combining multi-population fine-tuning with industrial vision-language model (VLM) pre-training for Geng rice eating quality prediction. Trained on natural and recombinant inbred (RI) population datasets, our optimal model (Model 4) showed high cross-population stability. It achieved R 2 values of 0.98, 0.57, and 0.61 in a natural population validation set (35 cultivars), an independent DA-RI population (201 lines), and a randomly collected set (30 Northeast and 28 Southern cultivars), respectively, consistently outperforming the widely used Satake STA1B analyzer. Furthermore, our approach enabled the mapping of a novel, robust quantitative trait locus, qIVOE7, for Geng rice eating quality on Chromosome 7. Further analysis suggested that Model 4 appears to rely on the Hue dimension of the HSV color space for its predictions. This framework provides a high-accuracy prediction model and an easy-to-use tool for rice eating quality evaluation, accelerating high-quality rice breeding as well as the development of the high-quality rice market.

Why it matches plant phenotyping methodsコメの食味という植物（種子）形質を画像ベースの深層学習で推定する手法を開発し、複数集団で検証・既存分析器と比較しており、フェノタイピング手法が研究の中心である。

abstractwe report a vision-based deep learning framework combining multi-population fine-tuning with industrial vision-language model (VLM) pre-training for Geng rice eating quality prediction.

Temporal profiling of floret developmental asynchrony for wheat-fertility studies. →

Wheat grain number integrates fate of individual florets, strongly affected by environmental stress during sensitive stages like meiosis. Because development is asynchronous across tillers, spikelets, and florets, it is hard to distinguish stress tolerance from stress escape. Based on 2400 destructive measurements of spike and anther length together with non-destructive morphological measurements from 158 plants grown in four experiments in controlled-environment, we developed a framework to track individual floret developmental stages at plant level. We applied it in two case studies for connecting within-plant developmental asynchrony to reproductive success under favorable or heat conditions. All florets showed a common relative growth rate, producing additive delays across tillers (1-7 d), spikelets (1-5 d), and floret positions (1-6 d). This generated a developmental map for every floret based on external traits. Under control conditions, grain set probability at floret level combined both positional and developmental effects within a spike. Under heat stress, grain loss occurred only in florets at meiosis during the stress, allowing to quantify a true "stress response", while later florets escaped damage. This framework allows understanding and predicting floret development and linking it to grain set, clearly distinguishing timing effects from positional influences and separating tolerance from stress escape.

Why it matches plant phenotyping methods外部形態測定から個々の小花の発育段階を追跡する方法・発育マップを開発し、複数実験で適用しているため、表現型取得・推定が研究の中心である。

abstractwe developed a framework to track individual floret developmental stages at plant level

Non-destructive durian locule counting from rotational RGB imaging using rotation-synchronized 2D-to-1D morphological energy inference →

The edible yield and commercial value of durian are strongly determined by the number of fully developed internal locules, yet their assessment still relies largely on subjective manual inspection or costly destructive analysis. Existing non-destructive approaches, including tapping-based evaluation and X-ray imaging, are either insufficiently standardized or impractical for high-throughput phenotyping. This study proposes the Rotation-Synchronized Structural Locule Evaluator (RS-SLE). The framework integrates convolutional spatial learning with deterministic signal processing to estimate internal locule number from rotational RGB imaging. The proposed system learns frame-wise spatial morphology using convolutional heatmap regression and performs temporal inference through deterministic kinematic signal processing over a mechanically bounded 360° rotation, thereby avoiding reliance on data-intensive recurrent video models. Specifically, sequential 2D locule-probability heatmaps are transformed into a synchronized 1D morphological energy signal, which is subsequently refined using gradient-based integration, Tikhonov regularization, and adaptive morphological thresholding to recover cycle-consistent structural peaks corresponding to fertile locules. This design provides a transparent and computationally efficient alternative to learned temporal memory while maintaining robustness to viewpoint variation, partial occlusion, and high-frequency spine noise. Evaluated on 260 fruits of the ‘Monthong’ cultivar, the proposed framework achieved a mean absolute error of 0.289 locules and a 71.1% exact-match rate on independent rotational videos. When combined with morphological correlation analysis, the framework attained a 100% tolerance accuracy (±1 locule) on the test set with destructive ground-truth measurements. These results demonstrate that dynamic external morphology can serve as a reliable optical proxy for internal locule development. Crucially, the final locule count is derived entirely from the CNN-generated one-dimensional morphological signal. This demonstrates the necessity of localized structural analysis over simple macroscopic shape indices. The findings highlight the value of explainable, kinematics-informed vision systems for practical precision agriculture.

Why it matches plant phenotyping methods回転RGB画像からドリアン内部のlocule数という植物器官状態を推定する画像ベース表現型計測法を開発し、独立動画と破壊的グラウンドトゥルースで検証しているため、方法が研究の中心である。

abstractThis study proposes the Rotation-Synchronized Structural Locule Evaluator (RS-SLE).

UAV-Hyp: UAV Dataset for Remote Sensing-Based Phenotyping of Hypericum perforatum →

Quantitative flowering phenotypes are needed to support breeding and harvest management in Hypericum perforatum L. (St. John’s wort), but manual flower assessment is slow and difficult to standardize under field conditions. UAV-Hyp is a multi-temporal UAV RGB dataset containing 12,653 high-resolution images acquired at 26 measurement dates across the complete flowering period of 15 H. perforatum accessions. The images represent variable illumination, soil moisture, weed pressure, and developmental stages. The dataset provides 59,163 plant bounding boxes and 107,054 flower bounding boxes. As an application example, cascaded YOLOv8 plant and flower detectors achieved mAP@0.50:0.95 values of 0.977 and 0.950, respectively. UAV-Hyp supports scalable flower quantification and the development of time-series phenotyping methods for genotype comparison and quality-oriented medicinal-plant breeding.

Why it matches plant phenotyping methods植物の開花形質を定量化するUAV画像データセットを提供し、検出性能も評価しているため、フェノタイピング用データセット・解析手法が中心です。

abstractUAV-Hyp is a multi-temporal UAV RGB dataset containing 12,653 high-resolution images acquired at 26 measurement dates across the complete flowering period of 15 H. perforatum accessions.

A Novel Drought-Resistance Index Balancing Foxtail Millet Yield and Quality and Its Prediction Based on UAV Multimodal Data →

Drought stress severely limits foxtail millet yield and quality, yet current drought-resistance indices are exclusively yield-oriented and ignore grain-filling quality. Our two-year (2024–2025) experiments with 24–48 varieties revealed that yield and blighted grain rate (BGR) are partially decoupled (e.g., Zhangzagu 18: yield 2307 kg/ha, BGR 0.444; Zhonggu 19: yield 1622 kg/ha, BGR 0.280). We therefore constructed the Yield–Quality Synergy Index (YQSI = DYI − BGR), which penalizes varieties with poor grain filling. The YQSI tied for first place with DYI in comprehensive screening performance and achieved the highest inter-annual stability (Spearman ρ = 0.823, Jaccard = 0.438, composite score = 1.261). Sensitivity analysis confirmed robustness of the equal-weight formula across a 4-fold range of quality-penalty weights. Six strongly drought-resistant germplasms with balanced yield and quality were identified. Using UAV multimodal data (RGB, multispectral, and thermal infrared) acquired during grain filling, a Random Forest model predicted a YQSI with overall R2 = 0.819 and an F1 score of 0.933 for variety screening. Feature-importance analysis highlighted NDVI, WDRVI, and red-edge texture as key predictors. This study provides a quality-constrained drought-resistance evaluation framework and demonstrates the potential of UAV-based high-throughput phenotyping for foxtail millet breeding.

Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル・熱赤外データから干ばつ耐性指標を予測する高スループット表現型解析手法が研究の中心であり、モデル性能も評価している。

abstractUsing UAV multimodal data (RGB, multispectral, and thermal infrared) acquired during grain filling, a Random Forest model predicted a YQSI with overall R2 = 0.819 and an F1 score of 0.933 for variety screening.

High‐throughput assessment of plant stand establishment, seedling vigor, and light interception in peanut using UAV‐based RGB and multispectral imagery →

Abstract In peanut ( Arachis hypogaea L.), plant stand establishment, seedling vigor, and canopy growth are key determinants of crop performance; yet traditional ground‐based assessment methods can be destructive, labor‐intensive, and limited in throughput. This study evaluated the potential of vegetation metrics derived from unmanned aerial vehicle (UAV)‐based red‐green‐blue (RGB) and multispectral (MS) imagery for high‐throughput, nondestructive assessment of plant stand establishment, seedling vigor, and light interception in peanut. Six runner‐type peanut cultivars were evaluated in 2024 and seven in 2025, with each cultivar represented by two seed size classes (small and large), to generate variation in these traits. Within‐row vegetation discontinuity‐based plant stand ratings for estimating plant stand count ( R 2 = 0.81–0.90), together with canopy coverage for assessing seedling biomass ( R 2 = 0.77–0.82) and light interception ( R 2 = 0.96–0.98), were the best‐performing vegetation metrics. These vegetation metrics provided similar or greater cultivar separation compared with ground‐based measurements. In contrast, several vegetation indices exhibited strong correlations with ground‐based measurements but provided inconsistent cultivar rankings and statistical groupings. MS imagery outperformed RGB imagery for plant stand and seedling biomass assessment. Overall, these results demonstrate that UAV‐derived canopy metrics provide reliable, high‐throughput tools for early‐ to mid‐season crop assessment and offer scalable alternatives to traditional ground‐based approaches for agronomic, crop physiological, and plant breeding research.

Why it matches plant phenotyping methodsUAV画像から植物体の出芽・苗勢・バイオマス・光 interception を推定する植 phenotyping 手法を開発・評価しており、取得指標の性能検証が研究の中心です。

abstractThis study evaluated the potential of vegetation metrics derived from unmanned aerial vehicle (UAV)‐based red‐green‐blue (RGB) and multispectral (MS) imagery for high‐throughput, nondestructive assessment of plant stand establishment, seedling vigor, and light interception in peanut.

Analytically derived sphere correction enables transferable RGB-D fruit sizing across fruit shapes and depth-sensing principles. →

Abstract Depth cameras measure the distance to a fruit's surface, whereas converting its silhouette into physical dimensions requires the depth of its center; corrections for this offset have so far been empirical, and therefore bound to the crop, sensor, and dataset they were fitted on. This paper derives the correction analytically. For a spherical fruit, integrating the surface-depth distribution over the visible hemisphere yields a closed-form sphere correction whose coefficient follows from sampling geometry, together with a theoretical justification of the median mask depth as the representative statistic. Combined with deep instance segmentation on RGB-D imagery of hydroponic melons, the empirically optimal coefficient coincided with the derived value, and the pipeline reached R 2 of 0.966 for fruit length (MAE 1.43 mm), 0.959 for width (1.84 mm), and 0.861 for end-to-end fresh weight (MAPE 4.6%). The analytical form made the measurement transferable. Applied unchanged to cylindrical mini-cucumbers, the pipeline held mm-level accuracy (width MAE 0.52 mm; fresh weight R 2 0.955 after coe cient refitting), with the correction's negligibility predicted in advance by an R / Z corollary; across active-stereo and time-of-flight cameras, the optimal coefficients proved non-interchangeable, identifying the coefficient as a physical parameter that absorbs geometry, sensor physics, and fruit shape. A field system that fuses and cross-verifies the two sensors, with an error-propagation confidence gate and parameterized grading logic, reproduced 2-3% fresh-weight error and 90.9% confirmed-judgment grading accuracy over four validation sessions in a commercial greenhouse unseen during development. Throughout, geometric components transferred unchanged while learned and regression components required recalibration - a boundary the model predicts and the system itself monitors.

Why it matches plant phenotyping methodsRGB-D画像と深度補正を用いて果実の寸法・重量を推定する手法を開発し、異なる果形・センサー・圃場で精度検証しており、植物表現型取得が中心である。

abstractThis paper derives the correction analytically.

Association of morphological markers of flower buds and anthers with the pollen developmental stage in Vicia faba L. ( Fabaceae ). →

The precise identification of microspore and pollen at the optimal developmental stages to be induced towards embryogenesis (vacuolated microspores and young pollen) is essential for induction of in vitro androgenesis in plants. Such identification is not always easy, and it is especially difficult in recalcitrant species such as Vicia faba . The present study evaluates the relationship between floral bud and anther morphometric parameters, and microspore/pollen developmental stages in V. faba using various methods including fluorescent staining, differential interference contrast microscopy, and morphometry. We measured flower bud and anther length and width, grouping them at different intervals, and performed a detailed microscopical and anatomical analysis of buds, anthers and microspores/pollen at different stages. Our results demonstrated that flower buds in V. faba exhibit complex and irregular morphologies, with considerable variation in both sepal length and shape. Furthermore, the determination of microspore and pollen developmental stages in this species is constrained by pronounced developmental asynchrony and strong genotype dependence. Although anther length measurements correlate closely with microspore and pollen developmental stages, their practical use can be challenging. Therefore, measuring flower bud length, while excluding sepals, remains the most practical criterion for routine applications. Combining this refined morphometric approach with microscopic validation appears to be the most effective strategy for improving the identification of flower buds containing microspores or pollen at developmental stages suitable for androgenesis induction in this recalcitrant legume species.

Why it matches plant phenotyping methods花蕾・葯の形態計測と顕微鏡検証を用いて、微小胞子・花粉の発達段階を推定する実用的な植物フェノタイピング手法を評価しており、方法開発・検証が中心である。

abstractThe present study evaluates the relationship between floral bud and anther morphometric parameters, and microspore/pollen developmental stages in V. faba using various methods including fluorescent staining, differential interference contrast microscopy, and morphometry.

Non-Destructive Watermelon Ripeness Assessment Using Deep Learning and Field-Based RGB Imagin →

Accurate, non-destructive assessment of watermelon ripeness remains a significant challenge in horticultural production, particularly under field conditions where traditional visual and tactile evaluation methods are subjective and often inconsistent. Although mechanical, acoustic, and spectroscopic techniques have demonstrated promising performance, their reliance on controlled laboratory environments limits their practical applicability in real-world agricultural settings. This study presents a field-deployable, AI-assisted computer vision system designed for objective, real-time classification of watermelon ripeness. The proposed prototype combines controlled illumination with RGB imaging and convolutional neural networks trained on thousands of annotated outdoor images collected over multiple growing seasons. A phased development strategy—encompassing proof-of-concept modelling, field integration, and multi-season validation—supports robustness against variable lighting conditions and environmental influences. The anticipated outcome is a reliable, non-destructive decision-support tool for growers, capable of identifying ripe fruit for manual harvesting while providing a technological foundation for future autonomous harvesting and precision agriculture applications.

Why it matches plant phenotyping methodsスイカ果実の成熟度という植物器官の状態を、RGB画像とCNNで非破壊推定する手法を開発し、圃場統合と複数季節の検証まで行うため、フェノタイピング手法が中心です。

abstractThis study presents a field-deployable, AI-assisted computer vision system designed for objective, real-time classification of watermelon ripeness.

An integrated YOLOv11-based framework for wheat spike phenotyping segmentation and grain yield estimation →

Spike count and spatial arrangement are among the strongest determinants of grain yield in wheat, yet reliable spike-level phenotyping under field conditions remains challenging. Field-acquired images are often affected by occlusion, heterogeneous illumination, and dense canopy overlap. In addition to these visual complexities, the effort required to generate large-scale instance-level annotations makes it difficult to build the extensive labeled datasets typically needed for robust segmentation models. This paper addresses both problems. The core methodological contribution is a semi-automated iterative annotation workflow: a YOLOv11x-seg model trained on a small manually annotated set is applied to unlabeled field images, and its predictions, after post-processing to remove duplicated, fragmented, and merged detections, are incorporated back into training. To prevent augmentation from distorting the training distribution, we introduce a distribution-aware augmentation strategy guided by Fréchet Inception Distance (FID), which retains only those augmented samples that remain within an acceptable distance from the original data distribution. Together, these components allowed us to build an effective training set from 3,899 high-resolution RGB images (4000 × 3000 pixels) of durum wheat collected at the CREA Research Centre for Cereal and Industrial Crops, with substantially reduced manual annotation effort. On the independent test set(242 images), the final YOLOv11x-seg model (M5 model) achieved a mask-level precision of 86.73%, recall of 83.02%, F1-score of 84.83%, mAP@50 of 89.42%, and mAP@50:95 of 60.51%. Spike masks were used to derive image-based traits including spike count, spike density, canopy coverage, spike area, spatial distribution, and vegetation indices. Their relationships with measured grain yield were explored through statistical analysis and machine-learning-based yield estimation.Both statistical and machine-learning analyses demonstrated that image-derived spike traits provided meaningful information for grain yield estimation, with canopy coverage showing the strongest positive association with yield. Using repeated nested cross-validation with out-of-fold (OOF) predictions, XGBoost achieved the highest yield estimation performance ( R OOF 2 = 0.312 , RMSE = 106.61 g/plot), supporting the potential of near-image phenotyping for late-stage yield estimation in wheat. These results show that semi-automated iterative annotation can enable practical wheat spike segmentation and image-based phenotyping under realistic open-field conditions. While grain yield estimation should be interpreted within the context of the experimental setting, the proposed framework highlights the value of image-derived spike traits for late-stage phenotyping and yield assessment in breeding experiments rather than for early-season yield forecasting.

Why it matches plant phenotyping methods半自動アノテーション、YOLOv11x-segによる小麦穂の画像セグメンテーション、分布認識型データ拡張、形質抽出と検証が中心的な方法論的貢献である。

abstractThe core methodological contribution is a semi-automated iterative annotation workflow

Smartphone-based high-fidelity 3D semantic segmentation of finger millet panicles using 3D Gaussian splatting for automated phenotyping and yield estimation →

Finger millet is an important cereal crop widely cultivated worldwide for food and fodder. Breeding programs aim to select genotypes with desirable architectural traits to develop new varieties with higher yields. In this effort, accurate high-throughput plant phenotyping is essential for accelerating crop improvement. To overcome the time-consuming and labor-intensive process of manual measurements, this study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images. First, multiple-view RGB images of a single finger millet plant were captured, and COLMAP was then utilized to estimate the camera poses of the images and reconstruct the sparse point cloud, followed by advanced 3D reconstruction through 3DGS and NeRF. Second, feature 3DGS and gaussian grouping models were used to generate the 3D gaussian representation of finger millet panicles. This single-process framework enabled the generation of high-fidelity 3D point clouds and semantic feature fields without the need for expensive depth sensors or manual annotations. Our results demonstrated the effectiveness of these models in capturing morphological variations across different panicle phenotypes, including compact versus open panicle architectures. In addition, the 3D point clouds of the panicles were utilized to extract structural traits for yield prediction, achieving biologically meaningful correlations with grain productivity. This work highlights the potential of 3DGS-based phenotyping pipelines as a low-cost, near real-time, photorealistic solution for trait quantification, segmentation, and yield estimation in real-world agricultural settings.

Why it matches plant phenotyping methods3D画像再構成・セグメンテーション・形質抽出を統合した植物フェノタイピング手法の開発が中心であり、収量関連形質の定量と予測まで技術的に評価している。

abstractthis study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images.

CEG-YOLO: A lightweight edge-optimized framework for in-field rice panicle counting →

Rice panicle number per unit area is a key determinant of yield, but manual counting remains time-consuming and labor-intensive. This study proposes CEG-YOLO, a lightweight deep learning model for in-field rice panicle detection and counting using consumer-grade RGB imaging devices. The model introduces three improvements to YOLOv11s to address specific challenges in field scenarios: C2f-Fast replaces standard convolutions with depthwise convolutions to reduce computational cost for edge deployment; SPPF-ECA integrates an attention mechanism to suppress complex background interference; and GhostConv reduces feature redundancy to improve detection of dense panicles. A dataset of 5,175 images was collected from four rice cultivars planted at three densities using an iPhone 12. The proposed model achieved 93.9% average precision (AP) on the test set, outperforming YOLOv11s which achieved 89.1%, while reducing parameters to 7.8 million and floating-point operations (FLOPs) to 16.5 billion. Robustness evaluation yielded coefficients of determination (R²) values of 0.91 to 0.94 for lighting, 0.89 to 0.94 for planting density, and 0.90 to 0.94 for cultivar. A cross-year field test using an NVIDIA Jetson Orin NX edge device on 120 quadrats in 2025 achieved R² of 0.91, root mean square error (RMSE) of 4.0, and mean absolute error (MAE) of 3.3 at 20 frames per second, confirming practical deployability. This study demonstrates that smartphone-based proximal phenotyping with an optimized deep learning model can provide accurate, low-cost rice panicle counting for breeding and production applications.

Why it matches plant phenotyping methodsイネ穂数という植物形質をRGB画像から推定する深層学習モデルを開発し、精度・頑健性・実地展開性能を検証しており、フェノタイピング手法が研究の中心である。

abstractThis study proposes CEG-YOLO, a lightweight deep learning model for in-field rice panicle detection and counting using consumer-grade RGB imaging devices.

High-Throughput Panicle Counting of Wild Rice Accessions for Germplasm Evaluation: An AI-Driven UAV Phenotyping Framework →

Wild rice (Oryza spp.) harbors abundant genetic variation and represents an important germplasm resource for improving yield-related traits in cultivated rice. Panicle number is a key phenotypic trait for evaluating tillering capacity and yield potential in wild rice. However, existing approaches for acquiring panicle-number phenotypes remain limited by low efficiency, high dependence on manual operation, and cumbersome matching between plant targets and accession identifiers. In this study, we proposed an AI-driven UAV phenotyping framework for high-throughput panicle counting of wild rice accessions for germplasm evaluation. The framework integrates field plant localization, accession identifier binding, flight route planning, plant-by-plant video acquisition, spatiotemporal registration, video slicing, and panicle detection and tracking, enabling structured panicle-number outputs indexed by accession identifier. To address the small scale, loose structure, morphological variation, and wind-induced swaying of wild rice panicles in UAV imagery, a wild rice panicle detection model was constructed, and WRPD-Tracker was developed for cross-frame identity association and non-redundant counting. The wild rice panicle detection model achieved an AP@50 of 91.56%, representing a 6.16-percentage-point improvement over the DEIM baseline, with 3.70 M parameters and 6.55 G FLOPs, while WRPD-Tracker achieved a HOTA of 65.1% and a MOTA of 79.0%, representing a 5.8-percentage-point improvement in HOTA over the baseline tracker. At the final counting level, UAV-based counts were highly consistent with manual ground counts, with an R² of 0.992 and an MAE of 0.37 panicles. This framework enables batch acquisition of panicle-number phenotypes in wild rice and provides quantitative support for germplasm evaluation, panicle-number trait comparison, and subsequent yield-related phenotypic studies.

Why it matches plant phenotyping methodsUAV画像とAIによるイネ穂数の取得・追跡・計数手法を開発し、手動計数と技術検証しており、植物フェノタイピング手法が研究の中心です。

abstractwe proposed an AI-driven UAV phenotyping framework for high-throughput panicle counting of wild rice accessions for germplasm evaluation.

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

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

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

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

Linking plant water status dynamics to yield and fruit cracking in citrus orchards using UAV multi-sensor data and machine learning →

Citrus fruit cracking causes substantial yield and economic losses, yet its relationship with plant water status (PWS) and irrigation management remains insufficiently characterized. Unlike previous UAV-based irrigation studies that focused on water-stress detection or yield estimation, this study introduces a dynamic, physiology-based framework that links temporal PWS trajectories during key phenological stages to fruit-cracking risk at the individual-tree scale. UAV-based multispectral, thermal, and LiDAR data, combined with field physiological measurements and machine-learning models, were evaluated in an irrigation management experiment in an ‘Ori’ mandarin orchard (Israel) across three contrasting growing seasons (2023–2025). Several irrigation treatments with different irrigation timings and water inputs were applied during the growing season to evaluate their effects on temporal PWS dynamics and fruit cracking. Trunk growth (TG), stem water potential (SWP), stomatal conductance (SC), and plant area index (PAI) were measured throughout the two seasons and estimated using Random Forest models (R 2 > 0.783). These indicators were subsequently used to predict yield and fruit cracking with high accuracy (yield: R² = 0.896; cracking: R² = 0.845). Cracking was lowest in 2023 (∼3%), with ∼25% lower irrigation, suggesting reduced irrigation may reduce cracking risk. Higher cracking in 2024 (∼14%, vs ∼8% in 2025) coincided with intense heat events. Mid-season SWP and SC were strongly associated with yield formation and cracking patterns. These findings demonstrate that monitoring temporal PWS dynamics can support precision irrigation management by identifying high-risk zones and enabling irrigation strategies that stabilize PWS, reduce the incidence of cracking, and improve yield under variable climatic conditions.

Why it matches plant phenotyping methodsUAVマルチセンサーと機械学習により、樹体水分状態などの植物形質を推定し、収量・果実裂果を予測する技術的枠組みが研究の中心である。

abstractthis study introduces a dynamic, physiology-based framework that links temporal PWS trajectories during key phenological stages to fruit-cracking risk at the individual-tree scale.

Automated Physical Quality Assessment of Harvested Seeds: A Critical Review of 2D and 3D Computer Vision Systems →

Purpose: This paper investigated automated physical quality assessment of harvested seeds. Design/Methodology/Approach: This study provides an extensive review of computer vision-based two-dimensional (2D) and three-dimensional (3D) deployments for the physical inspection of harvested seeds and grains. For this purpose, a total of 75 peer-reviewed articles published between 2022 and 2025 were identified from scientific databases, including Scopus, Web of Science, IEEE Xplore, and ScienceDirect. These articles were based on seed quality assessment, image processing, and artificial intelligence. The selected articles were systematically analysed according to different stages of the processing pipeline, including data acquisition, preprocessing, segmentation, feature extraction, and classification. Research Limitation: This review is restricted to physical quality assessment of harvested seeds, excluding chemical, biochemical, and nutritional parameters. It references 75 peer-reviewed articles published between 2022 and 2025. Findings: This study identified technical problems related to variations in seed samples, hardware setups, segmentation, feature selection, and classification. These problems significantly affect the performance of automated systems. Based on a critical examination of the present automated systems, this paper highlighted the scope for future research. Practical Implication: An advanced, future-ready system can address the need for integrated imaging methods and effective data processing. Social Implication: The adoption of automated seed inspection systems provides assurance of food security. Originality/ Value: This paper identified critical gaps such as the absence of a unified processing framework, the lack of cross-species generalisation, and the limited adoption of explainable AI.

Why it matches plant phenotyping methods収穫種子の物理品質を画像から評価する2D/3Dコンピュータビジョン手法を体系的にレビューしており、植物形質取得法が中心である。

abstractThis study provides an extensive review of computer vision-based two-dimensional (2D) and three-dimensional (3D) deployments for the physical inspection of harvested seeds and grains.

Deep Learning-Assisted Image Phenotyping for Genetic Dissection of Pod-Related Traits in Soybean →

Soybean pod-related traits are important for seed development, yield formation, and cultivar evaluation. However, conventional measurements are inefficient and have limited ability to characterize complex pod features such as curvature, local enlargement, and continuous color variation. In this study, mature pod images of 187 cultivated soybean accessions collected across two successive years were analyzed using a deep learning-assisted image phenotyping approach. Eight pod-related traits related to size, morphology, and color were extracted from pod images. A genome-wide association study (GWAS) was performed using 61,541 high-quality SNP markers to dissect the genetic basis of these image-derived pod traits. A total of 16 stable loci associated with pod size, morphology, and color traits were identified across 11 chromosomes. Among these loci, eight were not reported in the previous image-based soybean pod GWAS study. Based on SoyBase gene annotation and Gene Ontology biological process information, 32 biologically relevant candidate gene records were prioritized within the corresponding candidate genomic intervals, while pod-related expression profiles and SoyBase association information were used as supporting evidence for candidate gene evaluation. These findings indicate that refined image-derived traits can provide complementary genetic information beyond conventional pod measurements and offer additional opportunities for dissecting soybean pod development, morphology, and mature pod color variation.

Why it matches plant phenotyping methods深層学習画像解析によるダイズ莢形質の抽出が研究の中心であり、8種類の形態・色・サイズ形質を画像から定量化している。

abstractmature pod images of 187 cultivated soybean accessions collected across two successive years were analyzed using a deep learning-assisted image phenotyping approach.

Can Synthetic Data Overcome the Generalization Limits of AI-Based Flower and Pod Detection Across Cowpea Breeding Genotypes and Environments? →

High-throughput phenotyping requires AI-enabled computer vision models that generalize across genotypes, locations, and growing seasons, yet such models often lose accuracy under new conditions. Annotating real imagery for every genotype-by-environment (G x E) combination a breeding program encounters is prohibitively expensive. We quantify how G x E shifts affect AI-based detection of cowpea flowers and pods across two California locations and two growing seasons. Flower detection mAP@50 fell from 76.3% to as low as 50.6% under unseen shifts, and pod detection was more sensitive. Feature-space and image-quality diagnostics confirmed these losses track measurable distributional shifts. Because closing this gap with real data alone is not practical, we test whether synthetic imagery, rendered from a procedural 3D cowpea model, can substitute for that annotation burden. Synthetic supervision alone improved over pretraining but remained limited by a domain gap driven by camera image formation, not scene content. A domain-gap-aware camera-realism augmentation strategy, optimized against measured real-image statistics via Wasserstein distance, narrowed this gap, and a linear HDR representation converted a smaller measured gap into a larger detection gain than an 8-bit representation. Optimized HDR synthetic data combined with as few as five real images matched or exceeded the real-data baseline for spatial generalization, and pod detection benefited most at the lowest shot counts, with more modest gains under temporal shift. These results show that synthetic data can overcome the generalization limits of AI-based flower and pod detection, but only when the domain gap is measured and optimized rather than assumed away.

Why it matches plant phenotyping methods花・莢という植物器官の画像検出を対象に、異なる遺伝型・環境への一般化、合成画像、カメラリアリズム拡張、HDR表現を技術的に評価しており、植物表現型取得手法が研究の中心である。

abstractHigh-throughput phenotyping requires AI-enabled computer vision models that generalize across genotypes, locations, and growing seasons

Visualization and quantitative analysis of endosperm cavities in maize kernels via X-ray micro-computed tomography →

Endosperm cavities within maize kernels influence quality traits such as kernel plumpness and hardness, serving as a key phenotypic indicator for assessing maize yield and quality. Research on endosperm cavities remains relatively scarce due to the small size of maize kernels and limitations in technical approaches. This study employed X-ray micro-computed tomography (μCT) three-dimensional reconstruction technology to extract morphological parameters and spatial configurations of endosperm cavities in multiple maize varieties, enabling visualisation and quantification of endosperm cavities within maize kernels. Endosperm cavities exhibit spatial heterogeneity within the kernels: embryo-adjacent cavities (EACs) are distributed in a conical pattern around the embryo, whereas internal endosperm cavities (IECs) are located in the floury endosperm at the tip region of the kernel and exhibit a boat-shaped morphology. The volume ratio of EACs to IECs is approximately 5:1. A coordinate system was established with the kernel length axis perpendicular to the horizontal plane, revealing the spatial positions of IECs (x = 3.5 mm, y = 2.1 mm, z = 1.1 mm) and EACs (x = 2.5 mm, y = 2.3 mm, z = 7.1 mm). Significant differences in endosperm cavity characteristics were observed among the different varieties. The average volume of the endosperm cavities was 4.1 mm 3 , with kernel porosities ranging from 0.4% to 3.3%. These parameters exhibited highly significant positive correlations with kernel volume, kernel thickness, cavity surface density, etc. Although manual sectioning methods cannot capture the 3D features of endosperm cavities, their operational simplicity and rapid data extraction allow them to reflect, to some extent, the characteristics of endosperm cavities across different maize varieties, as confirmed by this study. This study elucidates the morphology and spatial distribution of endosperm cavities, revealing significant varietal differences in cavity characteristics that correlate with grain morphological traits. These findings lay the groundwork for research into maize grain digital characterisation and the relationship between grain structure and function.

Why it matches plant phenotyping methodsトウモロコシ種子内の内胚乳空洞をX線マイクロCTで3次元可視化し、形態・空間配置・体積などの表現型を抽出・定量化することが研究の中心である。

abstractThis study employed X-ray micro-computed tomography (μCT) three-dimensional reconstruction technology to extract morphological parameters and spatial configurations of endosperm cavities in multiple maize varieties, enabling visualisation and quantification of endosperm cavities within maize kernels.

A Simple Phenotypic Marker for Screening Pod Shattering in Cowpea (Vigna unguiculata (L.) Walp.) →

Pod shattering is a major domestication-related trait and one of the principal causes of pre-harvest yield losses in cowpea (Vigna unguiculata (L.) Walp.), particularly under hot and dry conditions. Rapid and reliable identification of shattering-resistant genotypes is essential for improving breeding efficiency. The present study evaluated a national core collection of 245 diverse cowpea genotypes over three consecutive years (2023–2025) to identify a simple phenotypic marker associated with pod-shattering resistance. Genotypes were screened using a modified Random Impact Method (RIM), and pod physical traits, including pod length, pod breadth, pod thickness, pod wall weight, seed-to-pod ratio, and dorsal suture morphology, were examined for their association with shattering response. A distinct and consistent morphological marker was identified in the dorsal suture of mature pods. Shattering-resistant genotypes exhibited a single, prominent dorsal ridge positioned above the dehiscence zone, whereas susceptible genotypes consistently displayed a two-ridged dorsal suture separated by a central depression that appeared to reduce tissue integrity and facilitate pod rupture under mechanical impact. The observed marker remained stable across years and environmental conditions, indicating its reliability as a rapid visual indicator of shattering resistance. The modified RIM provided a standardised, reproducible, and cost-effective approach for evaluating pod shattering while minimising environmental variation associated with field phenotyping. The identified dorsal ridge morphology offers a simple, non-destructive, and efficient phenotypic marker for large-scale germplasm screening and the selection of resistant genotypes. This marker can accelerate breeding for pod-shattering resistance, improve yield stability, and facilitate the development of climate-resilient cowpea cultivars adapted to drought-prone environments.

Why it matches plant phenotyping methods鞘の裂莢抵抗性を評価する標準化手法と、再現性のある形態マーカーを開発・検証しており、植物フェノタイピングが研究の中心である。

abstractGenotypes were screened using a modified Random Impact Method (RIM)

Large-scale GWAS integrated with image-based phenotyping reveals loci and trait-associated markers for Perilla frutescens seed traits →

Perilla ( Perilla frutescens ) is an important oilseed crop in East Asia with high nutritional and economic value. Seed size and seed coat color are key agronomic traits influencing yield, oil quality, and market preference. However, genetic studies in perilla remain limited by small population sizes and low-throughput phenotyping, restricting their application in breeding. A large-scale genome-wide association study (GWAS) was conducted by integrating high-throughput image-based phenotyping in a panel of 493 perilla accessions. Measurements of seed morphology, including area, perimeter, and length, and color traits (RGB components) were obtained. Genotyping-by-sequencing generated high-quality single-nucleotide polymorphism (SNP) datasets, and GWAS was performed using four statistical models (general linear model, mixed linear model, FarmCPU, and BLINK). A total of 44 significant trait-SNP associations were identified, corresponding to 20 unique SNPs, as several SNPs (including those on chromosomes 6, 10, 15, and 17) were associated with multiple correlated traits. Linkage disequilibrium-based analysis showed candidate genes involved in phenylpropanoid metabolism and carbohydrate pathways. Predicted protein-altering variants, including non-synonymous and stop-gained mutations, were detected in key genes. Derived cleaved amplified polymorphic sequence markers developed near peak SNPs distinguished phenotypic differences between allelic groups, demonstrating their effectiveness for trait differentiation. This study represents the first large-scale GWAS integrating image-based phenotyping for seed traits in perilla and provides candidate dCAPS markers with potential applicability to marker-assisted selection, pending validation in independent breeding populations. These findings offer valuable genetic resources and a practical framework for molecular breeding and crop improvement in perilla.

Why it matches plant phenotyping methods大規模GWASに統合された高スループット画像ベース表現型解析が中心で、種子形態・色形質の抽出方法を実質的に適用しているため。

abstractA large-scale genome-wide association study (GWAS) was conducted by integrating high-throughput image-based phenotyping in a panel of 493 perilla accessions.

Image-Based Estimation of Blueberry Yield Incorporating External Validation and Canopy Architecture Under Field Conditions →

Abstract Quantifying blueberry fruit yield and maturity is important for evaluating yield potential in breeding trials, but manual measurement remains slow, labor-intensive, and costly. Object detection and classification networks offer a high-throughput solution, yet few studies validate image-based counts against hand-harvested ground truth while explicitly accounting for canopy occlusion. Hence, this study developed a multi-class berry detection pipeline for immature and mature berries and validated image-based estimates against hand-harvest counts across 32 diverse southern highbush blueberry genotypes. Among the models evaluated, YOLOv8x achieved the highest detection performance, with an mAP50 of 0.82 and an mAP50–95 of 0.66. External validation produced F1 scores ranging from 0.74 to 0.91 for berry maturity classes. However, image-based detections systematically underestimated hand-harvested fruit counts, with R² values ranging from 0.40 to 0.61. Fruit occlusion varied widely among genotypes, from 42% to 90%, indicating that canopy structure strongly affects berry visibility. Incorporating image-derived canopy architecture, color, and texture features improved predictions of berry counts and maturity. Partial Least Squares regression provided the best performance, increasing R² values of hand-harvested fruit counts, ranging from 0.52 to 0.74. These results show that accounting for canopy occlusion improves image-based estimation of blueberry yield and supports more accurate high-throughput phenotyping.

Why it matches plant phenotyping methodsブルーベリーの収量・成熟度を画像から推定する検出パイプラインを開発し、手収穫値との外部検証および樹冠遮蔽・構造を考慮した改良を行っており、植物表現型取得法が中心である。

abstractthis study developed a multi-class berry detection pipeline for immature and mature berries and validated image-based estimates against hand-harvest counts

Maturity and size estimation with yield mapping for hydroponic strawberries using machine vision →

Strawberry production in hydroponic systems requires precise and spatially explicit information on fruit load, maturity, and size to support harvest planning and quality control. This study presents an integrated computer vision and GNSS RTK framework that detects individual strawberries, estimates their maturity and physical dimensions, assigns fruits to operational size–maturity categories, and generates high-resolution yield maps in an open-field hydroponic system. RGB-D images are processed with YOLOv8-seg and a BoT-SORT-based tracking module to obtain instance-level masks, from which a color-based ripeness index and 3D point clouds are derived. After DBSCAN-based filtering, fruit length and width are computed in metric space and combined with ripeness percentage to assign each strawberry to one of nine operational size–maturity categories using predefined threshold rules. The system was evaluated in a commercial hydroponic crop in Arcabuco, Boyacá, Colombia, achieving accurate segmentation, with median IoU values up to 0.83 for bounding boxes and 0.71 for masks, and mean absolute errors of 2.51 mm in length and 1.85 mm in width with respect to Vernier caliper measurements. Yield maps aggregated in 1 m2 cells and by crop row revealed marked spatial variability in fruit density, maturity state, and size–maturity composition, allowing the identification of zones with a high concentration of fruits ready for harvest versus areas dominated by immature fruits. The proposed framework provides a practical and low-cost decision support tool for hydroponic strawberry management and may be adapted to other high-value horticultural crops after recalibration under different crop architectures, cultivars, and environmental conditions.

Why it matches plant phenotyping methodsRGB-D画像と追跡・3D解析により、イチゴ果実の成熟度、寸法、果実密度および収穫状態を個体レベルで推定し、精度検証も行う中心的なフェノタイピング手法研究である。

abstractThis study presents an integrated computer vision and GNSS RTK framework that detects individual strawberries, estimates their maturity and physical dimensions, assigns fruits to operational size–maturity categories, and generates high-resolution yield maps in an open-field hydroponic system.

An End-to-End Precision Phenotyping Framework: Rice Panicle Detection and Counting in Complex Fields via Lightweight DETR →

Accurate, high-throughput quantification of rice panicles is important for yield estimation and breeding-oriented rice phenotyping. However, unmanned aerial vehicle (UAV)-based panicle detection remains challenging because flight-altitude variation produces large target-scale changes. Flooded paddy backgrounds, leaf occlusion, and illumination fluctuations further obscure small panicle targets. To address these challenges, we constructed a composite multi-altitude dataset covering UAV imagery acquired from 3 to 20 m under varying field conditions. We then propose Panicle-DETR, a lightweight detection and counting framework based on a frequency-aware Cross Stage Partial (CSP) backbone. Rather than treating the Fast Fourier Transform (FFT) as a filter by itself, the proposed FasterFD module uses frequency-domain representations with learnable frequency-response reweighting to enhance panicle-related texture cues and reduce redundant background responses. A Lossless Feature Encoder is designed to preserve fine spatial information for small targets across altitude-induced scale changes, while a composite metric loss based on Normalized Gaussian Wasserstein Distance (NWD) and Inner-IoU improves localization for adherent and overlapping panicle clusters. On the composite dataset, Panicle-DETR achieved a Precision of 90.97%, a Mean Absolute Error of 4.28, and an R2 of 0.957 for single-frame panicle counting. With 13.78 M parameters and 53.0 GFLOPs, the framework achieved 16.9 FPS with 1.96 GB peak GPU memory in a battery-powered notebook benchmark, supporting its potential for resource-constrained field-side UAV image analysis.

Why it matches plant phenotyping methodsUAV画像からイネ穂の検出・計数という植物形質推定を中心に、マルチ高度データセットと専用解析フレームワークを開発・評価しているため。

abstractAccurate, high-throughput quantification of rice panicles is important for yield estimation and breeding-oriented rice phenotyping.

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

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

Why it matches plant phenotyping methodsトマトの色・形状・欠陥という観察可能な器官形質を画像から抽出し、深層学習による自動等級判定法を開発・検証しているため、植物フェノタイピング手法が中心です。

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

PlaFe: an outdoor platform for crop phenotyping under progressive drought →

The selection of genotypes adapted to water stress requires experimental facilities that allow environmental control without compromising physiological and yield relevance. The objective of this study was to design and validate an outdoor phenotyping semi-controlled platform, PlaFe, which comprised sixty-two high-volume prismatic lysimeters arranged in rows 1.2 m long and spaced 0.6 m apart. Soil water dynamics were monitored weekly using a weighting system. To validate PlaFe, two soybean genotypes were exposed to two water scenarios for forty days from R2 + 7d, during two growing seasons. Two irrigation treatments were applied: irrigation to keep soil water content over 60–70 % of field capacity (EH0), and irrigation equivalent to 35 % of that applied in EH0 (EH1). Water consumption, crop biomass, and pod number were determined at maturity. On average, water stress reduced both biomass and pod numbers by 40 %. However, reproductive efficiency varied among genotypes. Canopy temperature increased by 0.56 °C as daily water consumption decreased, demonstrating its potential to assess drought. These results demonstrate PlaFe’s potential for the accurate evaluation of crop response and adaptation to diverse water scenarios without compromising the complex plant-environment interactions inherent to field conditions.

Why it matches plant phenotyping methodsPlaFeという屋外半制御型フェノタイピングプラットフォームを設計・検証しており、植物の水消費、バイオマス、莢数、群落温度などの表現型評価が研究の中心である。

abstractThe objective of this study was to design and validate an outdoor phenotyping semi-controlled platform, PlaFe

Dual-view weakly-supervised learning for apple tree flower counting. →

This paper presents a method to estimate apple tree flower cluster count using image analysis techniques. The main research question is how accurately flower clusters on apple trees can be counted using a camera-based approach and weakly-supervised learning, compared to traditional visual estimation. A camera is used to capture images of blooming apple trees from two sides. These images are processed by a weakly-supervised model based on a ResNet feature extractor and a feature pyramid network serving as the feature aggregator. The model is trained and validated using reference data obtained through manual flower cluster counts in the orchard and from estimations based on camera images. The model was trained using field-validated data and visually-estimated data, enabling a comparative evaluation. The proposed model surpassed conventional image segmentation methods in estimating both manually counted and visually estimated flower clusters. The proposed method achieves a relative error of 10.26% in estimating flower cluster quantities, demonstrating its effectiveness and improved accuracy over traditional approaches. Its reliance on field-validated reference data adds to its robustness and practical relevance.

Why it matches plant phenotyping methodsリンゴ樹の花房数という植物形質を、カメラ画像と弱教師あり学習で推定する手法の開発・検証が研究の中心であるため。

abstractThis paper presents a method to estimate apple tree flower cluster count using image analysis techniques.

From Phenomics to Genomics: Macro-GWAS of Almond Morphology and Quality →

Abstract In plant breeding and genetics, recent advances in high-throughput phenotyping are beginning to meet the growing demand for large-scale, high-quality phenotypic data that emerged after the development of next-generation sequencing technologies. Recent developments in phenomics have been incorporated into almond breeding programs, facilitating the large-scale acquisition of quantitative phenotypes and the dissection of the genetic architecture underlying morphological and quality-related traits. The implementation of a high-throughput phenotyping platform integrating RGB and hyperspectral imaging with genotyping using the 60K almond SNP array enabled the large-scale characterization of almond populations and the identification of 567 robust marker–trait associations across 66 traits. These analyses revealed two major genomic hotspots on chromosomes 2 and 5 associated with morphological and quality-related traits. These regions harbored biologically relevant candidate genes, including genes associated with OVATE family proteins, brassinosteroid signaling, protein ubiquitination, and acyl-CoA metabolism, as well as other regulators of organ growth, cell proliferation, hormone signaling, and seed development. Furthermore, a novel candidate gene encoding a COMT-like O-methyltransferase involved in lignin biosynthesis was identified and proposed to contribute to shell hardness, a major genetically controlled trait in almond. Together, these findings demonstrate the potential of integrating high-throughput phenomics and genomics to dissect complex traits, identify candidate genes, and accelerate genomics-informed breeding in almond.

Why it matches plant phenotyping methodsRGB・ハイパースペクトル画像を統合した高スループット表現型解析プラットフォームの実装と大規模形質取得が研究の主要部分であり、単なる形質のルーチン測定ではない。

abstractThe implementation of a high-throughput phenotyping platform integrating RGB and hyperspectral imaging with genotyping using the 60K almond SNP array enabled the large-scale characterization of almond populations

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

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

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

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

Physiology-informed high-throughput phenotyping of grain moisture dynamics provides enhanced insights into rice grain weight formation. →

Grain filling is the decisive period for rice grain weight formation. However, traditional static traits fail to capture its complex, nonlinear dynamics, while direct panicle weighing is hindered by canopy occlusion. Given the intrinsic synchronization between grain filling and dehydration from anthesis to physiological maturity, monitoring grain moisture content (GMC) dynamics serves as a robust proxy for characterizing the filling process. Here, we propose a high-throughput, physiology-informed phenotyping framework to monitor dehydration. Leveraging a 4-year dataset across 135 cultivar-environment combinations, we demonstrate that the GMC threshold for physiological maturity is relatively stable (≈25%). Concurrently, we developed 2 image-based models for GMC estimation, achieving high accuracies (R2 = 0.82 and 0.86). Integrating this physiological threshold with GMC estimation models enabled the successful reconstruction of the dehydration process. Validation on 26 independent cultivars across 2 sowing dates predicted physiological maturity with a root mean square error of 2.4 to 3.3 d. Traits extracted from these dehydration profiles accounted for 42% of the variance in grain weight, doubling the explanatory power of traditional traits. These gains are largely attributed to a new integrated trait, the moisture maintenance index, which showed a higher and more stable correlation with thousand-grain weight (r = 0.6). This framework offers a scalable approach for monitoring large-scale dehydration dynamics to deepen our understanding of grain weight formation, facilitating the genetic improvement of the filling process to enhance crop yield.

Why it matches plant phenotyping methods穀粒含水率の画像推定モデルと生理学的閾値を統合し、脱水動態や成熟期などの植物形質を高スループットに抽出・検証する枠組みが研究の中心である。

abstractwe propose a high-throughput, physiology-informed phenotyping framework to monitor dehydration

Pollen Germination as a High-Throughput Phenotyping Tool for Assessing Heat Tolerance in Soybean →

Abstract Background High temperatures during the reproductive stage of soybean severely disrupt reproductive processes and reduce yield. Heat stress causes ultrastructural damage in pollen grains, leading to reduced pollen germination, pollen size and shortened pollen tube length, ultimately lowering seed set and yield. This experiment aimed to evaluate pollen germination as a reliable, scalable phenotyping tool for assessing male gametophytic tolerance to high temperature stress in soybean. Sixteen soybean breeding lines (genotypes) were grown under controlled environments at optimal (28/18°C; day/night) and high temperature (38/28°C; day/night) regimes during flowering. In vitro pollen germination was quantified using a deep learning–based object detection tool to reduce the manual labor and improve accuracy. Several advanced object detection models belonging to the YOLO (You Only Look Once) family, specifically, YOLOv7–YOLOv12, were evaluated to identify the most reliable model. Results Comparative evaluations of different object detection models indicated that YOLOv9 model achieved superior performance in evaluating pollen germination relative to other YOLO models, especially for detecting germinated and non-germinated pollen in complex images. High temperature significantly reduced mean pollen germination from an average of 40% under optimal conditions to an average of 21% under heat stress (P

Why it matches plant phenotyping methodsダイズの耐暑性評価のため、花粉発芽を対象とした画像ベースの深層学習測定法を開発・比較検証しており、フェノタイピング手法が研究の中心である。

abstractThis experiment aimed to evaluate pollen germination as a reliable, scalable phenotyping tool for assessing male gametophytic tolerance to high temperature stress in soybean.

Lightweight Real-Time Strawberry Volume Estimation Based on Instance Segmentation and Principal-Axis Slicing →

Real-time strawberry volume estimation is a pivotal technology for automated harvesting and precision grading. However, conventional contact methods are prone to damaging fruits, while existing vision-based approaches struggle to balance high accuracy with low computational overhead. To address these challenges, this study proposes a two-stage real-time volume estimation framework coupling a red-green-blue-depth (RGB-D) sensor with an “Instance segmentation–Principal-axis slicing” framework. First, to precisely extract target contours in complex backgrounds, we designed Deformable Feature Aware-YOLO (DFA-YOLO) based on the YOLO11-seg architecture. This model enhances the geometric perception of irregular fruit edges and effectively overcomes the challenges of background noise and multi-scale variations, providing high-precision masks for subsequent spatial mapping. Subsequently, a principal-axis-slicing algorithm extracts the mask’s centroid and principal axis, perpendicularly slicing the mask into infinitesimal micro-slices. By computing and accumulating the pixel-space volume of these slices, the system converts them into precise 3D physical volumes based on RGB-D depth mapping. The entire system was deployed on an NVIDIA Jetson Orin edge computing platform and validated in a greenhouse. Experimental results demonstrate that the estimated volume highly agrees with the true volume, achieving a coefficient of determination (R2) of 0.945 and a mean absolute percentage error (MAPE) of 9.0%. Under typical operating conditions (1–5 targets per field of view), the system maintains an overall frame rate of 8–15 FPS, requiring only 55 ms for single-fruit estimation. This method exhibits favorable stability and lightweight efficiency under the tested greenhouse conditions, offering a reliable solution for real-time non-destructive crop phenotypic monitoring in computationally constrained agricultural environments.

Why it matches plant phenotyping methodsイチゴ果実の体積という植物器官形質を、RGB-D画像、インスタンスセグメンテーション、主軸スライシングで推定する方法を開発し、精度とリアルタイム性能を検証しているため、フェノタイピング手法が中心です。

abstractthis study proposes a two-stage real-time volume estimation framework coupling a red-green-blue-depth (RGB-D) sensor with an “Instance segmentation–Principal-axis slicing” framework.

High-performance 3D morphometrics via deep learning and tabular foundation models: a case study on complex cereal grain classification →

This study explores the integration of advanced 3D morphometric techniques and machine/deep learning (ML/DL) for the analysis of complex shapes. In this study, cereal grains were employed to develop a series of complex 3D classification tasks, aiming to improve previous 2D-based classifications of barley grain origins, type, and landrace. Traditional geometric morphometric methods in archaeobotany, typically reliant on 2D data, are expanded here using high-resolution 3D models, spherical harmonics (SH) capturing complex shape variations, and different classification models. Using the Northern European Barley Dataset (NEBD), this work tests multiple ML/DL approaches, including gradient boosting machines, multilayer perceptron (MLP), MeshCNN and Tabular Foundation Models (TFM), to determine optimal classification methods across various attributes. Results indicate that SH-based coefficients combined with MLP and TFM achieved the highest classification accuracies, with over 90% accuracy in binary tasks and over 80% accuracy in multi-class landrace classification. While MeshCNN showed potential, performance was limited by computational constraints resulting in the use of lower mesh resolutions. While MLP classification of SH-based 3D shape representation achieved similar results, TFM allowed the direct use of 3D grains measures within a simple workflow. Our results demonstrate that these methods allow for significant shape analysis advances in archaeobotanical studies and beyond. This approach can enable identification and differentiation of, up to now, non-identifiable grain attributes, underscoring its potential for broad application in morphometrics.

Why it matches plant phenotyping methods3D穀粒形状の取得・表現と機械学習による形態分類が研究の中心であり、植物器官の形態形質を抽出・分類する方法論的研究である。

abstracthigh-resolution 3D models, spherical harmonics (SH) capturing complex shape variations, and different classification models

A review: research progress on intelligent technologies for orchard yield monitoring →

Accurate yield estimation and crop load monitoring are essential for precision orchard management, supporting targeted fertilization, pruning, thinning, harvest planning, and marketing decisions. However, reliable in-situ monitoring remains challenging because commercial orchards are characterized by severe canopy occlusion, fruit overlap, heterogeneous tree architecture, variable illumination, and complex backgrounds. This review synthesizes advances in multi-modal sensing and deep learning for orchard yield estimation, breaking down the paradigm into intermediate fruit-counting or crop-load monitoring steps and supplementary spectral quality-assessment dimensions. First, yield-related indicators are summarized, including direct phenotypic traits such as fruit number, size, volume, and spatial distribution, as well as indirect structural and physiological proxies such as canopy volume, vegetation indices, flowering intensity, and spectral maturity attributes. Second, representative sensing devices and carrying platforms are reviewed, including red-green-blue (RGB) cameras, red-green-blue-depth (RGB-D) sensors, light detection and ranging (LiDAR), hyperspectral and multispectral systems, unmanned ground vehicles (UGVs), and unmanned aerial vehicles (UAVs). Third, the evolution of estimation methods is discussed, from traditional image processing and machine learning to object detection, instance segmentation, multi-object tracking, point-cloud analysis, remote-sensing regression, and multi-modal fusion. The review shows that no single sensor or algorithm can satisfy all orchard monitoring requirements. Ground-based vision and depth sensing are more suitable for fine-scale fruit counting and sizing, whereas UAV and spectral sensing provide advantages for regional yield mapping and quality-enhanced assessment. Future research should emphasize occlusion-aware perception, robust cross-environment generalization, lightweight edge deployment, standardized benchmarks, and integrated quantity-quality monitoring frameworks for actionable crop load management.

Why it matches plant phenotyping methods果実数・サイズ・体積・空間分布などの植物形質を対象に、センシング機器と画像解析・深層学習による収量推定法を体系的にレビューしており、フェノタイピング手法が中心である。

abstractThis review synthesizes advances in multi-modal sensing and deep learning for orchard yield estimation, breaking down the paradigm into intermediate fruit-counting or crop-load monitoring steps and supplementary spectral quality-assessment dimensions.

SeedMeasure: an efficient approach and open-source program to quantify seed size →

ABSTRACT Premise Seed size and morphology are critical traits in agriculture, ecology, and genetics, but high-throughput quantification of these traits is often limited by labor-intensive manual measurements or expensive, platform-specific imaging software. Methods and Results We developed SeedMeasure, a lightweight, open-source, and cross-platform command-line tool written in Python that automates the measurement of seed area, length, and width from images. Using a simple imaging setup, the program processes images by correcting for perspective skew, filtering debris, and exports quantitative data alongside quality-check images. We validated SeedMeasure across nine diverse species, ranging from small Arabidopsis thaliana seeds to large Zea mays kernels. The tool quickly handles images using multithreading and demonstrates high reproducibility, yielding low coefficients of variation across repeated runs. Conclusions Compared to existing software, SeedMeasure is free, offers faster processing through parallel computing, and provides standalone executables that require no programming dependencies. SeedMeasure offers an accessible, cost-effective, and high-throughput approach for rapid phenotypic profiling, making advanced seed morphological analysis available to researchers without specialized laboratory hardware.

Why it matches plant phenotyping methods種子画像から面積・長さ・幅を自動抽出するソフトウェアを開発し、複数種で検証しており、植物表現型取得法が研究の中心である。

abstractWe validated SeedMeasure across nine diverse species

Rapid rice seed vigor assessment: A machine learning and deep learning framework with multi-time-point image analysis. →

Automated rice seed vigor classification provides a non-invasive and scalable solution for improving agricultural decision-making. This study proposed an image-based framework to compare traditional machine learning and deep learning approaches for classifying individual rice seed vigor using standard RGB images. Machine learning models were developed using hand-crafted morphological and color features, while convolutional neural networks were employed to automatically extract visual patterns related to seed quality. Both single-time-point and multi-time-point image analysis strategies were investigated. Models trained on images captured at individual growth stages were compared with a multi-time-point ensemble approach that integrated visual information across multiple developmental stages. The ensemble approach achieved superior performance, highlighting the importance of incorporating temporal growth dynamics into vigor classification. Notably, traditional machine learning models performed comparably to deep learning models when informative features were carefully engineered. To improve transparency and reliability, interpretability techniques were applied to better understand model decisions. Overall, the findings demonstrate the practical potential of data-driven, image-based seed vigor assessment.

Why it matches plant phenotyping methodsRGB画像と機械学習・深層学習を用いてイネ種子の活力を自動推定する枠組みを開発・比較しており、表現型取得・抽出手法が研究の中心である。

abstractThis study proposed an image-based framework to compare traditional machine learning and deep learning approaches for classifying individual rice seed vigor using standard RGB images.

From Phenotyping to Supervised Agentic Decision Support: A Review of Sensing and Artificial Intelligence for Greenhouse Strawberry Cultivation →

Strawberry greenhouse cultivation is increasingly supported by sensing technologies, artificial intelligence (AI), and decision-support infrastructure, but their horticultural value depends on whether heterogeneous measurements can be translated into biologically meaningful crop states and practical management decisions. This review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation. The reviewed studies show substantial progress in measuring and interpreting vegetative, reproductive, fruit-quality, stress-related, and environmental crop states through imaging, spectral, environmental, root-zone, and modeling approaches. However, much of the literature still emphasizes measurement accuracy, model performance, or infrastructure capability, whereas fewer studies validate whether AI-derived outputs improve crop response, management decisions, workflow, resource use, or production outcomes. The review therefore distinguishes sensing technologies for data acquisition and measurement from AI-based methods for interpretation and prediction, and examines how crop-state information can be connected to practical greenhouse decision making. It also compares established decision technologies, including expert systems, model predictive control, digital twins, and closed-loop coordination, with supervised agentic coordination as bounded decision-support concepts rather than as evidence of unrestricted autonomous control. Future work should emphasize phenotype-to-action validation, domain-aware benchmarking, and supervised deployment studies that connect model outputs with decision rules, crop outcomes, operational constraints, and grower oversight. By grounding sensing technologies and AI-based interpretation methods in crop-response validation, strawberry greenhouse systems can progress toward supervised, crop-state-driven decision support.

Why it matches plant phenotyping methods温室イチゴのフェノタイピング、マルチモーダルセンシング、AIによる作物状態解釈を中心に整理する方法論レビューであり、植物状態の取得・推定手法が主題。

abstractThis review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation.

AnnonFruitTraits 1.0, a comprehensive dataset on frugivory-related traits for Annonaceae species worldwide →

Abstract Functional traits are critical for understanding species interactions within ecosystems and their responses to environmental changes. Yet, traits related to fruits and seeds are still underrepresented, especially in tropical ecosystems where mutualisms between fruits and fruit-eating animals are prominent. Here, we introduce AnnonFruitTraits 1.0, a comprehensive dataset of 34,772 records encompassing 26 frugivory-related traits for 2,266 species (ca. 90% of total species) of the pantropical plant family Annonaceae (Magnoliales). This dataset includes trait definitions and their significance for frugivory, as well as a description of our workflow from data acquisition to visualization. To facilitate data accessibility and reproducibility, we provide an accompanying R package (AnnonTraits) that enables users to explore, summarise, and visualise the dataset. By assessing species and trait coverage across genera and regions, we identified major data gaps in the Asia-Pacific region and in several Annonaceae genera (e.g., Artabotrys , Miliusa , Orophea, Polyalthia , and Uvaria ). Our findings show the importance of expanding trait data collection and taxonomic efforts, particularly in underrepresented regions and lineages. AnnonFruitTraits is a valuable resource for advancing research on seed dispersal, plant–animal interactions, and tropical forest conservation.

Why it matches plant phenotyping methods植物の果実・種子形質を大規模に整理した再利用可能なデータセットであり、データ取得から可視化までのワークフローと探索・要約・可視化用Rパッケージを提供しているため、形質データ資源として中心的です。

abstractHere, we introduce AnnonFruitTraits 1.0, a comprehensive dataset of 34,772 records encompassing 26 frugivory-related traits for 2,266 species

Curvature-aware 3D length estimation of greenhouse cucumbers using RGB-D imaging and cubic spline arc-length integration →

Commercial greenhouse cucumber production is graded by fruit length, which drives harvest scheduling, labour allocation, and logistics. Manual measurement with thread or caliper is accurate but infeasible at commercial scale. This paper presents CucumberVision, a non-contact length estimation framework using an Intel RealSense D435 RGB-D camera. A YOLO26n instance segmentation model locates cucumbers, and SAM (ViT-B backbone) refines each detection to a pixel-precise mask. Five methods are evaluated under matched conditions: (M1) a dominant-axis skeleton scan-line baseline; (M2) PCA on the bounding-box depth point cloud; (M3) SAM mask with medial-axis skeletonisation; (M4) a hybrid keypoint-guided approach using a YOLO26-pose model predicting five anatomical landmarks (KP0--KP4) with piecewise 3D arc-length; and (M5) a novel medial arc spline method fitting a cubic spline through the 3D medial axis of the SAM mask and computing arc length by trapezoidal integration -- the first such application to elongated vegetable measurement. All methods share five-frame burst depth averaging, colour-stream intrinsic alignment, and adaptive method selection with cascading fallbacks ensuring 100% coverage. A benchmark of 48 captures across seven cucumbers in three size categories (small ~8 cm, medium ~13 cm, large ~25 cm) with thread-based ground truth establishes a significant accuracy hierarchy: M1 (MAPE 9.68%) > M2 (5.31%) > M4 (5.51%) > M3 (5.82%) > M5 (4.13%). M5 significantly outperforms all competitors at Bonferroni-corrected alpha=0.0125. A secondary contribution is identifying a 12--18% length underestimation caused by using depth-stream rather than colour-stream intrinsics after rs.align(rs.stream.color) -- an under-reported error source. The complete system is released open source and runs in real time on a single consumer-grade GPU.

Why it matches plant phenotyping methodsRGB-D画像からキュウリ果実長を推定する手法を開発し、複数手法との比較検証、実測値によるベンチマーク、誤差要因分析まで行っており、植物形質取得が研究の中心です。

abstractThis paper presents CucumberVision, a non-contact length estimation framework using an Intel RealSense D435 RGB-D camera.

Research on digital fruit tree reconstruction method based on neural radiance field theory →

The objective of this study is to propose a digital fruit tree reconstruction method based on neural radiation field theory that will enable the efficient, accurate, and non-destructive acquisition of phenotypic information from fruit trees, while simultaneously reducing the cost of collecting this data. Firstly, a low-cost information acquisition platform is constructed for the purpose of shooting a multi-view video around a fruit tree. The video is then extracted and framed using a motion recovery structural algorithm, thereby obtaining a multi-view image sequence of the fruit tree with positional data. Secondly, the the image sequence is employed to train the neural radiation field, thereby obtaining a converged three-dimensional scene of the fruit tree. Ultimately, the three-dimensional scene is derived in the form of a point cloud, thus yielding a high-phenotypic detail point cloud model of the fruit tree. A multi-period point cloud model of an apple tree in an orchard environment, encompassing the flowering, fruiting, and dormant periods, was reconstructed using the aforementioned method. The experimental results demonstrated that the error associated with the tree shape data recorded by the multi-period point cloud model established by the aforementioned method was, on average, below 5%. Furthermore, the average error across all periods was 2.69%, representing a 75.50% reduction compared to traditional reconstruction methods. The dimensional accuracy of the point cloud model at the organ scale can reach the millimetre level, with an average error of 3.10% for fruit diameter, which is 66.19% lower than that of the traditional reconstruction method. The reconstruction method is robust to all periods of fruit trees and can meet the majority of cases of digital fruit tree reconstruction.

Why it matches plant phenotyping methods果樹の多視点画像からNeRFと点群を用いて樹形・器官寸法などの表現型を非破壊取得する手法を開発・検証しており、方法が研究の中心である。

abstractpropose a digital fruit tree reconstruction method based on neural radiation field theory that will enable the efficient, accurate, and non-destructive acquisition of phenotypic information from fruit trees

Apple Origin Classification and Sugar Content Prediction of 'Fuji' Apples Using Near-Infrared Spectroscopy and Deep Learning. →

Accurate apple origin identification and non-destructive internal quality evaluation are important for fruit traceability, quality grading, and post-harvest management. Unlike previous studies mainly focusing on origin classification, this study established a dual-task near-infrared spectroscopy framework integrating geographical origin classification and soluble solid content (SSC, °Brix) prediction for Fuji apples. Samples were collected from three representative production regions in China: Alar in Xinjiang, Yantai in Shandong, and Luochuan in Shaanxi. Near-infrared diffuse reflectance spectra were acquired from 375 apples, generating 3000 spectral samples for origin classification and 750 SSC-calibrated samples for sugar content prediction. For classification, six deep learning models were evaluated using standardized full-spectrum input without chemometric spectral preprocessing, and the Transformer achieved the best performance, with a test accuracy of 96.22%. For SSC regression, spectra were preprocessed using standard normal variate and Savitzky-Golay filtering. The DNN model achieved the best prediction performance, with MAE = 0.5958 °Brix, RMSE = 0.7333 °Brix, R 2 = 0.8646, and Pearson r = 0.9338. These results indicate that near-infrared spectroscopy combined with deep learning can support both Fuji apple origin authentication and non-destructive local tissue SSC assessment.

Why it matches plant phenotyping methodsリンゴ果実のSSC（糖度）という植物器官形質を、近赤外分光と深層学習で非破壊推定する方法を構築・評価しており、表現型取得手法が研究の中心である。

abstractthis study established a dual-task near-infrared spectroscopy framework integrating geographical origin classification and soluble solid content (SSC, °Brix) prediction for Fuji apples.

In-Field Assessment of Olive Fruit Quality Using a Low-Cost Multispectral Sensor and ANN Models →

Optimizing harvest time and oil production requires accurate olive fruit quality characterization. Traditional chemical methods are costly and tedious, leading to poor monitoring resolution and reliance on subjective visual assessments. While spectroscopy offers a non-destructive alternative, standard equipment remains complex and prohibitively expensive for smallholder farmers. To address this, we propose a methodology using a custom-made, low-cost multispectral device. Built upon the AS7265x board, the system acquires 18 spectral bands in the visible and near-infrared range (410–940 nm). We used these spectral data to feed artificial neural network (ANN) models for estimating the quality of intact olives. During a two-season field experiment, we monitored ripening to acquire spectral signatures and ground-truth values for oil content per fresh weight (OCFW), oil content per dry matter (OCDM), moisture (M), and titratable acidity (TA). External validation showed high accuracy for OCFW (R2p = 0.86), OCDM (R2p = 0.86), and M (R2p = 0.89), proving the system’s reliability. However, TA estimation showed lower performance (R2p = 0.21), indicating limited spectral correlation. These findings pave the way for affordable, real-time smart farming tools for olive quality monitoring.

Why it matches plant phenotyping methods低コスト multispectral センサーとANNによるオリーブ果実の品質形質推定システムを開発・外部検証しており、植物形質取得法が中心的である。

abstractwe propose a methodology using a custom-made, low-cost multispectral device.

Deep learning for real-time strawberry detection, ripeness classification, and picking point localization: A review of architectures, field studies, and open challenges →

Abstract Accurate detection of strawberry fruit, reliable ripeness estimation, and precise localization of the picking point are essential for automated harvesting and yield prediction in smart farming. However, real-world environments introduce significant challenges, including occlusion, illumination variability, and high visual similarity between ripeness stages. Deep learning (DL)-based object detection methods have become the dominant approach to address these issues. This paper presents a systematic review of 60 peer-reviewed studies published between 2023 and 2026, focusing on detecting strawberries and their ripeness using the YOLO family (v5–v11) of DL algorithms. The studies are analyzed with respect to dataset characteristics, preprocessing and augmentation strategies, model architectures, and evaluation protocols. Our results show a clear dominance of YOLOv8, used in 28 (46.7%) of the 60 reviewed works, due to its real-time capability and architectural flexibility. Despite its short history, YOLOv11 has been adopted in 13 studies (21.7%) owing to its balanced precision and computational efficiency. Hybrid CNN–ViT models that integrate Transformer modules or networks into YOLO are gaining attention (8 studies, 13.3%) and show improved performance in complex scenarios, but they still incur higher computational cost. However, we identify critical methodological issues that affect the validity of reported results. In particular, the improper application of data augmentation prior to dataset splitting — a practice observed in precisely one-third of the reviewed studies — poses a significant risk of data leakage and can result in overly optimistic performance estimates. Additional challenges include inconsistent evaluation metrics, limited dataset diversity, and a lack of standardized benchmarks. This review provides a structured overview of current approaches, a critical assessment of existing research practices, and actionable guidance for developing robust, deployment-ready DL solutions for precision agriculture.

Why it matches plant phenotyping methodsイチゴ果実の検出・成熟度推定を対象とする画像ベース手法の系統的レビューであり、データセット、モデル、評価法、データリークやベンチマーク不足を批判的に検討しているため、フェノタイピング手法レビューとして中心的である。

abstractThis paper presents a systematic review of 60 peer-reviewed studies published between 2023 and 2026, focusing on detecting strawberries and their ripeness using the YOLO family (v5–v11) of DL algorithms.

Deciphering "False Maturity" in Mountain Coffee: A Multimodal Hyperspectral Framework for Non-Destructive Sugar Content Assessment. →

In complex mountainous environments, the asynchronous development between external color turning and internal sugar accumulation (often termed "false maturity") in coffee cherries poses a severe challenge to post-harvest quality sorting and the consistency of final coffee products. To overcome the limitations of single-phenotype detection in raw material screening, this study proposed a multimodal quality discrimination framework integrating fruit hyperspectral imaging, micro-topography, and plant physiological characteristics. Taking typical mountain-grown fresh coffee cherries as the research object, and after comparing various spectral preprocessing and feature dimensionality reduction algorithms, the multimodal fusion efficacy of nine machine learning classifiers was systematically evaluated. The results demonstrated that: (1) Full-spectrum difference analysis quantitatively confirmed the limitations of visual harvesting; spectral reflectance differences between high- and low-sugar fruits were highly concentrated in the red and red-edge regions, with the maximum difference precisely located at 676 nm. (2) Compared to the single-spectrum model (mean accuracy of 75.93%), the fully fused Multilayer Perceptron (MLP) network effectively mitigated background noise induced by heterogeneous environments, improving the mean classification accuracy to 77.22% with a mean Area Under the Curve (AUC) of 0.827. (3) Correlation analysis clarified the quantitative association between topography and quality; micro-topographic slope (r = 0.346) was identified as the key environmental driver of spatial differentiation in fruit sugar content, while plant chlorophyll A content (r = 0.183) exhibited a corresponding physiological response trend. This study not only explains the root cause of visual assessment failure from a physical optics perspective but also reveals the spatial variation laws of quality driven by micro-topography, providing preliminary data support for the intelligent sorting of raw materials and ensuring post-harvest quality consistency of mountainous crops.

Why it matches plant phenotyping methodsコーヒー果実の糖含量という植物器官形質を、ハイパースペクトル画像・微地形・生理情報の融合で非破壊推定する方法が研究の中心であり、前処理、特徴削減、複数分類器の性能比較も行っている。

abstracta multimodal quality discrimination framework integrating fruit hyperspectral imaging, micro-topography, and plant physiological characteristics

An innovative screening method for heat stress tolerance in chickpea ( Cicer arietinum L.). →

Reliable field phenotyping for terminal heat stress (THS) tolerance in chickpea is constrained by conventional late-sowing approaches that confound reproductive stress with reduced vegetative growth. We developed and validated a deflowering (DF)-based field screening method that selectively imposes heat stress during the reproductive phase while maintaining normal vegetative vigour. Early flowers were removed to synchronize flowering and delay reproduction by 10-15 days, exposing flowering and pod set to high temperatures (>33 °C). Across two seasons and contrasting genotypes, DF maintained vegetative growth but significantly reduced pollen viability, pod set, and yield, with tolerant genotypes showing markedly lower yield penalties than susceptible ones. The method effectively discriminated reproductive thermotolerance and provides a simple, low-cost, and biologically grounded phenotyping tool for chickpea breeding under warming climates.•A DF-based field method selectively imposes reproductive-stage heat stress without compromising vegetative growth.•The approach reliably distinguishes heat-tolerant and susceptible chickpea genotypes under natural field conditions.

Why it matches plant phenotyping methods生殖期の耐暑性を選択的に評価するDFベースの圃場スクリーニング法を開発・検証しており、遺伝子型の識別に用いるフェノタイピング手法が中心である。

abstractWe developed and validated a deflowering (DF)-based field screening method that selectively imposes heat stress during the reproductive phase while maintaining normal vegetative vigour.

3D Quantitative Modeling for Stone Fruit Quality Assessment by LF-NMRI →

The core volume ratio (CVR) is a key indicator for evaluating the proportion of edible fraction in stone fruits. Traditionally, CVR is determined through destructive sampling by separately measuring the masses of the core and entire fruit. Recently, low-field nuclear magnetic resonance imaging (LF-NMRI) has been introduced as a non-destructive alternative, but its sparse sampling limits the ability to achieve accurate spatial and volumetric quantification of fruit quality. To address this limitation, we propose a novel method for high-precision three-dimensional (3D) modeling of stone fruits. The method acquires tomographic LF-NMRI sequences along three orthogonal axes. Each sequence is segmented into pulp and core regions using a SwinUNet deep learning model and converted into point clouds for each view. Point clouds from the three orthogonal views are registered via a genetic algorithm to align structural information from complementary perspectives and fused into a unified 3D model through Poisson surface reconstruction. Using prunes as a representative case, the method enables accurate quantification of core and entire fruit volumes, achieving a CVR estimation with a mean absolute error of 0.13% compared to manual measurements. The proposed three-view reconstruction strategy yields a volumetric error of only 0.73%, significantly outperforming single-view (4.57%) and dual-view (3.73%) approaches. This technology provides a robust and accurate non-destructive solution for 3D internal quality analysis of fruits.

Why it matches plant phenotyping methodsLF-NMRI、深層学習セグメンテーション、3D再構成を組み合わせ、果実内部の芯・可食部体積という植物器官形質を非破壊推定する手法を開発・検証しており、フェノタイピング手法が中心である。

abstractwe propose a novel method for high-precision three-dimensional (3D) modeling of stone fruits.

Defining critical drivers of cross-pollination for better hybrid grain set in wheat →

Hybrid wheat breeding offers a promising route to enhance grain yield and yield stability through heterosis, yet hybrid grain production remains constrained by limited cross-pollination efficiency due to high rates of autogamy. To achieve cross-pollination in an autogamous species like wheat, pollen must shed outside the floret. This is typically assessed by scoring visual anther extrusion (VAEX), a key floral trait that sets the foundation for cross-pollination. However, VAEX explains only part of the variation in hybrid grain set. To address this, we analyzed floral structures and reproductive processes underlying cross-pollination efficiency in wheat. From 24 elite winter wheat genotypes, we developed traits describing anther extrusion kinetics, pollen release, and floral bract architecture. These traits showed substantial genotypic variation and high heritability. While VAEX alone explained approximately 49% of the variation in hybrid grain set, combined trait analyses explained up to 77%, demonstrating that hybrid grain production is governed by coordinated floral and reproductive trait interactions. Together, our analyses define a hierarchical trait architecture linking floral bract mechanics, anther extrusion dynamics, and pollen shedding to cross-fertilization success. This establishes a systems-level phenotyping framework for improving male parent selection in hybrid wheat breeding. Highlight High cross-pollination efficiency in wheat is a multi-factorial process that requires lighter floral bract architecture combined with adequate anther extrusion and pollen release for improving hybrid grain production.

Why it matches plant phenotyping methods交雑受粉効率を評価するための葯突出動態、花粉放出、苞葉構造の形質を開発し、統合的なフェノタイピング枠組みとして解析しており、表現型取得・評価法が研究の中心である。

abstractFrom 24 elite winter wheat genotypes, we developed traits describing anther extrusion kinetics, pollen release, and floral bract architecture.

Skeleton-guided 3D digitization standardizes complex trait phenotyping and supports reproducible locus discovery in cucumber. →

Accurate and standardized phenotyping of complex, environmentally sensitive quantitative traits remains a major bottleneck for reliable locus discovery and breeding applications. Here, we established a skeleton-guided 3D digital phenotyping framework that generates standardized digital replicas and enables precise quantification of fruit and plant architecture traits in cucumber. The workflow was applied to a permanent recombinant inbred line (RIL) population (n = 211) evaluated across two seasons (2023-2024), from which nine traits were extracted from 3D models. All 211 RILs were whole-genome resequenced to generate genome-wide SNPs, enabling construction of a high-density linkage map and subsequent QTL mapping, complemented by GWAS for physical anchoring of association signals. Using this integrated design, we identified 29 QTLs across the nine traits and resolved cross-season major-effect loci with consistent genetic signals. Notably, two cross-season loci were detected as novel: FL4.1/FSL4.1 affecting fruit length and fruit stalk length, and NLB1.1/LLB1.1 affecting branching. GWAS further anchored lead variants to physical coordinates and supported cross-season associations. Together, these results demonstrate that standardized 3D phenotyping provides a reproducible and interoperable trait definition framework that supports cross-season locus discovery and downstream marker development for quantitative genetic dissection in cucumber.

Why it matches plant phenotyping methodsキュウリの果実・植物体形態を3Dモデルから定量化する標準化フェノタイピング手法の構築と応用が研究の中心であり、再現可能な形質抽出枠組みとして評価されている。

abstractwe established a skeleton-guided 3D digital phenotyping framework that generates standardized digital replicas and enables precise quantification of fruit and plant architecture traits in cucumber.

Hyperspectral phenotyping reveals the genetic basis of grain quality in rice. →

Rice ( Oryza sativa ) grain quality is an important breeding target, yet its genetic basis remains incompletely understood. In this study, we integrated hyperspectral phenotyping with genome-wide association study (GWAS) to investigate apparent amylose content (AAC) and protein content (PC) in 241 modern rice varieties. Using a visible-shortwave infrared hyperspectral system combined with optimized preprocessing and machine-learning pipelines, we achieved accurate predictions for AAC ( R 2 = 0.97) and PC ( R 2 = 0.92). Hyperspectral-based GWAS identified both known loci and previously unreported genetic associations. For AAC, qAAC (780.791nm) -1-3 was mapped to the Green Revolution gene SD1 , showing that the sd1 allele increases AAC while conferring high yields. For PC, we identified qPC (1998.98nm) -5-1 and confirmed GW5 as the causal gene, linking the high-yielding gw5 allele with high grain PC. Hyperspectral features outperformed traditional measurements, enhancing the detection of genetic signals. This study provides an efficient strategy for elucidating the genomic architecture of complex grain-quality traits.

Why it matches plant phenotyping methodsイネ穀粒の品質形質を対象に、ハイパースペクトル計測、前処理、機械学習による形質推定を開発・評価しており、フェノタイピング手法が研究の中心である。

abstractUsing a visible-shortwave infrared hyperspectral system combined with optimized preprocessing and machine-learning pipelines, we achieved accurate predictions for AAC ( R 2 = 0.97) and PC ( R 2 = 0.92).

From Occlusion to 3D: Amodal Completion Enables Single-View Wheat Reconstruction →

Abstract Occlusion is a major factor limiting accurate three-dimensional (3D) wheat phenotyping. In natural growth conditions, overlapping spikes, leaves, and stems often make only partial target regions visible in single-view images, hindering complete and reliable 3D reconstruction. To address this problem, this study proposes an amodal completion-driven framework for single-view 3D reconstruction of occluded wheat. The framework first uses visible prompts to recover the complete appearance and structural cues of occluded targets, and then feeds the completed images into single-view 3D reconstruction models to generate complete 3D structures. To support model training and evaluation, we construct the MMWO (Multi-view Multi-instance Wheat Occlusion) dataset from MMW by synthesizing diverse occlusion samples through organ-level cutouts, random geometric transformations, and region-constrained pasting, with annotations including visible masks, occlusion masks, and complete target images. Six representative reconstruction methods, including Direct3D, Real3D, SF3D, Spar3D, TRELLIS.2, and Hunyuan3D, are systematically evaluated. Hunyuan3D achieves the best geometric performance, with the lowest mean CD-L 1 and CD-L 2 values of 0.1286 and 0.0536, and the highest mean F-score of 0.5668. SF3D achieves the best rendering quality in terms of PSNR, SSIM, and LPIPS. In addition, Pix2Gestalt completion reduces the estimation errors of spike length, width, and area from 9.31%, 10.89%, and 32.23% to 4.64%, 9.70%, and 9.45%, respectively. These results demonstrate that amodal completion can effectively alleviate occlusion-induced information loss and provide more complete structural priors for single-view 3D wheat reconstruction. This study offers a feasible solution for robust 3D wheat phenotyping under occlusion and provides a systematic reference for applying general-purpose 3D generative models to agricultural phenotyping.

Why it matches plant phenotyping methods遮蔽下小麦の単視点3D再構成とアモーダル補完を開発し、データセット構築、複数手法の系統評価、器官形質推定誤差の検証まで行っており、植物フェノタイピング手法が中心である。

abstractthis study proposes an amodal completion-driven framework for single-view 3D reconstruction of occluded wheat

A Lightweight Shape-Aware YOLO Network for Field Strawberry Maturity Detection Under Complex Orchard Conditions →

Precise, non-destructive detection of fruit maturity is a cornerstone of modern precision agriculture, directly impacting harvest scheduling and post-harvest quality control. In the case of strawberries (Fragaria × ananassa), in-field automated assessment is persistently hampered by the fruit’s diminutive size, subtle physiological colour transitions, and frequent occlusion by foliage. To overcome these limitations, we developed SMLO-YOLO, a specialised lightweight vision system designed to reliably detect different maturity stages on edge devices under complex orchard conditions. The proposed architecture incorporates a Cross-Scale Aggregation Neck (HDP-Neck) driven by entropy-guided dynamic sampling, which effectively concentrates computational resources on fruit regions while filtering background noise. Additionally, we introduce a Shape-aware Intersection-over-Union (ShapeIoU) loss and a Boundary- and Class-aware Knowledge Distillation (BCKD) strategy to specifically address the challenge of detecting overlapping clusters and low-maturity fruits. Validation on custom datasets collected from commercial orchards in Sichuan and Shanxi demonstrated that the final SMLO-YOLO model, after BCKDloss-based knowledge distillation, achieved an mAP50 of 92.4% at an inference speed of 256.41 FPS, with 6.49 M parameters and 15.0 GFLOPs. These metrics indicate that the system successfully balances high-throughput detection with the non-harvestable low-maturity fruits of agricultural robotics, offering a robust tool for objective, real-time maturity monitoring.

Why it matches plant phenotyping methodsイチゴ果実の成熟段階という植物器官の状態を画像から推定する軽量YOLO手法を開発し、実圃場データで性能検証しており、フェノタイピング手法が研究の中心です。

abstractwe developed SMLO-YOLO, a specialised lightweight vision system designed to reliably detect different maturity stages on edge devices under complex orchard conditions.

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

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

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

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

Predicting wheat yield and grain quality with UAV multispectral imagery and deep learning. →

Bread wheat ( Triticum aestivum L.) is a major staple crop, and timely, in-season prediction of grain yield (GY) and grain quality traits, grain protein content (GP), and grain test weight (TW), is critical for informed management and field-based high-throughput phenotyping (HTP). Unmanned Aerial Vehicle (UAV) remote sensing, coupled with artificial intelligence and deep learning (DL), offers a practical pathway for rapid, plot-scale trait estimation. Here, we investigate the value of multitemporal, multispectral UAV imagery for predicting winter wheat GY, GP, and TW, and we systematically compare two modeling paradigms: (1) handcrafted feature-based workflows that use plot-aggregated spectral and texture descriptors derived from UAV imagery, and (2) image-based, end-to-end workflows that learn directly from plot-level reflectance image chips. During the 2022 growing season, multispectral UAV data were collected repeatedly over seven experimental wheat sites in South Dakota, USA. For handcrafted feature-based modeling, we evaluated Support Vector Regression (SVR) and Random Forest Regression (RFR), along with DL models including a feedforward Deep Neural Network (DNN) and a one-dimensional Convolutional Neural Network (1D-CNN). For end-to-end image-based modeling, we implemented 2D-CNN, 3D-CNN, and a hybrid 2D-CNN–LSTM architecture to leverage both spatial information and multi-date dependencies. Our results show that: 1) the image-based modeling workflow yielded comparable to slightly better performance than the handcrafted feature-based modeling workflow across wheat GY, GP, and TW predictions; 2) 3D-CNN outperformed all other methods with R 2 of 0.65, 0.61 and 0.69 for GY, GP and TW estimations, respectively; 3) multitemporal UAV data outperformed the data collected from a single growth stage; and UAV data from wheat Feekes 10 (booting) stage yielded slightly better estimation results compared to the data collected from other growing stages, with R 2 of 0.62, 0.55, and 0.62 for GY, GP, and TW estimations, respectively. The results indicate that DL applied to high-resolution multitemporal and multispectral UAV imagery holds strong promise for predicting winter wheat yield and grain quality during the growing season, while also informing HTP efforts and site-specific management.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から収量・品質形質を推定するワークフローを開発・比較・評価しており、植物表現型取得が研究の中心である。

abstractwe systematically compare two modeling paradigms: (1) handcrafted feature-based workflows that use plot-aggregated spectral and texture descriptors derived from UAV imagery, and (2) image-based, end-to-end workflows that learn directly from plot-level reflectance image chips.

Assessing robustness and transferability of image‐based semantic segmentation models for major field crops →

Abstract Plant breeding is essential for crop improvement, yet progress is often hindered by slow, laborious, and subjective field phenotyping methods. High‐throughput phenotyping (HTP), particularly image‐based methodologies powered by machine learning, offers a pathway to overcome these limitations. However, achieving robustness and generalization when analyzing diverse genotypes within a crop and across reproductive stages remains challenging and can affect model performance and the accurate extraction of phenotypic features. This study evaluated the performance of semantic segmentation models across a diverse panel of genotypes and distinct crop reproductive stages, using wheat ( Triticum aestivum L.), sorghum ( Sorghum bicolor L.), and corn ( Zea mays L.) as case studies. The primary objectives were to analyze (i) the overall prediction performance on the aggregated dataset for each crop, (ii) the stratified performance by genotype and collection date, and (iii) the temporal and genotypic transferability across growth stages and unseen genotypes. Four distinct smartphone cameras were used to collect images of the reproductive structure across crop growth stages (different collection dates) from 160 corn, 80 sorghum, and 40 wheat genotypes. The total number of images per crop was 2000 for wheat, 4000 for sorghum, and 3840 for corn. Five semantic segmentation models were tested in this study—DeepLabv3+, MaskFormer, SegFormer, SegNet, and U‐Net—using the images and respective binary masks for training and testing. The SegFormer model achieved the highest intersection over union (IoU) values for corn (0.90) and sorghum (0.92), while the U‐Net model performed best for wheat (0.89). A minor performance decline, with IoU differences up to 0.1, was observed when testing the same model across different genotypes. However, the temporal transferability drops up to 0.5 IoU when training and inferring on different crop growth stages. The main reason for those changes may lie in the natural color and organ architecture temporal changes between the trained and tested datasets when transferring the models across growth stages. These results highlight the urgent need to prioritize robustness and transferability when developing reliable in‐field HTP methodologies.

Why it matches plant phenotyping methods植物の生殖器官画像からの表現型抽出に用いるセマンティックセグメンテーション手法を、作物・遺伝子型・生育段階間で性能と転移性の観点から比較検証しており、方法論が研究の中心である。

abstractThis study evaluated the performance of semantic segmentation models across a diverse panel of genotypes and distinct crop reproductive stages

Development of a Compact Automatic Sorting Machine for Kazakhstani Apple Varieties Based on Computer Vision →

This article presents the development and experimental study of a compact, automated apple sorting machine based on computer vision, indirect fruit weight estimation, and color grading. It is designed for use in small and medium-sized farms, where the use of industrial lines is limited by high cost and complex maintenance. The proposed machine enables non-destructive assessing and sorting of apples in a continuous flow mode without the use of mechanical weighing, includes a fruit feeding and positioning module, a computer vision system, an image processing unit, and a sorting actuator synchronized with the conveyor movement. Fruit weight and color assessment is based on visual geometric parameters (diameter, fruit height, projected area, and the proportion of surface color), extracted from digital images, followed by classification by product category using regression models. As part of the experimental study, a correlation analysis was conducted between the actual weight of apples and their geometric parameters for five varieties typical of Kazakhstan. It was shown that the projected fruit area exhibits the most stable correlation with weight, justifying its use as the primary predictor in constructing a regression model for indirect weight estimation. An assessment of the accuracy of apple classification by product categories was carried out and the influence of conveyor speed on the stability and correctness of sorting was observed. The experimental results with a total 1250 apples of five varieties - Aport Alexander, Sinap Almaty, Kazakhski Yubileinyi, Ainur, and Nursat indicated that the optimal operating mode for the machine is an apple transport speed of 0.16 m/s. In this mode, sorting throughput is approximately 400 kg/hour, with an average accuracy of 92% for the automatic classification in accordance with GOST requirements. These results confirm that the proposed approach provides sufficient real-time sorting accuracy with a simple machine design. The machine can be used as a standalone sorting solution, as well as a base platform for further expansion of functionality by integrating surface defect assessment and grade identification modules.

Why it matches plant phenotyping methods果実の形態・色・重量を画像から推定し分類するコンピュータビジョン方式と装置の開発が中心であり、単なる品質測定ではなく、再利用可能な植物器官形質の取得・推定法を提示している。

abstractThis article presents the development and experimental study of a compact, automated apple sorting machine based on computer vision, indirect fruit weight estimation, and color grading.

A two-stage screening approach integrated with GWAS for rare phenotypes such as spontaneous haploid genome doubling in maize. →

Spontaneous haploid genome doubling (SHGD) is a valuable trait in maize breeding, enabling the development of doubled haploid (DH) lines without chemical chromosome doubling. However, SHGD is a rare phenotype, expressed in only a small fraction of maize germplasm, making its identification resource-intensive. This study evaluated the efficiency of a two-stage field screening approach designed to identify maize genotypes with high haploid male fertility (HMF), a key indicator of SHGD potential. Simulation analyses showed that evaluating 50 haploid plants per genotype, combined with a 25% HMF threshold, provides an optimal balance between detection accuracy and resource efficiency. Across three growing seasons, HMF exhibited a highly skewed distribution, with most genotypes showing low HMF and a small subset exceeding 30% HMF. Field evaluations conducted in 2022, 2023, and 2024 consistently identified high-performing genotypes, including A427, N525, N516, and NK778, which maintained stable HMF expression across years. A genome-wide association analysis identified genomic regions associated with HMF. Our two-stage screening approach identified both SHGD donor lines and genomic loci and candidate genes for HMF.

Why it matches plant phenotyping methods希少な植物表現型SHGDを検出する二段階フィールドスクリーニング法を開発・評価し、サンプル数と閾値の最適化および複数年検証を行っているため、表現型取得法が中心的である。

abstractThis study evaluated the efficiency of a two-stage field screening approach designed to identify maize genotypes with high haploid male fertility (HMF), a key indicator of SHGD potential.

Integrated Fruit Phenotyping and Electronic-Nose Profiling of Five Ilex Taxa from Eastern China for Germplasm Characterization and Utilization. →

Accurate characterization of closely related Ilex taxa is essential for the conservation, documentation, and utilization of plant genetic resources. In this study, five Ilex taxa from eastern China ( Ilex rotunda Thunb., Ilex chinensis , Ilex cornuta Lindl. & Paxt., Ilex cornuta 'Fortunei', and Ilex latifolia Thunb.) were evaluated using an integrated framework combining fruit morphometric traits, CIELAB color parameters, and electronic-nose (E-nose) volatile fingerprints. Fruit transverse diameter, longitudinal diameter, single-fruit weight, fruit shape index, and peel color traits (L*, a*, b*, and chroma, C*) differed significantly among taxa (one-way ANOVA, all p I . cornuta produced the largest and heaviest fruits, I . chinensis showed the most elongated fruit shape, and I . rotunda exhibited the highest redness and chroma values. Chemometric analyses of E-nose responses further improved taxon discrimination and revealed clear divergence in volatile-response patterns. Trait-space relationships were broadly consistent with the preset phylogenetic framework, with I . rotunda showing the greatest divergence and I . cornuta and I . cornuta 'Fortunei' showing the closest similarity. These findings indicate that integrated fruit phenotyping and rapid volatile profiling provide a practical approach for Ilex germplasm identification, comparative evaluation, and resource documentation, with potential value for conservation planning and horticultural utilization.

Why it matches plant phenotyping methods果実形態・色彩形質と電子鼻プロファイリングを統合した再利用可能な分類・遺伝資源評価フレームワークが研究の中心であり、単なる生物学的実験の routine 測定を超える応用と判断する。

abstractevaluated using an integrated framework combining fruit morphometric traits, CIELAB color parameters, and electronic-nose (E-nose) volatile fingerprints.

Optimizing soybean breeding: High-throughput phenotyping for stink bug resistance and high yields →

Abstract Background: The stink bug complex is one of the most damaging pests of soybean, reducing yield and seed quality. Genetic resistance remains the most sustainable and effective management strategy, but its quantitative inheritance and labor-intensive field phenotyping make its implementation in breeding programs challenging. Objective: This study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and the potential of phenotyping to stink bug resistance by correlating image-derived features and machine learning (ML) models. Methods: A population of 304 soybean lines was evaluated in alpha-lattice design trials across two seasons under natural infestations. Five resistance-related traits, grain yield (GY), healthy seed weight (HSW), number of days to maturity (NDM), tolerance (TOL), and leaf retention (LR), were manually scored and linked to UAV-derived vegetation indices (VIs) and texture indices (TIs). Three ML models (AdaBoost, SVM, MLP) were tested to predict these traits from aerial features. Results: Results showed that VIs, particularly Visible Atmospherically Resistant Index at the 25th percentile (VARI_P25), were consistently associated with resistance-related traits, while decision tree analysis highlighted TIs at 45° and 135° as complementary sources of structural information. Prediction ability was highest for GY, HSW, and NDM, especially in flights near flowering and maturity, but remained low for TOL and LR. Integrating multiple flights modestly improved accuracy, whereas cross-season predictions were unreliable. Nonetheless, indices such as VARI_P25 provided useful cross-season correlations for HSW and TOL, enabling early screening of less promising lines. Conclusion: This pioneering study demonstrates that UAV–ML pipelines can capture genetic signals of stink bug resistance in soybean, despite environmental complexity. These findings open new avenues for resistance phenotyping, supporting more efficient breeding strategies and accelerating genetic gains in soybean improvement.

Why it matches plant phenotyping methodsUAV画像と機械学習を用いて、ダイズの抵抗性関連形質や収量を推定するHTPパイプラインを技術的に評価しており、表現型取得・推定法が研究の中心である。

abstractThis study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and the potential of phenotyping to stink bug resistance by correlating image-derived features and machine learning (ML) models.

An Effective YOLOv11 Grain Detection Model Trained on Intact Barley Spikes Reveals a QTL Containing a Pivotal Regulator of Lateral Spikelet Formation. →

Grain number is a primary agronomic trait for targeted yield improvement, with the prospect of enhanced grain production leading to greater food security. Given the complex polygenic nature of the grain number trait, large sample sizes are essential for effective QTL identification. The implementation of trained computer vision models for grain detection offers a timely and cost-effective solution for rapid QTL isolation. In this study, we trained a grain detection model using Ultralytics' You Only Look Once (YOLOv11) framework. Training was completed on 1000 images of barley spikes, derived from a doubled haploid (DH) population descended from Hindmarsh and RGT Planet. The trained model, termed BarleyGC, achieved satisfactory accuracy metrics (mAP50-95 = 71.9%, recall = 96.7%, precision = 97.1%). Phenotypic characterisation of the DH population was completed with BarleyGC on a distinct collection of 973 images. The Pearson correlation coefficient (r) between model and manual-derived counts for the trait of grain number per spike was 0.895 ( p n = 153 DH lines), revealed a QTL peak at position 224.959 cM on the genetic map (LOD = 3.14), named qGN-2H. The QTL region contained 21 candidate genes-including HORVU2Hr1G092290 (HORVU.MOREX.r3.2HG0184740), encoding the six-rowed spike 1 ( Vrs1 ) gene-a well-characterised major regulator of row-type divergence and lateral spikelet development. Our study demonstrates the power of the YOLOv11 framework for grain quantification, with BarleyGC capable of grain detection directly from images of intact spikes in two-rowed barley varieties-thus achieving accelerated sample processing for the grain number trait.

Why it matches plant phenotyping methods大麦穂の画像から穀粒数を推定するYOLOv11モデルを開発・検証し、手動計数との比較およびQTL解析に応用しており、植物表現型取得法が研究の中心である。

abstractThe implementation of trained computer vision models for grain detection offers a timely and cost-effective solution for rapid QTL isolation.

High-throughput phenotypic analysis of plant and curd growth dynamics during the whole growth period of cauliflower based on instance segmentation →

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.

A Review of Crop Attribute Detection for Agricultural Harvesting Machinery →

Crop attribute detection, as a key component of intelligent agricultural harvesting machinery, plays a crucial role in harvesting efficiency, loss reduction, and autonomous operation control. Compared with existing reviews on artificial intelligence and sensing technologies in agriculture, this review focuses on crop attribute detection scenarios oriented toward the intelligent decision-making and control requirements of agricultural harvesting machinery. It mainly analyzes crop attributes that affect harvesting operations, as well as the sensors and algorithms involved in detecting these attributes, and further clarifies the relationship between detection methods and control decisions in agricultural harvesting machinery. For grain crops, the key attributes relevant to harvesting operations include plant height, plant density, spike number, crop lodging, canopy structure, and crop position. For fruit and vegetable crops, the key attributes relevant to harvesting operations include maturity, position, and quality. From the perspectives of multi-source data acquisition, data analysis, and attribute detection algorithms, the key technologies in the field of crop attribute detection are systematically summarized and analyzed, including sensors used in crop attribute detection, such as RGB, spectral, near-infrared, and LiDAR sensors, as well as data analysis and recognition approaches, such as image classification, object detection, and point cloud analysis. The complexity of field environments and the dynamics of machine operation are analyzed, highlighting the technical bottlenecks of current detection systems in environmental adaptability, real-time responsiveness, and resistance to interference. To address these challenges, feasible optimization directions were proposed, including multi-sensor fusion, weakly supervised learning, and few-shot learning. This review aims to provide systematic references and theoretical support for the coordinated development of crop detection and control decision-making in intelligent agricultural harvesting systems.

Why it matches plant phenotyping methods収穫機械向けではあるが、草丈・密度・穂数・倒伏・群落構造・成熟度など植物の形態・状態を検出するセンサーと解析手法を体系的にレビューしており、表現型取得法が中心である。

titleA Review of Crop Attribute Detection for Agricultural Harvesting Machinery

Image-based QTL mapping of grain size for establishment of predictive breeding in rice →

Rice is a staple food and a major source of calories for much of the global population. With the global population continuing to rise, breeding high-yielding rice cultivars is critical for future food security. Grain size is a key trait directly related to rice yield. In this study, QTL mapping was conducted using both phenotypic data collected with Vernier calipers and image-based phenotyping. All QTLs identified through caliper measurements were also detected using image data, which allowed for more precise localization with higher LOD scores. Grain size-related QTLs were identified on chromosomes 3, 5, 6, and 7, including major genes such as GS3, qSW5, and GW7. A novel QTL region between markers RM586 and RM1163 on chromosome 6 was identified, which has not been previously reported. Introgression of this region positively affected grain length, and an additive effect was observed when combined with qGL3. Within the RM586-RM1163 region, 16 open reading frames (ORFs) were annotated, and Gene Ontology (GO) analysis suggested their roles in regulating cellular structures and organelle functions during grain development. Among these, OsGSq6 showed a significant increase in expression from the panicle formation stage to the heading stage. Fifteen SNPs were identified within the gene, resulting in 11 distinct haplotypes, several of which were predominantly found in indica rice. OsGSq6 encodes a phosphotyrosyl phosphatase activator, suggesting its role in grain development. Image-based phenotyping also enabled the detection of varietal admixtures, contributing to improved genetic purity. This approach offers a promising strategy for enhancing rice breeding precision.

Why it matches plant phenotyping methods画像ベース表現型解析を用いてイネ粒サイズを定量し、ノギス測定との比較でQTL検出精度を評価しており、表現型取得法の実質的な適用が研究上重要です。

abstractQTL mapping was conducted using both phenotypic data collected with Vernier calipers and image-based phenotyping.

Integration of NIRS and GWAS identifies GhMYB86 as a potential regulator of cottonseed protein content with pleiotropic effects on fiber strength →

Abstract Cottonseed is rich in protein and oil, making the improvement of its nutritional quality essential for global food security. In this study, high-accuracy near-infrared spectroscopy (NIRS) models were developed for predicting cottonseed protein and oil content using least absolute shrinkage and selection operator (LASSO) regression, achieving validation R 2 values of 0.969 and 0.972, respectively. Using these models, 249 upland cotton accessions were phenotyped across five environments and subjected to a genome-wide association study (GWAS) based on a 10K liquid-phase SNP array, resulting in the identification of 24 significant loci. A novel stable QTL, qPO-A07-1 , was detected, within which GhMYB86 was prioritized as a candidate gene. This gene exhibited higher expression in high protein varieties during ovule development. Functional validation demonstrated that heterologous overexpression in Arabidopsis thaliana increased seed protein content by 2.61% – 3.34%, whereas expression in Saccharomyces cerevisiae reduced triglyceride content by 30.72% relative to the control. These results demonstrate that GhMYB86 positively regulates protein accumulation while negatively affecting oil content. A kompetitive allele-specific PCR (KASP) marker targeting a promoter A/T polymorphism revealed that the AA allele was associated with higher protein content, lower oil content, and increased fiber strength across both mapping and validation populations. Furthermore, the protein- and fiber strength-favorable allele has undergone positive selection during breeding. This study provides robust phenotyping tools, reliable genetic resources and molecular markers for cottonseed nutritional quality breeding, laying a foundation for the synergistic improvement of both fiber quality and nutritional quality in cotton.

Why it matches plant phenotyping methodsNIRSによる綿実タンパク質・油分の非破壊推定モデルを開発・検証し、多数系統の表現型取得に用いており、植物形質の取得法が実質的な中心要素である。

abstracthigh-accuracy near-infrared spectroscopy (NIRS) models were developed for predicting cottonseed protein and oil content using least absolute shrinkage and selection operator (LASSO) regression, achieving validation R 2 values of 0.969 and 0.972, respectively.

PAT: An Image Analysis Tool for Automated Scoring of Pollen in Alexander-Stained Anthers →

Quantitative pollen viability analysis is a critical but labor-intensive step in plant reproductive biology. Existing deep-learning Segment Anything Models (SAM) fail to reliably segment viable pollen in Alexander-stained anthers. To address this, we fine-tuned an existing Cellpose-SAM model for pollen segmentation. We integrated it into PAT (Pollen Analysis Tool), a cross-platform desktop application. PAT features instance segmentation with interactive quality control, an in-app model retraining module, and publication-ready statistical outputs. We deployed PAT in an EMS suppressor screen of semi-sterile Arabidopsis smg7-6 mutants, enabling efficient candidate prioritization for whole genome sequencing and mapping candidate mutation. This screen led to the identification of a point mutation in CAP-D2 ( capd2-2 ), a Condensin I subunit, that rescues the smg7-6 meiotic phenotype. Notably, mutation in a Condensin II subunits (CAP-D3 and CAP-H2) does not confer rescue. Further characterization suggests the capd2-2 allele is hypomorphic, showing no defects in vegetative growth, chromocenter compaction, or transposable element silencing. Collectively, we demonstrate that accessible AI tools have the potential to bridge gaps in plant phenotyping and accelerate the pace of biological discovery. Highlight We combined AI-powered image analysis with an easy-to-use desktop app to automate plant pollen counting, then used it to identify a new genetic suppressor of meiotic defects.

Why it matches plant phenotyping methods花粉の生存性を画像から自動定量するセグメンテーション手法とソフトウェアPATの開発が中心であり、植物表現型の取得・抽出手法に該当する。

abstractWe integrated it into PAT (Pollen Analysis Tool), a cross-platform desktop application.

Exploring morphological traits related to potential milling yield based on image-analysis. →

Wheat (Triticum aestivum L.) is a globally essential cereal crop whose productivity and processing efficiency are critically influenced by the morphological traits of the grain. While biotic and abiotic stresses reduce field yields, post-harvest milling losses further diminish flour output, underscoring the importance of optimizing grain morphology for processing efficiency. This study investigates the relationships between the wheat grain shape and size parameters and their impact on milling performance outcomes to identify optimal morphological characteristics that minimize yield losses. Using a Korean wheat core collection of 566 accessions, we applied image-based phenotyping to quantify key grain traits, in this case the width, length, area, perimeter, aspect ratio, circularity, roundness, and skewness. Multivariate analyses through k-means clustering and principal component analysis showed two distinct morphological groups and highlighted the kernel width and uniformity as potential indicators. Strong positive correlations between size traits and negative correlations between shape descriptors emphasize the trade-offs influencing milling quality. Optimal wheat grains for enhanced the milling yield exhibited large, plump, regular kernels with high circularity and low skewness. These findings provide quantitative criteria to guide wheat breeding programs with the goal of genetically optimizing the grain morphology to improve the milling yield and processing quality, thereby contributing to global food security.

Why it matches plant phenotyping methods画像解析による小麦粒の形態形質の定量が研究の中心であり、大規模コレクションに適用して形質抽出・解析を行っているため、植物表現型手法の実質的な応用に該当する。

abstractUsing a Korean wheat core collection of 566 accessions, we applied image-based phenotyping to quantify key grain traits, in this case the width, length, area, perimeter, aspect ratio, circularity, roundness, and skewness.

Machine vision RGB phenotyping reveals divergent and real‐time responses to different watering regimes in near‐isogenic wheat genotypes →

Abstract This study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences under well‐watered and reduced watering conditions in genetically uniform wheat ( Triticum aestivum L.) populations. It aims to support the design of breeding populations by identifying parents with complementary coping mechanisms that can be combined in crosses to produce superior progeny. We used RGB imaging to monitor side‐projected area (SPA) in BC 2 F 6 wheat progenies under well‐watered, pre‐anthesis, and post‐anthesis reduced watering conditions. SPA was modeled with logistic growth curves per genotype to extract dynamic canopy traits, which, together with the area under the SPA‐based growth curve, were then correlated with yield, straw biomass, harvest index, and spike traits measured at maturity. Despite genetic similarity, RGB‐based imaging revealed distinct phenotypes under normal conditions and stress response strategies among wheat lines, highlighting the value of dynamic, non‐destructive phenotyping for identifying complementary response patterns. Under well‐watered conditions ( n = 36), area under the curve was strongly associated with grain weight ( R 2 = 0.76, 95% confidence interval [CI]: 0.59–0.87), but relationships weakened under reduced watering, especially post‐anthesis, indicating a reduced association of canopy size with reproductive output. The data revealed contrasting response patterns among breeding lines based on characteristics of the logistic growth curve under normal conditions, their recovery slope after pre‐flowering reduced watering, or conversion of their straw biomass into harvestable grains. RGB imaging enables real‐time, non‐destructive detection of reduced watering responses in genetically similar wheat lines and provides complementary in‐season data to design next‐generation breeding populations for climate‐resilient cultivars.

Why it matches plant phenotyping methodsRGB画像で動的なキャノピー形質を抽出し、育種利用に向けた非破壊・リアルタイム表現型解析を実質的に評価しているため。

abstractThis study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences

Machine Learning-Based Non-Destructive Prediction of Juice Sac Granulation in Guanxi Honey Pomelo. →

The juice sac granulation of citrus fruits is a biological disorder that commonly occurs during the stages of growth, mature, and post-harvest, which severely affects the quality and reduces consumer acceptance of fruits. To explore the correlation between granulation and both external morphological characteristics and internal quality characteristics, 11 external and internal quality characteristics of Guanxi honey pomelo were collected and systematically analyzed by principal component analysis and linear regression. Then seven external quality characteristics and one critical characteristics, GR% were applied in machine learning modeling. The results indicated that several characteristics such as single fruit weight, single fruit volume, longitudinal diameter, and transverse diameter showed positive correlations with juice sac granulation rate (GR%), and were subsequently incorporated into classification model development. Among the five models evaluated, support vector machine demonstrated superior performance with a precision and recall rate of 100.00% and 100.00%, respectively, verifying its favorable accuracy and robustness. This research combined traditional statistical approaches with modern computational techniques, offering a reliable screening solution for juice sac granulation degree of Guanxi honey pomelo, which provided potential applicability in citrus processing industries and a theoretical foundation for non-destructive quality assessment.

Why it matches plant phenotyping methods果実の外観・内部特性から果肉粒化率を非破壊予測する機械学習モデルを開発・評価しており、植物状態の取得・推定手法が研究の中心です。

titleMachine Learning-Based Non-Destructive Prediction of Juice Sac Granulation in Guanxi Honey Pomelo.

Genetic improvement of papaya (Carica papaya L.) and fruit quality: a review of integrated digital phenotyping, physicochemical, and sensory approaches →

Papaya (Carica papaya L.) is a crop of great economic importance. However, factors such as low genetic variability, abiotic stresses, and technological limitations compromise fruit productivity and quality. In this context, the use of modern tools, such as digital phenotyping, combined with genetic improvement, has stood out in the search for more productive cultivars with improved postharvest quality. The objective of this study was to conduct a comprehensive literature review on papaya cultivation, addressing its main agronomic aspects, as well as postharvest evaluation methods, with an emphasis on digital image-based phenotyping techniques applied to fruit quality analysis. The study was based on scientific publications selected from the Scopus, SciELO, and Google Scholar databases, mainly covering the period from 2014 to 2024. Studies related to papaya cultivation, genetic improvement, digital phenotyping, sensory analysis, and physicochemical evaluation of fruits were included, as well as classical references relevant to the topic. The literature indicates that papaya breeding depends on the exploitation of genetic variability present in germplasm banks, aiming at the development of cultivars with superior agronomic traits and improved fruit quality. Digital phenotyping stands out as an efficient tool for collecting and analyzing phenotypic data, allowing greater precision, cost reduction, and optimization of the selection process. The integration of digital phenotyping with physicochemical and sensory analyses enables the identification of genotypes with higher productive potential and fruits with better consumer acceptance.

Why it matches plant phenotyping methodsデジタル画像ベースの植物表現型解析を果実品質評価に適用する方法を中心に扱うレビューであり、植物フェノタイピング手法レビューとして適格。

abstractThe objective of this study was to conduct a comprehensive literature review on papaya cultivation, addressing its main agronomic aspects, as well as postharvest evaluation methods, with an emphasis on digital image-based phenotyping techniques applied to fruit quality analysis.

High-throughput olive germplasm classification using morphological phenotyping and machine learning. →

This study presents a robust framework for high-throughput olive germplasm classification, addressing the phenotyping bottleneck that currently limits breeding programs. Unlike prior research restricted to narrow genotypic ranges or single-image modalities, we analyzed 65 genetically diverse olive cultivars from the Tarom Olive Research Station (Zanjan, Iran). We employed a dual-image phenotyping approach, integrating high-resolution imagery of both fruits and kernels with quantitative weight metrics. This methodology enabled the extraction of critical morphological traits—including eccentricity, solidity, and shape factors—to train and validate seven Machine Learning (ML) algorithms. Our comparative analysis of Discriminant Analysis (DA), Support Vector Machine (SVM), Neural Networks (NN), and ensemble methods reveals that the DA model achieves superior performance, attaining a recall and precision of 0.98 when integrating fruit, kernel, and weight data. This significantly outperforms standard models like KNN and Naive Bayes in this domain. These findings demonstrate that combining multi-view imaging with morphological feature extraction provides a highly accurate, cost-effective tool for managing olive genetic resources and accelerating crop improvement.

Why it matches plant phenotyping methods果実・核の画像から形態形質を抽出し、機械学習モデルを比較検証する高スループット植物フェノタイピング手法が研究の中心である。

abstractThis study presents a robust framework for high-throughput olive germplasm classification, addressing the phenotyping bottleneck that currently limits breeding programs.

High-throughput image-based seed phenotyping and multivariate analysis to characterize common bean ( Phaseolus vulgaris L.) accessions →

Common bean (Phaseolus vulgaris L.) seed phenotyping is essential for characterizing genetic diversity and identifying superior traits to support breeding for climate resilience and nutritional quality. Traditional manual techniques are increasingly being replaced by high-throughput, image-based digital phenotyping to ensure precision and efficiency in large-scale morphometric analysis. In this study, 30 common bean accessions from the Rural Development Administration (RDA) Gene Bank, South Korea, were phenotyped in 2025 using high-resolution image-based analysis to quantify key traits, including area, solidity, circularity, major/minor axis lengths, aspect ratio, and Feret diameter. One-way ANOVA revealed highly significant differences among accessions for all measured traits (p < 0.001), confirming substantial genotypic variability. Seed area ranged from 93.41 mm2 (IT160310) to 39.00 mm2 (IT337943), while roundness varied from 0.708 to 0.462, indicating pronounced morphological diversity. Spearman’s rank correlation showed a strong positive relationship between seed area and Feret diameter (r = 0.94), whereas aspect ratio and roundness exhibited a perfect negative correlation (r = −1.0). Hierarchical clustering and PCA effectively grouped accessions, with the first two components explaining 96.3% of total variation (PC1 and PC2). Validation against manual methods showed strong correlations (r = 0.95 for area; r = 0.94 for length), confirming ImageJ’s reliability. These findings provide a robust phenotypic foundation for breeding programs, enabling trait-based selection and supporting the integration of high-throughput pipelines into germplasm screening and future genomic studies, such as marker-trait association and genomic selection.

Why it matches plant phenotyping methods画像ベースで種子形態形質を高スループットに抽出し、手動測定との相関で検証しており、表現型取得手法が研究の中心である。

abstractTraditional manual techniques are increasingly being replaced by high-throughput, image-based digital phenotyping to ensure precision and efficiency in large-scale morphometric analysis.

Predicting Pollen Germination and Tube Elongation Responses to Different Plant Growth Regulators in Kiwifruit (Actinidia deliciosa L.) Using Random Forest Regression →

This study aimed to train a Random Forest regression model using pollen germination rate and pollen tube length data obtained after 3 h of in vitro germination at 0.005, 0.05, and 0.5 mM concentrations of 24-epibrassinolide, methyl jasmonate, spermidine, spermine, and putrescine, and to evaluate the model’s accuracy in predicting responses at 0.025, 0.25, and 2.5 mM concentrations. Experimental data were compared with Random Forest Regression model predictions, and model performance was assessed using Absolute Error and Root Mean Square Error. Prediction accuracy was classified as good, moderate, or low based on Absolute Error thresholds applied to both pollen germination and pollen tube length (0-6, 6-15, ≥15), and Root Mean Square Error thresholds defined separately for pollen germination (0-10, 10-20, ≥20) and pollen tube length (0-20, 20-40, ≥40). Results indicated that the Random Forest Regression model provided reliable predictions at low and moderate plant growth regülatör concentrations, with 24-epibrassinolide and putrescine treatments aligning closely with experimental data. However, for methyl jasmonate, spermidine, and spermine at higher concentrations, the model exhibited overestimations, particularly in predicting pollen germination rates at inhibitory doses. The study highlights the potential of machine learning approaches in pollen biology research and demonstrates the necessity of optimizing model parameters for high-dose predictions. These findings contribute to the integration of data-driven decision-making in artificial pollination and plant growth regulators treatment strategies.

Why it matches plant phenotyping methodsランダムフォレストによる花粉発芽率と花粉管長の予測モデルを構築・評価しており、植物形質の推定とモデル性能検証が研究の中心である。

abstractThis study aimed to train a Random Forest regression model using pollen germination rate and pollen tube length data

Seed imaging omics: A bridge from perception to cognition for the future of seed phenotyping →

Seeds are complex living systems that display rich diversity in morphology, physiology, biochemistry, and genetics. Yet phenotyping during seed dormancy remains hampered by limited imaging modalities, insufficient data integration, and underpowered intelligent analytics—constraints that impede the efficiency and accuracy of precision breeding and germplasm evaluation. In the AI-for-Science era, seed phenomics research urgently needs to establish an end-to-end “measure–compute–understand–apply” pipeline, spanning cross-scale multimodal data acquisition to insight. This article systematically reviews the technical evolution of dormancy-state seed phenotyping and delineates five stages—manual observation phenotypes, biochemical phenotypes, image-based phenotypes, digital seeds, and intelligent seeds—summarizing the defining features and principal limitations of each. The deep integration of advanced imaging with artificial intelligence offers new opportunities to overcome existing bottlenecks. Seed Imaging Omics has emerged to meet this need: leveraging multiscale, multidimensional imaging for comprehensive observation and multimodal data capture; coupling these data with multimodal fusion, foundation-model analysis, and virtual seed reconstruction to enable precise feature extraction and pattern discovery from large image corpora. These capabilities clarify complex traits, reveal morphology–function relationships, and advance systems-level understanding of seed biology, ultimately supporting precise germplasm management and evaluation, data-driven elucidation of biological mechanisms, and accelerated innovation in crop improvement. Looking ahead, continued progress in sensing and imaging, foundation models, and large-scale analytics will drive seed phenotyping toward “intelligent” systems capable of autonomous sensing, real-time analysis, and decision-making across the seed life cycle—transforming seeds from passive carriers of genetic and phenotypic information into smart units that integrate phenotypic logging, state monitoring, performance assessment, and management feedback.

Why it matches plant phenotyping methods種子休眠状態の表現型計測技術を体系的にレビューし、画像取得、マルチモーダル統合、特徴抽出、AI解析を中心に扱うため、植物フェノタイピング手法レビューとして含める。

abstractThis article systematically reviews the technical evolution of dormancy-state seed phenotyping

A Simplified Heat-Tolerance Evaluation System at the Pollen Development Stage in Rice ( Oryza sativa L.). →

Heat stress, particularly during the reproductive stage, poses a major challenge to rice production, as pollen development is highly sensitive to elevated temperatures. Accurate assessment of heat tolerance during this period is crucial for improving rice heat-stress tolerance but is hindered by asynchronous panicle development and imprecise staging. In this study, we identified a pair of near-isogenic lines, ZP15 and ZP17, which exhibited contrasting seed-setting rates under heat stress. We demonstrated that this divergence arises from differential tolerance during the pollen developmental stage, corresponding to a critical window (9-16 days before heading). Taking these lines as references, we established a reliable system that synchronizes developmental staging and quantitatively assesses heat-induced fertility loss. Validated using heat-tolerant N22 and heat-sensitive Wushansimiao, this system was applied to assess four conventional varieties and eight hybrids. Huanghuazhan and self-bred hybrids (Yangxianyou 912, Yangxianyou 903, and Yangxian 9A/P119-8) displayed high tolerance comparable to control varieties, whereas Yangdao 6 and multiple hybrids showed pronounced sensitivity. Collectively, this work provides a precise and reproducible framework for evaluating heat tolerance during pollen development, offering a valuable tool for accelerating the breeding of heat-resilient rice varieties.

Why it matches plant phenotyping methodsイネの花粉発育期における高温耐性と受精率低下を定量評価する、再現性のある評価システムを開発・検証しており、植物表現型取得が研究の中心である。

abstractwe established a reliable system that synchronizes developmental staging and quantitatively assesses heat-induced fertility loss.

Climate gradients drive the evolution of seed morphology and life history with impacts to seedling fitness in Fraxinus nigra →

Background and AimsClimate gradients influence seed morphology, emergence, and early life-history traits with cumulative impacts to individual fitness. For ex situ seed collections, which represent an invaluable repository of potential trait information for species management and conservation, climate data can guide preservation of adaptive variation and inform deployment strategies for restoration. Here we leverage a range-wide ex situ seed collection of critically endangered black ash seeds (Fraxinus nigra) to evaluate how climatic gradients shape variation in morphology and early life-history. MethodsTo test how climate of origin, seed morphology, and early life-history interact to impact first year fitness, high-throughput X-ray imaging and neural network-based segmentation were used to quantify variation in seed morphology for 701 maternal lineages spanning 76 populations across the range of F. nigra. Following this, a subset of seeds were used to establish a common garden experiment and quantify variation in emergence, early life-history transitions, and their cumulative impact to first-year survival and growth. ResultsOn average, differences within-population explained [~]43% of the variability in seed morphology, while among-population differences explained [~]14%. This suggests that substantial genetic variation exists within populations for natural selection to act upon and differences have evolved among populations. Climate associations indicated warmer and drier environments predicted heavier seeds with faster developmental transitions and increased first-year height. Together, climate of origin, seed mass, and timing of developmental transitions best predicted cumulative fitness, with populations from more continental environments exhibiting greater survival and first-year height accumulation on average. ConclusionsOverall, these results highlight the importance of climate of origin, seed traits, and early developmental transitions to first-year fitness in a perennial tree species. This work demonstrates how ex situ collections can be used to identify climatically structured trait variation and guide conservation strategies aimed at maintaining adaptive potential under environmental change.

Why it matches plant phenotyping methods701系統の種子形態を高スループットX線画像とニューラルネットワーク分割で定量しており、形態表現型の取得・抽出が研究の主要な技術基盤である。

abstracthigh-throughput X-ray imaging and neural network-based segmentation were used to quantify variation in seed morphology for 701 maternal lineages spanning 76 populations across the range of F. nigra

An Integrated Tunable-Focus Light Field Imaging System for 3D Seed Phenotyping: From Co-Optimized Optical Design to Computational Reconstruction →

Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 104 points/cm2 while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging.

Why it matches plant phenotyping methods種子の3D形態形質を取得する光学・計算イメージングシステムの開発と検証が研究の中心であり、フェノタイピング手法として明確に適格。

abstractThis paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation.

Deep learning based instance segmentation of mandarin fruit slices for precision assessment and morphological quantification. →

Accurate instance segmentation of mandarin fruit slices is essential for quantifying segment morphology and central core structure, which are key traits in cultivar evaluation, fruit quality assessment, and postharvest application. Manual measurement of these anatomical features, however, is time-consuming and prone to inconsistency. In this study, we developed an automated segmentation framework based on YOLOv8 to detect and semantic segment the fruit segments and the central core in high-resolution mandarin transversely cut images. The model was trained on a curated datasetderived from 58 original high-resolution cross-sectional images (5100 × 7019 pixels), which were systematically partitioned into 280 cropped sub-images (1712 × 1778 pixels), each containing a single complete citrus slice, and demonstrated excellent performance. YOLOv8 achieved near-perfect detection metrics, with bounding box metrics precision ~ 0.997, recall ~ 1.00, mAP50 ~ 0.995, and mAP50-95 ~ 0.922. Semantic segmentation accuracy was similarly strong, with precision = 0.997, Recall ~ 1.00, mAP50 ~ 0.995, and mAP50-95 = 0.965. Training and validation losses converged steadily, indicating stable learning without overfitting. The nonsignificant differences between YOLOv8-predicted measurements and ground truth data, together with low mean absolute error (MAE) values, demonstrate that the model not only performs well in semantic segmentation metrics but also maintains high accuracy in quantitative measurements, which is critical for cultivar discrimination and genetic studies. Our work provides a robust, high-precision, and reproducible framework for mandarin fruit slice phenotyping, offering significant potential for applications in agricultural research, breeding programs, and automated fruit quality evaluation.

Why it matches plant phenotyping methodsマンダリン果実スライスの形態・中心部構造を画像分割で定量する手法を開発し、精度検証と実測値比較を行っており、植物フェノタイピング手法が中心である。

abstractwe developed an automated segmentation framework based on YOLOv8 to detect and semantic segment the fruit segments and the central core

Image-based phenotyping of castor bean seeds for morphological traits, seed weight prediction, and assessment of genetic diversity →

Abstract Digital seed phenotyping offers an efficient and objective alternative to conventional, labor-intensive methods for morphological characterization in plant breeding programs. In castor bean, rapid and reliable tools are essential to support genetic improvement. This study evaluated the potential of digital phenotyping for seed characterization and its application in breeding. Seeds from 65 accessions (2023) and 51 accessions (2024) were photographed with an RGB camera and processed in ImageJ® for extraction of morphological traits. Agreement between digital and manual measurements was assessed by correlation and Bland–Altman analysis, while machine learning models were trained to predict hundred-seed weight (HSW). Genetic diversity was explored using principal component analysis (PCA) and clustering, and variance components and heritability were estimated with mixed linear models. Digital phenotyping showed strong agreement with manual measurements (r = 0.95–0.97) and enabled accurate HSW prediction, with Ridge Regression achieving the best performance (R 2 = 0.88; RMSE = 3.83; MAE = 3.19). PCA explained 85.7% of the variance and revealed three phenotypic clusters. Traits such as seed length (H 2 = 0.88) and aspect ratio (H 2 = 0.87) exhibited high heritability, while roundness (H 2 = 0.79), perimeter (H 2 = 0.72), and area (H 2 = 0.67) were moderate. These findings demonstrate that digital phenotyping is a reliable and high-throughput method for castor bean seed characterization, supporting genotype selection and the integration of machine learning approaches into breeding programs for greater precision and efficiency.

Why it matches plant phenotyping methods種子形態形質の画像取得・抽出、手動測定との技術検証、重量予測モデル評価が研究の中心であり、植物フェノタイピング手法として明確に適格。

abstractDigital seed phenotyping offers an efficient and objective alternative to conventional, labor-intensive methods for morphological characterization in plant breeding programs.

An End-to-End Precision Phenotyping Framework: Rice Panicle Detection and Counting in Complex Fields via Lightweight DETR →

Accurate, high-throughput quantification of rice panicles plays a vital role in advancing precision yield prediction. However, transitioning to real-time, edge-deployable unmanned aerial vehicle phenotyping is often impeded by extreme spatial scale variations from altitude fluctuations and complex unstructured backgrounds. To address this, we constructed a comprehensive composite dataset specifically capturing multi-altitude and varying illumination field conditions. We then propose Panicle-DETR, a highly optimized precision phenotyping framework incorporating a frequency-aware CSP backbone. By projecting visual perception into the frequency domain, the architecture inherently suppresses low-frequency environmental noise and minimizes computational redundancy. Furthermore, a Lossless Feature Encoder prevents the irreversible pixel decimation of micro-targets across varying operational altitudes, while a composite metric loss explicitly disentangles heavily adhered panicle clusters. Evaluated on our composite dataset, Panicle-DETR achieved an outstanding detection Precision of 90.97% alongside robust agronomic counting stability, demonstrated by a Mean Absolute Error of 4.28 and an \( R^2 \) of 0.957. With a compact footprint of only 13.78 M parameters, this framework fundamentally overcomes the computational and spatial limitations of traditional vision models, establishing a highly reliable paradigm for autonomous, onboard agricultural monitoring.

Why it matches plant phenotyping methods米の穂を画像から検出・計数する軽量なUAV表現型解析フレームワークを開発し、複合データセット上で性能評価しており、表現型取得・抽出手法が中心である。

abstractwe constructed a comprehensive composite dataset specifically capturing multi-altitude and varying illumination field conditions.

Identification of self-incompatibility in macadamia (Macadamia SPP.) using field-bagging and fluorescence microscopy →

Self-incompatibility (SI) significantly reduces crop yield, often far below its genetic potential. Developing self-compatible varieties is the most effective strategy for overcoming SI in crops. Most macadamia ( Macadamia SPP.) species exhibit SI or partial self-incompatibility (PSI), so the efficient identification of self-compatible germplasms has emerged as a crucial topic. To characterize self-incompatibility phenotypes in macadamia germplasm resources, we conducted a four-year field study using the field-bagging method and established a standardized classification system of self-incompatibility. That is, the degree of SI was based on the final self-incompatibility index (F_SI), which was calculated based on the open-pollination final nut set per raceme (OP_FNS) and self-pollination final nut set per raceme (SP_FNS) values (strong SI: F_SI ≥ 0.7, medium SI: 0.4 ≤ F_SI 35%). Through comprehensive analysis of the field-bagging and fluorescence-microscopy observations, thirteen varieties with strong SI (816, 778, 842, Special, 812, D, 820, 246, 772, A16, A4, 951, and 695), six varieties with moderate SI (851, 828, 508, 936, O.C, and D4), and four varieties with weak SI (915, HY, 836, and 814) were identified. Our study provides a theoretical foundation and technical support for advancing germplasm resource innovation, and the genetic improvement and breeding of self-compatible macadamia varieties.

Why it matches plant phenotyping methods自家不和合性という植物状態を対象に、圃場袋掛けと蛍光顕微鏡観察を用いた標準化分類体系を確立しており、表現型の取得・評価法が研究の中心である。

abstractTo characterize self-incompatibility phenotypes in macadamia germplasm resources, we conducted a four-year field study using the field-bagging method and established a standardized classification system of self-incompatibility.

Augmenting plant-pollinator interactions to promote biodiversity and global food security →

Global agricultural production is currently limited by ongoing climate change. Approximately 90% of crop species and numerous wild plants are dependent on pollinators for reproduction. The global threat to pollinators posed by climate change has grown considerably, as higher temperatures, shifting rainfall patterns, and more frequent extreme weather events disrupt the fragile relationships between plants and their pollinators. The decline in pollinators is also linked to shifts in land use, the widespread adoption of monocropping, and heavy reliance on agrochemicals. Therefore, the protection of pollinators and the preservation of agrobiodiversity are essential to uphold global food systems. Here, we synthesize the adverse impact of climate change on plant-pollinator interactions; throughput assay for phenotyping floral traits; assessing variability and molecular basis of floral display (flower size, shape, color, attractants etc.) and reward (nectar volume and composition, pollen, and fragrance in case of ornamental plants) traits; crop domestication and inbreeding, ploidy and mating systems differences impacting plant-pollinator interactions; volatiles and metabolites mediating plant-pollinator relationships; trade-offs involving reproductive and pollinator traits; and finally, progress in developing pollinator-friendly crop cultivars through conventional plant breeding and biotechnological interventions. Pollinator-assisted phenotyping and selection platform (DARkWIN) combined with other high-throughput phenotyping assays, has the potential to simultaneously quantify multiple interactions impacting pollinators’ visitation and foraging behaviors, and generate data on other parameters like stress tolerance, yield, and nutrition in the target populations. Assessing and exploiting functional diversity for plant-pollinator interactions, combined with the use of functionally characterized genes and associated markers for floral display ( AT2G31010 , AT4G17080 , CmGEG , CmCYC2c , CmJAZ1-like-CmBPE2 , Cyc2CL-1 , Cyc2CL-2 ) and reward ( SWEET9 , BrCWINV4A , EOBI , EOBII ) traits, can be deployed in breeding programs to develop pollinator-friendly crop cultivars. Numerous candidate genes, reported herein, must be functionally validated before being deployed in crop breeding programs.

Why it matches plant phenotyping methods植物―送粉者相互作用と花形質を対象とするレビューであり、花形質のスループット表現型解析やDARkWINによる送粉者支援型フェノタイピング基盤を主要な方法論として扱っている。

abstractthroughput assay for phenotyping floral traits

High-throughput phenomic survey of seed morphological diversity in common and tartary buckwheat germplasm. →

Buckwheat (Fagopyrum spp.) germplasm represents an underutilized source of morphological diversity for crop improvement. This study presents a high-throughput phenomic survey quantifying five seed morphological traits (area, length, width, circularity, roundness) across 563 RDA genebank accessions (519 common buckwheat (F. esculentum), 44 Tartary buckwheat (F. tataricum) using standardized imaging. Common buckwheat exhibits larger seeds (area: 21.12 ± 4.04 mm²) with lower coefficients of variation (CVs: 17.9%), while Tartary buckwheat shows smaller seeds (14.80 ± 3.23 mm²) with higher CVs (20.1%) and greater shape dispersion (PC2 variance: 3.73 vs. 1.06). Principal component analysis confirms species-level morphological separation and documents exploitable polymorphism, including notched/slender/round/rice morphotypes in F. tataricum. These standardized phenotypic baselines support genebank curation, accession ranking by seed size/shape extremes, and prioritization for multi-environment trials and genetic studies.

Why it matches plant phenotyping methods標準化画像を用いた大規模な種子形態形質の高スループット取得・解析が研究の中心であり、遺伝資源評価に再利用可能な表現型ワークフローとして substantive です。

abstractThis study presents a high-throughput phenomic survey quantifying five seed morphological traits (area, length, width, circularity, roundness) across 563 RDA genebank accessions

Classifying the Ripeness of Oil Palm Fresh Fruit Bunches Using a Mixed‐Order Relation‐Aware Recurrent Neural Network Approach for Enhanced Agricultural Monitoring and Optimized Crop Management in Agriculture →

ABSTRACT Oil palm production has increased rapidly since 2012, particularly in Guatemala, Malaysia, and Indonesia. Accurate Fresh Fruit Bunch classification is vital for oil yield and quality, but manual grading is inefficient and existing deep learning methods are computationally intensive. To overcome these challenges, this study proposes an optimized Oil Palm Fresh Fruit Bunch Ripeness classification framework based on a Mixed‐Order Relation‐Aware Recurrent Neural Network (OP‐FFBR‐MORA‐RNN). The proposed methodology integrates Confidence Partitioning Sampling Filtering (CPSF) for effective image localization, cropping, and resizing, followed by Revised Tunable Q‐Factor Wavelet Transform (RTQFWT) to extract discriminative features. These features are subsequently classified using a Mixed‐Order Relation‐Aware Recurrent Neural Network (MORA‐RNN), with its parameters optimized via the Fractional Pelican African Vulture Optimization (FPAVO) algorithm to enhance classification accuracy. The model categorizes FFBs into five ripeness classes: overripe, ripe, abnormal, empty fruit, and under‐ripe. Experimental evaluation on an oil palm dataset demonstrates that OP‐FFBR‐MORA‐RNN achieves 97.8% accuracy, 97.6% precision, and a low error rate of 2.4%, outperforming existing methods such as Oil Palm Fresh Fruit Bunch Ripeness categorization on mobile devices using Convolutional Neural Network (OP‐FFBR‐MD‐CNN), Machine Vision for maturity classification using Artificial Neural Network (MVM‐OP‐FFBR‐ANN), and Object Detection for Oil Palm Fruit Bunches using You‐Only‐Look‐Once (OB‐OPFBR‐YOLOv7). These results confirm the framework's effectiveness for reliable, scalable, and efficient ripeness classification, supporting improved agricultural monitoring and optimized crop management.

Why it matches plant phenotyping methods油ヤシ果房の熟度という植物器官の状態を画像から分類する手法を提案・評価しており、特徴抽出、分類モデル、精度比較が研究の中心である。

abstractthis study proposes an optimized Oil Palm Fresh Fruit Bunch Ripeness classification framework based on a Mixed‐Order Relation‐Aware Recurrent Neural Network (OP‐FFBR‐MORA‐RNN).

Description of morphological characteristics of wheat spike as a digital certificate in the SpikeDroidDB database. →

It has been repeatedly shown that spike productivity is the main component of wheat yield. The main spike parameters related to productivity are size, the number of grains and spikelets per spike, and the presence or absence of awns. In modern genetic research, morphometric analysis of hundreds and thousands of spikes is required to determine the loci that control spike productivity traits. On the other hand, thousands of accessions in modern collections of wheat genetic resources need detailed description. These considerations motivate the development of digital technologies for describing spike traits in wheat, which can be achieved through image analysis methods. These methods allow for automated acquisition of trait values that can serve as the basis for digital plant collections. Here we propose an extended set of spike characteristics obtained both manually and through digital image analysis and present plant characterization. These data form the basis of the updated version of the SpikeDroidDB database (http://spikedroid.biores.cytogen.ru/). The digital description of the spike consists of two blocks. The block of uploaded data includes a description of the plant and contains five tables: collection; variety sample (year of cultivation (vegetation), sowing identifier, taxonomic information, etc.), planting site, and characteristics of the spike determined manually (length, width of frontal and lateral views, type and color of the spike, etc.) The block of extracted features includes spike characteristics obtained by digital phenotyping and contains six tables: characteristics of the spike outline in the image; characteristics of the quadrangle model, values of the color components of the spike, dominant colors of the spike, and texture characteristics of the spike in the image. The most illustrative and significant features of the spike have been identified, allowing for the formation of the spike digital certificate, which includes size, shape, and color features derived from the digital images. The features forming the digital certificate have been compared between two wheat species, T. aethiopicum and T. carthlicum. It is shown that the features of the digital certificate allow for a clear representation of the spike model and the identification of distinct parameters: colors of the spike and awns and roundness of the frontal view of the spike. The database interface has been supplemented with the ability to upload data on plant and spike characteristics, as well as their images, in the batch mode.

Why it matches plant phenotyping methods小麦穂の画像解析による形態形質の自動取得、デジタル表現、データベース基盤を中心に開発・提示しており、植物フェノタイピング手法が研究の中核である。

abstractThese considerations motivate the development of digital technologies for describing spike traits in wheat, which can be achieved through image analysis methods.

Measurement of Fruit Diameter Using RGB‐D Cameras for the Purpose of Fruit Growth Assessment →

ABSTRACT This article describes a method for measuring the diameter of fruits with a near spherical shape using an RGB‐D camera in order to investigate fruit enlargement at different times of the year. In general, depth‐based measurement methods, when considering the diameter of an object with a near‐spherical shape, the diameter can be calculated by obtaining the Euclidean distance from the coordinates of the object's sides or by using the depth at the center of the object and the size of the object on the RGB image. However, it is difficult to accurately determine the diameter of an object because of errors in the calculated results due to the perspective projection of a general camera. Therefore, this study proposes a method to measure the diameter of fruits that have a shape similar to a sphere. Although this study focuses on young apple fruits, the proposed method can be applied to other agricultural crops, as well as to objects that are similar to spheres. In addition, we have also studied a correction that takes into account the rotation of the object so that the method can be applied to objects with circular cross‐sections.

Why it matches plant phenotyping methodsRGB-Dカメラを用いて果実径を測定する手法を提案・補正しており、植物形質の取得方法が研究の中心である。

abstractThis article describes a method for measuring the diameter of fruits with a near spherical shape using an RGB‐D camera

Predicting Rice Quality in Indica Rice Using Multidimensional Data and Machine Learning Strategies →

Integrating agricultural remote sensing and phenomics for full-growth-period rice quality prediction is vital for early non-destructive screening and breeding; however, studies integrating genomic and multi-source phenotypic data across multiple environments remain limited. This study addressed this gap by integrating genomic SNP data, UAV-based spectral data, and individual multidimensional phenotypic data of 61 indica rice varieties (field and greenhouse environments). As a proof-of-concept study, feature selection methods (LASSO, MI, RFE, SPA) were used to mitigate overfitting and the “p >> n” problem, with further validation needed in larger populations. The results showed that amylose content is genetically dominated, protein content is genetically determined and influenced by gene-environment interactions, and chalkiness traits are determined by three combined factors. For amylose content, SNP data under the Random Forest model at the population level (phenomics data from field UAV remote sensing of variety populations) achieved optimal performance (R2 = 0.92; MAE = 1.1; RMSE = 1.5), while the Stacking Ensemble method enhanced accuracy at the individual level (phenomics data from greenhouse single-plant phenotyping per variety). Chalky grain rate and chalkiness degree showed SNP-comparable prediction accuracy, with Stacking significantly improving performance at the population level (R2 = 0.89 and 0.85, respectively). Protein content prediction remained relatively low (optimal R2 = 0.56) due to strong environmental sensitivity and complex interactions. This framework extends traditional single-environment/single-data-source approaches, providing an effective strategy for early, high-throughput, non-destructive rice quality screening. Further validation with larger datasets, more growing seasons, or independent populations is required for reliable application in breeding-related practices.

Why it matches plant phenotyping methodsUAVスペクトル計測と個体フェノタイピングを統合し、機械学習でイネの品質形質を非破壊・高 throughput に予測する枠組みが研究の中心である。

abstractIntegrating agricultural remote sensing and phenomics for full-growth-period rice quality prediction is vital for early non-destructive screening and breeding

Application of digital imaging and genomic predictive models for improving Fragaria vesca berry quality traits →

• The investigation of the phenotypic variation of F. vesca has provided significant insights into the factors influencing fruit traits, including shape, size, and overall quality. • Digital phenotyping has been proven to be more effective in distinguishing F. vesca fruit traits that are difficult to phenotype in the selection process. • The integration of digital phenotyping and genomic analysis have proven to be a powerful strategy for improving the selection of quality traits in F. vesca . • Genome-based breeding and predictive models facilitate the development of climate- resistant F. vesca cultivars that meet consumer preferences. The quality of Fragaria vesca berry fruits is an important factor in their marketability, and therefore, it has become a major target of breeding programs. However, berry traits are difficult to dissect due to the complex interaction of genetic and environmental factors. In this study, we evaluated phenotypic variation in commercially relevant traits, including shape, size, pH and total soluble solids (SSC) in an open-pollinated F. vesca population grown in a region characterized by high temperature fluctuations. The observed variability underscores the intricate interplay between genetic background and environmental factors on fruit morphology and quality traits. Digital imaging phenotyping proved to be a robust and objective approach for capturing morphological traits difficult to phenotype, providing quantitative data necessary for effective selection. Moreover, the ddRAD sequencing facilitated the identification of significant genetic diversity in a F. vesca population, generating approximately 4000 SNP polymorphic markers used to investigate the population structure and the potential of genomic models for selecting desirable traits, such as fruit shape and size. Several genomic selection models were tested to predict breeding values for fruit morphological traits. Prediction accuracy was substantially improved through training set optimization strategies, particularly those based on CDmean criteria. The integration of digital phenotyping, high-throughput genotyping and genomic predictive modelling has proven to be a powerful strategy for improving the selection of desirable traits in F. vesca . Overall, the findings from this study provide a foundation for further genetic improvement efforts, which will ultimately enhance the quality and marketability of strawberry cultivars.

Why it matches plant phenotyping methodsイチゴ果実の形状・サイズを対象に、デジタル画像による表現型取得を明示的に評価しており、育種への応用だけでなく形態形質の定量的取得が研究の中心的要素である。

abstractDigital imaging phenotyping proved to be a robust and objective approach for capturing morphological traits difficult to phenotype, providing quantitative data necessary for effective selection.

High‐throughput phenotyping for the prediction and quantification of flower‐related traits in sugarcane →

Abstract Sugarcane ( Saccharum spp.), a C4 plant, is a vital renewable biofuel and sugar source for industries worldwide. However, synchronizing flowering between parental lines often poses challenges for breeders, hindering effective crossbreeding efforts. This study aimed to develop a high‐throughput phenotyping (HTP) strategy to evaluate flowering‐related traits using vegetation indices (VIs) and other metrics alongside artificial intelligence (AI)‐based prediction methods. A total of 154 genotypes were planted in an augmented block design at the IAC sugarcane breeding station in Serra Grande‐BA, Brazil. Raw RGB (Red, Green, Blue) images were captured using a DJI Mavic 3 Enterprise drone during the plant cane (PC) and first ratoon (FR) crop seasons. These images were processed to create orthomosaics and compute metrics/vegetation index; subsequently, machine learning (ML) and deep learning pipelines for systematic analysis were developed. A convolutional neural network (CNN) model achieved promising results, with an accuracy rate of up to 84% in the flowering detection task. Additionally, flower counts from the CNN model showed a moderate correlation with field data, evidenced by an R 2 value of 0.72 at the onset and an R 2 value of 0.29 at the conclusion of the flowering season for the PC. This resulted in an overall average regression R 2 of 0.46 with a root mean square error (RMSE) of 13.80. Furthermore, an artificial neural network classification model reached a notable accuracy of 0.87 in differentiating genotypes based on their flowering response (early‐flowering vs. late‐flowering), utilizing VIs and digital model‐based metrics as input parameters. The ML regression model demonstrated performance levels of R 2 = 0.51 and RMSE = 8.06 for days to flag leaf emergence in PC and R 2 = 0.52 and RMSE = 7.93 for days to flowering in FR. These results highlight the potential of HTP strategies, utilizing orthomosaics and AI, to accelerate data collection and analysis, offering significant insights for breeding programs in sugarcane.

Why it matches plant phenotyping methodsドローンRGB画像、オルソモザイク、植生指数、AIを組み合わせた開花形質のハイスループット取得・予測手法を開発し、精度検証まで実施しており、フェノタイピング手法が研究の中心である。

abstractThis study aimed to develop a high‐throughput phenotyping (HTP) strategy to evaluate flowering‐related traits using vegetation indices (VIs) and other metrics alongside artificial intelligence (AI)‐based prediction methods.

Leaf and cluster spectral signatures reveal trait-dependent prediction performance for grapevine cluster architecture and juice quality →

Abstract Grapevine cluster architecture is a key selection target in breeding programs because it influences disease susceptibility, yield stability and juice quality. High-throughput phenotyping offers a rapid and non-destructive approach to capture biochemical and structural variation in these traits, yet the influence of plant organ reflectance and data partitioning strategies on trait prediction remains poorly understood. In this study, we evaluated how hyperspectral reflectance from different grapevine organs contributes to the prediction of cluster architecture and juice quality traits in two clonal populations of Riesling and Pinot. Using partial least squares regression (PLSR), we assessed the prediction accuracy of eight cluster architecture and six juice quality traits under two data partitioning strategies. Models based on cluster reflectance outperformed those using dry leaf reflectance for most traits, except for pH. Partitioning the dataset by cluster type increased trait variance and improved predictions for number of berries (R² = 0.53), berry diameter (R² = 0.79), and total acidity (R² = 0.48). Visible, red-edge and NIR spectra were most informative regions to predict the traits studied. Together, our results highlight the importance of organ-specific data and appropriate calibration strategies to improve phenomic models for the development of scalable proxies for grapevine improvement. Highlight Spectral phenomics reveals that prediction accuracy in grapevine depends on organ spectral signatures and traits, with cluster reflectance outperforming leaves, informing new phenotyping strategies for breeding improvement.

Why it matches plant phenotyping methodsブドウの器官反射スペクトルとPLSRを用いて、房構造および果汁品質形質の予測性能を評価することが中心であり、スペクトル表現型解析手法の検証・応用に該当する。

abstractHigh-throughput phenotyping offers a rapid and non-destructive approach to capture biochemical and structural variation in these traits

A Precise Reproductive Calendar of Sexual and Apomictic Genotypes of Eragrostis curvula . →

Eragrostis curvula serves as a valuable model for studying diplosporous apomixis due to its unique reproductive mode, wide ploidy range, and extensive genomic resources. A major limitation for reproductive studies in this species is the difficulty of isolating female tissues at precise developmental stages, for example, for transcriptomics studies, since different floral tissues can introduce expression noise from non-target tissues. To overcome this, we performed a detailed cytoembryological and morphometric characterization of male and female development in seven E. curvula genotypes with different ploidy levels (2X-7X) and reproductive modes (sexual, facultative apomictic, and obligate apomictic). Using differential interference contrast microscopy and methyl salicylate clarification, we described key cytological stages of male and female development. These stages were then correlated with external floral parameters, including pistil, ovary, style, and anther length, to generate genotype-specific developmental calendars. Pistil length showed the strongest association with female developmental stage, particularly during the early phases of ovule development, enabling more precise staging. Synchrony between male and female development was also evaluated, revealing no consistent differences among reproductive modes or ploidy levels. This genotype-informed framework provides a practical tool for stage prediction and tissue selection, supporting future reproductive, developmental, and comparative studies in E. curvula and related grasses.

Why it matches plant phenotyping methods花器官の形態計測を細胞発生段階の推定に体系的に対応付け、遺伝子型別の発達カレンダーと再利用可能なステージ予測手法を構築しており、表現型取得が中心である。

abstractThese stages were then correlated with external floral parameters, including pistil, ovary, style, and anther length, to generate genotype-specific developmental calendars.

An AI-Driven Dual-Spectral Vision-Language Sensing Framework for Intelligent Agricultural Phenotyping. →

Seed varietal purity and physiological viability are critical determinants of crop yield and quality. However, non-destructive assessment faces significant challenges in fine-grained variety discrimination and the perception of internal defects. This study proposes S3-Net, an AI-driven multimodal sensing framework that integrates vision–language alignment with dual-spectral sensor fusion for autonomous seed quality evaluation. We introduce a Knowledge–Vision Alignment (KVA) module that incorporates encyclopedic morphological descriptions to guide feature learning, significantly enhancing few-shot generalization. Complementarily, a Dual-Spectral Fusion (DSF) module combines high-resolution RGB textures with penetrative Short-Wave Infrared (SWIR) sensing to jointly characterize external and internal traits. Experimental results on a custom multimodal dataset of 6000 samples across 12 crop categories demonstrate that S3-Net achieves 96.9% accuracy for species identification and 95.8% for viability detection. Notably, S3-Net outperforms ResNet-50 by 40.3% in extreme 1-shot scenarios. With a stable inference throughput of 95 fps, the system meets the high-throughput demands of industrial-scale applications, providing a robust and efficient solution for intelligent agricultural phenotyping.

Why it matches plant phenotyping methods種子の生理的状態（viability）をRGB・SWIR融合で非破壊推定するセンシング／AI手法が研究の中心であり、データセットと性能評価も提示しているため。種識別のみなら対象外だが、viability検出は植物状態のフェノタイピングに該当する。

abstractThis study proposes S3-Net, an AI-driven multimodal sensing framework that integrates vision–language alignment with dual-spectral sensor fusion for autonomous seed quality evaluation.

Research on Lightweight Apple Detection and 3D Accurate Yield Estimation for Complex Orchard Environments →

Severe foliage occlusion and dynamically changing lighting conditions in complex orchard environments pose significant challenges for visual perception systems in automated apple harvesting, including low detection accuracy, poor robustness, and insufficient real-time performance. To address these issues, this study proposes an improved lightweight detection network based on YOLOv11, named YOLO-WBL, along with a precise yield estimation algorithm based on 3D point clouds, termed CLV. The YOLO-WBL network is optimized in three aspects: (1) A C3K2_WT module integrating wavelet transform is introduced into the backbone network to enhance multi-scale feature extraction capability; (2) A weighted bidirectional feature pyramid network (BiFPN) is adopted in the neck network to improve the efficiency of multi-scale feature fusion; (3) A lightweight shared convolution separated batch normalization detection head (Detect-SCGN) is designed to significantly reduce the parameter count while maintaining accuracy. Based on this detection model, the CLV algorithm deeply integrates depth camera point cloud information through 3D coordinate mapping, irregular point cloud reconstruction, and convex hull volume calculation to achieve accurate estimation of individual fruit volume and total yield. Experimental results demonstrate that: (1) The YOLO-WBL model achieves a precision of 93.8%, recall of 79.3%, and mean average precision (mAP@0.5) of 87.2% on the apple test set; (2) The model size is only 3.72 MB, a reduction of 28.87% compared to the baseline model; (3) When deployed on an NVIDIA Jetson Xavier NX edge device, its inference speed reaches 8.7 FPS, meeting real-time requirements; (4) In scenarios with an occlusion rate below 40%, the mean absolute percentage error (MAPE) of yield estimation can be controlled within 8%. Experimental validation was conducted using apple images selected from the dataset under varying lighting intensities and fruit occlusion conditions. The results demonstrate that the CLV algorithm significantly outperforms traditional average-weight-based estimation methods. This study provides an efficient, accurate, and deployable visual solution for intelligent apple harvesting and yield estimation in complex orchard environments, offering practical reference value for advancing smart orchard production.

Why it matches plant phenotyping methodsリンゴ果実の検出と3D点群による個別果実体積・総収量推定を開発し、精度・速度・遮蔽条件下で検証しており、植物形質取得が中心的な方法論的貢献である。

abstractthis study proposes an improved lightweight detection network based on YOLOv11, named YOLO-WBL, along with a precise yield estimation algorithm based on 3D point clouds, termed CLV.

OzBarley: A genetic and phenotypic data resource capturing the Australian barley breeding history. →

OzBarley is a comprehensive genotype-to-phenotype resource to support research and enhance barley breeding by integrating genotypic and phenotypic data for gene discovery. This publicly available dataset comprises genotypic data from historical and modern elite barley cultivars of significance to Australian barley breeding. The phenotypic component includes high-throughput imaging and X-ray CT-based spike analysis, enabling trait measurements such as plant growth dynamics and seed morphology. Users can leverage genome-wide association studies (GWAS) and genomic selection to identify genetic variants associated with agronomically important traits in the OzBarley datasets, thereby accelerating targeted breeding strategies. The dataset is accessible for download under CC-BY 4.0 license and users are invited to contribute new data when using OzBarley plant material in their research. Through its FAIR-compliant design (Findable, Accessible, Interoperable, Reusable), OzBarley represents a resource to protect genotypes of historical relevance, explore the genetic architecture of adaptation to dryland environments, and to enhance knowledge of the resilience, yield, and quality of barley cultivars under diverse environmental conditions, contributing to global food security and agricultural sustainability.

Why it matches plant phenotyping methods高スループット画像およびX線CTによる形質取得を含む、再利用可能な遺伝型・表現型データ資源であり、植物フェノタイピング手法とデータセットが中心です。

abstractThe phenotypic component includes high-throughput imaging and X-ray CT-based spike analysis, enabling trait measurements such as plant growth dynamics and seed morphology.

YOLOv9s-multi: orientation-based fruit selection for robotic apple thinning →

Accurate detection and orientation estimation of immature apples are crucial for effective thinning decisions in robotic apple thinning. Existing research either relies on computationally expensive RGB-D approaches with 3D geometric fitting to estimate orientation and size or requires multiple separate models for thinning decision, limiting their real-time performance on robotic platforms. To address these issues, a multi-task model, YOLOv9s-Multi, is proposed. First, this model integrates segmentation and keypoint heads to perform instance segmentation of immature apples and detect their calyx keypoint positions. Second, the detection head employs an efficient and lightweight Depthwise Convolution module (DWConv) to reduce model parameters while accurately capturing spatial features across channels. Finally, the orientation is derived from the segmentation centroid and calyx keypoint, enabling pixel-based fruit selection for thinning decision-making. This model is evaluated on a self-developed dataset that divides immature apples based on developmental stage: Flower-Retained Stage (FR-Stage) and Fruit-Visible Stage (FV-Stage). Results show that instance segmentation and keypoint AP@0.5 for FV-Stage are 89.3% and 86.4%, respectively, while for FR-Stage they are 71.6% and 79.8%. The model further achieves prediction accuracies of 92.80% (FV-Stage) and 72.59% (FR-Stage) within an acceptable error of 30 °. The pixel-based fruit selection method achieves 74.00% and 70.31% selection accuracy on the test and an additional measurement dataset, respectively. Compared with the baseline YOLOv9s-seg, the number of parameters is reduced by 11.4%. In contrast to 3D fitting methods, our approach provides lower computational complexity, faster inference speed, and higher accuracy. These results demonstrate that the proposed model can efficiently estimate the orientation of immature apples and perform fruit selection in close-range scenes and complex lighting environments, which are challenging for depth cameras to handle. The code and datasets are publicly available on GitHub: https://github.com/DIANSLEE/YOLOv9s-Multi.

Why it matches plant phenotyping methods未熟リンゴのセグメンテーション、萼点検出、重心との関係から果実の向きという器官形質を推定する画像解析手法が研究の中心であり、精度評価とデータセット検証も行っているため。

abstracta multi-task model, YOLOv9s-Multi, is proposed.

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

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

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

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

Conformable Plant‐Attachable Janus E‐Skin with Sandwich Architecture for Plant Multimodal Phenotyping →

ABSTRACT Plant‐wearable sensors are essential for real‐time, in situ monitoring in smart agriculture, yet their adoption is constrained by functional simplicity and inadequate mechanical compliance. Herein, we present a conformable plant‐attachable Janus electronic skin (SPM/HMA@P) for multimodal phenotyping. Featuring a scenario‐specific encapsulation strategy fabricated via a bottom‐up approach, the device enables non‐invasive monitoring of multiple physiological parameters. The sensor integrates a conductive base composed of biocompatible quaternized chitosan, multi‐walled carbon nanotubes, and silver nanowires, a gas‐sensing functional layer, and an adhesive polydimethylsiloxane encapsulation. In the fully encapsulated configuration, SPM/HMA@P exhibits a 210.9% fracture elongation and an adhesion force exceeding 0.3 N on leaves, alongside excellent biocompatibility. This configuration allows for simultaneous monitoring of leaf temperature and growth‐induced strain, maintaining stable signals over 5 days. Conversely, the semi‐open configuration demonstrates high ethylene sensitivity (0.5–100 ppm, theoretical detection limit of 0.12 ppm), with response and recovery times of 300 and 120 s, and a lifespan of over 10 days. Coupled with a convolutional neural network (CNN), it achieves 96.8% accuracy in classifying ethylene signals from fruits. This robust multimodal platform addresses key challenges in plant physiological monitoring, holding great potential for smart crop breeding, postharvest assessment, and phenotyping.

Why it matches plant phenotyping methods植物に装着するマルチモーダルセンサーを開発し、葉温度・成長誘導ひずみ・エチレンなどの生理状態を非侵襲的に測定するプラットフォームであり、植物フェノタイピング手法が中心的に扱われている。

titleConformable Plant‐Attachable Janus E‐Skin with Sandwich Architecture for Plant Multimodal Phenotyping

Real-time multi-attribute quality grading of tabletop strawberries under occlusions for harvesting robots →

• Modular RGB–D pipeline for real-time multi-attribute grading of strawberries. • ResNet-101 multi-head classifier predicted maturity, disease, and deformity jointly. • CycleGAN restored strawberry shape and texture from partially occluded views. • QH and MADM strategies converted multi-attribute predictions to grading decisions. • Field tests validated end-to-end perception-to-placement pipeline in farms. Strawberry grading was critical for meeting market standards, yet manual post-harvest sorting remained labor-intensive, damage-prone, and inconsistent, while most vision-based methods targeted single attributes and lacked integrated in-field operation. We presented a real-time RGB–D grading framework for tabletop-cultivated strawberries in greenhouse environments. The system combined instance segmentation (YOLOv11–Seg), occlusion-aware completion via CycleGAN, and a three-branch multi-task network based on ResNet-101 to simultaneously infer maturity, disease status (including asymptomatic fruit), and deformity. Pose normalization and depth alignment supported calibrated volume-to-mass regression for non-destructive weight estimation. To improve robustness to rare morphology, 500 deformed-fruit samples were synthesized with a Gemini-based generative model to mitigate class imbalance. Built upon this perception stack, we implemented two grading strategies: a deterministic Quality Hierarchy (QH) for real-time robotic routing and a weighted multi-attribute decision-making (MADM) scheme for flexible batch evaluation. The multi-attribute classifier achieved 93.65% overall accuracy (disease 94.09%, maturity 94.07%, shape 94.42%) with an inference latency of 33.65 ms (29.7 FPS). Depth-assisted mass estimation yielded average errors of 8.11% for complete fruits and 10.47% under occlusion after completion; in field deployment on marketable fruits routed to size grading, it achieved MAE/RMSE of 1.85/2.20 g with R 2 = 0.9384 (MAPE 8.48%) and a three-bin size-grade accuracy of 90.91% (100/110). In single-target harvesting mode on an RTX 4060 GPU (640x480, 30 fps), end-to-end latency was 87.56 ms per harvested target in complete mode and 197.56 ms when completion was invoked (CycleGAN 110.00 ms/instance). These results demonstrated a practical, non-destructive, occlusion-aware, and deployable multi-attribute grading solution for intelligent strawberry harvesting in real greenhouse scenarios.

Why it matches plant phenotyping methodsRGB-D画像解析でイチゴの成熟度、病徴、形状、重量を推定し、性能検証とロボット実装まで行う中心的なフェノタイピング手法研究。

abstractModular RGB–D pipeline for real-time multi-attribute grading of strawberries.

Wheat spikelet detection on RGB images using deep machine learning. →

This study addresses the challenge of automated high-throughput phenotyping of wheat spike characteristics using modern computer vision and deep learning methods. Accurate estimation of spikelet number is a key indicator of plant productivity, yet traditional manual counting approaches are labor-intensive, slow, and difficult to scale to large breeding datasets. To overcome these limitations, we propose a spikelet detection strategy based on simplified point annotations, where an expert marks only the centers of spikelets rather than drawing detailed segmentation masks or bounding boxes. This significantly reduces annotation time and lowers the overall cost of preparing training datasets for machine learning models. To determine the most effective way of utilizing such simplified annotations, three computational methods were explored: segmentation of binary masks using a U-Net architecture, density regression based on two-dimensional Gaussian distributions optimized via Kullback-Leibler divergence, and detection of fixed-size bounding regions using the YOLOv8 object detection framework. The models were evaluated on dedicated test datasets using both quantitative metrics (MAE, MAPE) and spatial localization metrics (Precision, Recall, F1 score). The results demonstrate that U-Net-based approaches provide consistently high accuracy in spikelet localization and counting while maintaining robustness to annotation imperfections. In contrast, the YOLOv8-based method showed reduced performance, likely due to the geometric mismatch between fixed-size boxes and the natural elongated shape of spikelets. Overall, the proposed methodology highlights the effectiveness of combining minimalistic point-level annotation with advanced segmentation models for automating phenotyping workflows. This approach has the potential to accelerate breeding programs, enhance the efficiency of large-scale phenotypic data collection, and support further development of robust computer-vision tools for plant science applications.

Why it matches plant phenotyping methodsコムギの穂の小穂数をRGB画像から自動推定する画像解析・深層学習手法を開発し、複数モデルを定量評価しており、フェノタイピング手法が研究の中心である。

abstractThis study addresses the challenge of automated high-throughput phenotyping of wheat spike characteristics using modern computer vision and deep learning methods.

Inspection of pollination transfer and success in coffee flowering detection using intersection over union based cascade RCNN in a vision environment. →

The intricate process of coffee blossoming, pollination transfer, and successful development is crucial for creating every exquisite cup of coffee. During the flowering stage of coffee plants, delicate white flowers with a pleasant fragrance appear briefly, providing a limited opportunity for effective pollination. At this stage, the stigma of the flower, which is the female reproductive organ, becomes receptive and prepared to receive pollen. Existing research found methods such as machine learning and image analysis for monitoring crop pollination. Manual image annotation is conducted on pollen count disregarding spatial component of pollen collection which is essential for successful pollination. However, use of these strategies on coffee flowers by their complex structure and continuous changed in flowering stages. The paper represents novel methodologies by introducing methodological approach “IoU-AI” to monitor pollen transmission and success of pollination in coffee flower. IoU-AI utilize high resolution coffee flower image accurately track and detect floral organs by offering insight to pollen transfer. IoU-AI employs deep learning models to detect and observe floral components like stigma and anthers. Further computes overlap between structures and estimate pollen transmission. The flower detection accuracy was evaluated against ground truth measurements. The accuracy of coffee flower detection ranged from 94.77% to 85.34% for flower stages ranging from 20% to 100% blooming.

Why it matches plant phenotyping methodsコーヒー花の画像から柱頭・葯を検出し、重なりに基づいて花粉伝達と受粉成功を推定する画像解析手法を開発・精度評価しており、植物状態の取得が中心です。

abstractThe paper represents novel methodologies by introducing methodological approach “IoU-AI” to monitor pollen transmission and success of pollination in coffee flower.

ELSF-DETR: an efficient lightweight network for detecting strawberry flowers pollination status in non-structured greenhouse environments →

Accurate and efficient detection of the pollination status of strawberry flowers is essential for intelligent pollination robots, as it directly affects the determination of optimal pollination timing and improves fruit set rates. However, the small size of strawberry anthers, their visual similarity, varied opening states, and complex field environments make pollination status detection highly formidable. To overcome these constraints, this paper presents a streamlined and resource-efficient detection approach (ELSF-DETR), built upon the Real-Time DEtection Transformer (RT-DETR) and specially refined for detecting densely packed and visually similar small objects in agricultural scenes. A lightweight LS-ResNet backbone is constructed to better capture small and densely clustered anther structures in strawberry flowers while reducing model complexity for improved deployment efficiency. In addition, the integration of a P2 detection head with full-kernel convolution enhance the network’s capacity to focus on delicate anther contours and cracking characteristics. Furthermore, the Hierarchical Attention Fusion Block (HAFB) is employed to balance local detail extraction with global context understanding, reducing misjudgments caused by misleading fine-grained features. Lastly, by employing the Wise-IoU (WIoU) loss mechanism, the model achieves improved sensitivity to minor positional discrepancies in visually similar anther objects. Experiments conducted on a self-built strawberry flower dataset demonstrate that ELSF-DETR achieves superior performance, it achieves 88.2 % accuracy, 85.8 % recall, 87.1 % mAP@50, and F1 score of 86.98 %. Relative to the baseline architecture, mAP@50 and F1 improved by 7.1 % and 4.33 %, respectively, while the model parameters and GFLOPs were reduced by 6.86 MB and 13.7 G, meeting the requirements of high precision and low complexity. This work provides practical support for intelligent pollination systems in precision agriculture.

Why it matches plant phenotyping methodsイチゴ花の受粉状態という植物状態を画像から推定する検出モデルを開発・評価しており、植物フェノタイピング手法が中心である。

abstractthis paper presents a streamlined and resource-efficient detection approach (ELSF-DETR)

From segmentation to classification: Morphological phenotype extraction and classification analysis of tiny poplar seeds using the MP-Seed segmentation algorithm →

Extracting poplar seed morphological phenotypes is a core task in modern poplar breeding research. Accurate seed image segmentation is crucial for phenotype extraction and data quality. However, the small size of poplar seeds and their tendency to form dense clusters challenge the accuracy of current segmentation methods. Unlike current approaches that struggle with small-target segmentation and boundary delineation, this study develops the MP-Seed semantic segmentation algorithm, which combines a small-target attention module (based on Layer Across Feature Map Attention) with a multi-task learning mechanism that integrates boundary features. This novel integration targets small-seed key regions, fuses boundary features, and refines predictions to precisely segment densely clustered seeds, achieving superior accuracy and fine-grained delineation compared to current single-task methods. To address low efficiency and accuracy in poplar seed morphological phenotype extraction, this study further proposes a high-throughput extraction method leveraging the MP-Seed algorithm. To analyse the phenotypic data, an SVM classification model classifies eight types of poplar seeds. Experimental validation shows that the MP-Seed algorithm outperforms current methods on the test set, achieving Seed_IoU of 94.1 %, mIoU of 97.2 %, and Reference_IoU of 97.6 %. The high-throughput phenotyping method measures seed length and width with relative errors within 2.72 % versus manual measurements and extracts ten morphological traits at about 18.3 seeds per second. The overall classification accuracy reaches 91.1 %. Overall, this study provides technical support for accurate poplar seed segmentation and efficient morphological phenotype extraction, offering a valuable reference for other seed morphological phenotype research and analysis.

Why it matches plant phenotyping methodsポプラ種子画像のセグメンテーションアルゴリズムと高スループット形態形質抽出法を開発・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 develops the MP-Seed semantic segmentation algorithm

Explainable AI-Guided Hyperspectral Feature Selection in Fruit Quality Assessment and Spatial Visualization. →

The integration of hyperspectral imaging (HSI) with machine learning enables non-destructive prediction and visualization of food quality. However, multicollinearity and redundant features in spectral data can reduce model accuracy and increase computational time, emphasizing the need for key wavelength selection. In response, this study presents an inventive method combining a genetic algorithm (GA) with explainable artificial intelligence (XAI) to select key wavelengths for predicting apple dry matter content (DMC). A partial least squares regression (PLSR) model using the selected features outperformed recursive feature elimination (RFE) and competitive adaptive reweighted sampling (CARS), achieving a coefficient of determination (R 2 ) of 0.46 and a root mean squared error (RMSE) of 0.70%. The approach was further applied to hyperspectral images to visualize pixelwise DMC distribution, providing spatial insights into fruit composition. Results demonstrate that integrating XAI with evolutionary feature selection offers a noninvasive, transparent, and efficient strategy for assessing and visualizing fruit quality.

Why it matches plant phenotyping methodsリンゴ果実の乾物含量という植物器官形質を、ハイパースペクトル画像とGA・XAIによる波長選択で予測・可視化する手法が研究の中心である。

abstractthis study presents an inventive method combining a genetic algorithm (GA) with explainable artificial intelligence (XAI) to select key wavelengths for predicting apple dry matter content (DMC).

Optimizing seed anomaly detection in agricultural automation via lightweight ASD-YOLO and closed-loop control →

To address the high-throughput real-time detection requirements in industrial seed sorting scenarios, this study proposes an innovative solution coupling a lightweight detection algorithm with an industrial control system. By optimizing and integrating the YOLOv11-S architecture with the MobileNetV4 depth-wise separable convolution backbone, introducing the Focus operation for 4x downsampling via slicing concatenation without increasing computation, and embedding a mixed local channel attention mechanism, an industrially applicable model, Anomalous Seed Detection-YOLO(ASD-YOLO), with a parameter size of only 9.5 MB, was constructed. This model achieves a mean average precision (mAP) of 96.5 % while reaching a maximum processing capability of 62 FPS on a single device. Simultaneously, by incorporating algorithms such as a feedback error correction mechanism developed in conjunction with an industrial-grade pulse coordination control mechanism, the system achieves stable end-to-end latency control at the 35 ms level in a pepper seed anomaly detection production line environment. It supports continuous 24-h stable operation at a throughput of 10,000 seeds/min, with a relative error controlled to 3.3 mm. Based on the detection results, a fuzzy grading algorithm was developed to categorize the seed quality into five levels using membership functions. This provides a quantitative basis for refined storage management and differentiated processing, achieving a statistically significant 16.2 % reduction in the misjudgment rate compared with traditional grading methods. By constructing an “artificial intelligent algorithm-pulse coordination-protocol coupling” trinity architecture, the proposed model establishes a universal methodological framework for lightweight model deployment in agricultural intelligent manufacturing scenarios, offering a scalable standardized solution for seed quality control.

Why it matches plant phenotyping methods種子の異常を画像検出し品質を5段階評価する軽量モデルと産業用制御システムを開発しており、植物器官の状態取得・抽出が研究の中心である。

abstractthis study proposes an innovative solution coupling a lightweight detection algorithm with an industrial control system

Measurement of Fruit Diameter Using RGB-D Cameras for the Purpose of Fruit Growth Assessment →

This paper describes a method for measuring the diameter of fruits with a near spherical shape using an RGB-D camera in order to investigate fruit enlargement at different times of the year. In general depth-based measurement methods, when considering the diameter of an object with a near-spherical shape, the diameter can be calculated by obtaining the Euclidean distance from the coordinates of the object's sides or by using the depth at the center of the object and the size of the object on the RGB image. However, it is difficult to accurately determine the diameter of an object because of errors in the calculated results due to the perspective projection of a general camera. Therefore, this study proposes a method to measure the diameter of fruits that have a shape similar to a sphere. Although this study focuses on young apple fruits, the proposed method can be applied to other agricultural crops, as well as to objects that are similar to spheres. In addition, we have also studied a correction that takes into account the rotation of the object so that the method can be applied to objects with circular cross-sections.

Why it matches plant phenotyping methodsRGB-D画像から果実径を推定する手法の開発が研究の中心であり、植物器官の形態形質を直接測定するため。

abstractThis paper describes a method for measuring the diameter of fruits with a near spherical shape using an RGB-D camera

Energy-Efficient and Economy-Sustainable Technology for Online Seed Viability Detection Using Hyper Spectrum. →

The seed viability detection before sowing is indispensable in the agricultural production of mung beans. The conventional detection methods for seed viability are destructive, carry a risk of contamination, and fail to identify individual non-viable seeds. In this study, an efficient and sustainable method for online viability detection of mung bean seeds was developed, which utilized hyperspectral techniques and had characteristics of rapid speed, non-destructive analysis, and the ability to detect the viability status without pollution to the environment. A sample holder for mung bean seeds was designed to stably collect spectral data. The effects of different optimal spectral bands and modeling algorithms on the detection accuracy of seed viability were analyzed. Compared to the support vector machine (SVM) and the extreme learning machine (ELM) algorithms, the partial least squares (PLS) algorithm based on the visible and near-infrared spectra (380~980 nm) had better performance. The accuracy for the identification of non-viable seeds was 98.8%, and the error of viability prediction was 20.71%. The cost of a one-time viability test is $0.25 with energy consumption of 0.05 kWh -1 , which is much lower than the germination test with a cost of $80.2 and energy consumption of 50.4 kWh -1 . Furthermore, individual non-viable seeds can be identified and removed, and the revenue increases by $286.9 per hectare after sorting the non-viable seeds from the seeds with an 85% germination rate. This will promote the cleaner production of mung beans without additional chemical solutions added in the process.

Why it matches plant phenotyping methodsマングビーン種子の生存性という植物状態を、オンライン近赤外・可視ハイパースペクトル計測とモデルで非破壊推定する手法を開発・比較検証しており、表現型取得が中心である。

abstractan efficient and sustainable method for online viability detection of mung bean seeds was developed, which utilized hyperspectral techniques

A visualization framework for cell division activity and orientation in pre-anthesis ovaries of Prunus species →

Fruit size and shape, which influence horticultural quality, are determined by the number and the size of the cells in the local region. In fruit trees, however, the difficulty of applying molecular genetic approaches has hindered a detailed understanding of the localization and orientation of cell division in developing fruit tissues. In this study, we established a novel framework to visualize cell division in pre-anthesis ovaries of three drupe crops, peach ( Prunus persica ), Japanese apricot ( P. mume ) and the interspecific hybrid Japanese apricot ( P. salicina x P. mume ), providing clear insight into the spatial distribution and orientation of dividing cells. We systematically optimized a 5-ethynyl-2′-deoxyuridine (EdU) labeling protocol for thick ovary tissues by adjusting infiltration conditions and fixation methods. In addition, electron microscopy combined with wide-view tiling visualization was applied to directly identify dividing cells, including those undergoing chromosome segregation and cell plate formation. By combining with machine learning-based detection, we efficiently and objectively identified dividing cells. Using these complementary approaches, we found that cell division activity was broadly distributed throughout pre-anthesis ovaries in all three crops, without pronounced spatial restriction. In contrast, analysis of division orientation revealed region-specific patterns: cells in the outermost exocarp divided predominantly anticlinally, whereas cells in the mesocarp divided largely periclinally, consistent with subsequent ovary (fruit) enlargement. The integrated framework presented here provides a foundation for understanding the spatial and three-dimensional regulation of fruit development and for future studies in fruit morphogenesis and horticulture.

Why it matches plant phenotyping methods植物組織内の細胞分裂という発生状態を可視化・定量する統合フレームワークを開発し、EdU標識、電子顕微鏡、広視野タイリング、機械学習検出を組み合わせて検証・適用しているため、植物フェノタイピング手法が中心である。

abstractIn this study, we established a novel framework to visualize cell division in pre-anthesis ovaries of three drupe crops

Samplify: a versatile tool for image-based segmentation and annotation of seed abortion phenotypes. →

Automated seed phenotyping has wide applications in research and agriculture and relies on easy-to-use platforms and pipelines. Seed phenotyping in the model species Arabidopsis thaliana poses a significant challenge due to the large number of tiny seeds produced by individual plants, which are difficult to manually separate and count. Manual counting methods are time-consuming and prone to user bias, particularly for subtle phenotypic changes. To address these limitations, we developed Samplify, a scalable, automated pipeline for seed segmentation and classification by integrating classical image processing techniques with Meta's Segment Anything Model. Samplify effectively segments Arabidopsis seeds, even in dense clusters where conventional methods fail. To demonstrate its versatility, we quantified the seed abortion occurring in interploidy crossings in Arabidopsis, often referred to as 'triploid block'. Samplify includes a random forest classifier trained on a set of computed seed shape features that enable the categorization of seeds into normal, partially collapsed, and fully collapsed seeds, automating the manual classification process. The tool, designed as a command-line application, significantly reduces manual annotation workload. Our validation across multiple datasets demonstrates high segmentation and classification reliability, making Samplify a valuable resource for the plant research community.

Why it matches plant phenotyping methods種子画像のセグメンテーション・分類による表現型抽出パイプラインを開発し、複数データセットで信頼性を検証しているため、植物フェノタイピング手法が中心である。

abstractwe developed Samplify, a scalable, automated pipeline for seed segmentation and classification

Fruit Trait Variation Among Five Ilex Taxa: Insights from Morphology, Color, and Electronic Nose-Based Volatile Profiles →

Abstract Accurate discrimination of closely related Ilex taxa is essential for effective germplasm management and taxon‑specific applications. In this study, five Ilex taxa ( Ilex rotunda Thunb., I. chinensis , I. cornuta Lindl. et Paxt., I. cornuta ‘Fortunei’, and I. latifolia Thunb.) were evaluated using an integrated framework that combines fruit morphometrics and CIELAB color parameters with electronic‑nose (E‑nose) odor fingerprints and chemometric analyses. Fruit transverse diameter, longitudinal diameter, single‑fruit weight, fruit shape index, and color parameters (L*, a*, b*, and chroma (C*)) differed significantly among taxa (one‑way ANOVA, all p I. rotunda had the smallest fruit diameters but the highest redness (a*) and chroma (C*). E‑nose data from 10 metal‑oxide semiconductor (MOS) sensors were analyzed using principal component analysis (PCA), hierarchical clustering, and supervised OPLS‑DA. PCA explained 90.21% of the variance using the first two components (PC1, 71.8%; PC2, 10.5%) and revealed taxon‑specific clustering. A targeted OPLS‑DA model separating I. rotunda from the other four taxa showed strong goodness‑of‑fit and predictive ability (R²X = 0.785, R²Y = 0.944, Q² = 0.935). Permutation testing (n = 200) supported model validity (p I. rotunda consistently exhibiting the most divergent profile, consistent with the phylogenetic framework used in this study.

Why it matches plant phenotyping methods果実形態・色彩および電子鼻センシングとケモメトリクスを統合してIlex分類群の観察可能な果実形質を識別する方法適用が研究の中心であり、モデル性能の検証も行っている。

abstractwere evaluated using an integrated framework that combines fruit morphometrics and CIELAB color parameters with electronic‑nose (E‑nose) odor fingerprints and chemometric analyses.

Deep learning-based methods for phenotypic trait extraction in rice panicles. →

Introduction Key rice panicle traits (grain number, panicle length, grain dimensions, maturity) determine yield and quality, and high-precision/high-throughput measurement is critical for rice breeding. Traditional methods are. Methods A dataset of 5300 rice panicle images (loose/normal/dense types; milk/dough/full maturity/over-ripe stages) was constructed, with 3290 for training, 940 for validation, and 470 for testing. A deep learning pipeline integrating. Results The panicle length extraction achieved R²=0.9583, RMSE=5.69 mm. Grain counting R² values were 0.9799 (loose), 0.9551 (normal), 0.9278 (dense). Grain length R²=0.8823, grain width MAPE=6.64%. OPG-YOLOv8. Discussion This study provides a comprehensive, automated tool for rice panicle phenotyping, addressing occlusion challenges and bridging the gap between advanced models and breeding applications.

Why it matches plant phenotyping methodsイネ穂の画像から粒数・穂長・粒形などの形質を抽出する深層学習パイプラインを開発・評価しており、フェノタイピング手法が研究の中心です。

titleDeep learning-based methods for phenotypic trait extraction in rice panicles.

Dissecting the genetic architecture of seed-related traits in Brassica napus by integrating multi-omics analysis and VIS-NIR hyperspectral imaging. →

Background Brassica napus (B. napus) is globally important oilseed crop, yet traditional approaches for phenotyping of seed traits are labor-intensive and destructive. Results Here, we establish a non-destructive analytical framework integrating hyperspectral imaging (HSI) with machine learning for characterizing seed-related traits. We collect HSI data from seeds of 393 B. napus accessions over two consecutive years, generating 1,944 spectral indices per sample. We identify significant correlations between 1,293 hyperspectral indices and 956 seed metabolites. Flavonoid metabolites exhibit the most consistent interannual correlations with hyperspectral indices. Systematic benchmarking of 19 machine learning algorithms identifies nine optimal models for metabolite prediction, with 73.44% of metabolites achieving significant associations. Hyperspectral indices effectively predict nine key seed-related traits, including oil content, seed coat content, glucosinolate content and six fatty acid components. Genome-wide association studies (GWAS) of hyperspectral indices uncover three stable quantitative trait loci (QTL) hotspots, qHSI.hotA09, qHSI.hotA05 and qHSI.hotC05, that co-localize with QTLs for seed oil and seed coat content. Integration of GWAS with POCKET prioritization identifies BnaA09.MYB52 and BnaC05.PMT6 as candidate genes for the hotspots, qHSI.hotA09 and qHSI.hotC05, respectively. Functional validation using mutants demonstrates that both genes significantly influence seed flavonoid metabolites and hyperspectral profiles. BnaPMT6 is characterized as a novel positive regulator of seed coat content, similar to BnaMYB52. Conclusions This study establishes a novel, non-destructive approach for seed traits and metabolite assessment in B. napus seeds. It also provides a theoretical foundation and genetic basis for breeding of B. napus varieties with high oil content and improved nutritional quality.

Why it matches plant phenotyping methods種子形質を非破壊的に推定するハイパースペクトル画像と機械学習の分析フレームワークが研究の中心であり、多数の品種・複数年で検証されている。

abstractwe establish a non-destructive analytical framework integrating hyperspectral imaging (HSI) with machine learning for characterizing seed-related traits.

Rapid and accurate fruit volume estimation using a top-mounted mirror-based imaging system →

Abstract Size is one of the important external quality criteria of fruit and is often used in grading and sorting processes. Computer vision offers an effective solution for quickly and non-destructively estimating fruit volume. Several approaches involving three-dimensional (3D) reconstruction models of irregularly shaped fruit from multiple side-view images have been employed to enhance estimation accuracy. However, these approaches tend to be expensive and computationally complex. This study proposes a low-cost multi-view system for the rapid and accurate estimation of fruit volume. Using a top-mounted mirror-based setup, the system captures multiple fruit surfaces with a single camera, eliminating the need for rotation or multiple cameras. The captured multi-view image is analyzed using a global thresholding technique to calculate the multi-view area. A simple linear regression model is then built to estimate the fruit’s volume based on multi-view area, without requiring 3D model reconstruction. The proposed system was successfully tested for estimating the volume of two irregularly shaped fruits (pomelo and mango), achieving high coefficients of determination (0.986 and 0.988, respectively). Additionally, the system was tested with different fruit orientations, and the results showed that orientation did not affect volume estimation. These results demonstrate that this approach has strong potential not only for fruit quality assessment but also for other irregularly shaped solid objects where rapid and accurate volume estimation is needed.

Why it matches plant phenotyping methods果実の体積という植物器官形質を、鏡面マルチビュー画像と画像解析・回帰で推定する手法を開発・検証しており、形質取得法が研究の中心である。

abstractThis study proposes a low-cost multi-view system for the rapid and accurate estimation of fruit volume.

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

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

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

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

Predicting Faba bean yield and grain quality Pre-Harvest using chemometric modelling →

CONTEXT: Faba bean (Vicia faba L.) is a sustainable protein source, but in-season stresses such as heat, drought and diseases cause grain discolouration and shrivelling, leading to market downgrades. Grain quality assessments are only performed post-harvest, limiting growers’ ability to manage quality risks proactively on-farm. To address this limitation, this study explored the potential of in-season hyperspectral sensing as a non-destructive, data-driven tool for early grain quality assessment. AIMS: This study aimed to assess faba bean grain yield and quality pre-harvest by identifying optimal reproductive growth stage(s) and spectral regions linked to target grain traits. METHODS: Hyperspectral data were collected at five locations in Victoria, Australia across five critical reproductive growth stages: flowering (BBCH 65–69), podding (BBCH 70–79), pod fill (BBCH 80–82), pod maturity (BBCH 83–89), and crop senescence (BBCH 90–99). Partial least squares regression (PLSR) models were applied to canopy, leaf and pod level spectra to extract wavelength-trait relationships and identify predictive temporal windows for faba bean grain traits prediction prior to harvest. This approach enabled identification of both temporal (growth stage) and spectral (wavelength region) factors most informative for early trait prediction. Grain traits predicted include grain yield, harvest index, grain number, single grain weight, seed size index (SSI), grain protein content, seed coat brightness, redness and yellowness. KEY RESULTS: Canopy-level spectra provided the most reliable predictions. Harvest index (R² = 0.71, d-index = 0.75) and GPC (R² = 0.73, d-index = 0.76) were predicted as early as the flowering stage. The podding stage was optimal for predicting single grain weight (R² = 0.91, d-index = 0.76), SSI (R² = 0.71, d-index = 0.74), seed coat redness (R² = 0.68, d-index = 0.77) and yellowness (R² = 0.61, d-index = 0.68). Near-infrared (NIR) regions, 750–950 and 1000–1800 nm, were most informative for predicting grain quality traits. CONCLUSION: These findings demonstrate the potential of integrating hyperspectral sensing with chemometric modelling to enable pre-harvest prediction of faba bean grain agronomic and quality traits. IMPLICATIONS AND IMPACTS: Hyperspectral sensing as a precision agriculture application can mitigate on-farm grain quality downgrade risks by supporting early, data-driven harvest management decisions that maximise growers’ profitability and sustainability.

Why it matches plant phenotyping methodsハイパースペクトルセンシングとPLSRにより、収穫前の作物キャノピー・葉・莢から収量および品質形質を予測し、波長・生育段階と予測性能を評価しているため、表現型取得・推定手法が中心である。

abstractthis study explored the potential of in-season hyperspectral sensing as a non-destructive, data-driven tool for early grain quality assessment.

Light-resilient visual regression of strawberry ripeness for robotic harvesting →

Accurate real-time quantification of strawberry ripeness is critical for advancing selective strawberry harvesting robots. However, existing methods often overlook inconsistencies caused by color distortion under varying light intensities. This study aims to verify and quantitatively analyze the influence of illumination on strawberry ripeness, and further presents a novel light-resilient vision-based ripeness regression method that overcomes these limitations through three key innovations. First, the research established the first fine-grained ripeness metric under controlled lighting conditions and introduced the comprehensive LightStrawberry dataset, featuring multi-illumination strawberry images. Second, we propose SRR-Net, an innovative end-to-end strawberry ripeness regression network built upon YOLOv8/YOLOv11. The network incorporates a dedicated ripeness regression branch that operates in parallel with the detection and segmentation heads, enabling simultaneous and efficient estimation of strawberry maturity. To further mitigate lighting-induced color distortion, RetinexNet was integrated to decompose, adjust, and reconstruct images by normalizing illumination and reflectance. Experiments demonstrated that SRR-Net achieved 0.918 mAP@50 for segmentation and operated at 210.3 FPS based on YOLOv11, while SRR-Net with RetinexNet attained 0.898 mAP@50 and 43.39 FPS. Though slightly lower in precision than other methods, both significantly improved ripeness accuracy, with mean absolute errors (MAE) of 0.040 and 0.037, representing 68.75 % and 71.09 % improvements over conventional Mask R-CNN approaches. Field experiments further demonstrated that SRR-Net and SRR-Net with RetinexNet achieved superior performance in ripeness regression. However, their detection and segmentation performance showed limited adaptability to real orchard conditions due to the characteristics of the LightStrawberry dataset. Overall, both models outperformed the standard YOLOv8/v11 baselines but were slightly inferior to Mask R-CNN. This work provides a robust solution for strawberry-harvesting robotics, enabling reliable ripeness assessment in challenging field environments.

Why it matches plant phenotyping methods画像からイチゴの成熟度を定量推定する回帰手法、照明補正、データセットを開発・評価しており、植物形質取得が中心である。

abstractpresents a novel light-resilient vision-based ripeness regression method

Towards automated crop monitoring: A computer vision solution for maize tassel detection under center-pivot irrigation systems →

The center-pivot irrigation system is a highly efficient water-saving agricultural system. However, it typically operates solely based on predefined paths, speeds, and water volumes, and cannot assess the true needs of the crops due to its inability to monitor their growth status. To address this limitation, we deployed cameras on the center-pivot irrigation system to establish a mobile phenotyping platform, and developed a maize tassel detection model based on the YOLOv11 architecture. Several key improvements were implemented to address the complex morphology and challenging feature extraction of maize tassels: The ODConv (Omni-Dimensional Convolution) module was introduced to more comprehensively capture dynamic tassel features through a dynamic convolution strategy; To suppress interference from complex field environments on detection results, the SE (Squeeze-and-Excitation) attention mechanism was embedded to enhance effective feature responses and reduce noise impact; The BiFPN (Weighted Bi-directional Feature Pyramid Network) structure was adopted to strengthen multi-scale feature fusion capabilities, further improving the model’s detection performance for tassels at various scales; To tackle the difficulty of tassel recognition and localization in field environments, the C3K2 module was fused with the CSAM (Cross-Slice Attention Mechanism) to construct a C3K2-CSAM module, achieving more precise tassel identification and localization. Experimental results demonstrate that the OSBC-YOLO model achieved a precision of 91.8 % and a mean average precision (mAP) of 85.7 %, while reducing the number of parameters, FLOPs, and memory usage by 8.1 %, 28.1 %, and 7.3 % respectively compared to the original model. Field tests conducted on the center-pivot irrigation system revealed an error rate of 5.87 % between the number of tassels detected by the model and the ground truth count. These results verify that the OSBC-YOLO model can effectively perform tassel counting in practical operating environments and possesses the capability for recognition of crop phenotypic traits. This system provides critical data support for intelligent variable-rate irrigation decisions, promoting the transformation of center-pivot irrigation systems from traditional “single-function operation” to an integrated “perception-decision-execution” mode.

Why it matches plant phenotyping methodsトウモロコシ雄穂の検出・計数を行う移動型画像表現型解析プラットフォームとモデルを開発・検証しており、植物形態形質の取得が中心的貢献である。

abstractwe deployed cameras on the center-pivot irrigation system to establish a mobile phenotyping platform, and developed a maize tassel detection model based on the YOLOv11 architecture

Early prediction of coffee production per plant using morphological indices →

Purpose: Coffee farming plays an essential role in the global economy, making accurate productivity prediction methods indispensable for strategic decision-making in the sector. This study aimed to develop models for early prediction of coffee production per plant based on morphological indices.Methods:Two models were proposed using the following attributes: plant height, canopy width, and the number of fruits on the productive internodes of plagiotropic branches. In Model 1, fruit counts were manually conducted at the 4th and 5th productive nodes of the branches, while in Model 2, the average fruit count from the 1st to the 5th productive nodes was obtained automatically through branch image analysis using Detectron2, an open-source object detection library. Both models were developed using data collected at two distinct periods before harvest—the first five months prior and the second three months prior. The research was conducted in three coffee plots in Viçosa, Minas Gerais, Brazil, where 60 plants were selected to evaluate the production prediction model. During harvest, the production of each plant was individually recorded, enabling validation of the predictions. Results: The results revealed a strong correlation between the models and the field-observed production data, especially for the model based on data collected three months before harvest. Model 1 demonstrated a better fit (R² = 0.889; RMSE = 0.923 L/plant; MAE = 0.635 L/plant), while Model 2 had a lower absolute error (R² = 0.747; RMSE = 0.374 L/plant; MAE = 0.460 L/plant). Additionally, productivity maps were generated for each plot, showing good agreement with field-observed productivity data.Conclusions: It was concluded that the proposed models are promising for application in coffee farming, contributing to early production prediction.

Why it matches plant phenotyping methodsコーヒー果実数を枝画像から自動抽出し、個体あたり生産量を早期予測する手法を開発・検証しており、表現型取得と予測ワークフローが研究の中心である。

abstractBoth models were developed using data collected at two distinct periods before harvest—the first five months prior and the second three months prior.

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

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

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

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

A Comparative Study on the Identification of Xanthium sibiricum Patrin ex Widder and Xanthium italicum Moretti Based on Three Microscopy Technology. →

Xanthium sibiricum Patrin ex Widder and Xanthium italicum Moretti are morphologically similar fructus that are frequently misidentified. Xanthium italicum Moretti may possess inherent toxicity, and its adulteration of genuine medicinal materials poses a threat to clinical drug safety. Macroscopic observation and three microscopic techniques including stereo microscope, optical microscope, and 3D X-ray microscope were used for morphological identification of Xanthium sibiricum Patr ex Widder and Xanthium italicum Moretti in this study. 3D X-ray microscopy was applied as a novel tool for non-destructive, high-resolution discrimination of the two taxa. Intact fructus (n = 30 per species) were first screened macroscopically, then examined by stereo microscopy, optical microscopy, and 3D X-ray microscopy (0.3, 0.7, 1.5, 3.5, 18.06, 20.01 μm voxel size, Zeiss Xradia 520 Versa). The results showed that stereo microscopy, optical microscopy, and 3D X-ray microscopy collectively confirm the same conclusion from three distinct physical perspectives: surface topography, internal two-dimensional structure, and internal three-dimensional density distribution. The two Xanthium species differ significantly in burr spine morphology, fructus size and shape, the architecture and distribution of non-glandular and glandular trichomes, cotyledon conformation, and seed-coat cell patterning. In particular, 3D X-ray microscopy clearly resolves internal cotyledon spatial configurations and involucral cavity architectures, which furnishes critical endomorphic characters for taxonomic diagnosis. 3D X-ray microscopy provides unprecedented volumetric contrast of surface spines and internal seed architecture, permitting confident, non-destructive species identification. This study provides a basis for the safe clinical use of Xanthium sibiricum Patrin ex Widder. The frontier of 3D X-ray microscopy in plant systematics offers a novel, rapid, accurate and non-destructive protocol for the discrimination of morphologically elusive species.

Why it matches plant phenotyping methods3D X線顕微鏡を含む複数の画像計測法を用いて果実・種子の形態形質を抽出し、近縁2分類群の非破壊識別プロトコルとして比較・検証しており、植物フェノタイピング手法が中心である。

abstract3D X-ray microscopy was applied as a novel tool for non-destructive, high-resolution discrimination of the two taxa.

A Low-Cost RGB-Based Image Processing Method for High-Throughput Assessment of Rice Grain Chalkiness →

Although numerous rice genotypes have been developed worldwide, post-harvest evaluation of chalkiness, a key grain trait, remains a significant challenge in breeding programs. Conventional phenotyping methods rely on manual grain separation and analysis, which limits the speed and performance of decision-making. This study aimed to assess the efficiency of a low-cost, image-based phenotyping method for characterizing rice grain chalkiness and morphology traits (grain length and width) in comparison with traditional evaluation methods. Grains from 270 rice samples were imaged using a hyperspectral camera (VNIR, 400–1000 nm) and a Nikon digital single-lens reflex (DSLR) camera. Only RGB information was used for analysis, including RGB channels extracted from hyperspectral imagery to simulate low-cost setups. Python scripts were used to segment grains, estimate morphological parameters, and calculate chalkiness degree. Results from both imaging systems were compared with reference data obtained from the SeedCount platform. Strong correlations were observed with SeedCount data, reaching 93% for hyperspectral-RGB extraction and 76% for the RGB system. Binary classification metrics showed high discriminative performance, with area under the curve (AUC) values above 0.90 for most traits. The proposed method enabled image acquisition and processing in approximately 21 s per sample, compared to 1.5 min required by the conventional platform. The findings demonstrate the feasibility of a rapid and low-cost image-based phenotyping strategy to support rice breeding programs, particularly for chalkiness quantification and grain morphology assessment. The complete image-processing pipeline is provided as supplementary material, reinforcing the transparency and reproducibility of the method.

Why it matches plant phenotyping methods低コストRGB画像によるイネ粒の白未熟粒率・形態形質の抽出法を開発し、従来法およびSeedCountと比較検証しており、表現型取得・解析手法が研究の中心である。

abstractThis study aimed to assess the efficiency of a low-cost, image-based phenotyping method for characterizing rice grain chalkiness and morphology traits (grain length and width) in comparison with traditional evaluation methods.

Germplasm exploration and digital phenotyping reveal indigenous diversity and farmer preferences in pigeon pea (Cajanus cajan (L.) Millsp.) for climate-smart breeding →

Pigeon pea ( Cajanus cajan [L.] Millsp.) remains an underutilized legume in most African countries despite its potential to promote climate-resilient farming, diversify food sources, and enhance nutrition. Limited understanding of its indigenous diversity and farmer trait preferences hampers wider adoption, especially in the West African region. Between February and May 2025, a germplasm exploration was conducted across 18 Nigerian states, supplemented by accessions from the International Institute of Tropical Agriculture (IITA) genebank, Ghana, the Republic of Benin, and The Gambia, totaling 273 accessions. Ethnobotanical surveys documented farmer preferences, cultural uses, and local nomenclature, while seed morphometric traits were assessed using Videometerlab4 multispectral imaging. Farmer surveys revealed that cooking time (58.3%), commercial value (27.0%), and maturity cycle (14.7%) were key preferred traits. Gender and age influenced preferences; women and older farmers prioritized cooking time, whereas men and younger farmers emphasized the maturity cycle. Vernacular names (e.g., Otili, Fiofio, Waken Gwari ) reflected deep cultural ties and cross-border exchange in Ogun State and the Republic of Benin, highlighting transboundary diversity. Morphometric analysis showed moderate variation in seed size, shape, and color. Seed area (14.2–46.0 mm2), compactness (0.590–0.998), and eccentricity (0–0.808) distinguished rounded from elongated seeds, while CIELab_A values (− 0.04 to 29.98) captured color differences. The first two PCA axes explained 67.1% of the total variation, and cluster analysis grouped accessions into four morphotypes. By combining genetic, morphometric, and farmer preference data, this study offers a strong basis for conserving and developing climate-resilient, fast-cooking, and market-preferred cultivars for sub-Saharan Africa.

Why it matches plant phenotyping methodsVideometerlab4マルチスペクトル画像を用いた種子形態形質の取得と解析が研究の主要な構成要素であり、複数の形態・色指標と形態型分類を実施しているため、フェノタイピング手法の実質的な適用に該当する。

titleGermplasm exploration and digital phenotyping reveal indigenous diversity and farmer preferences in pigeon pea (Cajanus cajan (L.) Millsp.) for climate-smart breeding

Anther size quantification in wheat using deep learning under normal and heat stress conditions →

Key message We present an imaging-based deep learning phenotyping pipeline that classifies heat-stressed wheat anthers and quantifies size traits using YOLO, enabling fast, precise, scalable measurements to support breeding heat-resilient wheat varieties. Abstract Terminal heat stress is a major abiotic stress causing significant yield loss in wheat. Anther size, a key trait of terminal heat stress tolerance, is least studied in wheat due to complex and tedious scaling trait, and short live span. To provide user friendly approach to plant breeders, integration of the modern digital imaging and deep learning techniques together is the current need of high-throughput phenomic era. In this study, we introduced a hybrid approach that amalgamates the strengths of deep learning models for both binary classification and precise morphological analysis of anther images of 177 wheat accessions under normal and heat stress environments. ResNet18 with 94% accuracy, outperformed the traditional models like CNN and MobileNetV2, achieving high classification performance. For morphological trait extraction, we employed YOLOv8, a cutting-edge object detection model known for its high speed, accuracy, and computational efficiency. YOLOv8 successfully localized anthers and measured width and length with strong agreement to experimental measurements, as validated by Bland–Altman analysis. Its precise detection capability and lightweight architecture makes it ideal for high-throughput phenotyping using digital imaging approach. To further boost the interpretability of our deep learning models, we utilized Grad-CAM, a powerful technique for visualizing class-specific features in the network’s decision-making process. This facilitated in categorizing the key visual features within the anther images that had the greatest influence on the model’s decision-making process. This cohesive workflow not only sets a new benchmark in image-based classification and morphological measurement but also proposes an accessible tool for rapid, real-time phenotyping, supporting data-driven breeding strategies aimed at improving wheat resilience under terminal heat stress.

Why it matches plant phenotyping methodsコムギ葯の画像取得、深層学習による分類・形態形質抽出、実測値との技術検証を中心とする明確な植物フェノタイピング手法研究。

abstractWe present an imaging-based deep learning phenotyping pipeline that classifies heat-stressed wheat anthers and quantifies size traits using YOLO

Leveraging sensor technologies for seed phenotyping by genebanks →

Genebanks serve as critical repositories for preserving the genetic diversity of plant species, including crops, forages, and their wild relatives, which is essential for adapting to climate change, enhancing food security, and improving agricultural sustainability. Seed phenotyping, the process of evaluating observable seed traits influenced by genetics and environmental factors, plays a pivotal role in characterizing and utilizing this diversity. Traditional phenotyping methods, however, are labor-intensive and inadequate for the vast collections housed in genebanks. This paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum—from gamma rays to radio waves—to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor. We highlight the integration of advanced imaging systems (e.g., hyperspectral, X-ray, and thermal imaging) to enrich genebank datasets, facilitating trait discovery and crop improvement. Despite challenges like cost, scalability, and data standardization, opportunities arise from collaborative initiatives between genebanks and phenomics facilities through organizations such as International Plant Phenotyping Network. Our conclusions underscore how phenomics can revolutionize genebank operations, ensuring the efficient conservation and deployment of genetic resources to address global agricultural demands.

Why it matches plant phenotyping methods種子形質の高スループット取得に用いるセンサー・イメージング技術を中心に整理したフェノタイピングレビューであり、方法論的役割が明確です。

abstractThis paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum—from gamma rays to radio waves—to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor.

Leveraging sensor technologies for seed phenotyping by genebanks →

Genebanks serve as critical repositories for preserving the genetic diversity of plant species, including crops, forages, and their wild relatives, which is essential for adapting to climate change, enhancing food security, and improving agricultural sustainability. Seed phenotyping, the process of evaluating observable seed traits influenced by genetics and environmental factors, plays a pivotal role in characterizing and utilizing this diversity. Traditional phenotyping methods, however, are labor-intensive and inadequate for the vast collections housed in genebanks. This paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum—from gamma rays to radio waves—to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor. We highlight the integration of advanced imaging systems (e.g., hyperspectral, X-ray, and thermal imaging) to enrich genebank datasets, facilitating trait discovery and crop improvement. Despite challenges like cost, scalability, and data standardization, opportunities arise from collaborative initiatives between genebanks and phenomics facilities through organizations such as International Plant Phenotyping Network. Our conclusions underscore how phenomics can revolutionize genebank operations, ensuring the efficient conservation and deployment of genetic resources to address global agricultural demands.

Why it matches plant phenotyping methods種子形質を対象とする高スループット画像・センサー型フェノタイピング技術を中心に扱うレビューであり、方法論的役割が明確。

abstractThis paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum—from gamma rays to radio waves—to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor.

A Multidimensional Approach to Cereal Caryopsis Development: Insights into Adlay ( Coix lacryma-jobi L.) and Emerging Applications. →

Adlay ( Coix lacryma-jobi L.) stands out as a vital health-promoting cereal due to its dual nutritional and medicinal properties; however, it remains significantly underdeveloped compared to major crops. The lack of mechanistic understanding of its caryopsis development and trait formation severely constrains targeted genetic improvement. While transformative technologies, specifically micro-computed tomography (micro-CT) imaging combined with AI-assisted analysis (e.g., Segment Anything Model (SAM)) and multi-omics approaches, have been successfully applied to unravel the structural and physiological complexities of model cereals, their systematic adoption in adlay research remains fragmented. Going beyond a traditional synthesis of these methodologies, this article proposes a novel, multidimensional framework specifically designed for adlay. This forward-looking strategy integrates high-resolution 3D phenotyping with spatial multi-omics data to bridge the gap between macroscopic caryopsis architecture and microscopic metabolic accumulation. By offering a precise digital solution to elucidate adlay's unique developmental mechanisms, the proposed framework aims to accelerate precision breeding and advance the scientific modernization of this promising underutilized crop.

Why it matches plant phenotyping methods穀粒の3DフェノタイピングとAI画像解析を中核に据えた、方法論的な枠組みを提案するレビュー／展望論文である。

abstractThis forward-looking strategy integrates high-resolution 3D phenotyping with spatial multi-omics data

Integration of X-Ray CT, Sensor Fusion, and Machine Learning for Advanced Modeling of Preharvest Apple Growth Dynamics. →

Understanding the complex interplay between environmental factors and fruit quality development requires sophisticated analytical approaches linking cellular architecture to environmental conditions. This study introduces a novel application of dual-resolution X-ray computed tomography (CT) for the non-destructive characterization of apple internal tissue architecture in relation to fruit growth, thereby advancing beyond traditional methods that are primarily focused on postharvest analysis. By extracting detailed three-dimensional structural parameters, we reveal tissue porosity and heterogeneity influenced by crop load, maturity timing and canopy position, offering insights into internal quality attributes. Employing correlation analysis, Principal Component Analysis, Canonical Correlation Analysis, and Structural Equation Modeling, we identify temperature as the primary environmental driver, particularly during early developmental stages (45 Days After Full Bloom, DAFB), and uncover nonlinear, hierarchical effects of preharvest environmental factors such as vapor pressure deficit, relative humidity, and light on quality traits. Machine learning models (Multiple Linear Regression, Random Forest, XGBoost) achieve high predictive accuracy (R 2 > 0.99 for Multiple Linear Regression), with temperature as the key predictor. These baseline results represent findings from a single growing season and require validation across multiple seasons and cultivars before operational application. Temporal analysis highlights the importance of early-stage environmental conditions. Integrating structural and environmental data through innovative visualization tools, such as anatomy-based radar charts, facilitates comprehensive interpretation of complex interactions. This multidisciplinary framework enhances predictive precision and provides a baseline methodology to support precision orchard management under typical agricultural variability.

Why it matches plant phenotyping methodsリンゴ果実の内部組織構造をX線CTで非破壊・三次元計測し、構造パラメータを抽出する手法が研究の中心であり、環境データとの統合や機械学習による形質推定も行っているため。

abstractThis study introduces a novel application of dual-resolution X-ray computed tomography (CT) for the non-destructive characterization of apple internal tissue architecture in relation to fruit growth

ORBMO-RF: a non-destructive classification method for ginseng seeds based on multimodal fusion and improved red-billed blue magpie optimization algorithm. →

Introduction Ginseng, as a precious medicinal plant, requires precise classification of its seeds, which directly impacts production processes and the stability of herbal quality. Furthermore, this classification plays a critical role in advancing ginseng breeding and the modernization of the industry. Current research indicates that systematic automated precision classification technologies for ginseng seeds remain underdeveloped, necessitating breakthroughs in technical bottlenecks. Methods This study innovatively proposes a smart classification method based on multimodal data fusion. It employs recursive feature elimination (RFE) to select morphological features from images, followed by competitive adaptive reweighted sampling (CARS) to extract spectral bands from hyperspectral data within the 350~2500 nm range. Morphological and spectral features are then integrated to construct a random forest (RF) classification model optimized using an enhanced, red-billed blue magpie optimization (RBMO) algorithm. To address the RBMO algorithm's tendency to converge to local optima, the hybrid optimization framework is constructed by integrating three mechanisms: the improved Circle chaotic map, the golden sine search strategy, and the adaptive simulated annealing perturbation mechanism. Results Experimental results demonstrate that the proposed model outperforms the baseline model RF, achieving 4.69%、4.79%、4.69 and 4.74% improvements in classification accuracy, precision, recall, and F1-score on test datasets, respectively. Discussion The established multimodal data fusion classification system not only provides theoretical and technical foundations for industrial-scale ginseng seed classification but also offers a transferable intelligent decision-making paradigm for non-destructive testing in traditional Chinese medicine.

Why it matches plant phenotyping methods画像由来の形態特徴とハイパースペクトル特徴を統合し、種子を非破壊分類する手法自体が研究の中心であり、植物器官（種子）の観測可能な状態を推定しているため。

abstractThis study innovatively proposes a smart classification method based on multimodal data fusion.

Leveraging sensor technologies for seed phenotyping by genebanks. →

Genebanks serve as critical repositories for preserving the genetic diversity of plant species, including crops, forages, and their wild relatives, which is essential for adapting to climate change, enhancing food security, and improving agricultural sustainability. Seed phenotyping, the process of evaluating observable seed traits influenced by genetics and environmental factors, plays a pivotal role in characterizing and utilizing this diversity. Traditional phenotyping methods, however, are labor-intensive and inadequate for the vast collections housed in genebanks. This paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum-from gamma rays to radio waves-to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor. We highlight the integration of advanced imaging systems (e.g., hyperspectral, X-ray, and thermal imaging) to enrich genebank datasets, facilitating trait discovery and crop improvement. Despite challenges like cost, scalability, and data standardization, opportunities arise from collaborative initiatives between genebanks and phenomics facilities through organizations such as International Plant Phenotyping Network. Our conclusions underscore how phenomics can revolutionize genebank operations, ensuring the efficient conservation and deployment of genetic resources to address global agricultural demands.

Why it matches plant phenotyping methods種子形質を対象とする高スループットセンサー・画像フェノタイピング技術を総説しており、フェノタイピング手法が中心である。

abstractThis paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum-from gamma rays to radio waves-to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor.

Intelligent high-throughput recognition model for bitter gourd fruit morphology and tubercle characteristics. →

Bitter gourd, an important crop with both economic and medicinal value, requires precise identification of fruit shape and tubercle phenotypes to enhance breeding efficiency. To address the low efficiency and high subjectivity of traditional methods, this study proposes an improved YOLOv8-CEFC model for high-throughput automatic detection of the bitter gourd fruit shape and tubercle characteristics. First, the model integrates the ConvNeXt V2 module into the backbone network, combined with a Fully Convolutional Masked Autoencoder (FCMAE) framework and Global Response Normalization (GRN) layers to enhance feature extraction capabilities. Second, an Efficient Multi-scale Attention (EMA) mechanism is introduced, capturing local tubercle textures and global fruit shape contours simultaneously through a parallel dual-branch structure, while also improving the model’s robustness against cluttered backgrounds and environmental noise. Finally, Focal-CIoU Loss is incorporated to replace CIoU Loss, reducing the impact of class imbalance on model accuracy. The results show that the model achieves precision, recall, mAP50, mAP50-95, and F1 scores of 93.9%, 94.4%, 96.3%, 93.6%, and 94.15%, respectively, which represent improvements of 2.0%, 3.5%, 1.1%, 3.4%, and 2.75% compared to the original YOLOv8n model. The performance gain of the model was further examined using the bootstrap method, which confirmed that the improvement is statistically significant. Further validation through confusion matrix analysis, PR curves, and ablation experiments confirms the effectiveness of the improvements. Compared to other mainstream YOLO models, YOLOv8-CEFC demonstrates more accurate identification, better stability, and higher detection efficiency. The proposed improved YOLOv8-CEFC model provides an efficient solution for phenotypic analysis in Bitter Gourd breeding and holds significant importance for advancing the intelligentization of crop breeding.

Why it matches plant phenotyping methods苦瓜果実形状とこぶ形質を高スループットに自動認識するYOLOv8改良モデルを開発・検証しており、植物表現型の取得・抽出手法が研究の中心である。

abstractthis study proposes an improved YOLOv8-CEFC model for high-throughput automatic detection of the bitter gourd fruit shape and tubercle characteristics.

WheatAI v1.0: An AI-Powered High Throughput Wheat Phenotyping Platform →

High-throughput, low-cost phenotyping remains a critical bottleneck in wheat breeding, genetics, and crop management. This is particularly evident in the measurement of complex yield components (i.e., spike and spikelet counts), disease and grain-quality traits related to Fusarium Head Blight (FHB) and Fusarium-Damaged Kernels (FDK), and microscale physiological traits such as density and size of stomata and aperture. We introduce WheatAI (wheatai.net), an AI-powered web application designed to bridge the gap between advanced computer vision, AI and deep learning models, and high-throughput phenotyping (HTP) and practical agricultural applications. WheatAI v1.0 provides an accessible, browser-based interface that supports multiscale data ingestion from smartphones, Unmanned Aerial Vehicles (UAVs), and portable microscopes. The core functionalities of the platform include plot- and field-scale assessment via UAV- and smartphone-based wheat spike detection and counting, as well as smartphone-based spikelet counting. Additionally, it offers grain quality assessment through FDK ratio estimation and kernel morphometric measurements, such as length, width, and area, derived from smartphone images of kernel samples. For leaf-level analysis, WheatAI provides microscale phenotyping through automated stomatal counting, size, and aperture measurement from digital microscopy images. The system supports both single-image and bulk processing via a guided upload-and-run workflow. This platform is designed to reduce labor costs and rater subjectivity while accelerating field-to-lab decision cycles. By providing standardized, image-based outputs, WheatAI enables breeders, agronomists, and producers to implement high-throughput selection and precision scouting at scale.

Why it matches plant phenotyping methodsWheatAIは、画像から収量構成要素、病害関連形質、穀粒形態、気孔形質を抽出する高スループット植物フェノタイピング基盤そのものであり、方法・ソフトウェアの開発が中心です。

abstractWe introduce WheatAI (wheatai.net), an AI-powered web application designed to bridge the gap between advanced computer vision, AI and deep learning models, and high-throughput phenotyping (HTP) and practical agricultural applications.

Predicting Multiple Traits of Rice and Cotton Across Varieties and Regions Using Multi-Source Data and a Meta-Hybrid Regression Ensemble. →

Timely and accurate prediction of crop traits is critical for precision breeding and regional agricultural production. Previous studies have primarily focused on single crop yield traits, neglecting other crop traits and variety-specific analyses. To address this issue, we employed a Meta-Hybrid Regression Ensemble (MHRE) approach by using multiple machine learning (ML) approaches as base learners, integrating regional multi-year, multi-variety crop field trials with satellite remote sensing indices, meteorological and phenological data to predict major crop traits. Results demonstrated MHRE's optimal performance for rice and cotton, significantly outperforming individual models (RF, XGBoost, CatBoost, and LightGBM). Specifically, for rice crop, MHRE achieved highest accuracy for yield trait (R 2 = 0.78, RMSE = 0.59 t ha -1 ) compared to the best individual model (XGBoost: R 2 = 0.76, RMSE = 0.61 t ha -1 ); traits like effective spike also showed strong predictability (R 2 = 0.64, RMSE = 27.81 10,000·spike ha -1 ). Similarly, for cotton, MHRE substantially improved yield trait prediction (R 2 = 0.82, RMSE = 0.33 t ha -1 ) compared to the best individual model (RF: R 2 = 0.77, RMSE = 0.36 t ha -1 ); bolls per plant accuracy was highest (R 2 = 0.93, RMSE = 2.27 bolls plant -1 ). Moreover, rigorous validation confirmed that crop-specific MHRE models are robust across five rice and three cotton varietal groups and are applicable across six distinct regions in China. Furthermore, we applied the SHAP (SHapley Additive exPlanations) method to analyze the growth stages and key environmental factors affecting major traits. Our study illustrates a practical framework for regional-scale crop traits prediction by fusing multi-source data and ensemble machine learning, offering new insights for precision agriculture and crop management.

Why it matches plant phenotyping methods複数ソースデータとメタ・ハイブリッド回帰アンサンブルにより、イネ・ワタの収量や形態関連形質を推定し、モデル比較と品種群・地域横断検証を行っているため、植物形質推定手法が中心である。

abstractwe employed a Meta-Hybrid Regression Ensemble (MHRE) approach by using multiple machine learning (ML) approaches as base learners, integrating regional multi-year, multi-variety crop field trials with satellite remote sensing indices, meteorological and phenological data to predict major crop traits.

MEP3D: Improved clustering-based 3D point cloud method for comprehensive maize ear phenotypic trait extraction →

Maize ear traits are critical indicators for elucidating yield formation mechanisms and are widely used in genetic studies. Traditional two-dimensional (2D) phenotyping suffers from planar analysis constraints, occlusions in single-view imaging, and limited robustness to mixed textures or curved ears. To address these issues and support germplasm archiving and breeding research, we developed MaizeEar3DPheno (MEP3D), a 3D point cloud-based method for quantifying maize ear phenotypic traits. A structured-light 3D scanning system equipped with a motorized rotary platform was designed to acquire point clouds of 30 maize ears from three different varieties. Preprocessing involved axis alignment via PCA, uniform downsampling, and removal of non-kernel regions. Following preprocessing, key phenotypic traits, including ear length, diameter, and barren tip length, were calculated from the processed point clouds. MEP3D integrated directional erosion with density-based clustering to achieve robust kernel segmentation and counting. A spatial analysis algorithm was further developed to locate kernel row arrangements from geometric features. The results demonstrated that the proposed method achieved high-precision cross-variety kernel counting, with a mean absolute percentage error (MAPE) of 0.91%, and a coefficient of determination (R²) of 0.9917 across all maize ears. Kernel row quantification was fully consistent with manual measurements, allowing extraction of row inclination and average kernel number per row. Ear length, diameter, and barren tip length estimation achieved R² values of 0.9864, 0.9871, and 0.9670, respectively, demonstrating robustness. The generated high-fidelity 3D phenotypic data supports automated evaluation of ear and kernel traits and facilitates in-depth analysis of spatial morphological characteristics.

Why it matches plant phenotyping methodsトウモロコシ雌穂の3D点群取得・処理・形質抽出法を開発し、カーネル数や穂長などを手動測定と比較検証しており、フェノタイピング手法が研究の中心である。

abstractwe developed MaizeEar3DPheno (MEP3D), a 3D point cloud-based method for quantifying maize ear phenotypic traits.

High-throughput seed phenotyping of Populus cultivars in China using vibration-assisted machine vision with alternating back-lit and front-lit illuminations →

Seeds of major Populus cultivars were collected from across China in 2024 to build the image-data bank of over 1187000 images of singular seeds for the National Forestry and Grassland Science Data Center (NFGSDC). An innovative vibration-assisted machine-vision system was built with alternating back-lit and front-lit illumination, which incorporated a flexible vibratory panel (FVP) to manipulate the multitude of seeds to minimize the occurrence of butting or overlapping, and the lighting from alternating directions to capture phenotypic features both in silhouettes and in vivid color images. To investigate how illumination directions would affect phenotyping, morphological and chromatic metrics were measured, respectively from only the common front-lit images and through the combined use with back-lit images, and applied to distinguish different cultivars and harvest-batches. Results verified that back-lit excelled for reliable segmentation for feature images and accurate morphological metrics, especially when the closeness was clearly revealed in the clustering dendrogram between Nanlin 895 and Zhonglin 46, which shared a common genetic sourcing from P. Euramericana. In contrast, front-lit images were prone to occasional segmentation defects leading to inaccurate morphological measurements due to the highly dynamic range of seed colors, which caused the clustering to lose the genetic relevance. The power of the image-dataset of alternating illuminations was further demonstrated when a decent accuracy of 0.819 yielded from the simple support-vector-machine classification while working on only the back-lit morphological measurements, and the increase to 0.856 with statistical significance if with the addition of chromatic metrics from corresponding front-lit color images, while other image characteristics had been strictly held back. The vibration-assisted alternating illumination protocol established in this work to capture delicate seed-features of Populus cultivars may also be applied to other small grains facing similar imaging challenges, laying a sturdy step-stone of high-throughput phenotyping for large-scale breeding programs and genetic studies. Keywords: Populus seed, machine vision, camera calibration, flexible vibratory plate, back-lit and front-lit illumination DOI: 10.25165/j.ijabe.20261901.9850 Citation: Wang X W, Horly M M, Li Z P, Zhao M C, Wu B, Wang M M, et al. High-throughput seed phenotyping of Populus cultivars in China using vibration-assisted machine vision with alternating back-lit and front-lit illuminations. Int J Agric & Biol Eng, 2026; 19(1): 197–212.

Why it matches plant phenotyping methodsポプラ種子の形態・色形質を高 throughput に取得する画像計測システムと照明・振動プロトコルを開発・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。

abstractAn innovative vibration-assisted machine-vision system was built with alternating back-lit and front-lit illumination

A Machine Vision-Based Online Apple Grading System Toward In-Field Sorting →

Highlights A screw-conveyor-based machine vision system was evaluated for online apple grading. The developed vision pipeline enabled multi-view-based size estimation and comprehensive surface defect inspection. Apple sizing accuracy exceeded 95.9% across conveyor speeds of 1–2 apples s-1 per conveyor lane. The system achieved sample-level grading accuracies of up to 95.4%, demonstrating its potential for further integration and in-field validation. ABSTRACT. Apple quality grading is a critical operation in postharvest handling; however, most existing grading systems are designed for controlled packinghouse environments rather than in-field operation at harvest, which can achieve substantial cost savings for both growers and packers and improve postharvest inventory management. To address the need for in-field apple grading technology, building on our prior work, this study developed a machine vision-based apple grading system toward in-field sorting by integrating a screw-conveyor-based fruit handling mechanism with automated defect inspection and size estimation. The system enables continuous fruit transportation and rotation, allowing multi-view image acquisition for comprehensive surface assessment. The vision module comprises an enclosed image chamber equipped with uniform LED illumination and a top-mounted RGB-D (red-green-blue-depth) camera, ensuring stable and consistent quality of acquired imagery. A computer vision-based pipeline was developed to detect, track, and segment individual apples for surface defect evaluation and sizing. Multi-view images acquired during fruit rotation were fused to achieve full surface coverage. In addition, a geometry-based diameter estimation method integrating stem/calyx-aware boundary localization was introduced to improve fruit sizing robustness and accuracy. Experimental results demonstrate diameter estimation accuracy exceeding 95.9% and maintained sample-level grading accuracies of 95.4%, 94.2%, and 92.3% at conveyor speeds of 1, 1.5, and 2 apples s -1 per lane, respectively. These results demonstrate that the proposed system can support rapid apple grading and provide a practical step toward in-field fruit sorting. Both the dataset and software programs of this study has been made publicly available. Keywords: Apple, In-field grading, Machine vision, Multi-view imaging, Online inspection.

Why it matches plant phenotyping methodsリンゴのサイズ推定と表面欠陥評価を行う画像ベースのオンライン表現型取得・選別システムを開発し、精度検証しているため、植物フェノタイピング手法が中心である。

abstractA computer vision-based pipeline was developed to detect, track, and segment individual apples for surface defect evaluation and sizing.

Integrating UAV-Based High-Throughput Phenotyping, Genomics, and Deep Learning to Improve Prediction of Complex Traits in Winter Wheat →

Accurate prediction of complex agronomic traits such as grain yield and yield components remains a central challenge in winter wheat breeding because these traits are controlled by many genes and are strongly influenced by environmental variation. The integration of high-throughput phenotyping (HTP), genomics, and advanced machine learning approaches offers new opportunities to improve predictive accuracy and accelerate genetic gain in plant breeding. This study evaluates the use of an unmanned aerial vehicle (UAV)-based multispectral phenomics combined with genomic information, and deep learning approaches to enhance the prediction of grain yield (GY), test weight (TW), grain protein content (GPC), and tiller density (TD) in a winter wheat breeding program. The research was conducted during the 2022 to 2024 growing seasons at three locations in South Dakota: Brookings, Dakota Lakes, and Winner, across multiple breeding nurseries, including the Elite, Advanced, and Preliminary yield trials. UAV-derived spectral indices collected across multiple developmental stages were first integrated into deep neural network (DNN)-based phenomic prediction models and multi-trait genomic selection (MT-GS) frameworks. Significant associations were observed between UAV-based spectral indices and key agronomic traits. Phenomic prediction using DNN achieved strong accuracy for single-location trials (R² = 0.71, 0.62, and 0.49 for GY, TW, and GPC, respectively), with further improvement when models were trained on multi-location datasets (R² = 0.76, 0.64, and 0.75). Prediction accuracy for GY was highest at the Feekes 11 stage. Forward prediction of preliminary breeding lines using models trained on multi-location advanced lines improved accuracy by 32% relative to single-location training. Incorporating UAV-derived spectral indices as covariates in MT-GS models further improved predictive ability for GY (0.40) compared to single-trait genomic selection models (0.23), demonstrating the value of integrating phenomic information into genomic prediction frameworks. To address challenges associated with model transferability across environments, a deep transfer learning (DTL) strategy based on a one-dimensional convolutional neural network (1D-CNN) was implemented. Baseline cross-year and cross-location predictions showed poor performance (R² as low as -15.3); however, partial fine-tuning with 20-40% of target data substantially improved accuracy, achieving R² values up to 0.83 in cross-year and 0.29-0.69 in cross-location scenarios. Growth stage-specific modeling further revealed predictive performance highest at Feekes 10.5 and 11 (R² = 0.78-0.82), highlighting the importance of developmental timing in UAVbased trait prediction. Beyond predicting primary agronomic traits, UAV-based phenomics also provides opportunities to estimate important yield components that are difficult to measure at scale in breeding programs. Finally, UAV multispectral imagery was used to estimate early-season tiller density (TD). Among evaluated models, an attention-based convolutional neural network achieved the highest predictive performance (R² = 0.82; RMSE% = 15.20), outperforming conventional machine learning and standard deep learning approaches. Collectively, these findings demonstrate that integrating UAVbased HTP, genomic information, and deep learning approaches substantially improves the accuracy, generalizability, and scalability of complex trait prediction in winter wheat breeding. By enabling earlier and more reliable estimation of key agronomic traits across environments, these data-driven phenomic and genomic prediction frameworks accelerate breeding decisions and support the development of high-yielding and climate-resilient wheat cultivars.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像による作物形質推定と、深層学習・転移学習モデルの開発および検証が研究の中心であり、再利用可能なHTPワークフローとして実質的な方法論的貢献がある。

abstractThis study evaluates the use of an unmanned aerial vehicle (UAV)-based multispectral phenomics combined with genomic information, and deep learning approaches to enhance the prediction of grain yield (GY), test weight (TW), grain protein content (GPC), and tiller density (TD)

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

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

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

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

Composite interval mapping and genomic prediction of nut quality traits in American and American-European interspecific hybrid hazelnuts →

The native, perennial shrub American hazelnut (Corylus americana) is cultivated in the US Midwest for its significant ecological benefits, as well as its high‐value nut crop. Genetic improvement of perennial crops involves long‐term breeding efforts, and benefits from the use of genetic data in selection to reduce breeding cycle time. In addition, high‐throughput phenotyping methods are essential to the efficient and accurate screening of large breeding populations. This study reports novel advances in both of these domains, for American (C. americana) and interspecific hybrids between European (Corylus avellana) and American hazelnuts. Two populations of hazelnuts, one composed of C. americana and one composed of C. americana × C. avellana hybrids, were phenotyped over the course of 2 years in two locations using a digital imagery‐based method for quantifying morphological nut and kernel traits. These data were used to perform composite interval mapping using a recently released genetic map, and genomic prediction using a newly available chromosome‐scale reference genome for C. americana. Multiple quantitative trait loci were detected for all traits analyzed, with an average total R² of 52%. Genomic prediction exhibited high accuracy, with an average correlation coefficient between genotypic values and phenotypic observations of 0.78 across both environments. These results suggest that incorporating genetic data in selection is a tenable method for improving genetic gain for highly polygenic traits in hazelnut breeding programs.

Why it matches plant phenotyping methodsデジタル画像によるナッツおよび核の形態形質定量法を用い、その方法で得た表現型データを2年間・2地点で取得しているため、植物形質取得が研究の主要な方法的要素である。

abstractphenotyped over the course of 2 years in two locations using a digital imagery‐based method for quantifying morphological nut and kernel traits

Fusion of spectral and image information for generalized detection of SSC in multi-variety peaches →

Soluble solids content (SSC) is an important indicator for determining the commercial value of peaches. Visible/near-infrared (Vis/NIR) spectroscopy combined with chemometric methods is a primary technique for predicting peach SSC. However, the interference of fruit color with spectral signals makes it challenging to accurately detect SSC across different varieties. This study explored the feasibility of fusing spectral and image data to achieve accurate SSC prediction for multiple peach varieties. Diffuse reflectance spectra and images of three peach varieties (‘Hujing’, ‘Jinqiuhong’, and ‘Dongxue’) were collected. Multiple feature-level fusion strategies for spectral and image data were proposed. Partial least squares regression (PLSR) and support vector regression (SVR) models were developed based on the multimodal fusion data to predict the SSC of individual and multiple varieties, respectively. Their predictive performance was compared with that of models established using spectral data alone. To further improve the generalization ability of the multi-variety models, a spectrum-image fusion network (SIFNet) was proposed by extracting and leveraging high-level image features and integrating them with spectral information. The results showed that the SIFNet achieved superior performance in predicting the SSC of multi-variety peaches, with RP2, RMSEP, and RPDP of 0.8235, 1.0514, and 2.5208, respectively.

Why it matches plant phenotyping methodsスペクトル・画像融合とSIFNetを開発し、個々のモモ果実のSSCという器官形質を予測する方法が研究の中心である。

abstractThis study explored the feasibility of fusing spectral and image data to achieve accurate SSC prediction for multiple peach varieties.

Analysis of Gametophytic Apomixis Using Confocal Microscopy. →

Apomixis is an asexual reproductive mechanism that takes place deeply inside the female reproductive organs of the plant, in ovules and seeds. In gametophytic apomixis, an unreduced female gametophyte is produced by a modified meiosis of the megaspore mother cell (dipolspory) or from a somatic initial cell (apospory). The unreduced, nonrecombined egg cell develops subsequently into an embryo by parthenogenesis. The cyto-embryological study of apomixis is challenging because of the inaccessibility of these structures. Consequently, images of apomeiosis and parthenogenesis with high definition are limited to a few species. In this chapter, we show the application of a Feulgen staining protocol combined with confocal microscopy for the study of nonreductional megasporogenesis and autonomous embryo formation in diplosporous apomictic Taraxacum officinale and aposporous apomictic Pilosella piloselloides var. praealta. Using a rapid and technically simple method, performed on whole-mount ovaries, we have obtained high-resolution images of the female reproductive cells. Furthermore, we highlight the application of this protocol for the study of loss-of-diplospory and loss-of-parthenogenesis mutants in the same species.

Why it matches plant phenotyping methods全載卵巣にFeulgen染色と共焦点顕微鏡を組み合わせ、雌性生殖細胞・胚形成を高解像度で可視化する技術を提示・適用しており、植物の生殖状態を取得する方法が中心である。

abstractUsing a rapid and technically simple method, performed on whole-mount ovaries, we have obtained high-resolution images of the female reproductive cells.

Phenotypic characterization of Serbian bread wheat landraces for breeding-relevant traits →

The characterization of wheat genetic resources constitutes a fundamental prerequisite for their effective use in breeding programs aimed at preventing future food shortages. Continued technological developments in plant phenotyping for remote and proximal sensing have enabled multidimensional data acquisition and analysis, making the screening of large numbers of genotypes more accessible and costeffective. Within this framework, 36 bread wheat landraces collected from different localities across Serbia were grown under rainfed conditions during the 2024/25 growing season at Rimski Šančevi, near Novi Sad (45.20° N, 19.51° E) and analyzed using several proximal non-destructive phenotyping devices. In the field trials, genotypes were evaluated at two growth stages for seven traits associated with plant productivity: green cover, leaf area index, maximum plant height, normalized difference vegetation index (Literal sensor, Hiphen), chlorophyll content, and nitrogen balance index (DUALEX optical leaf clip meter, Metos). After harvest, the landraces were assessed for thousand grain weight and grain size fractions (length, width, area) using the MARViN system (MARViTECH), and basic technological parameters (protein, moisture, carbohydrates, oil contents) using the GrainSense Analyzer (Oulu). Principal Component Analysis revealed a clear separation among the analyzed genotypes, reflecting their substantial genetic diversity with respect to the evaluated traits, and highlighting their potential as a valuable source of novel alleles for enhancing breeding value and developing high-yielding varieties with improved technological quality.

Why it matches plant phenotyping methods複数の近位非破壊センシング機器を用いて、遺伝資源の生育・形態・生理・収量関連形質を体系的に取得することが研究の中心であり、実質的なフェノタイピング手法の適用に該当する。

abstractanalyzed using several proximal non-destructive phenotyping devices

Phenotypic, physiological, and technological evaluation of Serbian old wheat varieties for sustainable production →

Wheat breeding strategies, focused on the creation of varieties with high yield potential under optimized, intensive fertilization, have resulted in a loss of genetic diversity and reduced the ability of these varieties to perform well in low-input farming systems. Recent advances in plant phenotyping have opened new possibilities for the in-depth characterization of old and neglected wheat varieties considering their value for cultivation under stress-prone conditions. The aim of this paper was to assess the environmental sustainability of old wheat varieties that were grown in South East Europe before and at the beginning of the Green Revolution. The 30 wheat varieties were grown under rain-fed conditions during the 2024/25 growing season in experimental trials at Rimski Šančevi, Serbia. Field evaluation was conducted at three growth stages using 15 traits associated with yield and resilience to abiotic stresses with two non-destructive phenotyping devices - the Literal sensor (Hiphen) and the DUALEX optical leaf clip meter (Metos). After harvest, thousand grain weight and grain size were assessed using the MARViN (MARViTECH), while basic technological parameters were obtained by the GrainSense Analyzer (Oulu). ANOVA revealed statistically significant differences among the analysed genotypes for the studied traits, while the re-evaluation of old varieties under contemporary climate conditions, applying high-throughput phenotyping instruments, enabled elucidation of their value and potential role in wheat production under climate change.

Why it matches plant phenotyping methods複数の非破壊センサーと高スループット機器を用いて、品種の収量・ストレス関連形質を体系的に取得する方法適用が研究の主要部分である。

abstractField evaluation was conducted at three growth stages using 15 traits associated with yield and resilience to abiotic stresses with two non-destructive phenotyping devices - the Literal sensor (Hiphen) and the DUALEX optical leaf clip meter (Metos).

Improving Berry Uniformity in Grape (Vitis vinifera): Trait-Based Evaluation and Selection Perspectives →

This study explores the conceptual framework and evaluation methods of grape berry uniformity, elucidating its multidimensional nature arising from the coordinated contributions of berry size, shape, and cluster structure. Quantitative evaluation approaches based on the coefficient of variation, composite multi-trait indices, and high-throughput phenotyping technologies are systematically summarized. On this basis, key factors influencing berry uniformity are further analyzed, including genetic background, pollination and fertilization processes, berry developmental dynamics, plant growth regulator treatments, and water-nutrient environmental conditions. Integrating breeding strategies with production practices, a framework for improving berry uniformity is proposed, centered on “multi-trait selection, marker-assisted selection, and cultivation regulation.” Meanwhile, with the advancement of machine vision, high-throughput phenotyping, and multi-source data integration technologies, the evaluation of berry uniformity is shifting toward automation, precision, and intelligence. However, challenges remain in the standardization of evaluation systems, elucidation of molecular mechanisms, and integration of multi-source data. Future research directions toward data-driven precision improvement are discussed. This study aims to provide theoretical foundations and technical support for enhancing the quality and standardized production of table grapes.

Why it matches plant phenotyping methodsブドウ果実の均一性を対象に、評価指標、高スループットフェノタイピング、機械ビジョンによる自動評価を体系的に扱うレビューであり、フェノタイピング手法が中心です。

abstractQuantitative evaluation approaches based on the coefficient of variation, composite multi-trait indices, and high-throughput phenotyping technologies are systematically summarized.

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities →

Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection. This perspective paper synthesizes current advances, identifies major barriers, and proposes future directions to realize the transformative potential of AI‐enabled plant phenomics. We first provide an overview of AI technologies with the potential to address key challenges in phenomics, from data collection to phenotypic trait extraction and environmental sensing. We then present three case studies focusing on specialty crops (blueberry [ Vaccinium corymbosum L.] mechanical harvestability traits, strawberry [ Fragaria × ananassa (Duchesne ex Weston)] production, and citrus [ Citrus L.] disease) to illustrate practical applications of AI‐driven phenomics. Moreover, we highlight future perspectives and opportunities for further research and innovation. These include large foundation models, real‐time inference on edge devices, explainable AI, generative AI and digital twins, AI‐enhanced multi‐omics, agentic AI, and knowledge‐guided and data‐driven hybrid approaches. Finally, we discuss key challenges and limitations of applying AI to plant phenomics, including data curation, model generalization and bias, and ethical considerations related to equitable access to AI tools.

Why it matches plant phenotyping methods植物フェノミクスにおけるAIセンシング・形質抽出を主題とする展望論文であり、方法論のレビューとして中心的に扱っている。

abstractWe first provide an overview of AI technologies with the potential to address key challenges in phenomics, from data collection to phenotypic trait extraction and environmental sensing.

Towards a non-invasive monitoring of the soil-plant-atmosphere interactions: insights from a Mediterranean vineyard case study →

Abstract This study evaluated a non-invasive, integrated monitoring approach to characterize the soil-plant-atmosphere continuum (SPAC) in a commercial vineyard of Pignoletto (PG) and Trebbiano Romagnolo (TR). The approach is based on a cosmic-ray neutron sensor (CRNS) to continuously monitor soil water content (SWC), which was normalized into extractable soil water (ESW) to represent plant-available water. Moreover, vapor pressure deficit (VPD) was calculated based on weather data to characterize the atmospheric demand. Finally, remotely sensed NDVI data were used to detect canopy development and vine physiological responses. Over two growing seasons, measurements of midday stem water potential (Ψ stem ) and berry composition complemented the monitoring activities. In 2023, ripening was largely buffered from atmospheric demand, with Ψ stem values between − 0.66 and − 1.06 MPa, reflecting SWC as a non-limiting factor and uniform ripening. Conversely, the 2024 season showed more negative Ψ stem (-0.95 to -1.12 MPa) and an accelerated ripening process, particularly in TR. Principal Component Analysis (PCA) explained 65% of the variance in 2023 and 81.5% in 2024, revealing that environmental drivers (ESW, VPD) became more tightly linked to physiological and grape composition traits (Ψ stem , TSS, TA). Overall, the results showed the capability of the integrated approach to capture the main interactions within the SPAC offering a non-invasive and scalable tool for supporting precision and sustainability in Mediterranean viticulture.

Why it matches plant phenotyping methods土壌水分・大気需要・リモートセンシングNDVIを統合し、ブドウ樹の樹冠発達と生理応答を非侵襲的・スケーラブルにモニタリングする手法が研究の中心であるため。

abstractThe approach is based on a cosmic-ray neutron sensor (CRNS) to continuously monitor soil water content (SWC), which was normalized into extractable soil water (ESW) to represent plant-available water.

Leveraging foundation models to dissect the genetic basis of cluster compactness and yield in grapevine. →

Grape cluster compactness is a key trait that influences fruit quality, yield, and disease susceptibility. Understanding the genetic basis of this trait is essential for optimizing vineyard management and improving grapevine cultivars. In this study, we performed quantitative trait locus (QTL) mapping to identify genomic regions associated with cluster architecture and yield components in a bi-parental population derived from Vitis vinifera cv. Riesling × Cabernet Sauvignon. A total of 138 full-sibling progeny were evaluated over two growing seasons at Oakville, Napa Valley, California. Traditional yield-related traits were measured, including cluster number, total cluster weight, and average cluster weight. Additionally, an image-based phenotyping pipeline leveraging the foundation model Segment Anything Model (SAM) was employed to segment individual berries, measure their size and shape, and compute cluster compactness with minimal manual intervention. Trait correlations revealed that compact clusters tended to have a higher berry count but smaller berry size, highlighting the role of compactness in modulating cluster structure. Heritability estimates varied across traits, with berry dimensions and compactness displaying moderate to high heritability, indicating strong genetic control. Two parental linkage maps were constructed using a pseudo-test cross strategy. QTL mapping identified multiple loci associated with cluster architecture and yield components, with several stable QTLs detected across both years, with marker effects ranging from 7.6% to 22.1%. Notably, a QTL for cluster compactness was found in both seasons on chromosome 1 in Cabernet Sauvignon. Other stable QTLs were associated with berry size (chromosomes 6 and 17) and berry count (chromosome 5 in Cabernet Sauvignon and chromosome 7 in Riesling). Additional QTLs were detected in a single year, reflecting the influence of environmental variation. Our findings provide valuable insights into the application of foundation models requiring no prior training and minimal intervention for high-quality segmentation and enhance our understanding of the genetic architecture of cluster compactness and yield traits. The genomic regions identified in this study offer promising targets for breeding programs aimed at improving grape quality and disease resistance.

Why it matches plant phenotyping methodsSAMを用いた画像解析パイプラインで個々の果粒を分割し、サイズ・形状と房のコンパクトネスを算出する方法が、研究の主要な技術的要素として明示されている。

abstractAdditionally, an image-based phenotyping pipeline leveraging the foundation model Segment Anything Model (SAM) was employed to segment individual berries, measure their size and shape, and compute cluster compactness with minimal manual intervention.

High-Throughput Seed Phenotyping and GWAS Uncover Key Genetic Variants Influencing Seed Quality in Leymus chinensis →

Abstract Leymus chinensis (Trin.) Tzvel. (sheepgrass) is an important forage species, yet the relationships between seed phenotypic traits, agronomic performance, and their underlying genetic mechanisms remain unclear. In this study, we utilized the AIseed high-throughput phenotyping platform to systematically analyze 54 image-based traits (i-traits)—encompassing morphology, color, and texture—in 262 dehusked seeds of sheepgrass. Coupled with 50K single nucleotide polymorphism (SNP) chip genotyping data, we performed a genome-wide association study (GWAS) to elucidate genetic correlations among seed phenotypic traits. Elastic net regression was employed to identify informative phenotypic predictors, revealing significant associations between seed size, seed coat texture, and color with hundred-seed weight (HGW), hundred-seed weight without glumes (HGWwg), and germination rate (GR). Additionally, a germplasm screening approach based on principal component analysis (PCA) achieved a 71% accuracy rate in predicting high-germination germplasm and identified 10 germplasm lines with superior comprehensive performance. GWAS identified several SNPs significantly associated with seed color and morphology, mainly on chromosomes Lc2Xm and Lc6Xm. KEGG analysis highlighted the roles of phenylpropanoid and flavonoid biosynthesis pathways, with candidate genes such as PAL, PER18, PER50, BGLU16, BACOVA_02659, and ANR implicated. This study offers effective phenotypic screening strategies and valuable genetic resources for the molecular breeding of sheepgrass.

Why it matches plant phenotyping methodsAIseed高スループット画像プラットフォームによる54種の種子形質抽出とスクリーニングが研究の中心であり、GWASや遺伝資源評価に再利用可能な表現型取得手法を扱っている。

abstractwe utilized the AIseed high-throughput phenotyping platform to systematically analyze 54 image-based traits (i-traits)—encompassing morphology, color, and texture—in 262 dehusked seeds of sheepgrass.

Multimodal learning on RGB-D image for precise litchi phenotyping and weight estimation →

Accurate measurement of key phenotypic traits, including the horizontal and vertical diameters, the weights of both fruit and pit, is essential for the selection of elite litchi cultivars and the advancement of breeding research. Manual measurement, however, is laborious, inefficient, and subjective, highlighting the urgent need for automated and precise phenotyping tools. Unlike apples, mangoes, and grapes, litchi combines a spiny, highly variable pericarp (heterogeneous areoles/tubercles across cultivars) with diverse seed morphology (including irregular, wrinkled aborted seeds), thereby increasing the difficulty of semantic segmentation and biasing diameters and weight estimation. This study presents LitchiPhenoNet, a multimodal learning framework for litchi phenotypic analysis that employs a dual-branch architecture integrating RGB (color/texture) and depth (spatial/structural) information. Experiments were conducted on an RGB-D dataset comprising 1,198 image pairs (1280×720) across 10 cultivars, using a stratified train/test split of 958/240 pairs by cultivar. To address inherent semantic and scale inconsistencies between modalities, the framework incorporates the RD-Fusion module for precise cross-modal feature extraction, improving robustness under complex and variable pericarp surfaces. Comparative experiments show that LitchiPhenoNet consistently outperforms leading YOLO-based models, achieving millimeter-level diameter estimation with coefficients of determination approaching 0.98 and mean errors within 2 mm. For weight estimation, gram-level precision is attained across whole fruit, pit, and pulp, with coefficients of determination up to 0.98 and mean errors comparable to repeated manual measurements. By handling fine-scale surface relief and cross-cultivar variability, the framework is readily extensible to other textured fruits and scalable for high-throughput phenotyping in breeding programs. Collectively, these results demonstrate that LitchiPhenoNet provides an efficient, reliable, and accurate solution for quantifying litchi phenotypic traits, substantially advancing the objectivity and efficiency of phenotypic analysis and breeding selection.

Why it matches plant phenotyping methodsRGB-D画像を用いてライチ果実・種子・果肉の径と重量を自動推定する専用フレームワークを開発し、複数品種・比較実験で性能検証しているため、植物表現型取得法が中心である。

abstractThis study presents LitchiPhenoNet, a multimodal learning framework for litchi phenotypic analysis that employs a dual-branch architecture integrating RGB (color/texture) and depth (spatial/structural) information.

ICFMNet: an automated segmentation and 3D phenotypic analysis pipeline for plant, spike, and flag leaf type of wheat →

Three-dimensional high-throughput plant phenotyping technology offers an opportunity for simultaneous acquisition of plant organ traits at the scale of plant breeders. Wheat, as a multi-tiller crop with narrow leaves and diverse spikes, poses challenges for organ segmentation and measurement due to issues such as occlusion and adhesion. Therefore, building on previous research, this paper establishes a phenotyping pipeline and develops a 3D phenotypic automated analysis system for individual wheat plants at different growth stages. This system enables automated and precise three-dimensional phenotypic acquisition and analysis of wheat plant architecture, spike morphology, and flag leaf traits. To address the challenges posed by the significant structural differences among wheat spikes, leaves, and stems, as well as their compact spatial distribution, we propose a point cloud segmentation model based on deep learning called ICFMNet. ICFMNet relies on an instance center feature matching module, which extracts features from each instance’s central region and matches them with global point-wise features by computing feature similarity. This approach enables precise instance mask generation independent of the spatial structure of the point cloud. In the analysis of wheat phenotypes, we introduce a contour-based method to accurately extract the barren segment from 3D-scale wheat spikes. Furthermore, we perform the analysis of a total of 19 phenotypes, including flag leaf phenotypes and whole-plant phenotypes. In the organ point cloud segmentation tests for wheat spikes, stems, and leaves, the semantic segmentation achieves mPrec, mRec, and mIoU values of 95.9 %, 96.0 %, and 92.3 %, respectively. The instance segmentation attains mAP and mAR scores of 81.7 % and 83.0 %, respectively. Moreover, in comparison to five other segmentation network models, ICFMNet demonstrates superior segmentation performance. To better assess barren segment localization accuracy, additional evaluations are conducted using two metrics: interval overlap and interval error, achieving values of 92.33 % and 0.1123 cm, respectively. Experimental results indicate that our method excels in terms of accuracy, efficiency, and robustness, providing a reliable systematic platform for precise identification and breeding research of wheat plant types. The source code and trained models for ICFMNet are available at https://github.com/xiao-pl/ICFMNet.

Why it matches plant phenotyping methods小麦個体・器官の3D形質を自動取得・抽出するセグメンテーションおよび解析パイプラインの開発と技術評価が研究の中心である。

abstractthis paper establishes a phenotyping pipeline and develops a 3D phenotypic automated analysis system for individual wheat plants at different growth stages.

Field-scale single-plant sunflower head detection and geometric parameters measurement integrating multi-modal UAV data, deep learning and point cloud analysis →

Accurate monitoring of sunflower heads is critical for yield prediction, yet traditional methods are labor-intensive. This study proposed a novel framework integrating UAV remote sensing, deep learning, and point cloud analysis to address this challenge. The proposed method used a Dual-Branch YOLOv10n model, leveraging multi-modal data for precise detection of sunflower heads at various growth stages. Feature indices were designed and a two-step clustering technique was applied to extract sunflower head point clouds, from which geometric parameters such as diameter and volume are computed. The detection model achieved high accuracy (precision: 0.9, recall: 0.894, mAP@50: 0.932) across growth stages. A strong correlation (R² = 0.80) was found between diameter measurements from point cloud and ground-truth data, while volume showed good alignment with biomass (R² = 0.61). This method offers an innovative, efficient solution for field-scale crop monitoring and yield estimation, advancing agricultural practices.

Why it matches plant phenotyping methodsUAV画像・深層学習・点群解析を統合し、ヒマワリ頭部の検出から直径・体積という植物器官形質を抽出・検証する方法が研究の中心であるため。

abstractThis study proposed a novel framework integrating UAV remote sensing, deep learning, and point cloud analysis to address this challenge.

Tassel counting of individual ridge from UAV RGB imagery based on YOLOv8m with deep SORT and double-step Otsu thresholding algorithm by filtering abnormal IDs for maize breeding →

Accurate maize tassel counting on individual ridge plays a critical role in advancing maize breeding programs by providing insights into crop adaptability and agronomic performance. The current manual method of processing male inflorescences has low accuracy and high labor intensity. Moreover, the existing algorithms cannot be directly applied to the counting of an individual ridge maize tassels. An automated system with UAV-based RGB imagery is presented for maize tassel counting. The object detection model was developed based on You Only Look Once version 8-medium (YOLOv8m), which detects tassels. A double-step Otsu threshold algorithm (DSOTSUTA) was designed to extract individual maize tassel ridge, which eliminated the interference of two adjacent ridges on both sides. Individual ridge tassel counting was implemented by Deep learning based Simple Online and Realtime Tracking (Deep SORT). It assigned identifiers (IDs) to each tassel and filtered abnormal IDs by analyzing the displacement increments of IDs in consecutive frames eliminating errors caused by ID switching. The object detection model achieved a mean Average Precision (mAP) of 91.6 %. The DSOTSUTA was tested on 5340 images and effectively extracted individual maize tassel ridges. The system achieved a root mean square error (RMSE) of 22.14 tassels per video, a mean absolute percentage error (MAPE) of 8.43 %, and an accuracy of 92.23 %, signifying a mean absolute percentage error (MAPE) of 8.43 % between the predicted tassel counts and ground truth observations. These results indicate that this automated system has the ability to enhance the accuracy of individual ridge maize tassel counting in breeding programs.

Why it matches plant phenotyping methodsUAV画像からトウモロコシ雄穂を自動検出・追跡・計数する手法を開発し、精度評価まで行っており、植物形質取得が研究の中心である。

abstractAn automated system with UAV-based RGB imagery is presented for maize tassel counting.

Detection of water content and size of peas based on hyperspectral imaging combined with 2D-CNN and irregular polygon size measurement techniques →

Pea storage stability and germination rely on moisture content and morphology, but traditional destructive methods cause sample damage, low efficiency, and subjective errors, limiting practical use. To overcome the destructive and inefficient limitations of traditional methods for pea quality assessment, this study develops an integrated, non-destructive framework for the simultaneous and rapid measurement of pea moisture content and size using hyperspectral imaging combined with deep learning. We innovatively converted one-dimensional spectral data into two-dimensional texture images via Gramian Angular Field (GAF) encoding and input them into a residual 2D Convolutional Neural Network (2D-CNN) for moisture prediction. For dimensional analysis, a novel algorithm based on irregular polygon geometry was proposed to accurately measure pea length and width. The GAF-2D-CNN model achieved superior performance for moisture prediction (prediction set R²=0.9818, RMSEP=0.0318 %, RPD=7.4780), significantly outperforming 1D-CNN, Least Squares Support Vector Machine (LSSVM), and Partial Least Squares Regression (PLSR) models. The dimensional algorithm also demonstrated high accuracy, especially for length measurement (R²=0.9946, RPD=13.94). This framework provides a robust, accurate, and high-throughput solution for automated pea quality grading, offering significant potential for applications in precision agriculture and storage management.

Why it matches plant phenotyping methodsハイパースペクトル画像、深層学習、形状アルゴリズムを統合し、エンドウの水分含量とサイズという植物形質を非破壊・高スループットに推定する方法の開発が中心である。

abstractthis study develops an integrated, non-destructive framework for the simultaneous and rapid measurement of pea moisture content and size using hyperspectral imaging combined with deep learning.

Process, challenges and solutions of fruit 3D reconstruction: A review →

Existing tasks such as fruit growth monitoring, harvesting, and quality sorting still suffer from low precision and insufficient automation. 3D reconstruction technology can accurately capture the external characteristics of fruits and shows great potential for enhancing automated fruit detection and processing. This paper, guided by two core application needs in the fruit industry: real-time online sensing and offline high-precision analysis, provides a detailed overview of research progress in 3D reconstruction technology for fruits. It first introduces the principles, workflows, and advantages and limitations of classical 3D reconstruction methods. Then, it focuses on the basic framework of learning-based 3D reconstruction approaches, their improvement directions, and their applications in fruits and other agricultural products. In addition, the challenges encountered in fruit 3D reconstruction, such as occlusion and complex lighting conditions, are summarized, along with potential solutions. Finally, future research directions are discussed. This review serves as a valuable reference for promoting the integration of computer vision and agricultural intelligence and advancing the fruit industry chain’s digital and intelligent transformation.

Why it matches plant phenotyping methods果実の外部形質を取得する3D再構成手法を中心に、原理・ワークフロー・限界・応用・課題を体系的にレビューしており、植物フェノタイピング手法のレビューに該当する。

titleProcess, challenges and solutions of fruit 3D reconstruction: A review

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

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

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

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

Apple diameter prediction during mechanical picking based on flexible force-sensing and CNN-BiLSTM-Attention method →

Fruit diameter grading is essential for commercialization, packaging, and market sales, directly impacting the value and competitiveness of fruits. Traditional diameter grading is typically performed after harvesting, relying on manual or mechanical methods. This process introduces extra steps and increases the risk of fruit damage during transit, which can reduce economic efficiency. To address this issue, this study introduces a real-time apple diameter grading method utilizing a flexible force-sensing gripper and the CNN-BiLSTM-Attention deep learning network, enabling synchronized intelligent identification and grading of apple diameter during robotic mechanical picking. First, ionogel-based triboelectric nanogenerators (IG-TENG) were developed and mounted on the surface of a three-finger Fin-Ray flexible picking end effector. A contact force monitoring system was established using a modular apple model, and the force sensor was calibrated. This setup allowed for accurate measurement of the contact force between the fingers and the apple. Using a multi-layer perceptron (MLP) to integrate mechanical response data, robotic hand motor stroke, and apple posture information, an apple contact force model was created to accurately predict the actual gripping force under various grasping conditions. Finally, a CNN-BiLSTM-Attention diameter prediction model was designed to deliver real-time, precise fruit diameter estimates. Orchard experiments demonstrated that the apple diameter grading method, combining force sensing with deep learning, achieved a mean absolute error (MAE) of 2.13 mm and a grading accuracy of 92 %, supporting non-destructive gripping and accurate grading. This research addresses the limitations of traditional diameter grading methods, streamlines harvesting steps, enhances efficiency, and offers a cost-effective and reliable solution for non-destructive fruit diameter grading.

Why it matches plant phenotyping methodsリンゴ径という植物器官形質を、力覚センサーと深層学習でリアルタイム推定・等級化する手法の開発、校正、検証が研究の中心である。

abstractFinally, a CNN-BiLSTM-Attention diameter prediction model was designed to deliver real-time, precise fruit diameter estimates.

Exploring the efficacy of phenomic and genomic selection for yield and fruit quality traits in strawberry. →

Phenomic selection is a breeding approach that incorporates phenomic data into statistical models to predict new genotypes. There is still much to learn about the efficacy of phenomic selection compared to genomic selection and best practices for its application in different crops. We utilized multispectral imaging of 1122 strawberry (Fragaria × ananassa Duchesne) clones across four consecutive seasons to compare genomic selection and phenomic selection within and across seasons. Phenomic selection within seasons was more predictive than genomic selection for fruit yield but was less predictive than genomic selection for fruit quality traits. Phenomic models incorporating vegetation indices (VI) were 16% more effective than models with independent spectral bands. Models combining both phenomic and genomic data were most effective for across-season prediction of yield-related traits, with average predictive abilities of 56% for fruit size and 57% for yield. Models with single timepoints were 91% as predictive as models with weekly data across the season, but this was largely influenced by the specific timepoint of data capture. Lastly, we show that the predictive ability of phenomic selection increased significantly with the number of clonal replicates in the training set. Overall, these results suggest that phenomic selection is highly effective in strawberry breeding but is dependent on the trait, timepoint of data capture, and level of clonal replication.

Why it matches plant phenotyping methodsイチゴのマルチスペクトル画像から得たフェノミックデータを用い、ゲノム選抜との予測性能を比較・検証しており、植物形質推定法の技術評価が中心である。

abstractWe utilized multispectral imaging of 1122 strawberry (Fragaria × ananassa Duchesne) clones across four consecutive seasons to compare genomic selection and phenomic selection within and across seasons.

An interpretable nondestructive detection model for maize seed viability: Based on grouped hyperspectral image fusion and key biochemical indicators →

Seed viability is crucial for ensuring crop quality and yield. However, existing nondestructive detection methods, which primarily rely on spectroscopic techniques and simple data fusion strategies, often suffer from limited accuracy and reliability. To address these limitations, this study proposes a novel, highly accurate, and interpretable nondestructive approach for evaluating maize seed viability. With regard to enhancing the prediction accuracy of seed viability, a grouped hyperspectral image fusion (GHIF) strategy was proposed to more effectively integrate complementary information from visible-near-infrared hyperspectral imaging (VisNIR-HSI) and fluorescence hyperspectral imaging (Fluo-HSI) datasets. With respect to improving model interpretability, eight biochemical components in the embryo of maize seeds were measured, and two key biochemical indicators—catalase (CAT) activity and malondialdehyde (MDA) content—were identified and validated as highly correlated with seed viability and predictable from spectral data. Building on these findings, a two-stage detection model was constructed. In the first stage, the two key biochemical indicators were predicted from the fused data using regression models. In the second stage, seed viability was determined using a dual-threshold strategy based on the predicted biochemical values. Experimental results showed that the proposed method achieved 90 % classification accuracy, comparable to direct spectral models while offering greater interpretability. This approach provides a reliable and explainable solution for nondestructive seed viability evaluation.

Why it matches plant phenotyping methodsトウモロコシ種子の生存性という植物状態を、可視近赤外・蛍光ハイパースペクトル画像の融合と解釈可能な予測モデルで非破壊推定する手法が研究の中心である。

abstractthis study proposes a novel, highly accurate, and interpretable nondestructive approach for evaluating maize seed viability

Curvature sensing strategy for flexible gripper fingers during grasping deformation: enabling in-orchard online apple size grading →

In-orchard apple size grading remains challenging under occlusions, variable illumination, and irregular fruit morphology. We present a contact–curvature strategy that integrates bending sensors into fin–ray flexible fingers to estimate fruit size during grasping, unifying grasp–and–grade operations. Neural Radiance Fields (NeRF) reconstruction provides offline ground-truth curvature for calibration, yielding strong linear agreement between sensor readings and true curvature (R2=0.9852). Using temporal curvature features across approach, grasp, and steady phases, a gradient-boosting regression model predicts fruit diameter with R2=0.9577 and RMSE = 1.19 mm on the test set. In laboratory conditions, the system achieved an overall grading accuracy of 98.0 % for 200 apples classified into four grades, with a processing capacity of approximately 6 apples·min⁻¹, meeting real-time requirements. In a small-scale orchard pilot study, the system maintainedR2=0.94 andRMSE=1.27 mm, achieving 96 % grading accuracy versus 77 % for a camera-only approach. Compared with vision-only sizing methods, contact-curvature sensing demonstrates inherent robustness to occlusion and illumination while better tolerating morphological irregularities. A methylene–blue protocol confirmed non–destructive operation. Contact–curvature sensing is robust to occlusions/illumination and can, in principle, extend to other near–spherical crops.

Why it matches plant phenotyping methods果実径という植物器官形質を、接触・曲率センサーと回帰モデルで推定する手法を開発し、校正・精度検証・圃場評価まで行っており、表現型取得法が研究の中心である。

abstractWe present a contact–curvature strategy that integrates bending sensors into fin–ray flexible fingers to estimate fruit size during grasping, unifying grasp–and–grade operations.

Hierarchical 3-D Scene-Graph-Based Semantic-Metric SLAM for Plant Inspection and Fruit Counting in Intelligent Hydroponics System →

The synergistic development of Internet of Things(IoT), robotics, and Artificial Intelligence (AI) is reshaping the technological paradigms of interdisciplinary laboratories and industrial ecosystems. IoT-enabled vertical farming systems demonstrate significant advantages, achieving yield enhancement while reducing carbon emissions compared to traditional agriculture, thereby providing innovative solutions for sustainable food production. The advancement of robotic technologies further expands the application dimensions of mobile intelligent sensors in vertical farm IoT networks. Based on an autonomous farming system that integrates Unmanned Aerial Vehicle (UAV), sensors, and modular vertical farming units, this study proposes a three dimensional Scene Graph (3DSG)-based hierarchical mapping method for the dynamic monitoring of plant and fruit growth. Through feedback mechanisms, the system optimizes growth conditions by adjusting lighting and nutrient delivery, while the hierarchical mapping architecture reduces detection errors and enables comprehensive 3D visualization. The main contributions of this research include: 1) Pioneering application of 3DSG technology to establish a multi-dimensional spatiotemporal representation model for plant growth processes, supporting interpretable analysis and traceable monitoring; 2) Establishing an uncertainty model through error propagation by systematically analyzing sensor models (covering various common sensor combinations) and integrating these models into 3D object pose estimation algorithms. This highlights the necessity of hierarchical abstraction levels. The system is validated through simulations and real-world experiments, providing a quantitative evaluation of object pose estimation; and 3) An IoT-driven intelligent vertical farming architecture that integrating mobile robotic perception networks and environmental regulation devices, enabling dynamic acquisition and closed-loop control of plant growth parameters. Open-source code is available at https://github.com/allenthreee/scene_graph, video link: https://youtu.be/dhc8RLmX7hc.

Why it matches plant phenotyping methods植物・果実の成長監視と計数を目的に、3Dシーングラフ、SLAM、センサー融合、誤差伝播モデルを開発・検証しており、表現型取得手法が研究の中心である。

titleHierarchical 3-D Scene-Graph-Based Semantic-Metric SLAM for Plant Inspection and Fruit Counting in Intelligent Hydroponics System

Physiology at work to model apple expansion growth and skin pigment changes →

Fruit sizing is a major factor in determining yield while non-destructive monitoring of the fruit skin colour and internal quality attributes on the tree can provide valuable maturity and quality information for precision horticulture. Repeated spectral scanning and fruit sizing data of ‘Braeburn’ apples were collected on the tree from about 60 days after flowering until harvest. Assessed variables were: fruit diameter, dry matter (DMC), soluble solids content (SSC), a normalised difference vegetation index (NDVI) and a normalised anthocyanin index (NAI) analysed using indexed non-linear regression based on an adapted von Bertalanffy model (diameter, DMC, SSC), or a logistic model (NDVI, NAI). The reaction rate constants in the models were estimated in common for all fruit in a selection, while the biological shift factors (Δt) estimated the development stage or fruit maturity per individual fruit. Explained parts (R²ₐdⱼ) range from 85 to 97%. Tree location or crop load treatment only minimally affected the rate constants but did affect the estimated Δt values that describe almost all variation in the data. There is a close relationship between the Δt values for diameter, DMC and SSC but less with those of NDVI and almost none with the NAI. These data support the assumption that there is only one stage of fruit maturity, but it is estimated slightly differently depending on the measured variable. The actual relative growth rate strongly depends on the current size. Understanding apple expansion growth will therefore require a closer focus on the cell production period.

Why it matches plant phenotyping methodsリンゴ果実の非破壊スペクトル測定・果径測定と回帰モデルを組み合わせ、果実の成長・成熟・色素変化を個体ごとに推定する技術的ワークフローが中心であるため、植物フェノタイピング手法の応用として含める。

abstractnon-destructive monitoring of the fruit skin colour and internal quality attributes on the tree can provide valuable maturity and quality information for precision horticulture.

PhenoCitrus: An automated platform to phenotyping morphological traits of citrus fruit →

Citrus breeding critically relies on precise phenotyping, yet existing RGB/3D phenotyping methods lack standardized workflows balancing affordability, accuracy, and efficiency. An integrated hardware–software pipeline is introduced to address this gap: (1) a custom imaging device for standardized top/side-view capture of the sample fruit, and (2) optimized computer vision algorithms using enhanced 3D Gaussian Splatting (3DGS) to extract 3D structural traits (surface area, volume) and 2D algorithms (e.g., Unet++ and YOLO variants) for 2D trait (width, length, oil cell number, peel/pulp color, peel thickness, and segment number) quantification. Our approach achieved average Pearson correlations above 0.9 between algorithmic and manual measurements across five key traits, confirming measurement precision. The resulting phenotypic profiles revealed biologically significant inter-trait correlations to inform trait-oriented selection decisions and the refinement of breeding strategies. Finally, we operationalized this workflow through user-friendly software, delivering an end-to-end solution that enables high-throughput, low-cost citrus phenotyping with comparatively high accuracy for accelerated breeding applications. Code is available at https://github.com/liangzhao2000/PhenoCitrus . • Low-cost phenotyping device: Dual cameras enable comprehensive fruit imaging. • Precise trait extraction: Multiple deep learning methods extract fruit traits. • Phenotypic profiling: Statistical analysis supports trait-based variety selection.

Why it matches plant phenotyping methods柑橘果実の形態形質を取得・抽出するハードウェア、画像解析、ソフトウェアを開発し、手動測定との相関で検証した中心的なフェノタイピング研究。

abstractAn integrated hardware–software pipeline is introduced to address this gap: (1) a custom imaging device for standardized top/side-view capture of the sample fruit, and (2) optimized computer vision algorithms using enhanced 3D Gaussian Splatting (3DGS) to extract 3D structural traits (surface area, volume) and 2D algorithms (e.g., Unet++ and YOLO variants) for 2D trait (width, length, oil cell number, peel/pulp color, peel thickness, and segment number) quantification.

Advances in Growing Degree Days Models for Flowering to Harvest: Optimizing Crop Management with Methods of Precision Horticulture—A Review →

Temperature plays a vital role in plant metabolism, and effective crop temperature appears to be influenced by variables related to climate change. While extreme weather events are widely discussed, the effects of moderate temperature changes pose consistent yet underexplored challenges for farmers. The “growing degree days” (GDD) also termed “heat unit”, is the most widely used approach in agricultural and ecological studies to quantify the relationship between temperature and plant development. This review provides a comprehensive examination of GDD methodology as applied to horticultural crop production, specifically from initial fruit development to fruit maturity, and postharvest. It is the first integrated synthesis of the conceptual evolution, methodological refinement, and broad application of GDD, thereby highlighting the need to optimize GDD approaches in light of emerging technological tools. While the GDD model is valuable for predicting crop development based on heat accumulation, it has limitations in capturing the effects of other environmental factors. Additionally, air temperature may not provide precise data on each plant organ. Recent advances in remote sensing, such as the integration of thermal imaging, RGB cameras, and lidar have enabled the measurement of spatially resolved temperature distribution within crop canopies, including fruit surface temperature. Recent advances, highlighted in the literature, suggest that integrating sensor innovations with machine learning approaches holds high potential for improving the precision of modeling temperature-dependent growth responses and their interactions with other environmental variables. By addressing these challenges and expanding its applications, GDD can continue to serve as an essential tool in promoting sustainable horticultural practices and adapting to global warming.

Why it matches plant phenotyping methodsGDDを用いて温度から作物の発育段階・成熟を推定する方法論を中心にレビューしており、植物状態の計算的な表現型推定に該当する。

abstractThis review provides a comprehensive examination of GDD methodology as applied to horticultural crop production, specifically from initial fruit development to fruit maturity, and postharvest.

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

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

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

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

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

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

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

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

Optimizing Soybean Breeding: High-Throughput Phenotyping for Stink Bug Resistance and High Yields →

Abstract The stink bug complex is one of the most damaging pests of soybean, reducing yield and seed quality. Genetic resistance remains the most sustainable and effective management strategy, but its quantitative inheritance and labor-intensive field phenotyping make its implementation in breeding programs challenging. This study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and the potential of phenotyping to stink bug resistance by correlating image-derived features and machine learning (ML) models. A population of 304 soybean lines was evaluated in alpha-lattice design trials across two seasons under natural infestations. Five resistance-related traits, grain yield (GY), hundred-seed weight (HSW), number of days to maturity (NDM), tolerance (TOL), and leaf retention (LR), were manually scored and linked to UAV-derived vegetation indices (VIs) and texture indices (TIs). Three ML models (AdaBoost, SVM, MLP) were tested to predict these traits from aerial features. Results showed that VIs, particularly Visible Atmospherically Resistant Index at the first percentile (VARI_P25), were consistently associated with resistance-related traits, while decision tree analysis highlighted TIs at 45° and 135° as complementary sources of structural information. Prediction ability was highest for GY, HSW, and NDM, especially in flights near flowering and maturity, but remained low for TOL and LR. Integrating multiple flights modestly improved accuracy, whereas cross-season predictions were unreliable. Nonetheless, indices such as VARI_P25 provided useful cross-season correlations for HSW and TOL, enabling early screening of less promising lines. This pioneering study demonstrates that UAV–ML pipelines can capture genetic signals of stink bug resistance in soybean, despite environmental complexity. These findings open new avenues for resistance phenotyping, supporting more efficient breeding strategies and accelerating genetic gains in soybean improvement.

Why it matches plant phenotyping methodsUAV画像と機械学習による形質推定パイプラインを開発・評価し、抵抗性関連形質や収量を対象とするフェノタイピングが研究の中心である。

abstractThis study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and the potential of phenotyping to stink bug resistance by correlating image-derived features and machine learning (ML) models.

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

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

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

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

DIGITAL IMAGING-BASED PHENOTYPING OF WHEAT KERNELS UNDER DROUGHT STRESS →

Drought stress significantly impairs wheat growth and productivity, primarily by affecting kernel development.Given their strong association with kernel yield and quality, kernel traits offer reliable means to assess genotypic responses to drought stress conditions.In this study, 70 diverse wheat (Triticum aestivum L.) genotypes were evaluated under two moisture regimes: well-watered (four irrigations) and drought stress (irrigation withheld after the first watering).At maturity, the following agronomic traits were recorded: number of days to 50% heading, number of days to 50% physiological maturity, number of kernels per spike, number of spikelets per spike, thousand kernel weight, and kernel yield per spike.Nine kernels from each genotype were photographed in horizontal and vertical orientations using a 3 cm scale.Kernel traits were measured using Image-J software and included: horizontal area, vertical area, horizontal perimeter, vertical perimeter, horizontal length, horizontal roundness, horizontal width, vertical thickness, vertical roundness, factor from density, aspect ratio, kernel volume, horizontal deviation from ellipse, and vertical deviation from ellipse.Analysis of variance (ANOVA) showed significant differences among genotypes for all traits.Principal component analysis (PCA) highlighted kernel volume and horizontal area as the most variable traits.Genotype G17 had the highest thousand kernel weight under drought, while G30 and G41 performed best under normal irrigation.Biplot analysis showed that kernel yield per spike, number of spikelets per spike and number of kernels per spike were positively associated with horizontal kernel traits (horizontal area, horizontal length, and horizontal deviation from ellipse).In contrast, thousand kernel weight was positively associated with vertical kernel traits (vertical area, vertical perimeter, vertical thickness, vertical roundness, and vertical deviation from ellipse).In conclusion, digital imaging effectively captures variation in kernel morphology.The identified relationships between kernel traits and yield components can help breeders select drought-tolerant genotypes using both conventional and image-based traits.

Why it matches plant phenotyping methods小麦種子形態をデジタル画像とImageJで多数の形質として抽出し、干ばつ応答および収量関連形質との関係を評価しており、画像ベースの表現型取得が研究の中心である。

titleDIGITAL IMAGING-BASED PHENOTYPING OF WHEAT KERNELS UNDER DROUGHT STRESS

Temporal profiling of meiotic asynchrony between florets for wheat-fertility studies →

Wheat grain number integrates fate of individual florets, strongly affected by environmental stress during sensitive stages like meiosis. Because development is asynchronous across tillers, spikelets, and florets, it is hard to distinguish true stress tolerance from stress escape. Based on morphological and destructive measurements of spike and anther length in four controlled-environment experiments, we developed a framework to track all floret developmental stages at plant level, and particularly meiosis. We applied this framework in two case studies for connecting within-plant developmental asynchrony to reproductive success under favorable and heat stress conditions. All florets showed a common relative growth rate, producing stable and additive developmental delays across tillers, spikelets, and floret positions. This generated a developmental map for every floret based on simple external traits. Under favorable conditions, grain set probability at floret level combined both positional and developmental effects within a spike. Under heat stress, grain loss occurred only in florets at meiosis during the stress, allowing to quantify a true "stress response", while later florets escaped damage. This framework offers a quantitative tool to understand and predict floret development and link it to grain set, clearly distinguishing timing effects from positional influences and separating true tolerance from stress escape.

Why it matches plant phenotyping methods小花の発達段階を形態測定から追跡・推定する定量的フレームワークを開発し、植物体レベルで検証・適用しているため、表現型取得法が中心的です。

abstractwe developed a framework to track all floret developmental stages at plant level, and particularly meiosis.

AI-assisted Image-Based Phenotyping Reveals Genetic Architecture of Pod Traits in Mungbean (Vigna radiata L.) →

Abstract Mungbean ( Vigna radiata (L.) R. Wilczek) is a vital source of digestible proteins and is well-suited for the plant-based protein industry. In this study, we analyzed pod morphological traits in the Iowa Mungbean Diversity (IMD) panel with 372 genotypes (2022-23) with AI-assisted image phenotyping using 2,418 pod images. Pod morphological traits were extracted using deep learning image analysis, achieving excellent agreement with manual measurements (r>0.96 for pod length and seed per pod). Four complementary GWAS models identified 45 significant SNPs associated with pod curvature, length, width, and seed per pod traits. Notably, a significant SNP (5_35265704) on chromosome 1 was linked to pod dimensional traits, length, width, and curvature. A candidate gene, Vradi01g00001116 , was located within the linkage disequilibrium (LD) region of this SNP, is part of the GH3 gene family, and has an Arabidopsis ortholog ( AT4G27260 ) known for influencing organ elongation, pod, and seed development. Another SNP, 5_210437 on chromosome 2, has been found to be significantly associated with both pod length and seed per pod. A candidate gene, Vradi02g00003971 , located in the LD region of this SNP, belongs to the potassium transporter family and shares homology with the HAK5 gene family ( AT4G13420 ) in Arabidopsis , which influences pod and seed growth. Image-based measurements achieved genomic prediction accuracies ranging from 0.61 to 0.85 across various traits, exhibiting an improvement of 12-22% over manual methods. These results demonstrate the potential of AI-assisted phenomics integrated with genomic tools to accelerate selection for improved pod architecture in mungbean breeding programs across the Midwestern United States and globally.

Why it matches plant phenotyping methodsAI画像解析による莢形態形質の抽出と手測定との技術検証が研究の中心であり、GWAS応用も行っているため含める。

abstractwith AI-assisted image phenotyping using 2,418 pod images

Seed 3D Phenotyping Across Multiple Crops Using 3D Gaussian Splatting →

This study introduces a versatile seed 3D reconstruction method that is applicable to multiple crops—including maize, wheat, and rice—and designed to overcome the inefficiency and subjectivity of manual measurements and the high costs of laser-based phenotyping. A panoramic video of the seed is captured and processed through frame sampling to extract multi-view images. Structure-from-Motion (SFM) is employed for sparse reconstruction and camera pose estimation, while 3D Gaussian Splatting (3DGS) is utilized for high-fidelity dense reconstruction, generating detailed point cloud models. The subsequent point cloud preprocessing, filtering, and segmentation enable the extraction of key phenotypic parameters, including length, width, height, surface area, and volume. The experimental evaluations demonstrated a high measurement accuracy, with coefficients of determination (R2) for length, width, and height reaching 0.9361, 0.8889, and 0.946, respectively. Moreover, the reconstructed models exhibit superior image quality, with peak signal-to-noise ratio (PSNR) values consistently ranging from 35 to 37 dB, underscoring the robustness of 3DGS in preserving fine structural details. Compared to conventional multi-view stereo (MVS) techniques, the proposed method can achieve significantly improved reconstruction accuracy and visual fidelity. The key outcomes of this study confirm that the 3DGS-based pipeline provides a highly accurate, efficient, and scalable solution for digital phenotyping, establishing a robust foundation for its application across diverse crop species.

Why it matches plant phenotyping methods3DGSを用いた種子の3D再構成・点群処理・形質抽出パイプラインを開発し、精度を評価しており、植物表現型取得手法が研究の中心である。

abstractThis study introduces a versatile seed 3D reconstruction method that is applicable to multiple crops

Uncovering Indigenous Diversity and Farmer Preferences in Pigeon Pea (Cajanus cajan): Insights from Germplasm Exploration, Ethnobotanical Surveys, and Digital Phenotyping for Climate-Smart Breeding →

Abstract Pigeon pea ( Cajanus cajan [L.] Millsp.) remains an underutilized legume in most African countries despite its potential for climate-resilient farming systems, food diversification, and nutritional value. Limited knowledge of its indigenous diversity and farmer trait preference constrains wider adoption, particularly in the West African sub-region. Between February and June 2025, a germplasm exploration was conducted across 18 Nigerian states, complemented by accessions from the International Institute of Tropical Agriculture (IITA) genebank, Ghana, the Republic of Benin, and the Gambia, bringing the total to 273 accessions. Ethnobotanical surveys captured farmer preferences, cultural uses, and local nomenclature while seed morphometric traits were assessed using Videometerlab4 multispectral imaging. Farmer surveys revealed cooking time (58.3%), commercial value (27.0%), and maturity cycle (14.7%) as preferred varietal traits. Gender and age differences were evident; women and older farmers prioritized cooking time, while men and youth emphasized the maturity cycle as a preferred trait. Vernacular names (e.g., Otili , Fiofio , Waken Gwari ) highlighted deep cultural integration and cross-border exchange in Ogun State and the Republic of Benin, indicating transboundary diversity. Morphometric analyses revealed moderate variability in seed size, shape, and pigmentation. Seed area (14.2–46.0mm 2 ), Compactness (0.590–0.998), and eccentricity (0–0.808) differentiated rounded from elongated seeds, while CIELab_A values (–0.04–29.98) captured pigmentation differences. The first two PCA axes explained 67.1% of total variation, and cluster analysis grouped accessions into four morphotypes. By integrating genetic and morphometric information, as well as farmer varietal preference insights, this study provides a robust foundation for the conservation and development of climate-resilient, fast-cooking, and market-preferred varieties for sub-Saharan Africa.

Why it matches plant phenotyping methodsVideometerlab4によるマルチスペクトル画像から種子形態・色素形質を抽出し、PCAとクラスタリングで遺伝資源を分類するデジタル表現型解析が、研究の主要な構成要素である。

abstractseed morphometric traits were assessed using Videometerlab4 multispectral imaging

Diagnostic study of defoliation and boll opening effects on machine-harvested cotton using multi-source UAV remote sensing data →

Accurate assessment of cotton defoliation (DF) and boll opening (BO) is essential for optimizing yield and fiber quality during mechanized harvesting, as improper timing can reduce yield and impair fiber quality. Unmanned aerial vehicle (UAV)-based remote sensing has become an effective tool for monitoring these indicators, but most current methods rely on single-sensor data, limiting diagnostic accuracy and generalizability. To address this limitation, we propose a multi-source data fusion framework integrating RGB, multi-spectral (MS), and thermal infrared (TIR) sensors for comprehensive canopy information. The fused dataset includes vegetation indices (VIs), color indices (CIs), texture features (Tex), and canopy temperature (TC). Feature selection was performed using pearson correlation coefficients (PCCs), recursive feature elimination with cross-validation (RFECV), and the Boruta algorithm to identify key variables. Three machine learning models—partial least-squares regression (PLSR), random forest regression (RFR), and extreme gradient boosting regression (XGBR)—were developed and compared. The RFECV-selected RGB+MS+TIR features in the XGBR model achieved the highest predictive accuracy, with R² values of 0.918 for defoliation rate and 0.867 for boll opening rate, improving by 1.9 % and 4.3 %, respectively, over single-sensor models. Root mean square error (RMSE) and relative RMSE (rRMSE) were reduced by 1.11 %-1.99 % and 1.66 %-2.28 %, respectively. These findings demonstrate that multi-source UAV data fusion, combined with advanced machine learning techniques, significantly enhances the accuracy and robustness of cotton defoliation and boll opening diagnosis. This approach offers a practical solution for precision agriculture to improve harvest scheduling and defoliant management.

Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル・熱赤外データを融合し、綿花の落葉率と綿花開絮率という植物状態を推定する手法を開発・比較しており、フェノタイピング手法が研究の中心である。

abstractwe propose a multi-source data fusion framework integrating RGB, multi-spectral (MS), and thermal infrared (TIR) sensors for comprehensive canopy information.

Automated 3D wheat tissue analysis using x-ray CT and deep learning →

Understanding wheat grain internal structures is critical for improving quality, pest resistance, and breeding efficiency. While X-ray computed tomography (CT) enables non-destructive 3D imaging, existing segmentation methods rely on manual intervention, introducing inefficiency and subjectivity. This study introduces the Residual Depthwise Separable Convolution and Vision Mamba U-Net (RDVM-UNet), an automated framework combining Depthwise Separable Convolution (DSConv) for efficient local feature extraction and Vision Mamba for global contextual modeling. Trained over 200 iterations, the model achieved a mean Intersection over Union (mIoU) of 95.4 % in segmenting wheat tissues (epidermis, embryo, endosperm). Validation across 10 varieties demonstrated robust generalizability (mIoU is 94.78 %) and rapid processing (9.65 s/grain). The framework generated 3D reconstructions, enabling precise quantification of morphological parameters (volume, surface area) critical for analyzing genetic-environmental-morphological relationships. By establishing a non-destructive, high-throughput pipeline, this work advances precision breeding, functional genomics, and trait optimization in cereal crops. RDVM-UNet bridges computational imaging and agricultural science, offering scalable solutions for crop phenotyping and quality enhancement.

Why it matches plant phenotyping methodsX線CT画像から小麦組織を自動分割・3D再構成し、形態形質を定量化する深層学習パイプラインの開発と品種横断検証が中心であり、植物表現型計測法に該当する。

abstractThis study introduces the Residual Depthwise Separable Convolution and Vision Mamba U-Net (RDVM-UNet), an automated framework combining Depthwise Separable Convolution (DSConv) for efficient local feature extraction and Vision Mamba for global contextual modeling.

A real-time visualized TRSV-based gene silencing method using trichome as a selected marker in cucumber →

Cucumber (Cucumis sativus. L) is economically valuable vegetable crop worldwide. Although cucumber genomic sequence has been completed, the functions of most genes have not yet been characterized. Virus-induced gene silencing (VIGS) is an efficient system for investigating gene function in plants, however, detection of VIGS efficiency by PCR is a time-consuming method. In this study, a vacuum-agroinfiltrated Tobacco ringspot virus (TRSV)-based gene silencing method was developed in cucumber, and CsGLABROUS3 (CsGL3), which functions in initiation of trichome, was cloned into pTRSV2 vector to develop a TRSV-CsGL3 system. Gene silenced cucumbers were visible using trichome as a selected marker, and their glabrous phenotype exhibited throughout the life cycle in TRSV-CsGL3 system. Thereafter, a flower morphogenesis gene (UNUSUAL FLORAL ORGANS, CsUFO) was selected to silence by the TRSV-CsGL3 system, and CsUFO silenced cucumbers produced the flower defect phenotype as expected. In summary, TRSV-CsGL3 is a real-time visualized VIGS system using trichome as a selected marker, which simplify the VIGS identification procedure, and it can be used to investigate gene function throughout the life cycle in cucumber.

Why it matches plant phenotyping methodsキュウリのVIGS効率をトライコーム形態でリアルタイムに可視化・判定する手法の開発が中心であり、植物表現型の取得方法として適格です。

abstracta vacuum-agroinfiltrated Tobacco ringspot virus (TRSV)-based gene silencing method was developed in cucumber

Wheat3D PartNet: Annotated dataset for 3D wheat part segmentation →

High precision 3D data is becoming crucial for accurate feature extraction. Acquiring 3D data from plants with different growing patterns and thier growth under different environmental conditions is still a challenging task. The utilization of deep learning techniques can overcome some of these challenges, but these techniques often demand good quality training data for 3D point cloud analysis. One of the main challenges in plant phenotyping is the general lack of annotated 3D datasets available to the research community. Constructing such datasets is particularly difficult due to the complexity of capturing high-quality data that accurately represent the intricate structures and diverse morphologies of plants. The development of robust data sets is critical to advance plant phenotyping, allowing precise quantification of plant traits, and addressing challenges in modern agriculture. However, the lack of high-quality, annotated datasets for complex plant structures, such as wheat, hinders the development of effective methodologies. To address this, we introduce Wheat3D PartNet, a comprehensive repository of 1303 3D point cloud models of wheat (Triticum L.), comprising three cultivars: Paragon, Gladius, and Apogee. The 3D point clouds are reconstructed from RGB images of real plants that were acquired from multiple viewpoints and represent different plant structures at different growth rates. Wheat3D PartNet samples are manually labeled into two parts i.e., ears (wheat spikes) and non-ears (leaves and stems) and that captured in drought and watered conditions. Wheat3D PartNet is designed to support segmentation-based trait quantification tasks such as spike counting, spike length estimation, and stress detection-facilitating more precise yield prediction and enabling early agronomic intervention. Extensive experiments using several state-of-the-art 3D deep learning models validate the dataset's utility and challenge level. The methodology behind Wheat3D PartNet is extensible to other crops, including rice and potato, and is expected to significantly boost the research, understanding, and measurements of plants of interest.

Why it matches plant phenotyping methods植物の3D点群を用いた部位セグメンテーション用データセットの構築・検証が中心で、植物形質の定量化を直接支援するため。

abstractTo address this, we introduce Wheat3D PartNet, a comprehensive repository of 1303 3D point cloud models of wheat (Triticum L.)

Multimodal information fusion and precision harvesting system for fruit growth driven by flexible optoelectronic sensing and hierarchical attention networks →

To enhance fruit yield and quality, this study focuses on precise pre-harvest ripeness assessment and early disease detection. Addressing the limitations of conventional methods, we propose a multimodal flexible sensing and deep learning-based evaluation framework. The developed flexible optoelectronic in-situ sensing system integrates spectral (410-940 nm, 18 channels) and impedance (100 Hz-10 kHz) detection, allowing conformal attachment to mango surfaces for nondestructive monitoring throughout the growth cycle while collecting spectral, impedance, and physicochemical data. The proposed 1DCNN-ATT-BiLSTM-ATT network employs independent branches to extract local features from each modality, followed by attention mechanisms and temporal modelling for comprehensive feature fusion, achieving 97.5 % accuracy on test sets. Field experiments reveal systematic variations in soluble solid content (SSC), moisture content (MC), and optoelectronic signals during ripening. Correlation and Granger causality analyses underscore the necessity of multimodal fusion. This system supports intelligent harvesting and precision monitoring, advancing agricultural practices toward greater efficiency and sustainability while establishing a technical paradigm for precision agriculture. Future work will focus on improving environmental robustness and cross-cultivar applicability.

Why it matches plant phenotyping methodsマンゴー果実に装着する分光・インピーダンス統合センシングと深層学習による成熟度・品質状態推定を開発しており、植物状態の取得・抽出手法が研究の中心である。

abstractThe developed flexible optoelectronic in-situ sensing system integrates spectral (410-940 nm, 18 channels) and impedance (100 Hz-10 kHz) detection, allowing conformal attachment to mango surfaces for nondestructive monitoring throughout the growth cycle

Smartphone-based 3D imaging for canopy and berry cluster volume estimation in wine grapes →

• A smartphone-based 3D imaging approach was developed for canopy and berry cluster volume estimation in vineyards. • Machine learning-based segmentation using Gradient Boosting for canopy and YOLO11 with Structure from Motion (SfM) for clusters enabled accurate feature extraction. • Point cloud processing was utilized for accurate surface reconstruction and volume estimation. • The method provides an affordable and accessible alternative to traditional high-cost sensors for precision viticulture applications. Accurate estimation of vine canopy and berry cluster volumes is essential for precision viticulture, as it supports better vineyard management, yield prediction, and resource allocation. Traditional methods, such as manual measurements or expensive sensor-based systems, are often inaccessible to small and mid-scale growers. This study explores the use of smartphone-based 3D imaging and advanced machine learning techniques as an affordable and accessible alternative for estimating wine grape canopy and berry cluster volumes. In this study, point cloud data was collected using an iPhone 14 Pro Max to capture the spatial structure of grape canopies and berry clusters. Two separate datasets were used to evaluate canopy and cluster volumes independently, ensuring comprehensive analysis and validation. Canopy volume estimation involved segmentation using the Gradient Boosting Classifier, followed by computation of 3D point volumes, achieving an RMSE of 0.23 m³ and 98% classification accuracy for canopy point clouds. Berry clusters were segmented using YOLO11, and 3D point clouds were reconstructed using Structure from Motion (SfM) to create watertight meshes. Cluster volumes validated by water‑displacement ground truth yielded an RMSE of 14.68 cm. These findings demonstrate the potential of smartphone-based solutions to support precision viticulture through accurate estimation of vine canopies and berry clusters, which is expected to enhance vineyard productivity and berry quality.

Why it matches plant phenotyping methodsスマートフォン3D画像、機械学習セグメンテーション、SfM、点群処理を用いてブドウ樹冠・果房体積を推定し、実測値で検証する手法開発が中心である。

abstractA smartphone-based 3D imaging approach was developed for canopy and berry cluster volume estimation in vineyards.

Spectral markers and machine learning: Revolutionizing Rice evaluation with near infrared spectroscopy →

The evaluation of rice varieties is a complex, time-consuming process requiring advanced equipment. This study aimed to discriminate 22 commercial rice varieties from six types by analyzing biochemical, physicochemical, and cooking properties. Near-infrared (NIR) spectroscopy, combined with machine learning, linked molecular properties with quality traits, offering a high-throughput solution. Partial Least Squares (PLS) models accurately predicted parameters such as whiteness (R² = 0.94), width (R² = 0.94), resilience (R² = 0.96), and springiness (R² = 0.98), highlighting key wavelength regions. Principal Component Analysis (PCA) revealed distinct clustering patterns, while Partial Least Squares Discriminant Analysis (PLS-DA) achieved a 17 % error rate in external predictions. Spectral markers at A6032/4457 cm⁻¹, A7004/5241 cm⁻¹, and A7004/4749 cm⁻¹ reflected biomolecular differences among varieties. This innovative approach enables precise quantification, classification, and differentiation of rice types, enhancing quality control, improving consumer satisfaction, and optimizing breeding selection processes efficiently.

Why it matches plant phenotyping methodsイネ品種の穀粒・品質形質をNIR分光と機械学習で高スループットに定量・分類し、PLSモデルの予測精度や外部予測を評価しているため、形質取得法が中心です。

abstractNear-infrared (NIR) spectroscopy, combined with machine learning, linked molecular properties with quality traits, offering a high-throughput solution.

Identification of morphological and phenological traits for the characterization of cultivated Lycium barbarum L. plants →

The genus Lycium L. (Solanaceae) includes economically important species such as Lycium barbarum, L. chinense, and L. ruthenicum (goji). This study focuses on the morphological characterization of L. barbarum, a recently domesticated species lacking standardized descriptors. We evaluated traits including plant habit, vigor, leaf morphology, floral structure, fruit shape, and ripening stages using both qualitative and quantitative analyses. Growth habits were evenly distributed: 33.3 % were erect, 39.3 % were expanded, and 27.4 % were pendulous. The average leaf area was 166.22 ± 79.66 mm², and discriminant analysis of leaf shape achieved 83 % classification accuracy. Floral traits were consistent, with 94.3 % of plants having five petals and 5.7 % having six petals. Fruit ripened rapidly, completing four stages in ∼11.5 days. These findings highlight key diagnostic traits for developing harmonized descriptors. These traits will support future distinctness, uniformity, and stability (DUS) testing, which is essential for cultivar protection, genebank documentation, and product traceability in the growing global market for functional foods and nutraceuticals.

Why it matches plant phenotyping methodsゴジベリーの形態・生育段階を標準化された記述子として整理し、葉形分類やDUS試験に向けた診断形質を開発することが中心であり、単なる生物学的結果測定ではない。

abstractThis study focuses on the morphological characterization of L. barbarum, a recently domesticated species lacking standardized descriptors.

Grape sugar content prediction with multispectral alignment and improved residual network. →

Sugar content is a crucial indicator of grape ripeness and grading, and developing non-contact and non-destructive sugar content detection devices is essential for grape-picking robots and sorting platforms. Spectroscopy, which can detect the chemical composition of grapes, has become a key technology for developing non-destructive testing devices. In this paper, we collected 2,880 randomly labeled multispectral images of Sunshine Rose grapes with a Changguang Yuchen MS600 PRO multispectral camera and measured the sugar content (in Brix values) of the labeled grapes with a handheld refractometer, using data exclusively from this grape variety. To address noise and misalignment issues in the multispectral images, we proposed preprocessing methods including Gaussian denoising and ECC (Enhanced Correlation Coefficient) algorithm registration. Based on a ResNet-50 residual network, we constructed a grape sugar content prediction regression model Improved-Res with SE (Squeeze-and-Excitation) attention modules, DSC (Depthwise Separable Convolutions), and Inception modules. The model's performance was evaluated by MSE (Mean Squared Error), MAE (Mean Absolute Error), and R 2 (R-Square) metrics. We compared the performance of four feature extraction methods combined with four traditional machine learning models, as well as seven deep learning models. The results showed that among traditional machine learning methods, the combination of color histogram feature extraction and the XGBoost regression achieved the best performance, with MSE, MAE, and R 2 of 1.35, 0.90 Brix, and 0.78, respectively. Among deep learning methods, the ResNet-50 model demonstrated the best performance, with MSE, MAE, and R 2 of 0.95, 0.96 Brix, and 0.84, respectively. Effective improvements of SE attention module, depthwise separable convolutions, and Inception module in the ResNet-50 model was confirmed through ablation experiments: the proposed Improved-Res model achieved MSE, MAE, and R 2 of 0.49, 0.55 Brix, and 0.92, respectively, which significantly outperformed traditional machine learning methods and classical deep learning models.

Why it matches plant phenotyping methodsブドウ果実の糖度という植物形質をマルチスペクトル画像から非破壊推定する前処理・深層学習モデルを開発し、複数手法との比較とアブレーション検証を行っており、フェノタイピング手法が中心である。

abstractdeveloping non-contact and non-destructive sugar content detection devices is essential for grape-picking robots and sorting platforms.

Non-Contact Measurement of Sunflower Flowerhead Morphology Using Mobile-Boosted Lightweight Asymmetric (MBLA)-YOLO and Point Cloud Technology →

The diameter of the sunflower flower head and the thickness of its margins are important crop phenotypic parameters. Traditional, single-dimensional two-dimensional imaging methods often struggle to balance precision with computational efficiency. This paper addresses the limitations of the YOLOv11n-seg model in the instance segmentation of floral disk fine structures by proposing the MBLA-YOLO instance segmentation model, achieving both lightweight efficiency and high accuracy. Building upon this foundation, a non-contact measurement method is proposed that combines an improved model with three-dimensional point cloud analysis to precisely extract key structural parameters of the flower head. First, image annotation is employed to eliminate interference from petals and sepals, whilst instance segmentation models are used to delineate the target region; The segmentation results for the disc surface (front) and edges (sides) are then mapped onto the three-dimensional point cloud space. Target regions are extracted, and following processing, separate models are constructed for the disc surface and edges. Finally, with regard to the differences between the surface and edge structures, targeted methods are employed for their respective calculations. Whilst maintaining lightweight characteristics, the proposed MBLA-YOLO model achieves simultaneous improvements in accuracy and efficiency compared to the baseline YOLOv11n-seg. The introduced CKMB backbone module enhances feature modelling capabilities for complex structural details, whilst the LADH detection head improves small object recognition and boundary segmentation accuracy. Specifically, the CKMB module integrates MBConv and channel attention to strengthen multi-scale feature extraction and representation, while the LADH module adopts a tri-branch design for classification, regression, and IoU prediction, structurally improving detection precision and boundary recognition. This research not only demonstrates superior accuracy and robustness but also significantly reduces computational overhead, thereby achieving an excellent balance between model efficiency and measurement precision. This method avoids the need for three-dimensional reconstruction of the entire plant and multi-view point cloud registration, thereby reducing data redundancy and computational resource expenditure.

Why it matches plant phenotyping methodsヒマワリ花頭の形態形質を、画像セグメンテーションと3D点群で非接触抽出する手法を開発・評価しており、フェノタイピング手法が研究の中心である。

abstractThe diameter of the sunflower flower head and the thickness of its margins are important crop phenotypic parameters.

Amodal Segmentation and Trait Extraction of On-Branch Soybean Pods with a Synthetic Dual-Mask Dataset. →

We address the challenge that occlusions in on-branch soybean images impede accurate pod-level phenotyping. We propose a lab on-branch pipeline that couples a prior-guided synthetic data generator (producing synchronized visible and amodal labels) with an amodal instance segmentation framework based on an improved Swin Transformer backbone with a Simple Attention Module (SimAM) and dual heads, trained via three-stage transfer (synthetic excised → synthetic on-branch → few-shot real). Guided by complete (amodal) masks, a morphology-driven module performs pose normalization, axial geometric modeling, multi-scale fused density mapping, marker-controlled watershed, and topological consistency refinement to extract seed per pod (SPP) and geometric traits. On real on-branch data, the model attains Visible Average Precision (AP) 50/75 of 91.6/77.6 and amodal AP50/75 of 90.1/74.7, and incorporating synthetic data yields consistent gains across models, indicating effective occlusion reasoning. On excised pod tests, SPP achieves a mean absolute error (MAE) of 0.07 and a root mean square error (RMSE) of 0.26; pod length/width achieves an MAE of 2.87/3.18 px with high agreement (R 2 up to 0.94). Overall, the co-designed data-model-task pipeline recovers complete pod geometry under heavy occlusion and enables non-destructive, high-precision, and low-annotation-cost extraction of key traits, providing a practical basis for standardized laboratory phenotyping and downstream breeding applications.

Why it matches plant phenotyping methods大豆莢の遮蔽下形状を画像から復元し、種子数や幾何形質を抽出する画像解析パイプラインを開発・評価しており、植物表現型取得が中心である。

abstractWe propose a lab on-branch pipeline that couples a prior-guided synthetic data generator (producing synchronized visible and amodal labels) with an amodal instance segmentation framework

Deep learning versus geometric morphometrics for archaeobotanical domestication study and subspecific identification →

The identification of archaeological fruits and seeds is crucial for understanding the relationships between humans and plants within the cultural and biological history of both wild and cultivated species. We compared the relative performance of a deep learning approach, namely convolutional neural networks (CNN), and outline analyses via geometric morphometrics using elliptical Fourier transforms (EFT) at identifying pairs of plant taxa. We used their seeds and fruit stones that are the most abundant organs in archaeobotanical assemblages, and whose morphological identification, chiefly between wild and domesticated types, allows to document their domestication and biogeographical history. We used existing modern datasets of four plant taxa (barley, olive, date palm and grapevine) corresponding to photographs of two orthogonal views of their seeds that were analysed separately to offer a larger spectrum of shape diversity. Sample sizes ranged from 473 to 1,769 seeds per class, which constitute a relatively small dataset for training CNNs models yet typical within archaeobotanical research. On these eight datasets, we compared the performance of CNN and EFT coupled with linear discriminant analyses. Our objectives were twofold: i) to test whether CNN can beat geometric morphometrics in taxonomic identification and if so, ii) to test which minimal sample size is required. We ran simulations on the full datasets and also on subsets, starting from 50 images in each binary class. For the CNN network, we deliberately used a candid approach relying on pre-parameterised VGG19 network. For EFT, we used a state-of-the art morphometrical pipeline. The main difference rests in the data used by each model: our CNN used bare photographs where EFT used outline coordinates. This "pre-distilled" geometrical description of seed outlines is often the most time-consuming part of morphometric studies. Results show that our CNN beats EFT in most cases, even for very small datasets. We finally discuss the potential of CNNs for archaeobotany, and how bioarchaeological studies could embrace both approaches, used in a complementary way, to better assess and understand the past history of species.

Why it matches plant phenotyping methods種子・果実石の画像形態を対象に、CNNと幾何学的形態計測を比較し、分類性能と必要サンプル数を検証する方法中心の研究である。植物器官の形状という観測可能な形質の抽出・識別を扱う。

abstractWe compared the relative performance of a deep learning approach, namely convolutional neural networks (CNN), and outline analyses via geometric morphometrics using elliptical Fourier transforms (EFT) at identifying pairs of plant taxa.

CornPheno: Phenotyping corn ear kernels in the wild via point query transformer. →

Corn is a globally important economic crop. Certain trait parameters of corn ears kernels per ear are essential indicators for corn breeding. However, acquiring these parameters faces two challenges: i) manual measurement is labor-intensive and error-prone, and ii) vision-based corn phenotyping machines require fixed image capturing environment and are cost-prohibitive. To address these limitations, we introduce CornPheno, a user-friendly, low-end, smartphone-based approach capable of executing corn ear phenotyping in the wild. CornPheno highlights three corn ear parameters: kernels per ear, rows per ear, and kernels per row. Technically, inspired by crowd localization in computer vision, we first extract kernels per ear based on a Corn data-trained Point quEry Transformer (CornPET). CornPET generates interpretable per-kernel point predictions and supports subsequent row detection. To detect rows, we introduce a novel point-based corn row detection approach, termed unicorn, featured by sqUeezed clusteriNg and bI-direCtional pOint seaRchiNg, to phenotype rows per ear and kernels per row. With adaptive geometric modeling, our approach is robust to partial rows, curved rows, and missing kernels. To promote the use of CornPheno, we have integrated it into OpenPheno, a WeChat-based mini-program, and made it open-access for corn breeders. We hope our approach can provide the community with a user-friendly and cost-effective way to facilitate corn breeding.

Why it matches plant phenotyping methodsトウモロコシ穂の粒数・列数などの形質を、スマートフォン画像から抽出する手法とソフトウェアを開発しており、フェノタイピング手法が研究の中心である。

abstractwe introduce CornPheno, a user-friendly, low-end, smartphone-based approach capable of executing corn ear phenotyping in the wild.

Robust real-time strawberry maturity detection using UAV-mounted deep learning for precision agriculture. →

Background To address the challenge of real-time plant monitoring in greenhouse environments, this industry-driven research focuses on developing an autonomous quadrotor UAV system specifically designed for monitoring strawberry plants. Traditional methods for greenhouse monitoring are labor-intensive and lack scalability, particularly in precision agriculture applications. Method This research introduces a mature strawberry detection model specifically designed for greenhouse environments. The proposed YOLOv9-GLEAN approach enables the identification of small mature strawberries through an onboard camera mounted on the quadrotor. Additionally, a hybrid trajectory tracking controller for the quadrotor is developed and tested in both simulated and real-world conditions. The UAV navigates through the greenhouse using predetermined waypoints, operating as a semi-autonomous system for navigation while maintaining full autonomy in mature strawberry detection tasks. The system incorporates an integrated onboard vision platform that utilizes an innovative YOLOv9-GLEAN-based algorithm to perform real-time and offline detection and counting of mature strawberries. Results The YOLOv9-GLEAN model achieves high detection accuracy, as confirmed by evaluation metrics such as precision, recall, and F1-score. The proposed hybrid (PID+LQR) controller demonstrates superior tracking performance compared to other conventional controllers. The integrated control and perception system proves effective in both simulated and real-world greenhouse environments. Discussion The research validates the efficacy of deep learning models, with YOLOv9-GLEAN showing exceptional performance in enabling rapid, precise, and automated detection of ripe strawberries through quadrotor deployment in greenhouse environments. Such agricultural monitoring technologies represent a substantial advancement beyond conventional manual inspection approaches, empowering farmers and greenhouse operators to execute well-informed, time-sensitive management decisions that minimize crop losses and optimize production yields. This investigation underscores the revolutionary impact that deep learning technologies can have within greenhouse agriculture.

Why it matches plant phenotyping methodsイチゴ果実の成熟状態を画像から検出・計数する深層学習モデルとUAV搭載視覚プラットフォームの開発・評価が研究の中心であり、植物器官の状態を直接推定している。

abstractThis research introduces a mature strawberry detection model specifically designed for greenhouse environments.

Intelligent 3D Potato Cutting Simulation System Based on Multi-View Images and Point Cloud Fusion →

The quality of seed pieces is crucial for potato planting. Each seed piece should contain viable potato eyes and maintain a uniform size for mechanized planting. However, existing intelligent methods are limited by a single view, making it difficult to satisfy both requirements simultaneously. To address this problem, we present an intelligent 3D potato cutting simulation system. A sparse 3D point cloud of the potato is reconstructed from multi-perspective images, which are acquired with a single-camera rotating platform. Subsequently, the 2D positions of potato eyes in each image are detected using deep learning, from which their 3D positions are mapped via back-projection and a clustering algorithm. Finally, the cutting paths are optimized by a Bayesian optimizer, which incorporates both the potato’s volume and the locations of its eyes, and generates cutting schemes suitable for different potato size categories. Experimental results showed that the system achieved a mean absolute percentage error of 2.16% (95% CI: 1.60–2.73%) for potato volume estimation, a potato eye detection precision of 98%, and a recall of 94%. The optimized cutting plans showed a volume coefficient of variation below 0.10 and avoided damage to the detected potato eyes, producing seed pieces that each contained potato eyes. This work demonstrates that the system can effectively utilize the detected potato eye information to obtain seed pieces containing potato eyes and having uniform size. The proposed system provides a feasible pathway for high-precision automated seed potato cutting.

Why it matches plant phenotyping methods多視点画像と点群融合によりジャガイモの体積および芽の3D位置を推定し、精度を検証するシステム開発が中心であるため、植物形質取得手法として含める。

abstractA sparse 3D point cloud of the potato is reconstructed from multi-perspective images, which are acquired with a single-camera rotating platform.

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

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

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

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

DeepCanola: Phenotyping brassica pods using semi-synthetic data and active learning →

Phenotyping, the measurement of attributes or traits, is crucial in selecting superior cultivars for specific environmental situations. This is a time-consuming process when applied to large populations but can be accelerated through the use of deep learning, resulting in an algorithm that can phenotype images of specimens in negligible amounts of time. The primary issue with deep learning is the large quantities of high-quality training data required to make a viable phenotyping pipeline. To address this, we present a semi-synthetic training data generation system which significantly reduces the amount of human effort spent on data collection. We use active learning alongside this system to create DeepCanola, an instance segmentation model that successfully segments and measures the valves from Brassica napus pods. We demonstrate that the model accurately estimates the effect of different winter cold treatments on a range of different cultivars and crop types as effectively as manually curated measurements. Furthermore, the resulting model is effective on data from various experimental settings and on different, but related, species such as Arabidopsis thaliana, Allaria petiolate (garlic mustard) and Raphanus raphanistrum subsp. sativus (radish). This robust tool could be easily scaled, thereby accelerating breeding or fundamental research programs. Code and model weights: https://github.com/kieranatkins/deepcanola.

Why it matches plant phenotyping methods植物の莢画像からバルブを分割・測定する深層学習フェノタイピング手法を開発し、半合成データとアクティブラーニング、複数条件・種での性能検証を行っているため。

abstractWe use active learning alongside this system to create DeepCanola, an instance segmentation model that successfully segments and measures the valves from Brassica napus pods.

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

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

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

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

Strawberry fruit yield forecasting using image-based time-series plant phenological stages sequences →

Yield forecasting is crucial for growers, enabling efficient resource management and informed decision-making. Such decisions impact storage, product processing, and logistics, leading to increased productivity and cost savings. However, this heavily relies on accurate yield forecasts. This work addresses such a need by presenting the development and testing of a reliable method for yield forecasting. The proposed methodology combines high-resolution object detection with a multi-variate input forecasting model that accurately computes the yield for incoming harvests. The forecasting approach incorporates a physically-constrained model based on a Long Short-Term Memory (LSTM) network. This model dynamically applies weights to the time-series data composed of counts for the phenological stages: flower, green, small white, large white, pink, and red (ripe fruit). These counts are obtained from detections made by a YOLOv10s, achieving an mAP@50 of 0.74 for all classes. As a result, the forecasting model's capacity to interpret input data is enhanced, translating it into a valid ripe count forecast. To validate the proposed approach, the forecasting model was trained and evaluated using (a) untreated count sequences and (b) weighted count sequences. The results indicate that phenologically-weighted input sequences outperform untreated sequences, with the following evaluation metrics: R² = 0.74, Root Mean Square Error (RMSE) = 12.67, Mean Absolute Error (MAE) = 10.95, and Mean Absolute Percentage Error (MAPE) = 39.4, improving 15%, 19.26%, 17.13%, and 11.3%, respectively.

Why it matches plant phenotyping methods画像ベースのYOLO検出でイチゴの生育段階・果実数を抽出し、収量予測へ利用する方法を開発・検証しており、植物表現型の取得と解析が中心的な貢献である。

abstractThis work addresses such a need by presenting the development and testing of a reliable method for yield forecasting.

Evolving topological colour landscape unravels the final stages of pistachio nut development and the incidence of blank nuts. →

Pistachio is a major nut crop worldwide; however, there is a lack of standardized non-destructive methods to effectively evaluate maturity and kernel filling for improved management and harvest timing. This study presents an image-based approach to determine pistachio nut maturation and blank kernel incidence by analysing the surface colour patterns of individual nuts at three time points during late development. We identified eight major hull colours to represent the full colour spectrum and applied principal component analysis to divide each nut into seven spatial sections. Within each section, we constructed eight colour-based feature variables (covariates) and associated them with a binary response variable indicating kernel presence or absence. We explored the specific response-covariate relationships at each developmental time point using a data-driven method called categorical exploratory data analysis, which identified key first-order and second-order feature-categories that link hull colour patterns with kernel status. These relationships were visualized using block-structured heatmaps, revealing consistent distinctions between filled and blank nuts. Based on these findings, we developed an algorithm with two main functions: (i) identifying a nut's growth stage from its image for optimal harvest timing and (ii) estimating blank nut incidence for quality assessment and economic decision-making.

Why it matches plant phenotyping methods個々のピスタチオ果実画像から成熟段階と胚（カーネル）の有無を推定する画像解析手法・アルゴリズムの開発が中心であり、植物器官の状態を非破壊的に定量化している。

abstractThis study presents an image-based approach to determine pistachio nut maturation and blank kernel incidence by analysing the surface colour patterns of individual nuts at three time points during late development.

Applying machine learning for chili pepper phenotyping and feature extraction →