Plant phenotyping methods.

Hyperspectral–Region Aggregation Network for Maize Leaf Nitrogen Content Estimation via Spectral–Regional Joint Modeling →

Abstract Accurate estimation of maize leaf nitrogen content is important for improving nitrogen-use efficiency and supporting precision crop management. However, leaf-level hyperspectral modeling is challenged by high spectral redundancy and heterogeneous spectral responses among local leaf regions. This study proposes a Hyperspectral–Region Aggregation Network (HSRAN) for maize leaf nitrogen content estimation from region-level hyperspectral spectra. HSRAN consists of a Spectral Adaptive Recalibration Encoder (SARE) and a Context-Aware Gated Aggregation Module (CAGM). SARE performs band-wise residual recalibration and extracts regional spectral representations, whereas CAGM models contextual dependencies among regional features and performs gated attention-based aggregation for leaf-level prediction. Field experiments were conducted in 2024 and 2025 at the jointing, silking, and maturity stages. HSRAN was evaluated against PLSR, RF, XGBoost, SVR, 1D-CNN, MLP, and Transformer1D models. Across the stage-specific and pooled datasets, HSRAN achieved the highest R² and the lowest RMSE while maintaining competitive MAE values. On the pooled full-growth-period dataset, HSRAN achieved an R² of 0.84, an RMSE of 3.63 g kg⁻¹, and an MAE of 2.59 g kg⁻¹. At the jointing, silking, and maturity stages, the corresponding R² values were 0.56, 0.76, and 0.72, respectively. Ablation experiments indicated that integrating SARE and CAGM improved R² from 0.80 to 0.84. To interpret regional contributions, the learned attention weights were mapped back to the original leaf coordinates recorded during regional sampling. Regions near leaf veins, tips, and margins often received relatively higher attention weights, suggesting that their local spectra provided informative cues for model prediction. These findings indicate that spectral–regional joint modeling can improve leaf-level hyperspectral estimation of maize nitrogen content. HSRAN provides a practical framework for non-destructive nitrogen assessment in maize.

Why it matches plant phenotyping methodsトウモロコシ葉の窒素含量という植物形質を非破壊推定するためのハイパースペクトル深層学習手法を開発し、複数手法との比較・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。

abstractThis study proposes a Hyperspectral–Region Aggregation Network (HSRAN) for maize leaf nitrogen content estimation from region-level hyperspectral spectra.

Physiology-informed hyperspectral retrieval of leaf Vcmax across wheat and maize →

Leaf hyperspectral reflectance can provide a scalable way to estimate photosynthetic capacity (Vcmax), but models trained in one species or measurement context often lose accuracy in another. This transfer problem limits the use of spectral approaches in multi-species crop phenotyping and carbon-cycle applications. Here, we tested physiology-informed inputs for leaf-level Vcmax25 retrieval using paired gas-exchange and reflectance data from wheat (C₃; n = 198) and maize (C₄; n = 81) grown under contrasting nitrogen supply. The four input configurations were raw spectra (Mod1), spectra scaled by a PPFD–absorptance proxy (Mod2), scaled spectra augmented with radiative-transfer-derived traits (Mod3), and scaled spectra augmented with a spectral coordination proxy (Mod4). Within datasets, the best models reached R² = 0.82 in wheat, 0.41 in maize, and 0.76 in the combined dataset. In a matched comparison with a common random-forest learner, the spectral coordination proxy Mod4 improved accuracy only slightly over Mod2 in wheat (RMSE −0.51%; p = 0.0058) and maize (RMSE −1.26%; p = 0.0011) but not in the combined dataset (RMSE −0.15%; p = 0.074), and the trait-based Mod3 showed no consistent benefit. When wheat models were tested on measurement dates not used in training, accuracy remained moderate (R² = 0.563; RMSE = 15.07 µmol m⁻² s⁻¹). Despite this within-dataset performance, models applied to the other species without calibration failed in both directions (negative R²), and adding source-species data did not improve prediction even when a few samples of the new species were used for calibration. These results show that physiology-informed input design provides at most small within-dataset gains, and that reliable prediction across C₃ and C₄ crops requires calibration data from the target crop.

Why it matches plant phenotyping methods葉のハイパースペクトル反射から光合成能力Vcmaxを推定する手法を開発・比較・検証しており、植物形質取得が研究の中心です。

abstractLeaf hyperspectral reflectance can provide a scalable way to estimate photosynthetic capacity (Vcmax)

PlantC2USeg: Cross-Scale Consistent Pre-Training for Few-Shot Unified Plant Point Cloud Segmentation →

Modern crop breeding demands precise organ-level analysis for trait quantification, making plant point cloud segmentation (PPCS) increasingly important. However, conventional deep learning approaches rely heavily on densely annotated datasets that are labor-intensive to acquire. Unified PPCS adaptation from distribution-shifted examples with minimal additional training remains challenging. To address this, we propose PlantC2USeg, a deep transfer learning framework featuring cross-scale consistency learning to explicitly align features across spatial scales and an information-restricted decoding strategy that prevents reconstruction shortcuts and promotes robust adaptation. The resulting pre-training enables stable few-shot generalization across species and sensing conditions, while unified fine-tuning with inherited thresholds further reduces adaptation overhead. Under full supervision on Soybean3D, PlantC2USeg achieves the highest semantic IoU and instance mWCov among compared methods, at 91.91% and 94.62%. With 20 labeled samples, it leads both metrics at 89.78% and 90.27%; with only 10 samples, it retains the highest mWCov of 83.23% while achieving 83.19% IoU. Across HR3D, 10-shot transfer to tobacco, tomato, and sorghum averages 78.41% IoU and 79.42% mWCov, while 22-shot transfer to SYAU-Maize achieves the highest IoU and mRec at 92.75% and 93.51%. Furthermore, a leading category-averaged mIoU of 85.0% on ShapeNet Part demonstrates the framework's capability to handle diverse shape variations beyond agricultural domains. These results demonstrate that PlantC2USeg reduces overall adaptation effort under distribution shifts, enabling scalable plant phenotyping and transferable 3D representation learning beyond agriculture.

Why it matches plant phenotyping methods植物点群の器官レベル形質定量を目的とするセグメンテーション手法を開発し、複数データセット・作物・ショット条件で性能評価しているため、植物フェノタイピング手法が中心である。

abstractwe propose PlantC2USeg, a deep transfer learning framework featuring cross-scale consistency learning

A Lightweight Maize Pest and Disease Recognition Method Based on an Improved ShuffleNetV2 in Field Environments →

Abstract Agricultural pest and disease monitoring plays a vital role in ensuring crop productivity, reducing pesticide consumption, and promoting sustainable agricultural development. Although deep learning techniques have achieved remarkable success in plant health diagnosis, many existing models remain computationally intensive and are difficult to deploy on resource-constrained edge devices used in practical agricultural environments. To address these challenges, this study proposes a practical lightweight deep learning framework based on an improved ShuffleNetV2 architecture for real-time maize pest and disease recognition under complex field conditions.The proposed model incorporates the Ghost module to reduce redundant feature generation, the Efficient Channel Attention (ECA) mechanism to enhance feature representation, and the HardSwish activation function to improve nonlinear learning capability while maintaining computational efficiency. Extensive experiments were conducted on a maize pest and disease dataset containing multiple disease and pest categories collected under natural field conditions. Experimental results demonstrate that the proposed model achieves superior recognition accuracy while significantly reducing model parameters and computational complexity compared with several mainstream lightweight convolutional neural networks.The results show that the proposed method achieves an accuracy of 93.00%, a recall of 92.76%, and an F1-score of 92.42%, while maintaining extremely low computational cost (0.03 GFLOPs) and model size (1.16 MB). Furthermore, the proposed model was successfully deployed on a Raspberry Pi platform, demonstrating excellent real-time inference capability and low computational resource consumption. The framework is suitable for practical agricultural applications, including intelligent crop monitoring, UAV-assisted field inspection, and mobile diagnostic systems. By enabling rapid and accurate in-field identification of maize pests and diseases, the proposed approach supports timely crop protection decisions, reduces unnecessary pesticide application, and contributes to sustainable agriculture through practical edge-AI deployment.

Why it matches plant phenotyping methodsトウモロコシの病害状態を画像から認識する軽量深層学習法の開発・評価が中心であり、植物病害フェノタイプの取得手法に該当する。

abstractthis study proposes a practical lightweight deep learning framework based on an improved ShuffleNetV2 architecture for real-time maize pest and disease recognition under complex field conditions.

Yield-graph: multi-stage growth-aware maize yield prediction via graph neural networks. →

Key message Yield-Graph enables accurate maize yield prediction from incomplete multi-stage phenotypic and environmental data by modeling higher-order environment-trait interactions, with robust applicability across growth stages, regions, and crop species. Accurate yield prediction before maize harvest is crucial for advancing agricultural management and ensuring food security. Unlike conventional approaches that rely on traits from a single growth stage, this study models multiple traits across different developmental stages, all targeting final yield, thereby uncovering their stage-specific contributions and demonstrating the feasibility of early yield prediction. We introduce Yield-Graph, an innovative framework that evaluates phenotypic data at distinct developmental stages for yield prediction. The method employs a bipartite graph structure to impute missing trait values at each stage and leverages a hypergraph attention mechanism to capture high-order sample relationships. Comprehensive benchmark experiments demonstrate that Yield-Graph matches the top-tier predictive accuracy of exhaustively optimized tree models. Moreover, the framework exhibits strong robustness across growth stages, high adaptability to regional variations, and effective generalization across datasets. These findings highlight the potential of graph-enhanced multi-stage modeling for early-stage yield prediction, offering a scalable solution for precision agriculture and intelligent crop management.

Why it matches plant phenotyping methods作物収量という植物形質を、複数時期の表現型データから推定するグラフニューラルネットワーク手法を開発し、ベンチマーク評価しているため、計算型フェノタイピング手法が中心である。

abstractWe introduce Yield-Graph, an innovative framework that evaluates phenotypic data at distinct developmental stages for yield prediction.

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

Integrating UAV-derived enhanced disease detection index and texture features for monitoring southern corn rust severity →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsUAV画像から病害重症度を推定する検出指数・テクスチャ特徴の統合が題名上の中心であり、植物の病害状態を測定するフェノタイピング手法に該当します。

titleIntegrating UAV-derived enhanced disease detection index and texture features for monitoring southern corn rust severity

Autonomous Quadcopter Flight Path Generation via MAVLink and Ground Control Station Architecture for Precision Agricultural Crop Monitoring →

This paper presents an integrated system design for autonomous quadcopter flight path generation using the MAVLink protocol and a custom Ground Control Station (GCS) for precision agricultural crop monitoring. The system combines three coverage path algorithms (Boustrophedon, Spiral, and Energy-Optimized), a Pixhawk 4 / ArduPilot flight stack, a MicaSense RedEdge-P multispectral payload, and a ROS2-based GCS for mission planning, telemetry, and vegetation-index-based crop health assessment. The 2.8 kg quadcopter (450 mm frame, 4-cell LiPo) achieves 22–25 minutes of flight time. Across five field sizes (0.5–10 ha), the Energy-Optimized path achieved 96.5% coverage efficiency with 4.2% overlap and a 12.4% energy reduction over the Boustrophedon baseline. NDVI-based crop segmentation achieved pixel accuracy of 92.5% (maize), 94.1% (rice), and 90.8% (wheat), and four-class crop-health classification achieved a weighted F1-score of 90.0%. MAVLink 2.0 command latency averaged 15.8 ms with 99.3% packet delivery at ranges up to 800 m. An ablation study showed additional gains of 1.5–3.1% coverage from wind compensation and 2.1–2.8% from terrain-following.

Why it matches plant phenotyping methods自律ドローン、マルチスペクトル撮像、NDVIセグメンテーションによる作物健康状態推定を統合し、飛行・画像解析性能を定量評価しているため、植物状態の取得・抽出が技術的に実質的な構成要素である。

abstractThe system combines three coverage path algorithms (Boustrophedon, Spiral, and Energy-Optimized), a Pixhawk 4 / ArduPilot flight stack, a MicaSense RedEdge-P multispectral payload, and a ROS2-based GCS for mission planning, telemetry, and vegetation-index-based crop health assessment.

Intersection rotational invariance regression based plant disease detection. →

Emerged Technologies include imparted potentiality to generate adequate food to converge ultimatum of society. However, aspects like, climate change, plant disease, refuse at pollinators as well as others are demanding to farmers. The presence of diseases pulls up the maturation of their corresponding species. Plant disease recognition using image processing and machine learning (ML) with data collected from Internet of Thing (IoT) sensors has been receiving greater amount of interest in recent years, however timely disease detection remains a demanding issues. Despite early disease detection though reduces the risk of destruction to plants nevertheless the noise present in the sensor networks (i.e. Internet of Things) while collecting internet scrapped images compromise overall precision and accuracy. To address on this research gap, i.e., addressing noise with timely disease detection, in this work ML based plant disease detection method called, Intersection Histogram and Rotational Invariant Principal Component Regression (IH-RIPCR) is introduced. IH-RIPCR technique is dividing as pre-processing and feature extraction. First with the raw plant dataset obtained as input from Corn or Maize Leaf Disease Dataset, pre-processing is done employing Intersection Histogram based Contrast Enhancement model. Second with the obtained pre-processed contrast enhanced images is subjected as input to ML-based Rotational Invariance Principal Component Regression feature extraction model to extract relevant features pertaining to corn or maize leaf images for disease detection in an accurate and precise manner. Experiments are conducted with corn or maize plant leaf dataset with different existing methods to verify hypothesis potentiality of IH-RIPCR technique. Significance of technique is evaluated based on numerous parameters in terms of PSNR, processing time, precision, recall and accuracy respectively.

Why it matches plant phenotyping methodsトウモロコシ葉の病徴を画像から検出する画像処理・機械学習手法を開発し、既存法との比較評価を行っており、植物病害状態の表現型取得が中心です。

abstractin this work ML based plant disease detection method called, Intersection Histogram and Rotational Invariant Principal Component Regression (IH-RIPCR) is introduced.

YOLOv11-MPD: A Multi-Part Maize Disease Detection Algorithm for Complex Field Environments →

Maize diseases affecting leaves, stalks, and ears can substantially reduce yield and quality; therefore, rapid and accurate recognition in complex field environments is important for intelligent agricultural monitoring. To address the large-scale variation, weak fine-grained texture, and strong background interference associated with multi-part maize diseases, this study proposes YOLOv11-MPD (YOLOv11 for Maize Multi-Part Disease Detection), a maize disease detection algorithm based on YOLOv11n. The method jointly improves spatial position awareness, shallow detail preservation, local-context modeling, key semantic-region enhancement, and lightweight detection-head reconstruction. RFCAConv, C3k2_RFCAConv, and Detect_LSDECD are introduced into the baseline network to strengthen directional texture modeling, multi-scale feature aggregation, and detection-head feature representation. FG-RFCAConv, HGD-C3k2, LCA-C3k2, and GRN-BiAttn are further designed for high-frequency differential gated detail compensation, P3 high-resolution detail enhancement, local-context fusion, and global-response-normalized attention regulation, respectively. Experimental results show that YOLOv11-MPD achieves Precision, Recall, mAP50, and mAP50-95 of 72.3%, 72.8%, 79.5%, and 50.2%, improving YOLOv11n by 2.4, 2.5, 2.9, and 2.4 percentage points, respectively, while reducing parameters from 2.6 M to 2.4 M. These results indicate that, within the scope of the dataset used in this study, YOLOv11-MPD improves multi-part maize disease detection under complex field conditions. However, the current conclusions are limited to the constructed dataset, and further validation using larger multi-region, multi-season, and multi-device datasets is required to evaluate its broader generalization ability.

Why it matches plant phenotyping methodsトウモロコシの葉・茎・穂における病害状態を画像から検出するアルゴリズムを開発し、性能比較・検証しており、植物表現型取得が研究の中心です。

abstractthis study proposes YOLOv11-MPD (YOLOv11 for Maize Multi-Part Disease Detection), a maize disease detection algorithm based on YOLOv11n.

Procedures for Standardized Measurements of Core Traits in Maize Research and Breeding Programs. →

Maize ( Zea mays L.) is a highly adaptable crop grown worldwide across diverse climates and management practices, with uses across multiple sectors. Consequently, the traits prioritized in maize research and breeding programs vary depending on the specific objectives. Core traits, however, such as flowering time, plant and ear height, and stalk and root lodging, which are important for evaluating and improving the performance and stability of maize genotypes, are routinely evaluated across breeding programs, regardless of their goals. Standardized measurement of these core traits is essential to ensure data reliability and comparability, enabling the integration of phenotypic data across different experiments. Such efforts ultimately support better decision-making and accelerate the development of improved maize genotypes. This is particularly important in public sector programs, where large-scale evaluations, critical for assessing the value of specific genotypes, are often only feasible through collaboration across programs. Here, we provide a protocol for the standardized collection of phenotypic data, specifically focusing on how to measure core traits in maize field trials. These methods promote consistency and accuracy in the evaluation of these traits, and support communication and coordination among groups in the public sector and other research settings. Further, such standardization facilitates the integration and comparison of data across programs, enabling robust longitudinal and multienvironment analyses.

Why it matches plant phenotyping methodsトウモロコシの主要形質を対象に、圃場試験での表現型データ収集を標準化するプロトコル自体を提示しており、測定方法が研究の中心である。

abstractHere, we provide a protocol for the standardized collection of phenotypic data, specifically focusing on how to measure core traits in maize field trials.

Phenology-adaptive machine learning for early mapping of field-scale corn crop yield using fusion of Sentinel-2 satellite spectral imagery, and weather-based accumulated heat units →

Abstract Purpose Timely, accurate, and field-scale crop yield mapping is essential for precision crop management, yet most existing studies rely on late- or full-season data, limiting in-season decision-making. This study aims to develop a stage-aware earliest possible corn yield mapping framework that balances early data availability, predictive accuracy, and spatial fidelity by integrating Sentinel-2 imagery, vegetation indices (VIs), and accumulated growing degree days (AGDD). Methods Corn yield data were collected from a commercial farm over three growing seasons (2018–2020). The final modeling dataset included 51,794 Sentinel-2 10 m aggregated yield samples across three seasons: 2018 ( n = 16400), 2019 ( n = 18534), and 2020 ( n = 16860). Sentinel-2 raw spectral bands and derived VIs were organized for V4, V6, R1, R5, and R6 growth stages based on AGDD- and DAP-defined corn growth-stage windows, also confirmed visually on-ground, allowing observations from different planting and harvest dates across the three growing seasons to be aligned by crop developmental stage rather than calendar date. A stage-wise Pearson correlation and frequency-based selection identified informative and non-redundant VI subsets across crop development. Four machine learning models: Random Forest (RF), XGBoost (XGB), k-Nearest Neighbors (kNN), and a Neural Network (NNET; multi-layer perceptron) were trained using 13 input configurations, including individual growth stages and multi-stage combinations capturing phenological progression. Models were tuned via randomized search, trained on 2018–2019 data, and independently validated on the 2020 season. Yield predictions were mapped directly at 10 m Sentinel-2-pixel resolution without spatial interpolation to preserve fine-scale variability. Results Model performance was strongly influenced by phenological stage selection. Among single-stage inputs, R1 was the earliest stage where reliable yield mapping could be availed (RF: R² = 0.56, RMSE = 29.50%). While combining V6 with R1 stage inputs substantially improved predictive performance (RF: R² = 0.70, RMSE = 24.13%) for the yield mapping at the R1 stage, where V6-stage signals provided complementary yield-related information. The full-season combination (V4 + V6 + R1 + R5 + R6) produced the highest accuracy (RF: R² = 0.72, RMSE = 23.28%) but would be less suitable for early in-season decision-making. Early- or late-stage-only inputs (V4, R5, R6) showed weaker and less stable cross-year performance. Among algorithms, RF consistently generalized best to the independent 2020 dataset and is recommended for operational use. XGB showed strong training performance but reduced cross-year stability, kNN yielded moderate accuracy, and NNET achieved accuracy comparable to RF while closely reproducing observed spatial patterns such as center-pivot effects and edge gradients. Direct 10 m mapping preserved yield heterogeneity and avoided smoothing artifacts common in interpolation-based approaches. Conclusion Stage-aware feature selection and phenology-informed input combinations are critical for balancing yield prediction timeliness and accuracy. For operational in-season yield mapping, the RF model using the V6 + R1 stage combination provides a practical and reliable solution, enabling early, accurate, and spatially detailed yield estimates with robust cross-year performance. This study presents a deployable framework for integrating satellite time series and weather data into conventional (non-sequential) machine learning models to support proactive, within-season decision-making in precision agriculture.

Why it matches plant phenotyping methods衛星画像・気象データと機械学習を統合し、作物の収量という明示的な植物形質を圃場内10 m解像度で推定する手法を開発・独立年で検証しており、フェノタイピング手法が中心である。

abstractThis study aims to develop a stage-aware earliest possible corn yield mapping framework that balances early data availability, predictive accuracy, and spatial fidelity by integrating Sentinel-2 imagery, vegetation indices (VIs), and accumulated growing degree days (AGDD).

Development of High-Fidelity DenseNet Framework for Multi-Disease Real-Time Crop Health Surveillance and Farming Recommendation in Maize (Zea Maize) Cultivation in Nigeria →

In Nigeria, maize is the most widely cultivated grain, largely supporting food security for about half of the population. Research has indicated that there is annual production is declaiming to approximately 50% in maize, this is due to crop diseases and pest damage. This research presents a deep learning surveillance system for crop disease prediction and pesticides recommendations based on a DenseNet-121 architecture for continuous video streams. The research was evaluated on a curated field dataset collected from three states in Nigeria; Adamawa, Borno, and Taraba State. The system achieved a mean accuracy of 98.2% and a mean F1-score of 0.982. The results reflect a strong discriminative capacity across the diverse textural maize diseases.

Why it matches plant phenotyping methodsトウモロコシの病害状態を映像から推定する深層学習手法の開発・評価が中心であり、植物病害表現型の画像ベース計測に該当する。農薬推薦も含むが、病害分類性能が明示的に評価されている。

abstractThis research presents a deep learning surveillance system for crop disease prediction and pesticides recommendations based on a DenseNet-121 architecture for continuous video streams.

A System for the Recognition of Some Selected Grain Plant Leaves Using Deep Learning Algorithms →

Manual inspection of grain plant leaves for defects is subjective and labor-intensive. Few studies have compared deep learning methods on a combined multi-crop dataset. The study collected locally 5,640 leaf images from rice, maize, and guinea corn farms in Nigeria and grouped them into six classes representing defective and healthy leaves for each crop. Three models were trained: YOLOv8 for end-to-end detection and classification, EfficientNetB0 for standalone image classification, and a hybrid that used YOLOv8 for leaf detection followed by EfficientNetB0 for patch classification. The hybrid achieved 99.85% accuracy on the test set, slightly above EfficientNetB0 (99.82%) and YOLOv8 (mAP 0.995). The hybrid also supplies bounding box locations, helping farmers identify exactly where damage appears. This system offers a reliable, field-deployable tool for monitoring grain crop health.

Why it matches plant phenotyping methods穀物葉の健全・欠損状態を画像から検出・分類する深層学習システムの開発とモデル比較が中心であり、植物の病害・損傷状態を直接推定するため、植物フェノタイピング手法に該当する。

abstractManual inspection of grain plant leaves for defects is subjective and labor-intensive.

A Method for Measuring Plant Spacing of Maize Seedlings Based on Improved YOLOv8 →

The uniformity of maize plant spacing serves as a critical indicator for assessing sowing quality, seed vigor, and field seedling emergence stability. However, manual measurement is inefficient, and complex field conditions make automatic seedling detection and plant spacing measurement challenging. Aiming at the challenges of missed detection, insufficient accuracy for small targets, and large errors in plant spacing calculation under complex field conditions, this study constructs a high-quality dataset containing 693 maize seedling images and implements preprocessing enhancement for images degraded by haze or dust. An intelligent maize seedling detection and plant spacing measurement method based on improved YOLOv8 is proposed. The Global Attention Mechanism (GAM) is embedded into the backbone network to strengthen cross-dimension information interaction between channels and spaces, suppress background interference, and reduce the missed detection rate. The Bi-directional Feature Pyramid Network (BiFPN) is adopted to replace the original PAFPN for enhanced multi-scale feature fusion and deep semantic representation. A new 160 × 160 high-resolution small-object detection layer is added to significantly improve the detection performance of weak and small seedlings. Experimental results demonstrate that the improved model achieves a precision, recall, mAP50, and mAP50-95 of 89.4%, 90.3%, 94.4%, and 49.4%, respectively, which are 2.6, 0.5, 1.5, and 2.7 percentage points higher than those of the original YOLOv8 model. These results indicate that the proposed model improved maize seedling detection performance under complex field conditions. Automatic plant spacing calculation is realized based on detection outputs; the average plant spacing of the dataset is 30.42 cm, with a relative error of only 4.93% compared with the preset sowing spacing of 32 cm. The proposed method can efficiently accomplish field seedling identification, plant spacing quantification, and sowing quality evaluation, providing reliable technical support for precision maize sowing, seeder parameter optimization, and intelligent field management, which is of great significance for promoting the intelligent upgrading of grain crop production.

Why it matches plant phenotyping methodsトウモロコシ幼苗の検出画像から株間という植物形態・配置形質を自動抽出するYOLOv8手法を開発し、データセット構築と性能検証まで行っており、フェノタイピング手法が中心である。

abstractAutomatic plant spacing calculation is realized based on detection outputs

Decoding genetic basis of nitrogen use efficiency in maize using AI-generated deep phenotypes. →

Improving nitrogen use efficiency (NUE) is essential for sustainable agriculture, yet conventionally measured plant characteristics have limited value as NUE proxies. Here we show that artificial intelligence (AI) can uncover previously unrecognized phenotypic variation associated with NUE, revealing genetic variation that is largely missed by conventional phenotypes. We trained a convolutional neural network (CNN) on 25,080 maize images to learn features that distinguish how plants respond to low- and high-N conditions, achieving 96.7% accuracy. The learned features were defined as deep phenotypes. Compared with conventional phenotypes, deep phenotypes showed greater phenotypic variation and higher heritability, enabling the identification of 523 significant loci compared with 21 for conventional phenotypes. We next investigated candidate genes underlying these loci and used these findings to interpret the learned features. Lower CNN layers primarily reflected visual patterns overlapping with conventional phenotypes, whereas deeper layers encoded additional features associated with N-responsive genetic variation. To validate candidate genes identified by the AI framework, we functionally characterized Liguleless2 (LG2), a basic-leucine zipper (bZIP) transcription factor, and demonstrated that lg2 mutants exhibit enhanced root architecture and increased N uptake efficiency. Field trials of 200 hybrids across diverse N environments further supported the AI findings, with each beneficial allele increasing ear weight by an average of 18 g per plot under low-N conditions. These results show how integrating AI and biology can uncover biologically relevant variation underlying complex traits such as NUE and enhance the interpretability of AI models.

Why it matches plant phenotyping methodsCNNで植物画像からN応答に関連する「deep phenotypes」を抽出する手法が研究の中心であり、従来形質との比較や遺伝的妥当性検証も行っている。

abstractWe trained a convolutional neural network (CNN) on 25,080 maize images to learn features that distinguish how plants respond to low- and high-N conditions, achieving 96.7% accuracy.

Lightweight Deep Learning Models Deployment for Corn Growth Stage Classification Using PhenoCam Images →

Integrating precision agriculture (PA, a data-driven agricultural management system) with deep learning (DL) models can effectively support various activities, including yield prediction, crop health monitoring, field task automation, and decision-making. Taking advantage of such data-driven methodologies typically requires desktops, high-performance computing systems, and cloud clusters for data analysis, but their portability limits in-field applications. However, a single board computer, such as Raspberry Pi, offers a compact, lightweight, cost-efficient, easy-to-use, and feature-rich portable computing device which is ideal for in-field decision-making in PA applications. One such in-field application is crop growth stage classification for better crop management. Therefore, in this study, eight corn growth stages were classified using PhenoCam (near-surface [proximal] remote sensing network camera)imagery collected from ten PhenoCam sites. Four lightweight DL models were developed, ELiteCrop0, ELiteCrop1, ELiteCrop4, and MobNetCropV2, and evaluated across five image vertical clipping levels(0 %–40 %) using a supercomputer. The optimized model was subsequently deployed on a Raspberry Pi5 for edge inference. Model training accounted for the majority of the total CPU time, exceeding 97 %, while the testing times ranged from 0.01 min to 0.12 min, enabling real-time applications. Among the models, ELiteCrop0 achieved the most balanced performance with a confusion-matrix diagonal ratio(CMDR) of 0.93, followed by ELiteCrop1 (CMDR = 0.92). Overall, model performance decreased with increasing vertical clipping; therefore, a moderate image clipping (0 %–10 %) was recommended for improved computational efficiency. Analysis with a supercomputer produced an intrasite (same train sites)accuracy of 0.90–0.93 (Raspberry Pi: 0.78–0.81) and an intersite (new test sites) accuracy of 0.48–0.50(Raspberry Pi: 0.41–0.43), indicating challenges with model generalization. Raspberry Pi successfully processed ≈ 1000 images/min under safe operating conditions (68◦C). Future work should focus on extending the multi-site dataset to improve cross-site performance. Hence, this study presents a scalable and cost-effective solution for real-time corn growth stage monitoring in PA.

Why it matches plant phenotyping methodsトウモロコシの生育段階という植物状態をPhenoCam画像から推定する深層学習モデルを開発・評価し、Raspberry Piへ展開して性能検証しているため、フェノタイピング手法が中心である。

abstractTherefore, in this study, eight corn growth stages were classified using PhenoCam (near-surface [proximal] remote sensing network camera)imagery collected from ten PhenoCam sites.

AI-enabled simultaneous phenotyping of leaf vein and stomatal traits uncovers independent genetic control in maize →

Abstract Background Leaves maintain hydraulic homeostasis during photosynthesis through the coordinated action of stomata, which regulate gas exchange and transpiration, and veins, which supply water to the leaf lamina. While functional links between stomatal and vascular traits are known in dicots, their potential genetic coordination in C4 crops remains poorly understood. We investigated the genetic architecture of these traits in maize using a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis Results We phenotyped 285 recombinant inbred lines and the MAGIC founder lines, generating 8,072 images from 2,026 leaf samples taken from seedlings grown in controlled conditions. A YOLOv8-based model automatically detected stomata, while a custom and efficient image-processing pipeline quantified vein traits and stomatal spatial distribution patterns along cell bundles. This enabled simultaneous characterization of stomatal density, size, and distribution together with vein density, thickness, and bundle-associated spatial patterning. Substantial phenotypic variation was observed among genotypes, with strong correlations between abaxial and adaxial traits but no significant correlations between stomatal and vein traits. QTL mapping identified 37 genomic regions associated with stomatal and vein traits, including loci containing known developmental regulators such as stomatal density and distribution1 and stomagen1 , as well as novel loci controlling stomatal spatial patterns, divergence between leaf surfaces and veins traits. Conclusions These results support independent genetic control of stomata and veins and decoupled contribution to water-use efficiency, providing a novel genetic framework to independently optimize leaf hydraulic capacity and gas exchange in target environments.

Why it matches plant phenotyping methods葉の気孔・葉脈形質を自動画像解析で同時定量する高スループット表現型解析プラットフォームが研究の中心であり、形質抽出手法も具体的に記述されている。

abstractusing a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis

Three-Point Bend Testing for Quantification of Maize Brace Roots Mechanics. →

Root lodging, the agronomic term for plant mechanical failure, causes yield loss in crops, including maize. Brace roots can provide structural support and assist in preventing root lodging. While the mechanics of brace roots (e.g., stiffness and strength) can play a role in their ability to prevent root lodging, there has been limited characterization of individual brace root mechanical properties. Methods to quantify root mechanics can thus be useful for characterizing maize mechanical traits and breeding new varieties with improved root anchorage and lodging resistance. Here, we describe a protocol for evaluating mechanical properties of maize brace roots. Specifically, we outline the steps necessary to perform three-point bend mechanical testing of maize brace roots using an Instron Universal Testing Stand. We describe root preparation, instrument setup, method establishment, testing, and data analysis. While we exemplify the protocol using maize brace roots, the approach can be adapted for assessing the mechanics of other plants or root types.

Why it matches plant phenotyping methodsトウモロコシの根の力学特性という植物形質を定量する三点曲げ測定プロトコルの開発・手順化が中心であり、育種利用可能な表現型測定法に該当する。

abstractHere, we describe a protocol for evaluating mechanical properties of maize brace roots.

Dynamic Prediction of Maize Tasseling Stage Based on UAV LiDAR Time-Series Plant Height Growth Curves: A Framework Coupling UAV-CHM-POI →

Accurate identification and effective prediction of the maize tasseling stage are of great significance for guiding precision field management and ensuring stable crop yields. Conventional manual observation methods suffer from high labor intensity, poor timeliness, and strong subjectivity. In this study, based on an unmanned aerial vehicle (UAV) remote sensing platform, LiDAR point cloud data and RGB imagery were simultaneously acquired to construct digital surface models (DSMs) and digital terrain models (DTMs). Multi-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize. On this basis, the Logistic growth curve function was introduced to fit the dynamic changes in plant height, enabling the identification and early prediction of the maize tasseling stage based on the plant height growth curve. The research results indicate the following: (1) For maize plant height estimation, the LiDAR sensor outperforms RGB. The optimal accuracy is achieved by combining the 99th percentile of DSM with the minimum DTM, yielding a root mean square error (RMSE) of 0.17 m. (2) Based on the high-accuracy plant height time series, the point of inflection (POI) achieves the highest accuracy in tasseling stage identification, with an RMSE of 2.586 d under the reconstructed time series. (3) Prediction accuracy of the tasseling stage improves with increasing plant height threshold, and optimal performance is observed when the threshold is ≥1.6 m with a growth rate between 0.11 and 0.13. This study establishes a technical framework of "time-series perception-dynamic simulation-feature identification-early prediction", providing a scientific basis for automated monitoring and precision management of the maize tasseling stage. It holds significant theoretical and practical value for the advancement of smart agriculture and crop phenotyping research.

Why it matches plant phenotyping methodsUAV LiDAR/RGBによる草丈推定と時系列成長曲線から、トウモロコシの抽だい期を識別・予測する技術フレームワークが研究の中心であり、植物形質取得と検証を伴うため採用。

abstractMulti-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize.

High-Throughput Microbial Assay for Amino Acid Measurement in Ground Maize Seed Samples Utilizing Auxotrophic E. coli . →

Amino acids are important nutrients in maize grain used for food and feed. Because all 20 amino acids are required for growth and development, a deficiency in a single essential amino acid limits the utilization of dietary protein. In monogastric animals, 10 amino acids must be supplied by the diet and therefore are considered essential. The remaining amino acids can be made from the 10 essential amino acids. Lysine, tryptophan, and methionine are frequently limiting essential amino acids in grain-based diets. Therefore, increasing levels of limiting essential amino acids in grain is an important objective in crop improvement. Standard chromatographic methods for assessing levels of amino acids in grain are extremely accurate, but very expensive. Here, we present a protocol for high-throughput analysis of amino acids in grains, using microbial assays, conducted in 96 well plates, that can be carried out for a fraction of the cost of the standard chromatographic methods. We use Escherichia coli strains that have mutations in the biosynthetic pathway of the amino acid of interest. These strains are auxotrophic, so their growth is proportional to the amount of a specific amino acid in the media. The level of the amino acid of interest in a corn extract is determined by adding the corn extract to the microbial growth medium and measuring the growth of the culture as turbidity in a 96 well plate reader. This protocol is designed for analysis of methionine, but can be adapted for the analysis of any amino acid, by substitution of an appropriate auxotrophic strain of E. coli .

Why it matches plant phenotyping methodsトウモロコシ種子のアミノ酸含量という育種関連形質を、96ウェルで高スループット測定する新規プロトコル自体が中心であり、単なる生物学実験のルーチン測定ではない。

abstractHere, we present a protocol for high-throughput analysis of amino acids in grains, using microbial assays, conducted in 96 well plates, that can be carried out for a fraction of the cost of the standard chromatographic methods.

Grain Quality in Maize. →

Grain quality is defined as the suitability of grain for a particular use. It is usually designated by chemical composition or physical properties of the grain. The ability to measure grain quality is important for identity preservation of specialty grain market classes, for development of new varieties with improved quality through breeding, and for basic scientific studies on the genetic or biochemical control of grain quality traits. This review introduces official methods for measuring maize compositional traits, including protein, starch, oil, amino acid, phytate, and phosphorus content. Additionally, we discuss two nonofficial methods: measuring phytate and available phosphorus levels, and assessing amino acid balance. Phytate and available phosphorous impact the mineral nutrition of grain, while amino acid balance reflects the value of grain as a protein source and the bioavailability of protein. We also describe the use of near-infrared spectroscopy (NIRS) to assess levels of various compounds in maize. NIRS relies on the fact that compounds with differing molecular properties uniquely interact with the near-infrared region (750-2500 nm) of the electromagnetic radiation spectrum, and thus, generate spectral information that can be used to develop calibration models/equations for predicting the concentration of the compounds in grain samples. We discuss how sensitivity, accuracy, precision, throughput, and cost influence the choice of assay used to assess grain quality. Furthermore, we discuss how appropriate experimental design and data analysis can improve analytical outcomes when assessing grain quality.

Why it matches plant phenotyping methodsトウモロコシ穀粒の化学・物理形質を測定する方法を中心にレビューし、NIRSによる校正モデルと測定性能も扱っているため、植物形質計測法のレビューとして対象に含める。

abstractThis review introduces official methods for measuring maize compositional traits, including protein, starch, oil, amino acid, phytate, and phosphorus content.

Depth4PH: a vision foundation model-based framework for plant height estimation in agricultural scenes →

Plant height is a key 3D phenotypic trait for assessing crop growth, biomass accumulation, and lodging resistance. To overcome the practical limitations of conventional plant height measurement methods, this study proposes a novel vision foundation model-based framework named Depth for Plant Height (Depth4PH) for plant height estimation in agricultural scenes using low-cost monocular RGB imaging. As part of our contributions, a synthetic–real coupled multimodal dataset was constructed by integrating Blender virtual agricultural scenes (Blender VAS) with real field images. Building upon the existing Depth Anything V2 foundation model, we developed a novel module called Transfer-based Agricultural Metric Depth Anything V2 (TAM-Depth V2) for absolute metric depth estimation through parameter-efficient fine-tuning, depth decoder reconstruction, and joint loss optimization. Furthermore, we designed a novel multi-source prompt-based segmentation framework, MSP-SAM2, to generate positive and negative prompts for zero-shot crop instance segmentation. Finally, a new inverse physical plant height estimation algorithm, RANSAC-Per, was introduced to estimate plant height by combining truncated percentile statistics with local RANSAC micro-plane fitting, thereby reducing the effects of depth noise and field microtopographic variation. The result showed that TAM-Depth V2 achieved stable absolute depth estimation, with an RMSE of 0.1162 m and an AbsRel of 4.25%. Compared to the original box-prompted SAM 2, MSP-SAM2 achieved a 4.4% improvement in mIoU, reaching 91.6% and a recall of 93.2%. On a 350-plant multi-crop test set, Depth4PH achieved R 2 = 0.948, RMSE = 12.23 cm, and MAE = 8.82 cm, and MAPE =10.15%, with crop-specific RMSEs ranging from 3.99 cm (cucumber) to 20.73 cm (maize), significantly outperforming the traditional Global-MinMax baseline (which had an RMSE of 22.62 cm). These results indicate that Depth4PH provides a promising foundational pathway for high-throughput crop phenotyping. With future optimization for edge deployment, it holds significant potential to support high-throughput monitoring in precision agriculture.

Why it matches plant phenotyping methods植物高の画像取得・深度推定・セグメンテーション・高さ抽出アルゴリズムを一体化した植物表現型計測フレームワークの開発と検証が中心であり、データセット構築と性能評価も含む。

abstractthis study proposes a novel vision foundation model-based framework named Depth for Plant Height (Depth4PH) for plant height estimation in agricultural scenes using low-cost monocular RGB imaging.

Phenotyping maize stay green traits via in situ leaf hyperspectral reflectance sensing →

Advancements in stay-green phenotyping are increasingly utilizing hyperspectral sensing technology to assess crop response under extreme environmental conditions. Yet, the effectiveness of different spectral features in explaining stay green remains to be fully elucidated. This includes identifying which bands and spectral indices are more effective in capturing the genotypic differences in stay-green traits. The main objective of this study was to evaluate hyperspectral leaf reflectance as a means to estimate stay-green visual scores (SGVS) as an indicator of drought tolerance and to further understand whether chlorophyll absorption-band spectral indices can differentiate SGVS classifications during post-flowering stages of maize. The experiment was conducted over two growing seasons in Germany, comprising 18 maize genotypes under two contrasting water availability conditions. We measured leaf hyperspectral reflectance using a spectroradiometer in the second, fourth, and sixth week after flowering, along with stay-green traits measurements. We employed raw spectral reflectance, hyperspectral vegetation indices (VIs) in combination with random forest (RF) and ANN models to predict SGVS. Results showed that drought stress significantly affected stay-green-related traits and led to a 43.5% decrease in grain yield in the inbred lines. The grain dry yield (GDY) was positively correlated with stay-green visual scores (SGVS), with higher SGVS associated with higher GDY. Stay-green traits were correlated with various VIs, with the best correlation observed for the Chl_NDI (r = 0.91). Stay-green groups were successfully classified using the selected VIs, with the water-absorption band VIs performing better than the chlorophyll-absorption band VIs and other VIs. Similarly, for predicting the SGVS, the water absorption band indices (R² = 0.79 ± 0.04 and RMSE = 0.12 ± 0.01) outperformed the chlorophyll absorption band indices when using RF. Leave-one-out-location/year cross-validation revealed pronounced variation in model transferability driven by environmental and temporal domain shifts. RF consistently outperformed ANN, showing greater robustness to inter-site heterogeneity and interannual variability, whereas performance degraded most in spectrally distinct environments or atypical seasons. Interestingly, RDIS_3b (1280, 1250, 1180 nm), NDIS_2b (2190, 1510 nm), and NDWI2 (860, 1241 nm) were identified as the most critical predictors in the RF models, across merged and separated datasets. These findings demonstrate the potential of spectral signatures, particularly water-absorption band spectral indices, for quantitative phenotyping of stay-green as a proxy for drought tolerance in maize breeding programs; however, multisite, multiyear calibration is needed to enhance generalizability.

Why it matches plant phenotyping methodsトウモロコシのstay-green形質を対象に、葉のハイパースペクトル反射を用いた形質推定・分類モデルを評価し、交差検証で転移性と頑健性も検証しているため、センサー型表現型計測手法が中心である。

abstractThe main objective of this study was to evaluate hyperspectral leaf reflectance as a means to estimate stay-green visual scores (SGVS)

Methodological framework of automatic field-based plot extraction and maize emergence counting using UAV imagery and deep learning →

Accurate plant population estimation is critical for crop monitoring, yield prediction, and field management in precision agriculture. However, challenges such as complex backgrounds, overlapping plants, and the need for large-scale coverage make seedling counting from UAV imagery difficult. In this study, we propose a novel automated pipeline for maize seedling counting based on high-resolution UAV orthomosaic images. The pipeline begins with an automatic field-based plot extraction method that isolates individual planting regions without the need for manual intervention. A lightweight object detection model, YOLOv8n-CA, is then employed, incorporating Coordinate Attention to enhance feature localization while maintaining fast inference. To further accelerate the process, we introduce the ‘In-Range Sliding’ strategy, which limits inference to only planting regions, reducing computational overhead. Extensive experiments demonstrate the effectiveness of our approach, achieving a mean absolute error (MAE) of 0.587 and a coefficient of determination ( R 2 ) of 94.19 % at the plot level. Additionally, our system enables spatial feature extraction such as seedling spacing, supporting more refined agronomic analysis. This work provides an efficient and scalable solution for plant counting, offering valuable insights for large-scale, rapid post-processing agricultural monitoring.

Why it matches plant phenotyping methodsUAV画像と深層学習によるトウモロコシ幼苗の計数・圃場区画抽出パイプラインを開発し、計数精度を検証しているため、植物表現型取得法が中心である。

abstractwe propose a novel automated pipeline for maize seedling counting based on high-resolution UAV orthomosaic images.

Genomic selection and genome-wide association studies using UAV-derived plant height and aboveground biomass of maize →

Plant height (PH) and aboveground biomass (AGB) are critical agronomic traits that determine the yield potential of maize ( Zea mays L . ). However, the application of genomic selection (GS) and genome-wide association studies (GWAS) in maize breeding is often hindered by the limitations of phenotypic data collection, which is typically characterized by low throughput and inadequate accuracy. To address this challenge, we employed an unmanned aerial vehicle (UAV) equipped with LiDAR and RGB cameras for high-throughput assessment of pH and AGB in a panel of 817 maize hybrids derived from 364 inbred lines over two growing seasons. Our results demonstrated that the integration of UAV-derived LiDAR point clouds with crop surface models (CSMs) enabled robust estimation of pH across multiple years ( R 2 > 0.90). Furthermore, a three-dimensional AGB estimation model was developed using UAV-derived PH and canopy coverage (CC), achieving high estimation accuracy ( R 2 > 0.83). Subsequently, the UAV-derived PH and AGB were utilized for GS and GWAS analyses. Replicated 10-fold cross-validation showed that the mean predictability was 0.504 for PH and 0.402 for AGB across eight commonly used GS models. Moreover, of the 66,066 potential crosses derived from the 364 inbred lines, the top 200 crosses selected for AGB showed up to twice the AGB of the bottom 200 crosses. Field validation demonstrated that the mean ear weight (EW) in the AGB top group was 39.0% higher than that in the bottom group. A total of 16 and 11 significant SNPs were identified by at least two GWAS methods for PH and AGB, respectively. Based on these SNPs, 81 candidate genes were functionally annotated, six of which were simultaneously associated with both traits. The candidate gene association analysis suggested that variations in the promoter region of ZmFLA9 may affect both traits. Overall, our study highlights the potential of UAV-based high-throughput phenotyping to accelerate maize genomic breeding by enabling rapid, precise, and large-scale trait assessment.

Why it matches plant phenotyping methodsUAVのLiDAR・RGBデータから草丈と地上部バイオマスを推定する高スループット表現型測定モデルを開発・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 employed an unmanned aerial vehicle (UAV) equipped with LiDAR and RGB cameras for high-throughput assessment of pH and AGB

Assimilation of UAS remote sensing and deep learning-derived crop parameters into DSSAT model for grain yield prediction →

In-season fine-scale (i.e., within-field experiment plot scale) crop grain yield (GY) prediction is critical for optimizing inputs, minimizing environmental impacts, and supporting sustainable food production. Traditional approaches, such as field surveys, are often costly and inefficient over large areas. As an alternative, remote sensing combined with crop simulation models (CSMs) has been increasingly applied for in-season GY prediction. This study investigates the potential of integrating Uncrewed Aircraft Systems (UAS)-based remote sensing data, deep learning, and CSMs to predict maize and soybean GY using a data assimilation approach. UAS multispectral imagery was collected, along with field-measured maize above-ground biomass (AGB) and soybean leaf area index (LAI) during the 2022 and 2023 growing seasons at experimental fields in Brookings, South Dakota. Maize AGB was measured at two growth stages, while soybean LAI was collected across four stages. One-dimensional convolutional neural networks (1D-CNNs) were used to estimate maize AGB and soybean LAI from canopy spectral, textural, and structural features derived from UAS imagery. These UAS and deep learning–derived crop traits were assimilated into DSSAT-Maize and DSSAT-Soybean models to optimize parameters, and the optimized models were subsequently used to predict GY. For maize, the DSSAT-Maize model achieved an R² of 0.62, an RMSE of 717.8 kg ha⁻¹, and an rRMSE of 6.7% for GY prediction. For soybean, the DSSAT-Soybean model achieved an R² of 0.81, an RMSE of 207.3 kg ha⁻¹, and an rRMSE of 4.9%. Overall, these results highlight the potential of combining high-resolution UAS data and deep learning–derived crop traits within a CSM framework through data assimilation, enabling fine-scale, in-season yield predictions and supporting precise agricultural management.

Why it matches plant phenotyping methodsUAS画像と深層学習により、作物のAGBおよびLAIという植物形質を推定する取得・解析手法が研究の中心であり、作物モデルへの同化と性能評価も行っている。

abstractOne-dimensional convolutional neural networks (1D-CNNs) were used to estimate maize AGB and soybean LAI from canopy spectral, textural, and structural features derived from UAS imagery.

Early-stage diagnosis of nutrient deficiency by chlorophyll fluorescence in maize. →

Rapid, non-destructive phenotyping is vital for early nutrient deficiency detection in plant research and agriculture. Nitrogen (N) and magnesium (Mg) deficiency are hard to distinguish with instruments measuring only chlorophyll content since both deficiencies lead to loss of chlorophyll. This study evaluated the suitability of a commercially available dual-excitation fluorescence sensor (measuring chlorophyll (Chl) and epidermal UV absorbing compounds) for identifying nutrient deficiencies in maize seedlings. Both N and Mg deficiencies in maize seedlings caused a decrease in the Chl index and an increase in the flavonol (Flav) index, although the response of the Flav index was much weaker under Mg deficiency. This happened in spite of a much larger increase in sugar concentrations under Mg deficiency. Furthermore, the nitrogen balance index (NBI) detected N deficiency earlier in leaves developing under nutrient deficiency than in those present before treatment application. Spatiotemporal analysis revealed distinct patterns of Flav index increase: N deficiency caused a marked increase in upper (younger) leaves, whereas Mg deficiency initiated Flav index increases at the tips of lower (older) leaves. Utilizing green- and red-light excitation of chlorophyll to infer epidermal anthocyanins turned out to be not straightforward in nutrient-deficient maize leaves. Taken together, these findings reveal advantages and limitations of Chl fluorescence in diagnosing nutrient stress and underscore the importance of understanding spatial-temporal nutrient dynamics for accurate early detection. Leaf age should be considered for fertilization decisions based on the NBI. A strategy is suggested how anthocyanins can be detected without problems.

Why it matches plant phenotyping methodsトウモロコシの栄養欠乏を診断する蛍光センサーの適用性と性能・限界を評価しており、植物状態の取得手法が研究の中心である。

abstractThis study evaluated the suitability of a commercially available dual-excitation fluorescence sensor (measuring chlorophyll (Chl) and epidermal UV absorbing compounds) for identifying nutrient deficiencies in maize seedlings.

A high-resolution (500 m) dataset for mapping key agronomic growth stages of maize in Northeast China. →

Accurate monitoring of agronomic phenology is essential for yield estimation and food security assessment. However, currently available maize phenology datasets usually represent only a limited number of growth stages, restricting their application in process-based crop modeling and stage-specific agricultural management. Here, we present a high-resolution maize phenology dataset for Northeast China spanning 2001-2024 at 500-m spatial resolution and daily temporal resolution. By coupling MODIS spectral information with meteorological drivers in an energy-driven XGBoost framework, we retrieved eight key agronomic stages: Emergence, Three-leaf, Seven-leaf, Jointing, Flowering, Silking, Milking, and Maturity. Validation against observations from 91 agrometeorological stations during 2009-2024 demonstrates robust performance, with an overall RMSE of less than 5 days and R² values greater than 0.63 across all stages. Beyond overall accuracy, the dataset shows strong spatial consistency and temporal stability, preserves coherent regional phenological gradients, and captures interannual variations over the 24-year period. This long-term, multi-stage dataset provides a valuable benchmark for crop model calibration, climate change impact assessment, and the development of adaptive agricultural strategies in one of the world's major maize-producing regions.

Why it matches plant phenotyping methodsMODISスペクトル情報と気象データ、XGBoostを組み合わせてトウモロコシの8つの生育段階を推定し、観測データで検証した高解像度フェノロジーデータセットであり、植物形質の取得・抽出手法とベンチマークが中心です。

abstractHere, we present a high-resolution maize phenology dataset for Northeast China spanning 2001-2024 at 500-m spatial resolution and daily temporal resolution.

Integrating Multiple Disease-Related Traits Improves Phenotypic Stratification of Corn Stunt Tolerance in Tropical Maize →

Corn stunt is one of the most important diseases affecting maize (Zea mays L.) production in tropical regions of the Americas. The disease is caused by a complex of pathogens transmitted by the corn leafhopper (Dalbulus maidis), and its predominantly quantitative inheritance complicates the identification of tolerant genotypes under field conditions. In this context, we aimed to perform a comprehensive phenotypic stratification of corn stunt tolerance in a tropical public maize diversity panel and to identify contrasting inbred lines for breeding and genetic studies. A total of 360 inbred lines were evaluated under natural infection using three complementary disease-response traits: survivor plant health score (SPHS), proportion of survivor plants (PSP), and whole-plant health score (WPHS). Multi-trait mixed-model analyses revealed significant genotypic variation, moderate to high broad-sense heritability, and significant genotype × environment interactions for all evaluated traits. A multi-trait index (MSI), calculated from standardized best linear unbiased predictions (BLUPs), successfully integrated the three phenotypic components and enabled robust stratification of the diversity panel, identifying 60 highly tolerant and 60 highly susceptible inbred lines. Further, a genomic principal component analysis demonstrated that these phenotypic extremes were distributed across both tropical and subtropical germplasm, indicating that tolerance is not restricted to a single genetic background. The proposed phenotypic framework provides a robust and reproducible strategy for characterizing quantitative disease tolerance, identifying valuable parental germplasm, and establishing well-defined phenotypic extremes for future investigations of the genetic architecture of corn stunt tolerance.

Why it matches plant phenotyping methods複数の植物病害応答形質を統合する統計的フェノタイピング枠組みと指標を中核として、耐性の再現可能な層別化手法を提示しているため。

abstracta comprehensive phenotypic stratification of corn stunt tolerance

Explainable Deep Learning for Plant Leaf Diseases: A Comparative Study of Grad CAM →

Plant disease detection is critical for sustainable agriculture and food security. While deep learning models achieve high accuracy in leaf disease classification, their black box nature poses limitations for trust and adoption among agricultural practitioners. This study presents a comparative evaluation of three convolutional neural network architectures (ConvNeXt-Tiny, MobileNetV2, and VGG16) for classifying potato, maize, and pepper leaf diseases, with emphasis on explainability through Gradient-weighted Class Activation Mapping (Grad-CAM). The experimental results demonstrate that ConvNeXt-Tiny achieves 99-100% accuracy across all plant species, MobileNetV2 attains 97-100% accuracy with lower computational requirements, and VGG16 yields 97-99.5% accuracy. Grad-CAM visualizations reveal that modern architectures precisely focus on lesion regions, whereas older models occasionally attend to irrelevant features such as leaf veins and edges. Misclassification analysis identifies shadows and natural leaf patterns as primary error sources. This research demonstrates that explainable artificial intelligence is not merely complementary but essential for developing trustworthy agricultural decision support systems.

Why it matches plant phenotyping methods植物葉の病変領域を画像から分類・可視化する手法を比較評価しており、病害状態の表現型抽出が研究の中心です。

abstractThis study presents a comparative evaluation of three convolutional neural network architectures (ConvNeXt-Tiny, MobileNetV2, and VGG16) for classifying potato, maize, and pepper leaf diseases, with emphasis on explainability through Gradient-weighted Class Activation Mapping (Grad-CAM).

ResMDCL-PDM: An IoT-Enabled Multi-Task Deep Learning Framework for Precision Pest and Disease Management in Maize and Rice Production →

Abstract Pests and diseases are major constraints to cereal production, reducing crop yield, farm profitability, and food security worldwide. Timely detection of crop health threats and accurate assessment of infection severity are essential for effective crop protection, yet conventional field scouting remains labor-intensive, subjective, and unsuitable for real-time decision-making. Although recent advances in the Internet of Things (IoT) and deep learning have enhanced automated crop monitoring, most existing approaches focus on single-task disease classification and provide limited support for severity-aware management. This study proposes ResMDCL-PDM (Residual Network with Multi-Dimensional Compensation Layer for Pest and Disease Management), an IoT-enabled multi-task deep learning framework for precision pest and disease management in maize and rice production. The framework combines field-based environmental sensing with a modified ResNet-50 architecture enhanced by a Multi-Dimensional Compensation Layer (MDCL) to jointly identify crop species, classify pest and disease categories, and estimate infection severity. Field images collected from maize and rice farms at the Federal University of Agriculture, Abeokuta, Nigeria, were integrated with publicly available benchmark datasets. Following preprocessing and data augmentation, 8,556 annotated images were used for model development and evaluation. The proposed framework achieved an overall classification accuracy of 97.8% , outperforming AlexNet, VGG16, MobileNetV3, DenseNet121, EfficientNet-B0, and the baseline ResNet-50. High precision, recall, and F1-score, together with ablation analysis, confirmed the effectiveness of the proposed MDCL. The results demonstrate that integrating IoT-enabled monitoring with multi-task deep learning provides reliable, severity-aware decision support for targeted crop protection and offers a practical, scalable solution for sustainable precision agriculture.

Why it matches plant phenotyping methods植物画像から病害・害虫カテゴリーと感染重症度を推定するIoT・深層学習フレームワークの開発と評価が中心であり、感染植物の状態を直接測定する方法論的研究である。

abstractThis study proposes ResMDCL-PDM (Residual Network with Multi-Dimensional Compensation Layer for Pest and Disease Management), an IoT-enabled multi-task deep learning framework for precision pest and disease management in maize and rice production.

Maize Yield Prediction via Data Fusion of UAV Multi/Hyperspectral Imagery and In-Field Measurements →

Timely forecasting of maize productivity is essential to support precision agriculture and optimize management practices. In this study, we analyzed the potential of integrating ground-based measurements and UAV-derived spectral data for predicting maize grain yield (GY) under different fertilization conditions. Field data were collected at two key phenological stages: early vegetative stage (V7) and pre-harvest (R4). Ground-based measurements included SPAD, above-ground biomass (AGB), and leaf area index (LAI), while multispectral and hyperspectral imagery was acquired by drone. A series of Ordinary Least Squares (OLS) models was developed to evaluate the predictive performance of individual variables and their combinations. Model robustness was assessed using two validation strategies: Leave-One-Treatment-Out (LOTO) to assess model performance across the treatments included in the experimental design and random sampling to assess performance within the dataset. The results showed that yield prediction was less accurate during the early growth stages, where data fusion significantly improved the model’s accuracy (R2 = 0.82; MAE = 6.36 q ha−1; MAPE≈7 %). The predictive performance of VIs alone increased substantially in the pre-harvest stage, with the combination of red-edge indices and LAI proving to be the best model for late yield prediction (R2 = 0.86; MAE = 6.56 q ha−1; MAPE≈7%). Comparison of multispectral and hyperspectral data revealed comparable predictive performance, suggesting that multispectral sensors may already capture the key spectral information needed for yield forecasting. Furthermore, random validation consistently produced more optimistic results than the LOTO method, highlighting the importance of using validation strategies that explicitly account for the experimental design when evaluating model performance across the treatments included in the study. Overall, the present study demonstrates that yield prediction is highly dependent on the phenological stage and validation approach, and that integrating complementary data sources can improve model performance, particularly during the early growth stages. These findings should be interpreted as a proof-of-concept based on a single-site, single-season experiment with a limited sample size (n = 12), and therefore require further validation across multiple environments and growing seasons.

Why it matches plant phenotyping methodsUAVマルチ／ハイパースペクトル画像と地上測定を統合し、トウモロコシ収量を予測する方法を開発・比較・検証しており、植物形質の取得・推定が研究の中心である。

abstractA series of Ordinary Least Squares (OLS) models was developed to evaluate the predictive performance of individual variables and their combinations.

A multimodal geospatial foundation model anticipates crop stress and yield failure across climates and species →

Introduction Climate extremes increasingly threaten agricultural production, yet many artificial intelligence systems in agriculture remain local, reactive and narrowly trained for one crop, region or sensing modality. Methods We present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions. AgriFM was pretrained using self-supervised objectives on 2.4 million field-season sequences and evaluated on a curated benchmark spanning maize, wheat, soybean, rice and sorghum across five agroclimatic regions. Results In held-out geography and time-split evaluations, AgriFM improved early stress detection and yield-failure prediction over statistical, crop-model and deep-learning baselines. The largest gains occurred during compound drought and heat events, for which AgriFM produced alerts 18 to 24 days earlier than the satellite-only baseline while maintaining improved calibration. Phenology-conditioned fusion improved transfer across planting calendars, and uncertainty calibration reduced false alerts at fixed recall. Discussion Because the study is based on retrospective datasets, these findings establish cross-region retrospective performance rather than prospective field efficacy. The results support further field-based evaluation of multimodal foundation models for climate-resilient crop monitoring.

Why it matches plant phenotyping methods作物ストレス状態と収量失敗リスクを衛星・レーダー・熱画像等から推定する基盤モデルを開発し、複数作物・地域のベンチマークで評価しており、植物状態の取得・推定手法が中心である。

abstractWe present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions.

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.

Near-Infrared Spectroscopy Non-Destructive Detection Modeling for Starch Content in Kernels of 58 Rainfed Corn Varieties. →

Traditional methods for determining starch content in corn kernels are labor-intensive, destructive, and inefficient. To overcome these challenges, this work developed a rapid, non-destructive approach based on near-infrared hyperspectral imaging, applied to 58 rainfed corn varieties. A spectral preprocessing scheme combining wavelet transform, multiplicative scatter correction, and standard normal variate transformation was employed to enhance spectral quality. A two-stage wavelength selection framework was established using competitive adaptive reweighted sampling and sparrow search algorithm optimization. From the selected optimal wavelengths, four predictive models, namely partial least squares regression, artificial neural network (ANN), convolutional neural networks, and gradient boosting decision tree, were established, implemented, and systematically compared. The results identify 14 key wavelengths (1020.65-1647.71 nm) strongly correlated with starch content, with clear assignments to specific chemical bonds and good physical interpretability. Among these models, the ANN exhibited the best performance. The R 2 , RMSE, and RPD of the test set were 0.826, 0.759%, and 2.40, respectively, indicating favorable prediction accuracy and generalization ability. These key wavelengths provide a foundation for developing portable detection instruments. This work supports corn quality grading, breeding of high-starch varieties, and rapid raw material screening, thereby enhancing the quality and efficiency of the corn industry.

Why it matches plant phenotyping methodsトウモロコシ穀粒のデンプン含量という植物器官形質を、近赤外ハイパースペクトル画像と予測モデルで非破壊推定する手法を開発・比較検証しており、フェノタイピング手法が中心である。

abstractthis work developed a rapid, non-destructive approach based on near-infrared hyperspectral imaging

Single-kernel near-infrared spectroscopy enables haploid kernel sorting in field and sweet corn using high-oil haploid inducers across diverse donor-inducer combinations →

Doubled haploid (DH) technology significantly shortens the breeding cycle for developing homozygous inbred lines in maize ( Zea mays ). Manual sorting of haploids from a larger bulk of hybrid kernels in an induction cross is a major bottleneck in DH development. Automated systems based on near-infrared (NIR) reflectance spectroscopy can be valuable tools for rapid haploid sorting, provided that sorting accuracy is sufficient for incorporation into the DH process. In this study, we evaluated the accuracy of a custom-built single-kernel NIR (skNIR) sorter for classifying haploid kernels from 12 high-oil haploid induction populations generated from two sweet corn and two field corn donors and four high-oil haploid inducers (HOHIs). We evaluated several general classification models that can be applied without population-specific recalibration or prior genotyping, including models that classified haploids based solely on predicted oil content, as well as multivariate methods that used all wavelengths of the NIR spectra. The highest classification accuracy was obtained using a general multivariate support vector machine (SVM) model. When combined with the two best-performing HOHIs, the general SVM model accurately sorted induction populations from two of the three donor backgrounds crossed with these inducers. Two oil-based methods showed less accurate classification than the multivariate SVM model, due to overlapping oil content distributions across the two kernel classes. Overall, this study demonstrates effective skNIR-based sorting of haploid kernels from diverse induction populations using a single general model. The practical deployment of this instrument in maize breeding programs is discussed.

Why it matches plant phenotyping methods単粒NIR分光装置と分類モデルによるハプロイド種子の判別・選別が研究の中心であり、複数集団で精度評価とモデル比較を行っているため、植物表現型計測手法として含める。

abstractwe evaluated the accuracy of a custom-built single-kernel NIR (skNIR) sorter for classifying haploid kernels from 12 high-oil haploid induction populations

Sentinel-2 and Unmanned Aerial Vehicle (UAV) Imagery for Irrigation Scheduling in Fodder Maize: A Comparative Remote Sensing Approach. →

Accurate estimation of crop water requirements is essential to improve irrigation efficiency for forage maize production. This study compared satellite- and UAV-derived normalized difference vegetation index (NDVI) models for estimating crop coefficients (K c ) and evaluated their operational performance for irrigation scheduling. K c -NDVI models were developed during the 2023 growing season and subsequently validated under field conditions during the 2024 season in two forage maize hybrids (N83N5 and Matador) under three irrigation strategies: conventional producer irrigation (ID1), satellite-based irrigation scheduling (ID2), and UAV-based irrigation scheduling (ID3). Both NDVI sources exhibited strong relationships with K c , with higher calibration accuracy for the UAV model (R 2 = 0.9414) than for the satellite model (R 2 = 0.8278). The UAV-based model applied 23-30% less irrigation water, maintaining high water productivity but also reducing crop growth, forage yield, and nutritional quality. In contrast, satellite-based irrigation scheduling promoted greater crop growth and produced the highest forage yield, reaching 59.8 t ha -1 in hybrid N83N5 while maintaining efficient water use. This treatment also improved forage quality by increasing dry matter and starch concentrations while reducing fiber fractions. The findings highlight the complementary potential of satellite and UAV imagery in precision irrigation and underscore the trade-offs between spatial detail, temporal resolution, and operational scalability. Furthermore, the results demonstrate that a stronger K c -NDVI relationship does not necessarily translate into improved irrigation scheduling performance. Under the conditions evaluated, the satellite-based model provided the best balance between water use, forage yield, and nutritional quality.

Why it matches plant phenotyping methods衛星・UAV画像からNDVIを用いて作物係数を推定する手法を開発し、別年・圃場条件で検証しており、植物群落状態の取得・推定が研究の中心である。

abstractK c -NDVI models were developed during the 2023 growing season and subsequently validated under field conditions during the 2024 season

Artificial Intelligence and Machine Learning for Genomic Prediction, High-Throughput Phenotyping and Climate-Adaptive Breeding In Maize and Rice: A Comprehensive Review →

Climate change is intensifying abiotic stresses such as drought and heat, posing significant threats to global food security and the productivity of staple crops including maize (Zea mays L.) and rice (Oryza sativa L.). Conventional breeding approaches are often constrained by the complex genetic architecture of stress-adaptive traits and lengthy breeding cycles, highlighting the need for more efficient, data-driven strategies. This review summarizes recent advances in artificial intelligence (AI) and machine learning (ML) for genomic prediction, high-throughput phenotyping (HTP), and climate-adaptive breeding in maize and rice. We discuss the applications of machine learning architectures, including multilayer perceptron (MLP), convolutional neural networks (CNN), random forest (RF), deep neural networks (DNN), gradient boosting methods, and explainable artificial intelligence (XAI), in improving genomic selection and capturing complex genotype–environment interactions. The review further explores the integration of AI with HTP technologies, including autonomous robotic platforms, drones, hyperspectral imaging, and LiDAR, to enable rapid, accurate, and non-destructive phenotypic assessment. In addition, we examine the role of AI-driven predictive models in identifying stress-responsive genes, improving trait prediction, and accelerating the development of climate-resilient crop varieties. Current challenges, including data heterogeneity, computational demands, model interpretability, and biological validation, are also discussed alongside emerging solutions such as multi-view learning, transfer learning, and intelligent precision design breeding. Overall, the convergence of AI, ML, multi-omics, and advanced phenotyping technologies represents a transformative framework for next-generation crop improvement, offering new opportunities to accelerate sustainable breeding programs and strengthen global food security under changing climatic conditions.

Why it matches plant phenotyping methodsAI・MLを用いた高スループット植物表現型解析と、ロボット、ドローン、ハイパースペクトル、LiDARによる表現型評価を中心的にレビューしているため。

abstractThis review summarizes recent advances in artificial intelligence (AI) and machine learning (ML) for genomic prediction, high-throughput phenotyping (HTP), and climate-adaptive breeding in maize and rice.

Nano-flow liquid chromatography-mass spectrometry for highly sensitive quantification of indole-3-acetic acid from small tissue sections prepared by laser micro dissection. →

Plant hormones play critical roles in many aspects of plant life cycles including development, growth, reproduction and responses to environmental stimuli. These processes are often associated with changes in endogenous plant hormone levels and locations. Therefore, to understand the modes of action of plant hormones, it is important to accurately quantify these chemical compounds in a high-definition tissue map. In this study, we developed a system to quantify indole-3-acetic acid (IAA), the major endogenous auxin, from small tissue samples using laser microdissection (LMD) coupled with nano-flow liquid chromatography (nano-LC)-mass spectrometry (MS), which improved detection limits, allowing quantification of IAA from a single 10 μm cryosection of maize coleoptile. Our results reveal that IAA is actively synthesized in the apical 400 μm region of the coleoptiles and is preferentially accumulated in vascular tissues. This technique can provide a precise view of the spatiotemporal distribution of plant hormones and their significance in regulating physiological responses at tissue or cellular levels.

Why it matches plant phenotyping methods植物組織中のIAAの空間分布を高感度に定量するLMD-nano-LC-MS法そのものを開発しており、植物の生理状態を組織・細胞レベルで取得する技術が研究の中心である。

abstractIn this study, we developed a system to quantify indole-3-acetic acid (IAA), the major endogenous auxin, from small tissue samples using laser microdissection (LMD) coupled with nano-flow liquid chromatography (nano-LC)-mass spectrometry (MS)

Three-dimensional reconstruction and light interception quantification of maize/soybean system based on UAV cross-surround photography →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsUAV周囲撮影による三次元再構成と光 interception の定量化が題名上の中心であり、作物群落の構造・光環境という植物状態の取得手法に該当する。

titleThree-dimensional reconstruction and light interception quantification of maize/soybean system based on UAV cross-surround photography

UAV remote sensing for yield prediction in staple crops: a review. →

Accurate yield prediction for major grain and oilseed crops, including soybean, corn, wheat, and rice, is essential for food-security assessment and precision field management. This study presents a structured integrative review of UAV-based crop yield prediction and follows PRISMA-guided procedures for literature search, screening, and evidence synthesis. Seventy peer-reviewed studies published between 2018 and 2025 were synthesized within a "Data-Ground Truth-Model-Decision" framework. Beyond summarizing UAV platforms, sensor configurations, feature-engineering strategies, and model architectures, the review explicitly distinguishes among microplot, field, and regional prediction scales, and evaluates the characteristics and limitations of yield-label acquisition methods, including manual harvest, plot-combine harvest, and combine yield-monitor data. Existing evidence indicates that the reliability of UAV-based yield prediction depends not only on optimal image acquisition windows, multi-source feature fusion, and model architecture, but also on scale-consistent yield labels, spatially aware validation strategies, and clearly defined model outputs, such as plot-level scalar yield, field-scale yield maps, and regional yield estimates. Major bottlenecks include scale mismatch between UAV imagery and yield labels, error propagation during yield-map generation, limited cross-year and cross-region transferability, weak causal interpretability, and difficulties in deploying models under complex operational field conditions. Future research should emphasize scale-explicit benchmark datasets, quality-controlled ground-truth yield acquisition, UAV-satellite-ground data fusion, spatiotemporal deep learning, and edge-cloud collaborative systems that can translate prediction outputs into agronomic decisions. This review provides a practical pathway for developing robust, interpretable, and deployable UAV-based yield prediction systems for major grain and oilseed crops.

Why it matches plant phenotyping methodsUAV画像から作物の収量という植物形質を推定する手法を中心に、プラットフォーム、特徴量、モデル、検証尺度、グラウンドトゥルースを体系的にレビューしているため、方法レビューとして収載。

abstractThis study presents a structured integrative review of UAV-based crop yield prediction and follows PRISMA-guided procedures for literature search, screening, and evidence synthesis.

Soil-Temperature-Compensated Growing Degree Days Improve Unified Simulation of Maize LAI Dynamics Across Film Mulching Treatments. →

Film mulching can promote maize canopy development by altering soil thermal conditions. However, commonly used air-temperature-based growing degree days (GDDs air ) may not adequately reflect mulch-induced soil warming or the effects of biodegradable film degradation on leaf area index (LAI) dynamics. To improve unified simulation of maize LAI under different film mulching conditions, field experiments were conducted in 2023 and 2024. Five treatments were established: 0.006, 0.008 and 0.010 mm biodegradable films (DM1, DM2 and DM3, respectively), a 0.010 mm conventional plastic film (PM), and a no-mulching control (CK). The compensation of increased soil temperature for air-temperature-based thermal accumulation during early maize growth was quantified. Modified Logistic LAI models were then developed using days after emergence (DAEs), GDDs air , soil-temperature-compensated growing degree days (GDDs stc ), and normalized GDDs stc (NGDDs stc ) as driving variables. The models were calibrated with observations from 2023 and independently validated with observations from 2024. The compensation effect acted through mulch-induced increases in 0-10 cm soil temperature during early maize growth and was stronger at the seedling stage than at the jointing stage. Compared with DM1 and DM2, daily compensation values were higher by 0.25-0.78 °C under DM3 and by 0.26-0.76 °C under PM. Independent validation showed that the GDDs stc -driven model had lower prediction error than the DAEs- and GDDs air -driven models. The NGDDs stc -driven model performed best; its RMSE values were 17.61%, 15.17% and 10.91% lower than those of the DAEs-, GDDs air - and GDDs stc -driven models, respectively. These results indicate that incorporating mulch-induced soil temperature compensation into the thermal time scale can more accurately represent maize canopy development under film mulching conditions.

Why it matches plant phenotyping methodsマルチ処理下のトウモロコシLAIという植物形質を推定するモデルを開発し、別年データで独立検証しており、形質推定手法が中心である。

abstractModified Logistic LAI models were then developed using days after emergence (DAEs), GDDs air , soil-temperature-compensated growing degree days (GDDs stc ), and normalized GDDs stc (NGDDs stc ) as driving variables.

A Single-Operator Push-Cart Multi-Beam LiDAR Platform for Multi-Trait Field Phenotyping →

Here, we present a single-operator push-cart platform equipped with a 16-beam LiDAR. A push-button interface controls data acquisition, and the data processing pipeline removes ground points, filters noise, performs 5-cm voxelization, and produces plot-level canopy metrics. We validated biomass estimation in hairy vetch (Vicia villosa) and corn (Zea mays) leaf- and whole-plant thinning experiments. In vetch, voxelized estimation of plant volume correlated strongly with destructively measured biomass (r2 = 0.88), showing that the multi-beam LiDAR can produce biomass estimates comparable to previously reported methods. In corn, comparisons of perpendicular (0°) and multi-angle LiDAR beams showed significantly greater voxel counts in the upper canopy when angled beams were used (beam angle × height interaction, p < 0.001), demonstrating that multi-beam scanning provides greater penetration into the upper canopy than a single perpendicular scan plane. We also extended the suite of LiDAR-derived traits to include apparent leaf area index (LAI), mean tilt angle (MTA), persistent homology-based stand density, and plot-bounded foliage area density (FAD). The persistent homology algorithm distinguished between leaf-removal and plant-removal treatments (removal type × removal amount, p = 0.0039). LiDAR-derived LAI has been used to estimate canopy leaf area, but gap-fraction approaches do not fully exploit the ability of LiDAR to resolve distance. Plot-bounded FAD used ray length and interception distance within defined plot volumes and was more sensitive to plot-level treatments than apparent LAI or MTA, detecting differences associated with both the removal amount and removal type. These results show that a robust, portable, multi-beam LiDAR cart can reproduce plot-level canopy measurements and improve trait especially in research-sized plots.

Why it matches plant phenotyping methods携帯型マルチビームLiDARプラットフォームと処理パイプラインを開発・検証し、バイオマス、LAI、葉面積密度などの作物形質を推定しているため、フェノタイピング手法が研究の中心である。

titleA Single-Operator Push-Cart Multi-Beam LiDAR Platform for Multi-Trait Field Phenotyping

Double Transfer Learning-Based Capsule Network for Multi-Crop Plant Disease Classification Using Heterogeneous Leaf Image Datasets →

Abstract Crop disease is a major worldwide problem in agricultural production and food security, adversely affecting yield and quality for a variety of plant species. To overcome these drawbacks, this research provides a Double Transfer Learning-based Capsule Network (DTL-CapsNet) approach for automated plant disease classification with multiple crops. Based on the image pre-processing, segmentation, double transfer learning, and Capsule Networks technologies, the proposed framework extracts discriminative features of the diseases and maintains spatial relations between the leaves symptoms effectively. This double transfer learning approach involves extracting general visual features from pre-trained deep learning models and then fine-tuning these features to classify plant diseases. Capsule Networks then leverage the visual similarity of disease patterns to make the recognition more robust, while simultaneously adding hierarchical part–whole relationships in leaf structures, thereby improving the feature representation. Experiments were performed on a heterogeneous data set consisting of 21,927 leaf images belonging to 17 different healthy and diseased classes of apple, chilli, cotton, corn and potato crops. The experimental results proposed DTL-CapsNet framework is more accurate compared to the traditional CNN-based models and conventional transfer learning models. The proposed method of double transfer learning and Capsule Networks offers an efficient and scalable approach for intelligent plant disease diagnosis, offering significant potential in precision agriculture and real-time crop monitoring systems.

Why it matches plant phenotyping methods葉画像から植物病害状態を分類する画像・計算手法が研究の中心であり、提案手法の開発と既存モデルとの比較検証が行われているため。

abstractBased on the image pre-processing, segmentation, double transfer learning, and Capsule Networks technologies, the proposed framework extracts discriminative features of the diseases and maintains spatial relations between the leaves symptoms effectively.

Toward Autonomous Crop Sensing: High-Frequency UAV-Based RGB and Thermal Imaging of Maize and Soybean →

Abstract Precision field management and high-throughput plant phenotyping increasingly rely on remote sensing to capture spatial and temporal variability in crop performance. Unmanned aerial vehicle (UAV) – based sensing offers unique advantages for field-scale data collection, including high spatial resolution, flexible deployment, and scalable throughput. However, the full potential of UAV platforms remains constrained by labor-intensive operations across flight execution, data transfer, and processing workflows. This study presents a systematic evaluation of an automatic UAV-based crop sensing platform through a season-long, multi-crop field experiment. Data acquisition was conducted over a maize irrigation trial and a soybean breeding experiment, resulting in 176 completed flights over 28 days during the growing season. High-frequency flights on selected days captured diurnal dynamics in key canopy traits, including maize leaf rolling under drought stress and genotype-dependent plot temperature variation in soybean. In the maize irrigation experiment, significant differences in diurnal canopy cover ratio (CCR) were observed among irrigation treatments. The predictive relationship between CCR and final grain yield strengthened throughout the day, with the coefficient of determination (R 2 ) increasing from 0.05 in the early morning (RMSE = 3.05 Mg ha − 1 ) to 0.65 at midday (RMSE = 1.87 Mg ha − 1 ), highlighting the importance of temporal optimization in UAV-based sensing. Temperature measurements from the onboard thermal infrared camera showed a strong overall linear correlation with ground truth measurements (R 2 = 0.85). In the soybean trial, the highest plot temperature was observed on the fast-wilting genotype. Additionally, regression models were developed to estimate key crop traits, including canopy height (CH) and leaf area index (LAI), demonstrating the platform’s quantitative sensing capability. Overall, this study demonstrates that automatic UAV systems enable high-temporal-resolution crop monitoring while substantially reducing operational cost. The results highlight their potential for precise crop management and scalable field phenotyping. Future work will focus on integrating automated data processing pipelines to support near-real-time analytics and decision-making.

Why it matches plant phenotyping methods自動UAVのRGB・熱画像センシング platform を圃場で系統的に評価し、温度・キャノピー被覆率・高さ・LAIなどの植物形質を定量化しているため、フェノタイピング手法が研究の中心である。

abstractThis study presents a systematic evaluation of an automatic UAV-based crop sensing platform through a season-long, multi-crop field experiment.

EDISP: a hybrid CNN-ViT framework for robust maize leaf disease detection and classification. →

Introduction Maize is one of the most important food crops in the world, and foliar diseases can lead to significant yield losses if identification is not performed on time. Experts conducting manual inspections find it less effective and more subjective. Deep learning-based approaches utilizing Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have been demonstrated as a viable approach to automate disease diagnosis. Thus, while CNNs fail to capture wider context due to their local feature focus and ViTs need larger datasets and tend to miss finer-grained details. To overcome these limitations, we present EDISP a hybrid framework that connects Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for local feature extraction, as well as global contextual learning. Methods The EDISP framework brings together the strengths of CNNs and ViTs to overcome their individual weaknesses. It is trained on a dataset that includes both controlled-environment and real-field maize leaf images, which helps it handle different environmental conditions. The data undergoes thorough preprocessing, including normalization, augmentation, and stratified splitting into training, validation, and test sets to support generalization. The CNN focuses on detailed local disease features, while the ViT captures broader contextual information across the maize leaf surfaces. Results The proposed EDISP model significantly outperforms standalone CNN and ViT Models in multiple performance metrics, achieving an overall classification accuracy of 99.40%, precision of 99.43%, recall of 99.38%, and an F1-score of 99.40%. Experimental results demonstrate that EDISP excels in identifying maize leaf diseases, including Common Rust, Gray Leaf Spot, Northern Leaf Blight, and Healthy leaves, with minimal false positives and negatives. External validation with an independent dataset further highlights the model's robustness and ability to generalize to real-world conditions. Discussion The EDISP hybrid architecture, integrating CNNs and ViTs, provides a stronger method for accurate, automated maize leaf disease detection. Its robust performance, consistent results on controlled and field datasets shows robustness in diverse environments. However, EDISP's effectiveness may be limited by image quality, lighting, or disease types not seen in training. These results highlight the promise of hybrid deep learning in precision agriculture and offer a scalable solution for disease detection, supporting farmers without expert diagnostic resources.

Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から検出・分類するCNN-ViT手法の開発と独立データセットによる検証が研究の中心であり、植物フェノタイピング手法に該当する。

abstractwe present EDISP a hybrid framework that connects Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for local feature extraction, as well as global contextual learning.

Estimating maize canopy water content using UAV-based multispectral-thermal infrared imagery and canopy signal distributional features. →

Introduction Canopy water content (CWC) is an important indicator of crop water status **and** supports precision irrigation decision-making. Plot-level CWC estimation using UAV imagery often relies on canopy mean features, whereas the role of within-plot canopy-signal distributional information remains insufficiently examined. Methods In this study, spring maize at the Shiyanghe site was monitored using UAV-based multispectral and thermal infrared imagery. Mean, percentile, and dispersion features were extracted from effective canopy pixels within each plot. RFECV feature selection, 50 repeated random train-test splits, paired statistical tests, simulated spatial aggregation, and four regression models were used to evaluate the stage- and scale-dependent contribution of these features. Results and discussion Water stress affected both overall spectral-thermal responses and within-plot signal distributions. Before tasseling, percentile and dispersion features were frequently selected and provided complementary information, especially for tree-based models and finer aggregation scales. After tasseling, mean features generally showed more stable performance, although some distributional features still contained CWC-related information. The supplementary Xinxiang site-internal analysis suggested that, under weak water-gradient and small-sample conditions, distributional features may be frequently selected but may not consistently improve prediction accuracy. Overall, the contribution of distributional features was growth-stage-, scale-, and model-dependent.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外画像からトウモロコシ群落の水分含量を推定する特徴抽出・選択・回帰手法を中心に、反復分割や統計検定で技術的に評価しているため。

abstractPlot-level CWC estimation using UAV imagery often relies on canopy mean features, whereas the role of within-plot canopy-signal distributional information remains insufficiently examined.

ROOTQUANT: AUTOMATED ROOT TRAIT QUANTIFICATION FROMMINIRHIZOTRON IMAGES USING DEEP LEARNING →

A bstract Quantifying root traits such as root length (RL) and root surface area (RSA) from minirhizotron imagery is a valuable approach for overcoming the phenotyping bottleneck that limits understanding and improvement of crop productivity, resource use efficiency and resilience in field experiments. However, current approaches remain labor-intensive, and deep learning (DL) methods suffer from limited generalization ability. We present RootQuant, an end-to-end DL model that simultaneously predicts RL and RSA directly from minirhizotron images using only whole-image trait values as supervision, thereby eliminating the need for pixel-level annotations. The model’s generalization ability was evaluated across species and fine-tuning configurations. The practical applicability of the model was further assessed under field conditions by converting image-derived RL estimates into volumetric root length density (vRLD). Using 118,191 maize and soybean images collected between 2009 and 2020, RootQuant trained on both species achieved an R 2 of 0.90 and an RMSE of 2.9 mm for RL, and an R 2 of 0.88 and an RMSE of 4.2 mm 2 for RSA. The same mixed-species model generalized strongly across species, yielding an 8% relative improvement in R 2 and a 30% lower RMSE on maize compared with the same architecture trained on a single species and applied zero-shot. Image-derived RL predictions converted to vRLD showed the expected depth-dependent decline in vRLD, as was also found by coincident destructive quantification of roots washed out of soil cores. By providing a generalist backbone model trained on a large dataset from two major crop species, RootQuant enables high-throughput simultaneous estimation of two relevant root traits directly from raw imagery without task-specific fine-tuning, thereby accelerating in situ root system analysis and phenotyping applications.

Why it matches plant phenotyping methodsミニライゾトロン画像から根長・根表面積を推定する深層学習手法を開発し、種間一般化と圃場適用性を評価しており、植物フェノタイピング手法が研究の中心である。

abstractWe present RootQuant, an end-to-end DL model that simultaneously predicts RL and RSA directly from minirhizotron images using only whole-image trait values as supervision

Hyperspectral imaging combined with texture features for maize hybrid purity detection: a multi-model comparison based on machine learning and SHAP interpretability study. →

Hybrid maize performance depends strongly on the genetic purity of hybrid seeds, but female self-pollinated seeds and target hybrid seeds are difficult to distinguish by conventional visual inspection because of their highly similar phenotypes. This study developed a nondestructive and interpretable maize hybrid purity detection framework by integrating hyperspectral imaging and RGB-derived texture features. Hyperspectral images were acquired from the embryo and endosperm sides of five female parents, one common male parent, and their corresponding hybrids. Texture-based, full-band spectral, characteristic-band spectral, and texture-spectral fusion models were systematically constructed and compared. Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), and Synchronous Two-Dimensional Correlation Spectroscopy (Sync2D) were used for characteristic wavelength selection. The results showed that the embryo side provided more stable and discriminative spectral information than the endosperm side. Texture-only models showed limited ability to distinguish hybrids from female self-pollinated seeds, whereas embryo-side texture-spectral fusion models combined with CARS or SPA and Support Vector Machine (SVM) or Partial Least Squares Discriminant Analysis (PLS-DA) achieved average test accuracies of 0.99-1.00, meeting the national maize hybrid seed purity requirement of 97%. In the optimal low-dimensional models, the retained high-dimensional spectral variables were compressed to 28-69 key features, corresponding to a dimensionality reduction ratio of approximately 88%-95%. SHAP analysis identified mean saturation and seed size as important texture features; among spectral intervals, the 450-462 nm region appeared among the top-ranked embryo-side SHAP features in all five maize lines and showed the highest embryo-side mean absolute SHAP magnitude (0.0107 ± 0.0028). Overall, the proposed framework provides a high-throughput, low-dimensional, and interpretable solution for maize hybrid seed purity detection.

Why it matches plant phenotyping methodsハイパースペクトル画像、RGBテクスチャ、特徴選択、機械学習を統合し、トウモロコシ種子のハイブリッド純度を非破壊推定する手法の開発・比較が研究の中心である。

abstractThis study developed a nondestructive and interpretable maize hybrid purity detection framework by integrating hyperspectral imaging and RGB-derived texture features.

UAV-Derived Multispectral Datasets and Index-Guided Segmentation for Maize Water Stress and Common Rust Detection Under Real Field Conditions →

The segmentation model achieved Mean IoU values of 0.7723 for Water Stress 2025, 0.9164 for Common Rust 2025, and 0.9531 on the benchmark dataset. The classifier achieved 99.54% accuracy for the five-class task; however, the improvement over the strongest baselines and the RGB + multispectral configuration was limited. Therefore, the classification component is not presented as a substantially superior classification-only model. Instead, it is interpreted as an exploratory multimodal analysis that quantifies the contribution and limitation of RGB, multispectral, Wavelet, and GLCM branches under the adopted UAV dataset protocol. For classification-only deployment, simpler alternatives such as DenseNet201 or the RGB + multispectral configuration may be more practical because they provide comparable accuracy with lower architectural or preprocessing complexity. Ablation, modality-controlled, and 21-run stability experiments showed reproducible segmentation results and clarified the behavior of the classification branches. RGB and multispectral branches mainly provided the peak classification accuracy, whereas Wavelet and GLCM branches mainly affected offline convergence rather than final accuracy. RGB, NDVI, and NDRE visualizations were also added for qualitative support. Since direct physiological ground measurements were not available for all samples, the masks are interpreted as adaptive index-guided labels rather than direct physiological ground truth. Overall, the main evidence of practical benefit is associated with UAV-based dataset construction, adaptive index-guided segmentation, and field-scale stress/disease mapping, while the classification experiments should be interpreted as modality-contribution and convergence analyses rather than proof of a practically superior complex classifier.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像によるトウモロコシの水ストレス・病害状態のセグメンテーション、データセット構築、再現性評価が研究の中心であり、植物表現型の取得・抽出手法として実質的です。

abstractOverall, the main evidence of practical benefit is associated with UAV-based dataset construction, adaptive index-guided segmentation, and field-scale stress/disease mapping

Validating superior maize hybrids in all India coordinated trials using REMATTOOL-R: a decision support approach. →

Introduction The identification and advancement of superior maize hybrids under the All India Coordinated Research Project (AICRP) on Maize rely on multi-environment evaluation integrating grain yield, maturity, and agronomic performance. Interpretation of large multi-environment datasets is often complex, time-consuming, and susceptible to subjectivity, highlighting the need for objective and reproducible decision-support tools. This study evaluated the effectiveness of REMATTOOL-R (Relative Maturity Adjustment Tool in R) in validating the existing hybrid advancement framework adopted under the AICRP on Maize. Methods Multi-environment trial data from the National Initial Varietal Trial (NIVT)-Late conducted during Kharif 2020-21 across five locations representing the Central West Zone (CWZ) of India were analysed. The dataset comprised 45 entries, including 40 experimental hybrids, four commercial checks, and one filler entry. REMATTOOL-R integrated grain yield with days to 50% anthesis, grain moisture at harvest, and harvested plant stand to facilitate simultaneous evaluation of grain yield, maturity, and adaptation-related traits. Least-square means generated from mixed-model analysis were used to identify superior hybrids based on a predefined grain yield superiority threshold (≥5%) over the standard check while maintaining comparable maturity and agronomic performance. Results REMATTOOL-R enabled rapid visualization and integrated assessment of multiple agronomic traits, allowing objective identification of superior hybrids. Five experimental hybrids-PM 21109L (Entry 30), R8050 (Entry 35), PM 21111L (Entry 32), BIO 978 (Entry 4), and DKC 9226 (Entry 9)-recorded ≥5% higher grain yield than the standard check Bio 9682 while maintaining statistically comparable days to 50% anthesis, grain moisture at harvest, and harvested plant stand. All five hybrids identified by REMATTOOL-R corresponded with the official AICRP decisions for advancement from NIVT to Advanced Varietal Trial-I (AVT-I), while three hybrids (R8050, PM 21111L, and DKC 9226) progressed further to AVT-II during subsequent testing cycles, confirming the reliability of the analytical framework. Discussion The findings demonstrate that REMATTOOL-R provides an efficient, transparent, and reproducible framework for the simultaneous evaluation of grain yield, maturity, and adaptation-related traits in maize multi-environment trials. By complementing the existing AICRP hybrid evaluation procedure, the tool facilitates objective advancement decisions and reduces subjectivity associated with manual interpretation of complex datasets. REMATTOOL-R therefore represents a valuable decision-support approach for coordinated maize breeding programmes and has considerable potential for application in large-scale hybrid evaluation systems.

Why it matches plant phenotyping methodsREMATTOOL-Rという解析ツールを開発・評価し、収量、成熟期、収穫時水分、植立本数を統合してハイブリッドの表現型・適応性を客観的に評価することが中心である。

abstractREMATTOOL-R integrated grain yield with days to 50% anthesis, grain moisture at harvest, and harvested plant stand to facilitate simultaneous evaluation of grain yield, maturity, and adaptation-related traits.

Estimating plant density of maize across the growing season via dynamic fusion of UAV-based multimodal data →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsUAVマルチモーダルデータを動的融合し、栽培期間中のトウモロコシ個体密度という植物群落形質を推定する手法が題名上の中心である。

titleEstimating plant density of maize across the growing season via dynamic fusion of UAV-based multimodal data

Effects of tree-stripes on crop growth in agroforestry systems using unmanned aerial systems-based analysis →

Abstract Agroforestry systems (AFS) offer a promising strategy to address environmental challenges while supporting rising food demands. However, the complex interactions between trees and crops complicate research, particularly regarding their effects on crop yields. This study presents a methodological approach using multispectral unmanned aerial system (UAS) data to investigate a maize-cultivated alley cropping system in eastern Germany as a case study. Growth parameters, namely the Normalized Difference Vegetation Index (NDVI) and plant height, were derived as proxies for yield and analyzed in relation to the distance from tree stripes. Additionally, direction-dependent regression analyses were conducted to assess whether spatial variations in the field could be attributed to the trees. Two distinct patterns emerged: first, a pronounced increase in NDVI was observed at close proximity to the trees, correlated with tree height and schematically illustrated for two representative tree stripes; second, at greater distances, fluctuations in NDVI were associated with the trees but lacked consistent directional trends. Considerable inconsistencies were also observed in plant height variations. The discussion highlights potential drivers of the close-range NDVI increase, the applicability of UAS for AFS research, and limitations in generalizing findings from a single case study. Overall, the results demonstrate that tree effects on crop growth and vitality are detectable but marginal in terms of their influence on maize yields at this site, while showcasing the utility of UAS-based approaches for field-scale analysis of AFS.

Why it matches plant phenotyping methodsマルチスペクトルUASからNDVIと植物高を抽出し、樹木からの距離に伴う作物形質を解析する手法の実質的適用が研究の中心であり、単なるルーチン測定を超える。

abstractThis study presents a methodological approach using multispectral unmanned aerial system (UAS) data to investigate a maize-cultivated alley cropping system in eastern Germany as a case study.

From Scalar Summaries to Functional Comparisons: A Framework for Analyzing Plant Disease Progress Curves. →

Disease progress curves (DPCs) are central to evaluating disease management strategies, including host plant resistance. Although widely used and often appropriate, scalar summaries such as the area under the disease progress curve (AUDPC) may obscure meaningful differences in epidemic timing and trajectory shape. Here, I introduce a curve-based framework for comparing plant disease epidemics that treats DPCs as epidemic phenotypes, enabling trajectory-based comparisons beyond conventional scalar summaries. Using a hierarchical generalized additive model, environment-adjusted mean epidemic curves were estimated for each treatment (corn hybrid) while accounting for repeated assessments and environmental heterogeneity. Similarity among hybrids was quantified using a functional distance defined over the epidemic time domain, and hierarchical clustering was used to identify epidemic phenotypes based on differences in curve shape. Applied to multi-environment field data (6 environments; 74 DPCs) for southern corn leaf blight in 13 hybrids, this approach identified distinct epidemic phenotypes that were not fully reflected by AUDPC-based comparisons, despite similar overall disease levels. In addition, a distance-based permutation test indicated that breeder-defined resistance classes (moderately resistant versus resistant), established independently of the curve analysis, were associated with systematic differences in epidemic trajectory shape across environments. By shifting emphasis from scalar summaries to curve-based epidemic representations, this framework provides a complementary tool for host resistance phenotyping and comparative epidemiology and establishes a foundation for hierarchical synthesis and trajectory-based inference across environments.

Why it matches plant phenotyping methods植物病害進展曲線を植物病害表現型として解析する統計的・計算的フレームワークを開発し、複数環境・ハイブリッドで適用しているため、方法が研究の中心である。

abstractHere, I introduce a curve-based framework for comparing plant disease epidemics that treats DPCs as epidemic phenotypes, enabling trajectory-based comparisons beyond conventional scalar summaries.

FewShotCropNet: Real-Time Detection of Emerging Crop Diseases with Limited Labels Using Spectral-Temporal Attention Prototypical Networks →

The acquisition of labelled data for new or emerging plant diseases is difficult due to the high cost and logistical complexity of ground-truth collection, challenges that are further compounded by the limited infrastructure available to smallholder farmers in sub-Saharan Africa. This study presents FewShotCropNet, a few-shot learning model based on Spectral-Temporal Attention Mechanisms and Prototypical Networks that utilises multispectral time-series data from Sentinel-2 to classify crop diseases. Two principal innovations are incorporated in the proposed model: (1) a spectral attention mechanism based on the Squeeze-and-Excitation approach to learn disease-relevant spectral band weights; and (2) a temporal attention pooling mechanism to identify the most discriminative growth stages for disease classification. The model employs a two-phase training strategy comprising supervised pre-training followed by episodic meta-learning, enabling the generation of optimal feature representations under extreme label scarcity. Crop disease detection experiments were conducted in Edo State, Nigeria on cassava and maize using monthly Sentinel-2 composites from 2024 (10 spectral bands and five vegetation indices across twelve temporal steps). Under a 4-way 5-shot classification scenario with 100 GPS-validated labelled samples (25 per-class), FewShotCropNet achieved a mean accuracy of 98.15% with a 95% confidence interval of ±0.58%. An equitable comparison was enabled by introducing a Pre-trained Simple Prototypical Network, a variant sharing the same two-phase training strategy as FewShotCropNet but without the attention modules—which achieved 97.75% (±0.63%). FewShotCropNet exceeded the Pre-trained Simple ProtoNet by +0.40 percentage points (t = 1.52, p = 0.13), with the attention module contribution verified as positive though not statistically significant on the current dataset. Statistically significant improvements over models trained without pre-training were observed: FewShotCropNet outperformed the Relation Network (94.40%), Matching Network (94.40%), and the Optimised Baseline Convolutional neural networks (CNN) (86.65%, pre-trained backbone with 5-shot linear probe), with p

Why it matches plant phenotyping methods植物病害状態を対象に、Sentinel-2時系列データから病害を分類するFewShotCropNetを開発し、比較評価しているため、病害フェノタイピング手法が中心である。

abstractThis study presents FewShotCropNet, a few-shot learning model based on Spectral-Temporal Attention Mechanisms and Prototypical Networks that utilises multispectral time-series data from Sentinel-2 to classify crop diseases.

Additively Manufactured in planta Integrated Microneedle–Microfluidic Sensing: Nondestructive Electrochemical Tracking of Glucose and Water Stress in Agricultural Crop Plants →

Abstract Timely quantification of crop stress physiology remains challenging because conventional assays are destructive, labor-intensive, and poorly suited for continuous monitoring and field deployment. Here, we report a microneedle-enabled electrochemical biosensing platform with smartphone-based data collection for the in planta monitoring of plant stress that integrates three design innovations in a single architecture: (i) a fully integrated hollow microneedle–microfluidic measurement pathway for sap access, (ii) physical isolation of the metal electrodes from direct tissue contact to reduce insertion-zone abrasion of the sensing interface, improving biocompatibility and potentially lowering fouling pathways, and (iii) lithography-free fabrication of a modular transducer on an additively manufactured substrate. The platform comprises a three-electrode gold (Au) transducer modified with a nanostructured reduced graphene oxide (rGO)–chitosan layer. The biosensing platform enabled dual sensing channels via functionalized glucose oxidase (GOx) and horseradish peroxidase (HRP) for the detection of glucose and water stress-associated hydrogen peroxide (H2O2), respectively. The glucose channel showed a strong linear calibration over the tested range, with Pearson’s r = 0.99, R2 = 0.98, sensitivity of 62.34 μA/mM, and a limit of detection (LOD) of 102.50 μM (∼1.85 mg/dL), while the H2O2 channel exhibited Pearson’s r = 0.99, R2 = 0.99, sensitivity of 3.65 μA/decade, and an LOD of 3.22 μM. Repeatability across measured standards remained high for both channels, with mean coefficients of variation of 1.31% for glucose and 1.16% for H2O2. Ex vivo measurements in plant sap, including standard-addition experiments and comparison with commercial benchmark assays, provided validation of analyte concentration determination in plant-derived samples. In planta measurements on maize plants (Zea mays L.) grown under graded watering treatments revealed statistically significant treatment-dependent glucose and H2O2 signatures over time (p

Why it matches plant phenotyping methods植物体内のグルコースとH2O2を非破壊・連続測定し、水ストレス状態を推定する電気化学センシング基盤の開発と検証が中心であり、植物フェノタイプ取得手法に該当する。

abstractwe report a microneedle-enabled electrochemical biosensing platform with smartphone-based data collection for the in planta monitoring of plant stress

Comparison of Isotope Mass Balance and AquaCrop Model in Evapotranspiration Partitioning in a Maize Field of North China. →

Understanding evapotranspiration (ET) partitioning into soil evaporation (E) and plant transpiration (T) is crucial for improving agricultural water use efficiency in water-scarce regions. The isotope mass balance (IMB) method and AquaCrop model are two widely used approaches for ET partitioning, yet their comparative performance across different crop growth stages remains poorly characterized. This study systematically compared these two methods using two consecutive years (2012-2013) of field isotopic observations in a summer maize field on the North China Plain, a core maize production area facing severe agricultural water scarcity. Stable isotope analysis showed that the local meteoric water line (LMWL) had a slope lower than the global meteoric water line. The 0-5 cm surface soil water evaporation lines had slopes of 5.84 (2012) and 8.06 (2013), confirming significant evaporative enrichment in the topsoil. Plant water isotopic composition closely resembled that of 40-100 cm deep soil water, indicating limited root uptake from the surface layer. IMB-estimated transpiration ratio (T/ET) exhibited distinct phenological patterns, increasing from 37 to 44% at jointing to a peak of 94-96% at filling, then declining to 84-85% at maturity. The two methods agreed well during filling to maturity (differences of 2-10%), but compared with the IMB method, AquaCrop substantially underestimated T/ET at jointing (0.9% vs. 43.8% in 2013) due to its canopy-cover-based transpiration algorithm. These findings identify the filling stage as the critical water demand period, providing a quantitative reference for precision irrigation management under similar climate and soil conditions.

Why it matches plant phenotyping methodsトウモロコシの蒸散比を対象に、同位体質量収支法とAquaCropモデルを比較・検証しており、植物の水利用状態を取得する測定手法の性能評価が中心である。

abstractThe isotope mass balance (IMB) method and AquaCrop model are two widely used approaches for ET partitioning, yet their comparative performance across different crop growth stages remains poorly characterized.

Leaf area extraction framework: Transformer-based segmentation and geometric-based completion approaches for accurate extraction of field maize leaf area from point clouds →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methods圃場トウモロコシの葉面積を点群から抽出するセグメンテーション・幾何学的補完手法の開発が題名の中心であり、植物形質の取得方法に該当する。

titleLeaf area extraction framework: Transformer-based segmentation and geometric-based completion approaches for accurate extraction of field maize leaf area from point clouds

Lightweight contour-aware 2D Gaussian splatting under Plant-to-Camera →

The objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud. A lightweight, contour-aware framework leverages the view-consistent and surface-oriented representation of 2D Gaussian Splatting, making it suitable for plant surface reconstruction under the Plant-to-Camera mode. A contour-weighted Laplacian regularization suppresses depth discontinuities around plant boundaries, while simplified Gaussian primitives improve computational efficiency without compromising geometric fidelity. Organ-level semantics are integrated into the reconstructed geometry to distinguish plant organs such as leaves, stems, and ears. On maize and wheat datasets, our method outperformed existing approaches in terms of morphological fidelity, organ-level structural consistency, and processing speed, demonstrating its suitability for plant phenotyping

Why it matches plant phenotyping methods植物器官の3D再構成と形態情報抽出を目的とする計算手法を開発し、既存法と形態忠実度・器官構造整合性・処理速度で比較評価しており、フェノタイピング手法が中心である。

abstractThe objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud.

Using UAV Multispectral Imagery to Predict Leaf SPAD Dynamics During Maize Growth Under Different Plant Densities →

Chlorophyll content represents a key growth indicator for maize. The traditional SPAD (Soil and Plant Analyzer Development) method, though easy to operate, is inefficient, destructive, and unsuitable for high-throughput field monitoring. UAV (Unmanned Aerial Vehicle) remote sensing technology is highly efficient and detects abundant indicators, enabling large-scale SPAD measurement. In this study, 18 vegetation indices and eight texture features were selected as the indicator system by combining prior knowledge and experimental analysis. In a two-year maize density experiment, multispectral images were collected in the growth period. The correlations among SPAD values, multispectral indices and texture features were analyzed using Pearson correlation coefficients. Then the detection accuracies of three algorithms, i.e., RF (Random Forest), PLSR (Partial Least Squares Regression), and SVR (Support Vector Regression), were compared under this indicator system. Compared with models constructed using single vegetation indices or single texture features, the estimation accuracy of the indicator system at the jointing stage was improved by 0.13 and 0.22, respectively. The results showed that SVR achieved the highest estimation accuracy among the three algorithms, with determination coefficients (R2) of 0.73, 0.77and 0.70 at the jointing, silking, and grain-filling stages, respectively. This study established a non-destructive monitoring framework for chlorophyll content during the entire maize growth stage based on UAV data.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と回帰モデルにより、トウモロコシの葉緑素量（SPAD）を非破壊推定する手法を構築し、複数アルゴリズムの精度比較も行っており、表現型取得手法が中心である。

abstractThis study established a non-destructive monitoring framework for chlorophyll content during the entire maize growth stage based on UAV data.

Comparative evaluation of precision planter performance via UAV remote sensing: A workflow for maize emergence monitoring →

To overcome the inefficiency and subjectivity of manual seedling surveys, this study presents a unsupervised framework for evaluating maize sowing quality and emergence uniformity via UAV-based remote sensing. Centimeter-level multispectral imagery was captured to reconstruct 3D point clouds using SfM and MVS techniques. At the algorithmic level, an improved unsupervised pipeline was developed: the Otsu method was employed for plant segmentation, followed by a Fourier Transform to extract 2D spatial frequency features for precise crop row identification and automated spacing measurement. Subsequently, the Combined Entropy Uniformity (CEU) index was developed using Shannon entropy, and a proxy for canopy closure (CCP) was derived using a porosity model, thereby enabling the simultaneous relative quantification of seedling height consistency, spatial distribution uniformity, and canopy geometric structure variability. At the application level, the framework was validated through field trials involving 19 precision planters of diverse configurations. Performance was assessed using indices such as qualified spacing, miss-sowing, and the Coefficient of Variation of Plant Spacing (PSCV). Results indicate that: (1) Vacuum-type planters exhibited optimal stability at speeds of 7–9 km/h, achieving an average qualified spacing rate of 76.7% and a PSCV of approximately 24%, whereas finger-pickup planters were more sensitive to seed size variation and mechanical vibration. (2) The results from the Generalized Additive Model (GAM) suggest a possible nonlinear relationship between seeding rate and certain uniformity indices, indicating that appropriately adjusting operational parameters could help balance operational efficiency and seeding quality; however, this trend requires further validation with larger sample sizes and repeated observations. (3) Point cloud CEU metrics and canopy structure proxies based on the Gap Fraction model showed statistical correlations with certain manually collected indicators, indicating that this method has the potential for rapid screening of seeding quality and relative evaluation of seedling population structure at the field scale under the current experimental conditions.

Why it matches plant phenotyping methodsUAV画像・3D点群から作物の出芽、草丈均一性、空間分布、群落構造を抽出する解析ワークフローを開発し、19種のプランターで検証しており、植物表現型取得法が中心である。

abstractthis study presents a unsupervised framework for evaluating maize sowing quality and emergence uniformity via UAV-based remote sensing.

Enhancing Vision Transformers with Kolmogorov–Arnold Networks for Plant Leaf Disease Classification →

Abstract Early and accurate detection of plant diseases is vital for global food security and sustainable agriculture. While deep learning offers promising solutions, there is a continuous need for architectures that enhance learning capacity and efficiency. This study introduces ViT-KAN, an innovative hybrid model merging the powerful feature extraction of Vision Transformers (ViT) with the flexible, learnable activation functions of Kolmogorov-Arnold Networks (KAN). By replacing the standard Multilayer Perceptron (MLP) classification head of ViT with a KAN module, the proposed architecture aims to better capture nonlinear patterns in agricultural images. Evaluated on the PlantVillage dataset for potato and maize leaf diseases using standard fivefold cross-validation, with final results reported as mean ± standard deviation across the five folds, the model was trained entirely from scratch. ViT-KAN achieved 99.49 ± 0.13% accuracy on the maize dataset and 98.28 ± 0.51% on the potato dataset, compared with 98.92 ± 0.40% and 97.77 ± 0.88%, respectively, for the standard ViT model. Beyond mean accuracy, ViT-KAN showed lower standard deviation across folds, while representative fold curves suggested smoother early training trajectories under the shared training configuration. These findings suggest that ViT-KAN is a promising alternative to conventional ViT-based classification models for plant disease diagnosis.

Why it matches plant phenotyping methods植物葉画像から病害状態を分類する新規ViT-KANモデルを開発・交差検証しており、病害表現型の取得・推定手法が研究の中心である。

abstractThis study introduces ViT-KAN, an innovative hybrid model merging the powerful feature extraction of Vision Transformers (ViT) with the flexible, learnable activation functions of Kolmogorov-Arnold Networks (KAN).

AI-Driven Crop Detection and Plant Disease Prediction →

Crop disease identification is still a big problem in agriculture, which results in large yield losses and food lacks, especially in regions dependent on manual monitoring. Traditional methods of identifying plant diseases are often labor intensive, error-prone, and ineffective in early-stage diagnosis. To overcome these limitations, this study proposes a hybrid machine learning model for accurate crop type identification, disease classification, and severity prediction using image data. The methodology utilizes the PlantVillage dataset, encompassing over 50,000 annotated leaf images across 14 crops. After rigorous preprocessing involving image resizing, normalization, and cleaning, Improved Deep Joint Segmentation is applied to localize disease-affected regions. Feature extraction incorporates color, texture (GLCM, LBP), and shape attributes to enhance classification accuracy. A hybrid approach integrating XGBoost for feature selection and Support Vector Machine (SVM) for classification is proposed to capture both overarching trends and intricate details. Experimental results across four major crops—potato, tomato, corn, and grape—demonstrate superior performance, with the hybrid model achieving 98.6% accuracy, 98.3% precision, 99.0% recall, and 99.1% F1-score. The model outperforms existing approaches, offering a robust, scalable, and accurate solution for early crop disease detection in precision agriculture.

Why it matches plant phenotyping methods画像から病変領域を抽出し、植物病害の分類と重症度を推定する機械学習ワークフローが研究の中心であり、植物状態の表現型計測に該当する。

abstractthis study proposes a hybrid machine learning model for accurate crop type identification, disease classification, and severity prediction using image data.

Advanced Progressive Graph Convolutional Networks for Early Detection and Monitoring of Plant Infections and Disease Progression in Smart Agriculture →

ABSTRACT Plant disease is a physiological or structural problem caused by pathogens such as fungi, bacteria, viruses, or environmental factors, which disrupts plant development, yield, and overall health. Furthermore, the formation of new and more aggressive diseases complicates disease control, making it harder for farmers to preserve their crops while ensuring consistent food production. In this manuscript, to advance Progressive Graph Convolutional Networks enable early detection and continuous monitoring of plant infections in smart agriculture (PGCN‐EDM‐PID) is proposed. Initially, input images of food grains such as rice, wheat, and maize are collected from internet sources. To implement this, the input image is preprocessed using the Adaptive Two‐Stage Unscented Kalman Filter (ATSUKF), which performs resizing, sharpening, cropping, contrast enhancement, brightness adjustment, and Gaussian blurring on the images from the dataset. Then the preprocessed images are augmented based on horizontal flip, width shift, height shift, vertical flip, rotation range, shear, zoom and brightness. Additionally, Make Sense AI is proposed to annotate the images in the dataset under each class. Then the preprocessed and augmented images are fed to Progressive Graph Convolutional Networks (PGCN) to detect and classify the plant diseases. Generally, PGCN does not show adapting optimization approaches to find ideal factors to assure accurate plant disease detection. Therefore, the Augmented Red Panda Optimizer (ARPO) was proposed to optimize the weight parameter of PGCN, which accurately detects the plant disease. Then the proposed PGCN‐EDM‐PID is executed in Python and the performance metrics such as Accuracy, Precision, False Positive Rate (FPR), True Positive Rate (TPR), Specificity, Recall, F1‐score, Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are analyzed. Performance of the PGCN‐EDM‐PID approach attains high accuracy, high Precision, high Recall when analyzed through existing techniques like Real‐time plant disease dataset improvement and detection of plant disease utilizing DL (PDD‐DPD‐CNN), Detection of plant leaf diseasesusing deep convolutional neural network methods (DPLD‐DCNN), New DL algorithm for cross‐crop detection of plant disease: A generalized model for detecting unhealthy leaves (CPDD‐SVM) methods respectively.

Why it matches plant phenotyping methods植物画像から病害を検出・分類する画像解析手法の開発と性能評価が研究の中心であり、感染状態という植物表現型を直接推定している。

abstractProgressive Graph Convolutional Networks enable early detection and continuous monitoring of plant infections in smart agriculture (PGCN‐EDM‐PID) is proposed.

Regional-Scale Estimation of Maize Plant Moisture Content in Arid Regions Integrating Multi-Source Remote Sensing and Machine Learning →

Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R2 of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R2 increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments.

Why it matches plant phenotyping methodsマルチソースリモートセンシングと機械学習により、トウモロコシの植物含水量という明示的な植物状態を地域スケールで推定・検証する手法開発が中心である。

abstractthis work develops a regional PMC estimation approach by combining multi-source remote sensing data.

Real-Time Crop Stress Monitoring and Early Warning System for Paddy and Maize Using Multi-Temporal Sentinel-2 Data and Deep Learning in Semi-Arid Regions →

Semi-arid regions with a high potential for rice and maize cultivation have become some of the most actively farmed areas. They now face the challenge of achieving food security despite the threats of crop water stress, nutrient loss, and environmental changes. In this paper, we develop a real-time crop stress monitoring and early warning system that utilizes multi-temporal Sentinel-2 images and deep learning models in Mahabubabad district, Telangana, India. Different types of crop stresses such as water stress, nutrient deficiency, and phenological anomalies were detected and classified using a hybrid CNN-LSTM architecture with an attention mechanism. The methodology was based on 874 field polygons with extensive in-situ data collection during 2023-24, incorporating multi-temporal spectral indices (NDVI, EVI, NDWI, REP), weather variables, and soil characteristics. The total classification accuracy reached 89.4% for paddy and 87.2% for maize over all stress types, showing that stress detection from satellite images is quite reliable. Water stress was the category that was detected most accurately (92.1% for paddy and 89.8% for maize), followed by nutrient stress (88.7% and 86.3%) and phenological stress (85.2% and 83.9%). The warning system made it possible to identify the problem 15-25 days before there were visible symptoms, making it possible for the farm management to respond in time. Activities of the farm that were most vulnerable to detection were air and water temperatures, precipitation, and crop growth stages for water stress 45-60 days after sowing, 30-45 days for nutrient stress, and during the reproductive phase for phenological stress. The system could be extended for industrial crop stress monitoring across the semi-arid agricultural systems which might lead to precision agriculture and climate-resilient farming practices.

Why it matches plant phenotyping methods衛星画像と深層学習を用いて作物の水ストレス・栄養ストレス・生育異常を直接推定し、精度検証と早期検出性能を評価しているため、植物表現型取得法が中心である。

abstractwe develop a real-time crop stress monitoring and early warning system that utilizes multi-temporal Sentinel-2 images and deep learning models

Performance Evaluation and Cross-Dataset Validation of Machine Learning Algorithms for Crop Disease Identification Using Rice and Maize Leaf Images →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsイネとトウモロコシの葉画像による病害識別について、機械学習アルゴリズムの性能評価とデータセット間検証が主題であり、植物病害状態の画像ベース推定手法を検証している。

titlePerformance Evaluation and Cross-Dataset Validation of Machine Learning Algorithms for Crop Disease Identification Using Rice and Maize Leaf Images

A multi-source remote sensing and machine learning framework for maize mapping and yield estimation in fragmented Loess gully regions. →

Accuracy crop distribution mapping and reliable yield estimation are essential for overcoming fragmentation and decentralization in smallholder farming systems of the Loess Plateau gully region. Multi-source remote sensing data, ancillary datasets, and machine learning techniques were integrated to map maize distribution and estimate yield. First, Sentinel-2 temporal spectral features, vegetation indices, and topographic variables were integrated to identify the optimal maize mapping model by a comparing machine learning algorithms: Random Forest (RF), Extra Trees (ET), Gradient Boosting Decision Tree (GBDT), and Histogram-Based Gradient Boosting Decision Tree (HGBDT). Subsequently, Sentinel-2 optical data and ERA5-Land meteorological data were dynamically resampled and spatiotemporally fused. A maize yield estimation model was then developed by integrating these fused predictors with in-situ measured maize yield samples. Finally, SHapley Additive exPlanations (SHAP) analysis was applied to quantify the feature contribution to both crop mapping and yield estimation, improving model transparency and interpretability. The results indicate that RF model achieved superior performance for maize identification in heterogeneous agricultural landscapes, with an overall Accuracy of 0.825, Precision of 0.849, Recall of 0.933, and F1-Score of 0.889. In the multi-source fusion-based yield estimation task, the HGBDT model yielded the highest predictive accuracy, with an R² of 0.6, RMSE of 1.07 t/ha, relative RMSE (rRMSE) of 11.49%, and MAE of 0.86 t/ha. Here, a methodological advancement is presented toward accurate and interpretable crop mapping and yield estimation in the ecologically complex and topographically fragmented Loess Plateau.

Why it matches plant phenotyping methodsトウモロコシの収量という植物形質を、リモートセンシング・気象データ融合と機械学習で推定する手法を開発・評価しており、単なる農業実験の routine 測定ではない。

abstractA maize yield estimation model was then developed by integrating these fused predictors with in-situ measured maize yield samples.

Phenomics-derived temporal maize health and environmental index enhance physiology-informed genomic prediction of yield across environments. →

Integrating multi-omics data, including phenomic, genomic, and environmental inputs, offers a powerful approach for enhancing maize performance and predicting grain yield. In this study, crop health was quantified using temporal NGRDI (Normalized Green Red Difference Index) trajectories collected from 16 unoccupied (unmanned) aerial vehicle or system (UAV or UAS, drones and sensors) flights (from 19 to 117 d after planting) in maize trials conducted in Texas. Crop health indices (CHIs) were calculated through area under the curve (CHIAUC) and functional principal component analysis (CHIFPCA), capturing dynamic plant health responses throughout the growing season. Heritability estimates of NGRDI fluctuated between 0.3 and 0.7, averaging 0.51 ± 0.02, reflecting consistent genetic contributions to growth dynamics. CHIs derived from a favorable (irrigated) trial in Texas effectively separated high- and low-yielding hybrids across 41 environment-tester combinations, achieving significant differentiation in 27 (CHIAUC ) and 28 (CHIFPCA1) environments. In comparison, only 21 environments were differentiated when using grain yield alone. Genomic mapping of temporal NGRDI revealed key quantitative trait loci (QTLs) linked to maize growth, containing candidate genes including br2, phyC1, wus1, mads69, cct1, rap2, miR172, and gl15, associated with canopy development, flowering regulation, and drought adaptation. Integrating multi-omics data into phenomic- and environment-informed genomic prediction models improved yield prediction accuracy by approximately 18.5%, particularly for untested genotypes in both tested and untested environments. These findings demonstrate that multi-omics integration provides a scalable framework for enhancing maize performance and advancing grain yield prediction across diverse agricultural systems.

Why it matches plant phenotyping methodsUAV画像から時系列NGRDIを抽出し、作物健康・生育動態の表現型指標を構築して検証・予測に利用しており、フェノタイピング手法の適用が研究の中心的要素です。

abstractCrop health indices (CHIs) were calculated through area under the curve (CHIAUC) and functional principal component analysis (CHIFPCA), capturing dynamic plant health responses throughout the growing season.

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

Genetic dissection of dynamic leaf area index variation in maize using UAV-based phenotyping and time-series genome-wide association studies →

Abstract The leaf area index (LAI) is a key determinant of canopy architecture and yield potential in maize, primarily through its influence on photosynthetic efficiency. Although unmanned aerial vehicle (UAV) technology has greatly advanced field-based phenotyping, its potential for deciphering the genetic mechanisms underlying dynamic and complex trait development remains underexplored. In this study, multispectral UAV images were collected from a diverse maize panel across eight developmental stages in four environments over two consecutive years. Using multi-temporal data, a random forest model accurately predicted LAI (R² = 0.82–0.83), significantly outperforming models based on single time-point data. By integrating high-throughput phenotypic predictions with time-series genome-wide association studies (GWAS), 36 dynamic SNPs associated with LAI variation were identified. Principal component analysis (PCA) of temporal LAI data revealed two principal components that together explained 84.2–86.5% of the total phenotypic variance. GWAS based on these components identified an additional 51 SNPs, seven of which overlapped between the two analytical approaches. Among the 72 candidate genes identified, Zm00001d048615 exhibited significant variation in both phenotype and expression among different inbred lines. The heterologous overexpression of Zm00001d048615 in Arabidopsis induced leaf curling and a significant reduction in leaf size, indicating its potential role in regulating leaf development. Collectively, these findings establish a robust framework that integrates UAV-based phenomics with temporal GWAS to identify key genes regulating complex dynamic traits. This approach provides valuable insights and genetic targets for improving maize canopy architecture and yield potential through molecular breeding.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と時系列データからLAIを推定するモデルを開発・評価し、高スループット表現型解析に中核的に用いているため。

abstractmultispectral UAV images were collected from a diverse maize panel across eight developmental stages in four environments over two consecutive years.

TDAVM-UNet: task-driven attention VM-UNet for crop disease detection from UAV imagery →

Crop diseases pose a serious threat to agricultural yield and global food security. Accurate detection using Unmanned Aerial Vehicle (UAV) remote sensing imagery is of great significance for precision agriculture. However, this task remains challenging due to complex field backgrounds, diverse spectral-spatial characteristics of diseased leaf regions, irregular lesion boundaries, and variable texture patterns. To address these issues, this paper proposes a Task-Driven Attention VM-UNet (TDAVM-UNet), a novel deep learning model for crop disease detection from UAV imagery. The model integrates two task-driven attention modules: (1) Disease-Aware Dynamic Attention (DADA), which enhances the representation of diseased regions through disease feature enhancement, multi-scale dynamic channel attention, and texture-guided spatial attention; and (2) Channel-Spatial Visual State Space (CSVSS), which enables efficient long-range dependency modeling and local-global feature fusion while maintaining linear computational complexity. A hybrid loss strategy combining binary cross-entropy (BCE) loss, Dice loss, and cross-entropy (CE) loss with optimized coefficients is employed to address class imbalance and boundary delineation challenges. Extensive experiments are conducted on a self-constructed UAV crop disease unified mix dataset, comprising soybean disease images from Maharashtra, India, and rust disease images from wheat, corn, and other crops in Yangling, China, totaling 6,680 raw collected images, which after deduplication yields 5,000 images for experimentation. The results demonstrate that TDAVM-UNet achieves 26.87M parameters and 31.45 GFLOPs for 256×256 inputs, maintaining O(N) linear complexity (80% lower than TransUNet’s 156.78 GFLOPs), with 82.22% mIoU. This work provides a high-accuracy, robust, and computationally efficient method for UAV-based crop disease detection, offering significant technical support for precision agriculture applications.

Why it matches plant phenotyping methodsUAV画像から作物病害の病変領域・病害状態を推定する深層学習モデルを開発し、データセット上で性能評価しており、植物表現型取得・抽出手法が研究の中心である。

abstractthis paper proposes a Task-Driven Attention VM-UNet (TDAVM-UNet), a novel deep learning model for crop disease detection from UAV imagery.

An Edge AI and Mobile Sensing Framework for Real-Time Multi-Crop Disease Detection and Geospatial Surveillance in Smallholder Agricultural Systems →

Abstract Maize and cassava are staple crops in Nigeria, but their productivity is limited by viral and fungal diseases. This study created a mobile system based on lightweight CNN for smartphone portable real-time detection of cassava and maize diseases. In the 2025 planting season, 10,800 leaf images were gathered from a 10-acre experimental farm in Araromi Area, Bakatari Farm, Ido Local Government, Ibadan that cut across the seven classes of healthy cassava (1,620, 15%), cassava mosaic disease (1,540, 14.3%), cassava brown streak disease (1,410, 13.1%), healthy maize (1,880, 17.4%), maize leaf blight (1,540, 14.3%), maize rust (1,360, 12.6%) and maize streak virus (1,450, 13.4%). In order to make the dataset more diverse, data augmentation was done in the form of rotation by +/-30°, flipping, brightness by +/-20% and random cropping in the range of 80-100%. Lightweight CNN architectures MobileNetV2, EfficientNet-Lite, ShuffleNet, and custom CNN were trained in an 80:20 ratio for train and test. Out of 11 models tested, EfficientNet-Lite model forecast the highest where it achieved an accuracy of 94.6%, precision of 0.95, recall of 0.94, F1-score of 0.94, and an ROC-AUC of 0.97. As for MobileNetV2, it achieved an accuracy of 93.8% while ShuffleNet was estimated to achieve the fastest mobile inference at 65 ms. As for the class-wise analysis, it can be seen that the maize leaf blight (95.2%) and cassava mosaic disease (94.1%) had the most accurate predictions. The offline prediction from mobile deployment showed that EfficientNet-Lite occupied 92 ms and 135 MB. The findings show that low-cost and practical smartphone-based disease diagnosis requires the use of lightweight CNN models that can deliver accuracy and alertness that can allow farmers to manage the crop and food security on their own. Smallholder farmers in a resource-poor rural environment will be able to benefit from these models.

Why it matches plant phenotyping methods植物葉画像から病害状態を推定するCNNベースのモバイル画像解析手法を開発・比較・実装しており、植物状態の取得と技術性能評価が研究の中心である。

abstractThis study created a mobile system based on lightweight CNN for smartphone portable real-time detection of cassava and maize diseases.

DFA-YOLO: an enhanced YOLOv11-OBB and knowledge distillation-based maize stomata detection system. →

Introduction Stomata are vital gatekeepers of plants that regulate the fundamental trade-off between carbon gain and water loss. Precise, high-throughput identification of stomatal traits is therefore essential for assessing plant stress tolerance and water-use efficiency. However, conventional bounding box detection struggles to accurately localize densely distributed and arbitrarily oriented stomata. Methods This study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction. Based on a maize stomatal dataset expanded from 1,053 to 3,597 microscopic images, DFA-YOLO integrates three task-specific components: (1) a cross-dataset MGD distillation strategy that transfers structural priors from single-stomata images to dense multi-stomata scenes; (2) a fixed-threshold Focaler-CIoU localization-loss reweighting strategy (u stomata = 0.95, d stomata = 0.00) to emphasize hard positive samples during OBB regression; and (3) a C3k2_AssemFormer feature-aggregation module that combines convolutional local feature extraction with linear attention-based context aggregation. Results DFA-YOLO achieved 94.1% mAP50, 84.8% mAP75, 74.1% mAP50-95, and 90.0% recall, with higher recall, mAP50, mAP75, and mAP50-95 than the YOLOv11-OBB baseline under the same OBB evaluation protocol. When deployed on an automated platform, the system processed images at 44.9 FPS and supported stomatal localization, density estimation, and preliminary orientation-aware size description. Discussion Under the tested maize microscopic imaging workflow, DFA-YOLO enables rapid extraction of detection-oriented stomatal traits and provides a prototype tool for high-throughput maize stomatal phenotyping.

Why it matches plant phenotyping methodsトウモロコシ気孔の画像検出・配向局在化と形質抽出を目的とするYOLOベース手法を開発し、データセット、精度比較、自動化プラットフォームでの性能を評価しているため、植物フェノタイピング手法が中心である。

abstractThis study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction.

UAV-Based Multi-Sensor Fusion for Leaf Age Detection in Maize Inbred Line Population Seedlings →

Accurate and rapid detection of maize seedling growth is critical in early breeding decisionmaking, smart management, and yield improvement. Traditional leaf age detection still relies heavily on labor-intensive and low-efficiency manual field surveys, underscoring the urgent need for high-throughput phenotyping. Integrating multisource sensor data from unmanned aerial vehicle (UAV) with measured information such as crop height can further enhance the estimation accuracy of crop phenotypic parameters. Accurate field plot segmentation is critical for field-scale phenotypic analysis. However, current approaches remain largely dependent on slow, manual segmentation. Automating this step would greatly reduce the workload of agronomists. This study used UAV RGB and multispectral imagery collected over maize inbred line population plots before the canopy closure stage to perform automatic plot segmentation on field orthophotos and combined measured plant height with relative flight dates to achieve high-throughput detection of leaf age during the maize seedling stage. First, this study proposed a maize plot automatic segmentation method based on orthophotos. Then, it extracted texture features, RGB, and multispectral vegetation indices of each plot. Combined with relative flight date and plant height, four datasets were constructed. Support vector regression (SVR), random forest regression (RFR), and automatic machine learning (AutoML) regression algorithms were used to build the leaf age detection model. The results showed that the orthomosaic from March 23 achieved the best plot-segmentation performance, with minimum intersection over union (IoU), mean IoU, and IoU standard deviation of 14.67%, 96.47%, and 8.65%, respectively. Incorporating relative flight dates and plant-height measurements improved model performance, and the AutoML demonstrated the greatest robustness, achieving a validation R2 of up to 0.862 and an RMSE as low as 0.715. This study proposed a leaf age estimation method that offers practical technical support for field-based maize seedling assessment and reduces manual labor demands.

Why it matches plant phenotyping methodsUAV RGB・マルチスペクトル画像、圃場区画 segmentation、特徴抽出、回帰モデルを統合し、トウモロコシの葉齢という植物形質を推定する方法を開発・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。

abstractThis study used UAV RGB and multispectral imagery collected over maize inbred line population plots before the canopy closure stage to perform automatic plot segmentation on field orthophotos and combined measured plant height with relative flight dates to achieve high-throughput detection of leaf age during the maize seedling stage.

Satellite data reconstruction under cloud cover for corn yield forecasting via multimodal fusion →

A comprehensive intelligent system for corn yield prediction based on the synergy of a Spatio-Temporal Generative Adversarial Network (ST-cGAN) and a recurrent CNN-LSTM architecture has been developed and tested. A multimodal fusion of weather-independent Sentinel-1 radar data, ERA5-Land meteorological factors, and historical Sentinel-2 optical observations was applied. The problem of "biochemical blindness" in radar signals, where radar captures physical plant structure but fails to detect photosynthetic activity, and cloud cover limitations in optical remote sensing was successfully resolved. To achieve this, an early fusion strategy was implemented. The ST-cGAN simultaneously processes current SAR data for the structural macro-state, historical optical data for the last known biochemical baseline, and a 10-day history of meteorological priors to enforce physiological constraints. Consequently, if radar indicates high biomass but meteorological data reveals severe drought, the neural network mathematically recognizes biological stress and synthesizes proportionally depressed NDVI and NDRE indices, preventing the hallucination of falsely healthy crops. Dynamic synthesis of missing NDVI and NDRE vegetation indices was conducted under prolonged continuous cloud cover (up to 21 days), achieving a high structural similarity index (SSIM = 0.89). Temporal discriminators for frame sequence analysis were introduced into the architecture, improving the edge-preserving index by 18 % and minimizing spatial artifacts at field boundaries. Pixel-level regression was performed for a 15,000-hectare test area in the Western Forest-Steppe of Ukraine based on reconstructed time series covering 15 critical phenological stages. It was established that the proposed architecture reduces the root mean square error (RMSE) to 0.48 t/ha with a coefficient of determination (R2) of 0.90. Statistical analysis proved the significant superiority of the developed method over industry-standard gap-filling algorithms (e.g., STARFM). A scalable decision-support system for optimizing harvest logistics and financial planning under any atmospheric conditions was presented. Future research directions involving neural network knowledge distillation for IoT devices and UAV data integration were outlined.

Why it matches plant phenotyping methods雲下で欠測するNDVI・NDREなど植物状態指標を再構成する深層学習手法の開発と、SSIM・RMSE・R2および既存手法との比較検証が中心であり、単なる収量予測ではなく植物表現型推定手法に該当する。

abstractDynamic synthesis of missing NDVI and NDRE vegetation indices was conducted under prolonged continuous cloud cover (up to 21 days), achieving a high structural similarity index (SSIM = 0.89).

MaizeStereo: 2D Gaussian splatting-driven stereo foundation model for in-field proximal 3D maize phenotyping →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsタイトルから、圃場近接3Dトウモロコシ表現型計測のためのステレオ基盤モデル開発が中心であり、植物フェノタイピング手法に該当する。

titleMaizeStereo: 2D Gaussian splatting-driven stereo foundation model for in-field proximal 3D maize phenotyping

CIELab-Based Digital Phenotyping of Plant Pigments in Popcorn Seedlings Under Salt Stress →

Salt stress represents one of the main challenges for global agricultural production, and digital phenotyping has emerged as a promising alternative for identifying popcorn genotypes tolerant to salt stress. This study evaluated the accumulation of plant pigments in response to salt stress in 49 popcorn genotypes (7 inbred lines and 42 F1 hybrids). Seeds were subjected to two saline conditions: without salt stress (NS—0 mM NaCl) and salt stressed (SS—100 mM NaCl). The evaluation included physiological parameters, and morphological and colorimetric attributes based on the CIELab color space were analyzed using the GroundEye® system. Additionally, the salt stress tolerance index (SSTI) was calculated for all assessed genotypes. The SSTI ranged from 0.55 to 0.83, with values closer to 1.0 indicating higher tolerance to the stressor. Among the evaluated genotypes, L472 and four of its hybrids stood out for their salinity tolerance, as they combined efficient maintenance of chlorophyll content with higher SSTI estimates. In contrast, L217 and two of its hybrids were identified as sensitive, exhibiting some of the lowest SSTI estimates and significant accumulation of anthocyanins, which, in this study, indicated a response mechanism to oxidative damage. Digital phenotyping associated with CIELab colorimetric analysis constitutes an objective tool for identifying tolerant genotypes, thereby accelerating breeding programs aimed at developing cultivars adapted to saline environments.

Why it matches plant phenotyping methodsCIELab色空間とGroundEye®を用いた植物色素・色彩形質のデジタル表現型解析が、耐塩性遺伝子型評価の中心的手法として明示されている。

abstractdigital phenotyping has emerged as a promising alternative for identifying popcorn genotypes tolerant to salt stress

Interpretable machine learning and causal inference for maize LAI estimation from UAV multimodal imagery →

Accurate retrieval of leaf area index (LAI) is vital for crop monitoring and genetic breeding. Although multi-modal unmanned aerial vehicle (UAV) remote sensing has advanced LAI estimation, conventional empirical models often overfit on small breeding populations and cannot disentangle the true causal effects of genetic backgrounds from confounding factors within a statistically rigorous framework. This study presents a robust framework for plot-scale maize LAI estimation across 800 breeding plots from four genetic subgroups: doubled haploid (DH), mixed, temperate (TEM), and tropical/subtropical (TST). From UAV RGB and multispectral imagery acquired at three phenological stages, we extracted 76 multi-modal features comprising point-cloud structural metrics, spectral vegetation indices, and texture features. Following a dual-criterion mutual information and multicollinearity filter, four algorithms, including traditional tree-ensembles and the Tabular Prior-data Fitted Network (TabPFN), were evaluated using nested validation. TabPFN achieved superior generalization performance, yielding a mean test R2 of 0.778 ± 0.031, an RMSE of 0.264 ± 0.024, and an MAE of 0.203 ± 0.022, significantly outperforming tree-ensemble models (p < 0.01). Across growth stages, retrieval accuracy peaked at the expanded bell-mouth stage (R2 = 0.802) and successfully captured the unimodal trajectory of canopy development. SHAP-based attribution showed that spectral indices contributed most to the predictions (55.9%), followed by canopy texture (26.3%), with the spatial heterogeneity metric Tex_Entropy being the most influential single feature (22.8%). When embedded as the nuisance estimator within a Double Machine Learning framework for causal inference, TabPFN confirmed that, relative to the TEM subgroup, only the DH genetic background exerted a consistent and significant negative causal effect on LAI (ATE = −0.070, p = 0.030). These results establish TabPFN as a reliable and extensible tool for non-invasive, high-throughput phenotyping in precision breeding.

Why it matches plant phenotyping methodsUAVマルチモーダル画像からトウモロコシLAIを推定する計算・画像解析ワークフローを開発・比較検証し、高スループット表現型解析への応用を示しているため、方法が中心的である。

abstractThis study presents a robust framework for plot-scale maize LAI estimation across 800 breeding plots from four genetic subgroups

Next-Generation Crop Breeding: Harnessing Genomics, Phenomics and Machine Learning: A Review →

Global food security requires crop improvement strategies that can respond to population growth, climate variability and increasing constraints on agricultural resources. Conventional plant breeding has contributed substantially to crop productivity, yet long selection cycles and dependence on extensive field evaluation can limit the rate of genetic gain. This review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding, with emphasis on their combined contribution to selection accuracy and breeding efficiency. Key genomic approaches discussed include whole-genome sequencing, reference and pan-genome resources, genome-wide association studies, genomic selection and CRISPR-Cas-based genome editing. The review also examines high-throughput phenotyping platforms, including controlled-environment systems, ground-based robots, UAV-based remote sensing and root phenotyping tools. Machine learning approaches, ranging from random forest and support vector machines to convolutional neural networks, recurrent networks, transformers and explainable artificial intelligence, are considered in relation to genomic prediction, image analysis and breeding decision support. Multi-omics integration, data management, FAIR principles and an integrated genomics-phenomics-ML breeding pipeline are reviewed as enabling components for practical deployment. Crop-specific examples from wheat, rice, maize, soybean and legumes illustrate the potential and constraints of these technologies. The review further identifies key challenges, including phenotyping bottlenecks, genotype-environment interaction, data governance, model interpretability and regulatory uncertainty.

Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、ハイスループット計測プラットフォーム、画像解析、機械学習、UAV・ロボット・根系計測などをレビューしているため。

abstractThis review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding

Challenges in Maize Root Phenotyping: Preprocessing Limits and Class Imbalance in Deep Learning →

Doubled Haploid (DH) technology significantly accelerates the development of homozygous lines in maize breeding; however, its scalability is constrained by the reliable discrimination of haploid and diploid individuals. The widely used R1-nj anthocyanin marker at the seed stage is susceptible to genetic suppression and environmental variability, leading to high misclassification rates. This limitation has driven a shift toward seedling root morphology as a more robust phenotypic marker, yet it introduces major challenges, including complex image noise and severe class imbalance. In this study, we systematically evaluate the limitations of standard computer vision pipelines and baseline deep learning models for root-based classification. Automated background removal methods (HSV, Rembg) are shown to misinterpret fine root hairs as noise, resulting in significant morphological data loss. Additionally, experiments conducted under a realistic class imbalance (1:5.4) demonstrate that widely used CNN architectures (ResNet50, VGG16, EfficientNetB0, DenseNet121) exhibit strong majority class bias, with haploid recall dropping to 0.00% and 27.7%. These findings reveal a critical limitation in existing approaches and highlight the need for domain-informed datasets and imbalance-aware learning strategies for robust and scalable AI-based maize breeding systems.

Why it matches plant phenotyping methodsトウモロコシの根形態画像を用いた倍加半数体・二倍体分類について、画像前処理と深層学習モデルの限界を体系的に評価しており、表現型取得・抽出手法が中心である。

abstractIn this study, we systematically evaluate the limitations of standard computer vision pipelines and baseline deep learning models for root-based classification.

Joint Modeling of Grain Yield and Root Lodging in Maize Using Multi-Output Neural Network and Machine Learning Models Under Defined Environmental Conditions →

We evaluated a multi-output neural network framework for jointly analyzing maize grain yield (GY) and root lodging percentage (LP) using above-ground morphological traits measured under defined environmental conditions. To address model robustness, the multi-output neural network was compared with linear regression, elastic net, random forest, and XGBoost using repeated five-fold cross-validation, an 80/20 holdout split, and independent year-wise validation. Under repeated cross-validation, XGBoost provided the strongest average predictive performance for both traits, with R2 values of 0.57 for GY and 0.67 for LP. The multi-output neural network showed moderate performance, with R2 values of 0.49 for GY and 0.57 for LP. Final holdout performance for the neural network for GY and LP was R2 = 0.64 and R2 = 0.92, respectively. Year-wise validation showed weak temporal transferability because the two seasons differed not only in environmental conditions, but also in lodging mechanism. Repeated permutation importance identified ear width (EW), kernel row number (RNE), thousand kernel mass (KM1000), and kernel number per ear (KNE) as important predictors of GY, while LP prediction was most strongly associated with internode major diameter (IDmajor), ear length (EL), and the number of green leaves (NGL). Across both permutation importance and SHAP, only RNE and NGL were consistently shared between GY and LP. Supplementary ALE diagnostics indicated that RNE showed increasing model-estimated effects for both predicted GY and LP, whereas NGL showed a positive association with predicted GY but a decreasing or nonlinear association with predicted LP. These results show that joint modeling can support exploratory trait interpretation, but the predictive relationships remain environment-specific and should not be interpreted as causal or broadly transferable without further multi-environment validation.

Why it matches plant phenotyping methods穀粒収量と倒伏率という植物形質を推定する多出力ニューラルネットワーク等のモデルを開発・比較し、交差検証、ホールドアウト、年次外部検証で性能評価しており、計算的形質推定が中心である。

abstractWe evaluated a multi-output neural network framework for jointly analyzing maize grain yield (GY) and root lodging percentage (LP) using above-ground morphological traits measured under defined environmental conditions.

Decoding the hidden half: Advances in root growth dynamics and functional architecture of maize (Zea mays L.) →

Maize (Zea mays L.) is a major cereal crop whose productivity across diverse agro-ecological environments is strongly influenced by belowground traits. The root system functions as the primary plant-soil interface, regulating water and nutrient uptake, providing mechanical support and enabling adaptive responses to abiotic and biotic stresses. Despite its central importance, maize root biology has historically received less attention than aboveground characteristics. The maize root system comprises primary, seminal, nodal and lateral roots differing in developmental origin, growth behaviour and physiological role. Key architectural traits-such as rooting depth, root growth angle and branching density-play a crucial role in root system development. These traits, along with anatomical features like cortical aerenchyma and root hair development, are governed by complex genetic networks involving regulatory genes, quantitative trait loci and hormone-mediated signalling pathways. Root growth and spatial distribution are further shaped by soil properties and agronomic practices, including irrigation and nutrient management. Recent advances in high-throughput phenotyping, three-dimensional (3D) reconstruction and artificial intelligence (AI) based image analysis technologies have enhanced quantitative assessment of root traits. This review consolidates recent progress in maize root research with emphasis on root system architecture (RSA), developmental regulation and their functional relevance to crop productivity. Uniquely, it integrates structural, genetic and phenotyping advances in maize root research into a unified framework, while explicitly linking RSA with its functional significance, an aspect often treated separately in earlier reviews. Optimising maize root systems is therefore essential for improving productivity, resource-use efficiency and agricultural sustainability under changing climatic conditions.

Why it matches plant phenotyping methodsトウモロコシ根系研究のレビューであり、根系形態形質の定量評価に用いるハイスループット表現型解析、3D再構成、AI画像解析を明示的に扱うため、表現型解析手法レビューとして中心的です。

abstractRecent advances in high-throughput phenotyping, three-dimensional (3D) reconstruction and artificial intelligence (AI) based image analysis technologies have enhanced quantitative assessment of root traits.

Editorial: Plant phenotyping for agriculture →

Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-

Why it matches plant phenotyping methods植物フェノタイピングの技術動向を扱うEditorialであり、画像解析、UAVセンシング、深層学習、形質抽出などの方法が中心的に整理されている。

titleEditorial: Plant phenotyping for agriculture

An in situ image-based phenotyping system for hydroponic maize seedling roots based on DB-UNet and customized skeleton-based analysis. →

monitoring capabilities, and existing models have limited accuracy in root segmentation. To address these issues, we developed a crop root phenotyping system integrating crop cultivation and data collection. We also proposed a DB-UNet model for hydroponic maize root segmentation. DB-UNet builds a CNN-ViT dual-branch parallel structure during encoder downsampling level. The lightweight ViT branch uses sequential downsampling to achieve global topological dependency modeling while reducing computational costs. An attention fusion module dynamically calibrate dual-branch features weights, achieving complementary fusion of local root edge details and global context information. we constructed a mixed loss function combining Dice loss, Focal loss, and structural consistency KL loss to solve class imbalance, hard sample segmentation, and semantic divergence of dual-branch features. On our custom hydroponic maize root dataset, DB-UNet achieved an mIoU of 91.02%, an FG IoU of 82.78%, and a Centerline-Dice of 97.72%.Compared to classic UNet, mIoU, FG IoU, and Centerline-Dice increased by 0.92%, 1.84%, and 1.99%, respectively. Plant-level five-fold cross-validation further showed that DB-UNet maintained stable segmentation performance across different plant-level partitions. Based on DB-UNet segmentation results, we propose a custom skeleton-based algorithm for multi-trait root phenotyping, enabling the extraction of total root length and root branch points. Root area is calculated from binary mask pixel statistics. Compared to the traditional Zhang-Suen algorithm, the average relative error of root length measurement is reduced to 3.14%, which is 8.42 percentage points lower than the traditional method. Furthermore, we analyzed relationships between segmentation accuracy metrics and phenotypic relative errors. Higher segmentation quality generally led to lower phenotypic relative errors and more reliable trait measurements. In particular, Centerline-Dice was closely associated with root length estimation, whereas pixel-level segmentation consistency was more closely related to root area measurement. Pearson and Spearman correlation analyses showed a strong positive correlation between maize plant height and total root length, with coefficients of 0.8466 and 0.8634, respectively.

Why it matches plant phenotyping methods画像ベースの根セグメンテーションと骨格解析を開発・検証し、根長や分枝点などの形質を抽出するシステムが研究の中心であるため。

abstractwe developed a crop root phenotyping system integrating crop cultivation and data collection.

Imaging Double Fertilization in Maize →

Sexual reproduction in flowering plants relies on double fertilization, a process marked by two fusion events between the male and female gametes that lead to seed formation. Because this process unfolds within the embryo sac embedded deep inside the ovule, direct observation remains technically demanding, especially in maize, where the large size of female reproductive organs presents additional obstacles. The described method enables high-resolution visualization of cellular events unfolding during maize double fertilization. The approach integrates optimized fixation, clearing and confocal imaging of embryo sacs from ears pollinated with fluorescent pollen marker lines. Precise timing of embryo sac fixation is critical, allowing capture of key events such as pollen peri-germ cell membrane break-down or gamete karyogamy. The protocol provides detailed guidance for ovule dissection, fixation, preparation and renewal of the clearing solution and confocal imaging of embryo sacs. This method offers unprecedented access to the cellular events of double fertilization in maize, establishing a robust framework for studying reproductive processes and supporting future discoveries in plant reproduction.

Why it matches plant phenotyping methodsトウモロコシの二重受精過程を高解像度で可視化する固定・透明化・共焦点 imaging プロトコルが研究の中心であり、植物の生殖状態を取得する方法として該当する。

abstractThe described method enables high-resolution visualization of cellular events unfolding during maize double fertilization.

LSL-YOLO11n: a YOLO11n-based model for maize leaf disease detection in complex field environments →

Maize leaf diseases in field environments often exhibit large variations in lesion scale, irregular morphology, blurred boundaries, and complex backgrounds. These factors pose challenges for existing detection models, particularly in detecting small lesions and achieving precise bounding-box localization. To address these issues, this study proposes LSL-YOLO11n, a maize leaf disease detection model based on the YOLO11n framework. The proposed model improves feature representation, localization quality modeling, and bounding-box regression to enhance disease detection performance under complex field conditions. Experiments were conducted on a dataset containing 15,119 images and 29,366 annotated instances across eight categories, including seven maize disease categories and healthy leaves. To evaluate the effectiveness of the proposed model, ablation experiments, comparative experiments with mainstream object detection models, and visual detection analyses were carried out. The ablation results show that the improved components contribute positively to the overall detection performance. LSL-YOLO11n achieves a Precision of 84.4%, Recall of 73.9%, and mean Average Precision (mAP) of 83.3%, which is 3.1 percentage points higher than that of the baseline YOLO11n model. Compared with YOLOv8n, YOLOv9t, YOLOv10n, and YOLOv12n, the proposed model improves mAP by 4.7, 3.3, 5.3, and 10.9 percentage points, respectively. The visual detection results further indicate that LSL-YOLO11n performs more stably in complex backgrounds and small-lesion scenarios. These findings provide technical support for rapid maize disease recognition and intelligent field monitoring.

Why it matches plant phenotyping methodsトウモロコシ葉の病斑・病害状態を画像から検出するYOLOベース手法を開発し、アブレーション比較や既存モデルとの性能評価を行っており、植物病害表現型の取得法が中心である。

abstractTo evaluate the effectiveness of the proposed model, ablation experiments, comparative experiments with mainstream object detection models, and visual detection analyses were carried out.

FloraForge: Procedural generation of editable and analysis-ready 3D plant geometric models using LLM-assisted template design →

Accurate 3D plant models are crucial for computational phenotyping and physics-based simulation; however, current approaches face significant limitations. Learning-based reconstruction methods require extensive species-specific training data and lack editability for hypothesis-driven research. Procedural modeling offers parametric control and large model variability but demands specialized expertise in geometric modeling and an in-depth understanding of complex procedural rules, making it inaccessible to domain scientists. We present FloraForge, an LLM-assisted framework that enables domain experts to generate biologically accurate, fully parametric 3D plant models through iterative natural language Plant Refinements (PR) during template creation, minimizing the need for programming expertise. Our co-design workflow leverages LLM-assisted code generation to progressively refine Python scripts that generate parameterized complex plant geometries as Non-Uniform Rational B-Spline (NURBS) surface representations, with botanical constraints. Plant organs are represented as spline surfaces that can be easily tessellated into polygonal meshes with arbitrary precision, ensuring compatibility with functional structural plant analysis workflows such as light simulation, computational fluid dynamics, and finite element analysis. We demonstrate the framework by generating procedural models of multiple maize genotypes, soybean (which shares the procedural generator with mung bean), and mung bean, with one plant tracked across different developmental stages. We fit procedural models to empirical LiDAR and NeRF-derived point cloud data through manual refinement of the Plant Descriptor (PD), a human-readable YAML file originally templated by the LLM, obtaining consistently low mean symmetric Chamfer distances that indicate close agreement between generated models and measured plant geometry. The pipeline generates dual outputs: triangular meshes (represented as STL or OBJ files) for visualization and triangular meshes with additional parametric metadata for quantitative analysis (stored as SMESH files). We further illustrate analysis-ready use by coupling the procedurally generated models to the HELIOS framework to simulate diurnal photosynthetically active radiation interception in virtual maize and mung bean fields across growth stages. Our framework uniquely combines pre-trained LLM-assisted template creation, mathematically continuous representations that support both phenotyping and rendering, and direct parametric control through the PD. The framework makes sophisticated geometric modeling accessible to plant science researchers while maintaining mathematical rigor through biologically interpretable parameterizations; additionally, the iterative PR dialogue produces an explicit record of model properties that is typically absent in conventional procedural modeling pipelines.

Why it matches plant phenotyping methods植物の3D形状を生成・編集し、LiDAR/NeRF点群との適合で検証する、計算機フェノタイピング向けの中心的手法開発である。

abstractWe present FloraForge, an LLM-assisted framework that enables domain experts to generate biologically accurate, fully parametric 3D plant models

Stage-Specific Estimation of Maize Flavonoids Using UAV Multispectral Imagery and Spectral, Texture, and Phenological Features →

Rapid and non-destructive estimation of maize (Zea mays L.) leaf flavonoid (Flav) content is important for crop stress monitoring and precision agriculture. This study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages in the Guanzhong Plain, China. Maize Flav content was measured in situ using a Dualex Scientific+ meter, while canopy reflectance was acquired with a DJI M300 RTK UAV equipped with an MS600 Pro multispectral camera. A comprehensive feature set, including spectral bands, vegetation indices, texture features, texture indices, and logistic curve-derived phenological parameters, was constructed. Three feature selection methods, competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), and the successive projections algorithm (SPA), together with three regression models, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN), were evaluated for Flav estimation. The results showed that integrating spectral, texture, and phenological information significantly improved model performance compared with spectral variables alone. CNN and XGBoost generally outperformed PLSR. Across the six growth stages, the stage-specific optimal models achieved coefficient of determination (R2) values ranging from 0.7749 to 0.8686 and residual prediction deviation (RPD) values ranging from 2.0046 to 2.6019, indicating high to outstanding predictive ability. The highest accuracy was obtained at R3 using the CARS-XII-CNN model, with R2 = 0.8686, root mean square error of validation (RMSEV) = 0.0382, and RPD = 2.6019. Texture features and phenological metrics, especially the start of season derived from the normalized difference vegetation index (NDVI_SOS) and the rate of senescence derived from the enhanced vegetation index (EVI_ROS), contributed substantially to model accuracy. In addition, maize Flav showed a unimodal response to nitrogen supply, with moderate nitrogen levels associated with higher Flav content. This study demonstrates the potential of UAV-based multisource feature integration and machine learning for accurate maize Flav estimation, and provides a useful framework for digital crop phenotyping and stress diagnosis.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と特徴量・機械学習を用いて、トウモロコシ葉フラボノイド含量という植物形質を推定する手法を開発・比較検証しており、フェノタイピング手法が中心である。

abstractThis study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages

Accurate detection of full-surface ear rot in maize using hyperspectral imaging and deep learning. →

Maize ear rot severely restricts maize yield and quality, making the breeding of disease-resistant varieties the core strategy for disease prevention and control. Due to the highly uneven spatial distribution of lesions on maize ears, precise full-surface detection is essential for objectively quantifying disease severity. However, traditional manual disease grading is highly subjective, and conventional RGB-based detection methods struggle to precisely identify lesion regions associated with maize ear rot. These limitations hinder the precise identification and quantitative analysis of maize ear rot infection regions, thereby limiting the reliability of phenotypic data used for resistance evaluation and subsequent genome-wide association studies (GWAS). To address these challenges, this study developed an integrated full-surface hyperspectral imaging system featuring line-scan imaging and synchronous rotation control. Non-redundant full-surface ear images were then generated using the oriented FAST and rotated BRIEF (ORB) algorithm combined with random sample consensus (RANSAC), hereafter referred to as ORB-RANSAC. Furthermore, after Savitzky-Golay (SG) preprocessing and feature selection using a genetic algorithm (GA), three machine learning models and three deep learning models were established, and their classification performance was compared. The results showed that the convolutional neural network-bidirectional long short-term memory network (CNN-Bi-LSTM) model achieved the best average performance, with an average overall accuracy (OA) of 95.61 ± 0.36%. It also achieved higher overall accuracy than traditional machine learning models such as random forest (RF), indicating that CNN-Bi-LSTM can achieve high-precision pixel-level detection of lesion regions showing Fusarium-associated maize ear rot symptoms. Additionally, this model was deployed in locally developed automatic analysis software, enabling an integrated analysis workflow from raw hyperspectral data input to the quantification of disease-related phenotypic parameters. This study not only fills the technical gap in the non-destructive full-surface detection of maize ear rot but also provides an efficient and reliable automated tool for high-throughput phenomics research, which holds great significance for accelerating the discovery of maize resistance genes and ensuring food security.

Why it matches plant phenotyping methodsトウモロコシ穂の病斑をハイパースペクトル画像と深層学習で定量し、全表面撮像システム、解析モデル、ソフトウェアを開発したため、植物表現型取得法が中心である。

abstractthis study developed an integrated full-surface hyperspectral imaging system featuring line-scan imaging and synchronous rotation control.

Supplemental figures for "Integrating Machine Learning and Remote Sensing to Determine Crop Nitrogen Content of Maize" →

This study developed machine learning models to predict maize crop nitrogen content (CNC) using vegetation indices derived from UAV and satellite imagery. Several models were evaluated, with Random Forest demonstrating the best performance. The study emphasizes that combining vegetation indices enhances prediction accuracy more than using individual spectral bands. Results show reliable CNC estimation across growth stages, although predictions at early stages are less precise due to low canopy cover and soil interference. The approach allows for real-time, field-to-regional-scale nitrogen monitoring, supporting improved fertilizer management and precision agriculture decision-making.

Why it matches plant phenotyping methodsUAV・衛星画像からトウモロコシの窒素含量を推定する機械学習・リモートセンシング手法の開発とモデル比較が中心であり、植物形質の取得方法に該当する。

abstractThis study developed machine learning models to predict maize crop nitrogen content (CNC) using vegetation indices derived from UAV and satellite imagery.

Supplemental figures for "Integrating Machine Learning and Remote Sensing to Determine Crop Nitrogen Content of Maize" →

This study developed machine learning models to predict maize crop nitrogen content (CNC) using vegetation indices derived from UAV and satellite imagery. Several models were evaluated, with Random Forest demonstrating the best performance. The study emphasizes that combining vegetation indices enhances prediction accuracy more than using individual spectral bands. Results show reliable CNC estimation across growth stages, although predictions at early stages are less precise due to low canopy cover and soil interference. The approach allows for real-time, field-to-regional-scale nitrogen monitoring, supporting improved fertilizer management and precision agriculture decision-making.

Why it matches plant phenotyping methodsUAV・衛星画像と機械学習により、トウモロコシの作物窒素含量という植物形質を推定し、モデル性能を比較・評価しているため、リモートセンシング型フェノタイピング手法の適用が中心です。

abstractThis study developed machine learning models to predict maize crop nitrogen content (CNC) using vegetation indices derived from UAV and satellite imagery.

Using UAV Multispectral Imagery to Predict Leaf SPAD Dynamics During Maize Growth Under Different Plant Densities →

Chlorophyll content represents a key growth indicator for maize. The traditional SPAD method, though easy to operate, is inefficient, destructive, and unsuitable for high throughput field monitoring. Unmanned Aerial Vehicle (UAV) remote sensing technology is highly efficient and detects abundant indicators, enabling large-scale SPAD measurement. In this study, 18 vegetation indices and 8 texture features were selected as the indicator system by combining prior knowledge and experimental analysis. In a two-year maize density experiment, multispectral images were collected in the full growth period. The correlations between SPAD values, multispectral indices and texture features were analyzed using Pearson correlation coefficients. Then the detection accuracies of three algorithms i.e. Random Forest (RF), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), were compared under this indicator system. Compared with models constructed using single vegetation indices or single texture features, the estimation accuracy of the indicator system at the jointing stage was improved by 0.13 and 0.22, respectively. The results showed that SVR achieved the highest estimation accuracy among the three algorithms, with determination coefficients (R²) of 0.73, 0.77and 0.70 at the jointing, silking, and grain-filling stages, respectively. This study established a non-destructive monitoring framework for chlorophyll content during entire maize growth period based on UAV data.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からトウモロコシ葉のSPAD／クロロフィル含量を推定する指標体系と機械学習モデルを構築・比較しており、表現型取得手法が研究の中心である。

abstractThis study established a non-destructive monitoring framework for chlorophyll content during entire maize growth period based on UAV data.

Aerial imagery and deep learning accurately estimate maize foliar disease severity →

ABSTRACT Southern leaf blight (SLB) is a foliar disease of maize (Zea mays L.) caused by the necrotrophic fungal pathogen Cochliobolus heterostrophus. Genetic resistance is the most effective control method for SLB. Developing disease resistant maize lines requires field trials during which disease phenotypes must be visually assessed. Remote sensing using drones is an emerging technology that can be leveraged for high-throughput phenotyping of disease severity that is otherwise labor-intensive and subjective. This project used a deep learning approach to estimate SLB disease severity of single-row maize plots from drone imagery. Over 26,000 plot-level images produced from flights conducted across three growing seasons were labeled with in-field visual scores taken contemporaneously by expert raters. Variation in environmental conditions contributed to a labeled image dataset that reflects the complexity of agronomic field experiments. We assessed the ability of nine deep learning models from three architectural families to estimate disease severity. The best-performing model, EVA-02-B, achieved strong cross year generalization (R 2 = 0.697). Error analysis found that performance was more strongly associated with seasonal disease progression and flight-score time offset than with image-level noise. UAV-based deep learning estimated SLB severity with comparable precision to expert raters. This study lays the groundwork for integrating automated phenotypes into genetic studies of disease resistance. PLAIN LANGUAGE SUMMARY Southern leaf blight (SLB) of maize is a disease that causes yield loss worldwide and developing resistant varieties offers the best hope for controlling the disease. Studying SLB resistance requires plant pathologists to visually score severity in the field, a labor-intensive method that requires expertise. To address these challenges, we asked whether SLB severity scoring could be automated using drone images and artificial intelligence (AI). We trained AI models using three years of image and score data then compared the results to visual scores taken by five plant pathologists. The best performing AI model showed a similar level of consistency to the experts and proved capable of scoring severity despite unpredictable and uncontrollable conditions that affect field imaging experiments such as weeds or shadows. These findings provide a validated method that improves the efficiency of maize disease research, a critical area of study for agricultural sustainability and productivity.

Why it matches plant phenotyping methodsドローン画像と深層学習により、トウモロコシの葉病害重症度という植物状態を推定し、複数年データで性能と汎化性を評価した手法研究である。

abstractRemote sensing using drones is an emerging technology that can be leveraged for high-throughput phenotyping of disease severity

Spring Maize Yield Prediction and Optimal Phenological Stage Assessment in the Junggar Basin Based on UAV and Stacked Ensemble Learning →

Abstract Accurate prediction of maize yield is crucial for improving field management and enabling timely yield estimation. To improve the accuracy and determine the optimal timing of field-scale spring maize yield estimation in the Junggar Basin, this study focuses on spring maize in this region. In 2023, UAV-based multispectral images were acquired at three key growth stages: jointing, filling, and milk stages. Eighteen spectral features significantly correlated with yield were selected. Spring maize yield prediction models were constructed using XGBoost, CatBoost, RF, DT, SVR, GP, LR, and a stacked ensemble learning model, respectively, revealing differences in prediction accuracy across growth stages. Finally, SHAP was used for model interpretability analysis. The results show that: (1) The milk stage achieved the highest prediction accuracy (R² = 0.761, MAE = 0.067 kg·m⁻², RMSE = 0.089 kg·m⁻², MAPE = 5.055%), outperforming the jointing and early grain-filling stages, thereby resolving the uncertainty regarding the optimal timing for UAV-based yield estimation of spring maize in the Junggar Basin. (2) Compared with traditional machine learning algorithms, the stacked ensemble model exhibited stronger robustness and generalization ability. This study provides a technical reference for timely yield estimation and field management of spring maize in irrigated areas of the Junggar Basin, supporting regional food production stability.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からトウモロコシ収量を推定し、複数生育段階・機械学習モデルの精度と頑健性を比較して最適推定時期を評価しており、表現型推定手法が研究の中心である。

abstractUAV-based multispectral images were acquired at three key growth stages: jointing, filling, and milk stages.

Recent trends in crop water stress monitoring using remote sensing technologies: A review →

Unmanned aerial vehicle (UAV) based remote sensing has emerged as a disruptive technology for detecting crop water stress (CWS) in real time, precisely and at low cost offering significant advancements over conventional approaches. The study examined the red green blue (RGB), multispectral (MSP), hyperspectral (HSP), thermal image sensors integrated with UAVs, which offers a high-spatial and temporal resolution of physiological indicators such as chlorophyll content and canopy cover, canopy temperature, stomatal conductance. The study highlights that in spring maize, random forest (RF) models using UAV-derived MSP and thermal indices with leaf area index (LAI) performed well (R² > 0.575, root mean square error (RMSE)

Why it matches plant phenotyping methodsUAV搭載センサーによる作物の水ストレスや生理形質のモニタリング技術をレビューしており、表現型取得法が中心である。

titleRecent trends in crop water stress monitoring using remote sensing technologies: A review

Integrated UAV-Based Multispectral Scouting and Variable-Rate Aerial Spraying for Enhanced Crop Yield and Input Efficiency: A Field-Validated Study →

Precision agriculture demands integrated systems that couple accurate crop stress detection with targeted intervention to mitigate climate volatility and input overuse. Traditional manual scouting and uniform chemical application are spatially imprecise, labour-intensive, and environmentally burdensome. This study field-validates a closed-loop unmanned aerial vehicle (UAV) framework integrating AI-driven multispectral scouting with prescription-mapped variable-rate aerial spraying (VRS). A randomized complete block design with four replications was implemented in maize (Zea mays L.) across a 2.4 ha field in Davangere Karnataka, India. Scouting flights at 25 m altitude (1.8 cm ground sampling distance) utilized a MicaSense RedEdge-P and FLIR thermal sensor, with imagery processed through a radiometrically calibrated YOLOv8-Seg pipeline to detect early-stage disease, nutrient deficiency, and water stress. Prescription maps derived from NDRE and CWSI thresholds directly controlled a DJI Agras T40 centrifugal sprayer calibrated to ASABE S572.1 standards. The integrated system achieved an AI detection F1-score of 0.91, reduced agrochemical volume by 34.2%, and improved spray deposition uniformity (coefficient of variation = 18.4%) relative to conventional blanket spraying. Grain yield increased significantly by 11.7% (p

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像とAI解析により、作物の病害、栄養欠乏、水ストレスを検出する方法を開発・現地検証しており、植物状態の取得が統合システムの中心的要素である。

abstractThis study field-validates a closed-loop unmanned aerial vehicle (UAV) framework integrating AI-driven multispectral scouting with prescription-mapped variable-rate aerial spraying (VRS).

An Ai-Powered Based Solution for Automated Plant Disease Detection →

Reducing agricultural yield and food security all over the world are major impacts of diseases on crops. Particularly in developing regions this is becoming a severe issue. Early disease identification followed by necessary steps is the way to control further infection. But, identification of crop diseases on the spot can be quite tough because of the scarcity of skilled agronomists who can recognize different plant diseases. A web application based framework is introduced in this paper for real time, automatic recognition of leaf diseases using an AI application. With this framework the plants which have got infected with 38 categories of diseases over 14 plants of apple, corn, tomato, grape, peach, strawberry, citrus, etc can be detected automatically. Size of the image can be anything but here, 160 x 160 pixel leaf image is used as input to the algorithm. It would helps to identify the category of the plant, disease, probability of detection and reason of the infection along with suggesting the appropriate solutions. It is being developed in Python language with the support of TensorFlow, Keras and flask web application for instant response via an interactive web application with Drag and Drop facility. In this, experimental results show good accuracy to classify and recognize the leaf diseases making this system a smart application for farmers, researcher and the expert.

Why it matches plant phenotyping methods植物葉の画像から病害状態を自動推定するAI手法とWebアプリケーションが中心であり、植物の病徴・病害状態を直接評価するため、植物フェノタイピング手法として収録する。

abstractA web application based framework is introduced in this paper for real time, automatic recognition of leaf diseases using an AI application.

Improving maize LAI estimation by integrating multispectral imagery and digital surface models with ensemble learning. →

To overcome the limitations of single remote-sensing features in estimating maize canopy leaf area index (LAI), this study developed a UAV-based estimation approach by integrating multispectral vegetation indices (VIs) with digital surface model (DSM) features and stacking ensemble learning. Field experiments were conducted in Dehong, Yunnan Province, China, during 2023-2024, and UAV multispectral images and DSM products were acquired for maize grown under three planting-density treatments. Five vegetation indices and three DSM-derived texture/structural features were retained according to their correlation with measured LAI, statistical significance, and complementary spectral or structural information. The VI-based random forest (VI-RF) model achieved an R 2 of 0.835 and an NRMSE of 9.5%, whereas the DSM-based model showed lower performance (R 2 = 0.641; NRMSE = 14.2%). Under the same random-forest modeling framework, fusing VIs with DSM features improved the overall model performance to R 2 = 0.892 and NRMSE = 7.6%, indicating that DSM-derived structural information mainly enhanced the feature representation of maize LAI. Using the same VI-DSM feature set, the stacking model with support vector machine (SVM) as the meta-learner further improved the overall performance to R 2 = 0.930 and NRMSE = 6.3%. The additional gain from stacking was moderate but consistent, whereas feature fusion contributed the dominant improvement. The combined VI-DSM-Stacking workflow improved prediction stability across planting densities, especially under low- and high-density canopy conditions where soil background interference and spectral saturation were more evident. These results demonstrate that integrating spectral and DSM-derived structural information with stacking ensemble learning can improve the accuracy and robustness of UAV-based maize LAI estimation.

Why it matches plant phenotyping methodsUAV画像・DSM・アンサンブル学習を統合し、トウモロコシのLAI推定法を開発・比較検証しており、表現型取得・推定手法が研究の中心である。

abstractthis study developed a UAV-based estimation approach by integrating multispectral vegetation indices (VIs) with digital surface model (DSM) features and stacking ensemble learning

Pathogen Inoculation and Rating Strategies for Studying Maize Diseases. →

Maize is a globally important staple that is used as food for human and animal consumption, fuel, and other industrial applications. Pathogens affect all stages of the plant life cycle and every plant organ, and lead to significant yield losses. An integrated strategy incorporating cultural and chemical management practices, as well as development of resistant plant varieties, is needed to prevent yield losses due to plant diseases. Large numbers of breeding material must be screened to develop pathogen-resistant maize varieties. Inoculation methods must be high-throughput to accommodate the large screening experiments. Additionally, there needs to be an extensive understanding of the plant-pathogen interaction to use a targeted biotechnology-based approach, which takes advantage of knowledge of the system to engineer resistance. To evaluate germplasm for breeding and biotechnology approaches, inoculation methods must replicate natural infection, and disease severity must be rated consistently to accurately screen germplasm or gather data on pathogens of interest. Here, we review inoculation and rating methods for Gibberella ear rot, seedling blight caused by Globisporangium ultimum var. ultimum , and Goss's wilt that are efficient and high-throughput. We also introduce fluorescence microscopy techniques for leaf samples infected with Exserohilum turcicum , the causal agent of northern corn leaf blight. These pathogens all cause significant yield losses, and in particular, Gibberella ear rot is associated with the accumulation of harmful mycotoxins. Understanding how pathogens cause disease and how plants defend against attack is a major goal of maize pathology studies and critical for developing integrated management strategies.

Why it matches plant phenotyping methodsトウモロコシ病害の接種および病害重症度評価法をレビューし、高スループットで一貫した植物病害表現型の取得を扱うため、方法論が中心です。感染葉の蛍光顕微鏡法も紹介されています。

abstractInoculation methods must be high-throughput to accommodate the large screening experiments.

High-Throughput Fluorescence Microscopy Using Aniline Blue Staining to Study the Maize -Exserohilum turcicum Pathosystem. →

Maize is a globally important grain crop that is important for food and fuel. Northern corn leaf blight, caused by Exserohilum turcicum , is an important fungal foliar disease of maize that is highly prevalent and causes yield losses globally. Microscopy can be used to visualize plant-fungal interactions on a cellular level, which enables pathology and genetics studies. Host resistance and isolate aggressiveness can be characterized at different stages of disease development, which enables a more detailed understanding of the pathogenesis process and host-pathogen interactions. Our protocol outlines an efficient, cost-effective method for staining E. turcicum tissue on inoculated maize leaves and visualizing samples using a compound fluorescence microscope. This protocol uses KOH treatment followed by aniline blue staining, which stains glucans present in plant and fungal cell walls, and samples are visualized using fluorescence microscopy. Quantitative data about fungal structures including the conidia, hyphal structures, and appressoria, the structures formed to push through the plant leaf surface after conidia have germinated, can be obtained from the images generated using this technique. Visualization of these structures can help pathologists understand plant-pathogen interactions for maize and E. turcicum This method has advantages over other methods because the stain is less toxic than other available stains, samples can be processed in a more high-throughput manner than other protocols, and the required supplies are relatively inexpensive.

Why it matches plant phenotyping methodsトウモロコシ葉上の病原体感染構造を蛍光顕微鏡画像から定量する高スループット染色・画像化プロトコルが研究の中心であり、植物病態の表現型取得法に該当する。

abstractOur protocol outlines an efficient, cost-effective method for staining E. turcicum tissue on inoculated maize leaves and visualizing samples using a compound fluorescence microscope.

RSCM: A Bayesian remote sensing-integrated crop model software framework for yield estimation →

RSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization. This approach automates estimation of leaf area index, aboveground dry matter, and grain yield without extensive ground-based calibration. The system couples a Python data interface with a high-performance C simulation engine, enabling efficient regional-scale processing. Validation using independent datasets for rice, wheat, and maize demonstrated robust performance: yield Model Efficiency reached 0.99, with a minimum ME of 0.67 for wheat. The Bayesian prior regularization constrained parameter estimates while maintaining predictive accuracy. Regional applications in South Korea, North Korea, and the U.S. Corn Belt captured spatial yield gradients and inter-annual variability across millions of pixels. RSCM provides a computationally efficient tool bridging process-based modeling and remote sensing for precision agriculture and food security monitoring.

Why it matches plant phenotyping methods衛星データと作物モデルを統合し、LAI・地上部乾物量・収量という植物形質を推定するソフトウェア手法を開発・検証しており、形質取得・推定法が研究の中心である。

abstractRSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization.

Predicting complex phenotypes using multi-omics data in maize. →

Understanding and predicting complex traits in plants remains a fundamental challenge due to the emergent nature of most phenotypes and their dependence on genetic, regulatory, and environmental interactions. Accurate prediction of traits and identification of underlying genetic elements have broad applications for plant breeding, systems biology, and biotechnology. Here, we tested if multi-omic datasets could improve predictive accuracy of 129 diverse maize phenotypes across 9 environments using genomic markers, field-based transcriptomic data from 2 locations, and drone-derived phenomic data of vegetative indices. We trained and compared linear (rrBLUP) and nonlinear (support vector regression) models using single- and multi-omics inputs. Multi-omics models consistently outperformed single-omics models for most traits, with genomic and transcriptomic inputs contributing distinct biological features. Phenomic features alone yielded the lowest predictive power but improved predictions for specific trait categories like root architecture. Transcriptomic datasets enabled cross-environment prediction, demonstrating that gene expression patterns from one field site could accurately predict traits measured in another. Environment-specific expression of benchmark flowering time genes highlighted the value of transcriptomics in capturing genotype-by-environment (G × E) interactions not detectable through genomic data alone. Analysis of model feature weights further indicated that predictive signal is distributed across many genes, consistent with complex traits such as yield arising from coordinated, network-level processes rather than a small number of dominant loci. These findings demonstrate that integrating transcriptomic and phenomic data with genotypes enhances trait prediction, improves model generalizability across environments, and provides deeper insight into the genetic and regulatory architecture of agriculturally important traits in maize.

Why it matches plant phenotyping methods複数オミクスとドローン由来フェノミックデータを統合し、植物形質を予測するモデルを比較・評価しており、形質推定ワークフローが研究の中心である。

abstractWe trained and compared linear (rrBLUP) and nonlinear (support vector regression) models using single- and multi-omics inputs.

Intelligent Crop Disease Detection System using Leaf image Analysis with Computer Vision →

Burkina Faso's agriculture sector faces major challenges, with annual crop losses reaching 40% due to plant diseases, affecting 2.7 million people in a situation of food insecurity. This research presents an innovative automatic plant disease detection system using computer vision, specifically developed for West African constraints. The system is based on the YOLOv11 (You Only Look Once) unified detection architecture, recognised for its optimal balance between speed and accuracy in real time, essential for mobile deployment to detect diseases in maize, tomatoes and chillies with an overall accuracy of ~99%. The image dataset from Kaggle has been validated by local agronomic expertise from INERA, ensuring the relevance of disease classes specific to the Sahelian context. The proposed architecture demonstrates superior performance to existing approaches while being optimised for mobile deployment. This solution contributes to the development of decision support tools for precision agriculture in West Africa.

Why it matches plant phenotyping methods葉画像から植物病害を自動検出するコンピュータビジョン手法の開発が中心で、植物の病害状態を直接推定しているため、植物フェノタイピング手法として採用。

abstractThis research presents an innovative automatic plant disease detection system using computer vision

WaveST-Yield: a novel spatio-temporal deep learning framework with frequency-domain refinement for UAV-based maize yield prediction. →

Accurate and generalizable plot-scale maize yield prediction is critical for precision agriculture and food security. While UAV-based multispectral remote sensing provides rich phenotyping data, existing yield prediction models often struggle with insufficient mining of complex spatio-temporal dynamics, ineffective separation of spatial details from background noise, and inadequate focus on yield-sensitive features throughout the crop growth cycle. To address these limitations, this study proposes WaveST-Yield, a novel hybrid deep learning framework tailored for multi-temporal multispectral data. The proposed model integrates three core modules: a Spatio-Temporal Phenology Encoder (SPE) based on ConvLSTM to capture the temporal dynamic patterns and spatio-temporal correlations across the entire growth period; a Multiscale Frequency-Spatial Refiner (MFSR) utilizing Haar Wavelet Downsampling (HWD) to preserve image details and decouple noise without early loss of key physiological features; and an Adaptive Yield-Sensitive Re-calibrator (AYSR) leveraging a 3D-CBAM attention mechanism to enhance the extraction of critical yield-related traits while suppressing background interference. The model was rigorously evaluated on two independent maize experimental fields using 5-fold cross-validation and cross-plot external validation. Results demonstrate that WaveST-Yield consistently outperforms traditional machine learning algorithms and single-structure deep learning models, achieving the highest prediction accuracy (Overall R² of 0.883 and 0.775 in Field 1 and Field 2, respectively) with superior error control. Extensive ablation and multi-model comparison experiments confirm that the synergistic integration of spatio-temporal encoding, frequency-domain refinement, and 3D attention mechanisms significantly improves model robustness and cross-regional generalization ability. This study provides a highly accurate, robust, and generalizable methodological framework for high-throughput crop yield monitoring.

Why it matches plant phenotyping methodsUAVマルチスペクトル時系列からトウモロコシ収量を推定する深層学習フレームワークの開発と、独立圃場・交差検証による技術評価が研究の中心である。

abstractthis study proposes WaveST-Yield, a novel hybrid deep learning framework tailored for multi-temporal multispectral data.

Protocol for evaluating salt and alkaline tolerance in maize seedlings using a sand-bed germination assay. →

Here, we present a protocol to independently and concurrently evaluate salt and alkaline tolerance in maize at the seedling stage using a controlled sand-bed germination assay. We describe steps for preparing standardized stress solutions, establishing the sand-bed assay, applying stresses either independently or in combination, and measuring multiple phenotypic traits. Furthermore, we detail procedures for ranking germplasm using a comprehensive scoring system based on a membership function. This protocol enables reproducible, large-scale screening of maize germplasm at the seedling stage.

Why it matches plant phenotyping methods幼苗耐盐碱性状の再現可能な大規模フェノタイピング用アッセイとスコアリング手順を中心に提示しており、単なる生物学的処理実験ではない。

abstractwe present a protocol to independently and concurrently evaluate salt and alkaline tolerance in maize at the seedling stage using a controlled sand-bed germination assay

Multi-Context Validation of Global Fractional Vegetation Cover Products in Croplands Using Multi-Source Crop FVC References →

Fractional Vegetation Cover of Crops (CropFVC) is a critical canopy parameter for monitoring crop growth, yet the behavior of widely used global FVC products (GLASS, GEOV1, GEOV2, and GEOV3) over croplands remains insufficiently understood due to fragmented validation references and limited crop-specific assessments. This study compiled a multi-source global CropFVC reference dataset (2000–2024) by integrating five international validation networks, the literature-derived samples, and newly acquired UAV and Jilin-1 satellite-derived CropFVC samples from China in 2024. The references were organized into three complementary validation contexts (V1~V3) to examine product behavior under different temporal coverage, crop purity, and reference conditions, together with spatio-temporal observations at the KONZ site. Results show that (1) across validation contexts, the evaluated products showed consistent behavior patterns, including shared overestimation under dense canopy conditions and reduced differences at low FVC levels; (2) spatio-temporal analysis at the KONZ site confirmed that peak-season deviations reflect shared response behavior rather than site-specific reference uncertainties; (3) historical mixed references (V1~V2) showed similar bias structures, whereas crop-specific validation (V3) preliminary revealed clearer crop-dependent responses, with predictive difficulty following winter wheat > maize > rice > soybean and improved stability after integrating 2024 observations. The integration of recent high-resolution crop observations expands existing global CropFVC references and enables behavior-oriented interpretation of global FVC products beyond simple accuracy ranking, providing an updated validation perspective for future development and application of global CropFVC products in agricultural monitoring.

Why it matches plant phenotyping methods作物キャノピーのFVCという植物形質を対象に、複数の全球FVC推定プロダクトを多様な参照データで体系的に検証し、UAV・衛星観測を含むCropFVC参照データセットを構築している。形質取得・検証が研究の中心である。

titleMulti-Context Validation of Global Fractional Vegetation Cover Products in Croplands Using Multi-Source Crop FVC References

An Automated Information Processing Framework for UAV-Based Detection and Spatial Mapping of Crop Damage Using Deep Learning →

The early detection and spatial characterization of crop damage are critical for improving decision-making in precision agriculture, particularly in regions where traditional monitoring methods are limited in scalability and objectivity. This study presents an integrated information processing framework that couples UAV-based image acquisition, instance segmentation, slicing-aided inference of large orthomosaics, and georeferenced spatial analysis into a single reproducible pipeline for the detection and mapping of crop damage. The framework is applied to maize cultivated under traditional milpa systems in Yucatán, Mexico, a region characterized by intercropping, irregular plant spacing, and complex backgrounds rarely represented in mainstream agricultural deep learning benchmarks. High-resolution RGB images were systematically acquired over maize fields in Yucatán, Mexico, and curated into specialized datasets representing parcels, individual plants, and damaged vegetation. Instance segmentation models based on the YOLOv11 architecture were trained and evaluated to extract visual information related to crop condition, while the Slicing-Aided Hyper Inference (SAHI) method was integrated to enable efficient processing of large orthomosaic images. The proposed framework achieved high performance in detecting maize plants, with a precision of 92.9% and an mAP50 of 94.2%, and demonstrated reliable identification of damage patterns associated with Spodoptera frugiperda, reaching a precision of 79.2% and an mAP50 of 71.7%. The resulting georeferenced outputs provide spatially explicit information that supports quantitative analysis of crop health and damage distribution. The results indicate that the proposed framework constitutes a scalable and reproducible approach for UAV-based visual information extraction, with potential applicability to broader agricultural monitoring and data-driven decision support systems.

Why it matches plant phenotyping methodsUAV画像、インスタンスセグメンテーション、SAHIを統合し、作物の損傷状態を抽出・定量化する再現可能な手法が研究の中心であり、性能評価も実施している。

abstractThis study presents an integrated information processing framework that couples UAV-based image acquisition, instance segmentation, slicing-aided inference of large orthomosaics, and georeferenced spatial analysis into a single reproducible pipeline for the detection and mapping of crop damage.

Curvelet Decomposition-Based Tri-Branch Coupling Network for Hyperspectral Unsound Maize Seeds Identification. →

The rapid and nondestructive classification of maize kernels is of great significance for seed screening and quality evaluation. Existing hyperspectral image classification methods based on the Mamba architecture can effectively represent spectral and spatial features; however, they still face limitations in time-frequency analysis and multimodal feature fusion. In addition, traditional approaches often rely heavily on spectral preprocessing, which may introduce additional errors and compromise the model's robustness and generalization ability. To address these challenges, this paper proposes a novel cross-modal classification framework named CD-TriMamba, which jointly leverages hyperspectral data and visible-light images for comprehensive feature extraction and deep fusion. Specifically, an innovative feature extraction module is designed, consisting of a Spectral Curvelet Convolution (SCC) module for hyperspectral data and a Curvelet-Decomposed Convolution (CDC) module for spatial modeling. A feature rearrangement mechanism is further introduced to mine critical information from both spectral and spatial modalities. Finally, a ConvNeXt-guided tri-branch cross-fusion structure (TriMamba) is constructed to achieve deep collaboration and efficient integration between spectral and spatial features. Experimental results demonstrate that the proposed model achieves outstanding performance in seed classification, with an accuracy (Acc) of 99.2% and a Kappa value of 99.1%. These results strongly confirm the effectiveness and broad application potential of cross-modal feature fusion in maize kernel classification.

Why it matches plant phenotyping methodsマルチモーダル画像からトウモロコシ種子の健全性を推定する新規分類フレームワークを開発しており、種子状態の取得・抽出手法が研究の中心である。

abstractthis paper proposes a novel cross-modal classification framework named CD-TriMamba, which jointly leverages hyperspectral data and visible-light images for comprehensive feature extraction and deep fusion.

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

Explainable machine learning to predict root biomass of field crops using UAV multispectral data →

Understanding below-ground biomass dynamics is essential for improving crop performance in water-limited regions. Yet field-scale root monitoring remains constrained by destructive and labor-intensive sampling. This study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits. Across 405 samples collected during the 2024 growing season, eight algorithms were evaluated, among which Random Forest and XGBoost achieved the highest predictive accuracy (R² = 0.763 for millet, 0.688 for maize, and 0.659 for sorghum). SHAP analysis revealed that leaf area was the dominant predictor across all crops, with 2-3 times greater influence than other traits, while leaf water content and chlorophyll-related parameters exhibited species-specific effects associated with drought adaptation. Under the conditions tested, these results suggest that UAV-based multispectral phenotyping, combined with interpretable machine learning, can enable non-destructive estimation of root biomass at the field scale. Within the limits of this single-site, single-season study, the approach demonstrates potential for large-scale root phenotyping and for supporting crop improvement in semi-arid regions. We quantify a 15-25% reduction in R² relative to above-ground trait prediction, which we term the 'cost of indirect inference'-highlighting the inherent challenge of estimating below-ground biomass from canopy-level data. These findings offer insights for precision agriculture, subject to broader validation.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と説明可能な機械学習を用いて根 biomass という植物形質を非破壊推定する手法が研究の中心であり、実証・比較評価も行っている。

abstractThis study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits.

Sensitive Spectral and Temporal-Spatial Characteristic Analysis of Leaf SPAD in Maize Under Variety and Nitrogen Coupling Effects. →

Accurate estimation of leaf SPAD is crucial for maize growth and yield formation. Many methods for monitoring SPAD currently lack the analysis of sensitive leaf position in different stages of maize. In this paper, the spectra and temporal-spatial characteristics of maize leaf SPAD were analyzed to describe the sensitive stage and leaf position. After exploring the dynamic growth effects of SPAD in maize leaves, the sensitive stage of SPAD was determine. Several preprocessing methods and spectral vegetation indices were used to analyze the spectral reflectance of typical leaf positions in sensitive stages. The function regression methods based on single vegetation index and the random forest regression (RFR) based on multi-vegetation indices were employed. The results showed that the twelve-leaf (V12) and the silking (R1) were the sensitive stages. The strongest RVI at the V12 stage and NDRE for the ear leaves at the R1 stage were observed under SG-SNV method. The best prediction data ( R 2 = 0.7) was showed at the V12 stage under MSC-RF. The prediction effect of the ear leaves after MSC pretreatment was slightly better ( R 2 = 0.69). In addition, SPAD value can indirectly reflect the chlorophyll content, nitrogen content and yield status of maize leaves, and its accurate monitoring provides effective guidance for maize leaf nutrition information and yield prediction.

Why it matches plant phenotyping methodsトウモロコシ葉のSPADをスペクトル情報と回帰モデルで推定する方法を中心に、感受性時期・葉位や予測性能を分析しており、植物表現型取得手法が主要な内容である。

abstractAccurate estimation of leaf SPAD is crucial for maize growth and yield formation.

Hyperspectral data-driven corn nitrogen monitoring: application and interpretability analysis of multi-source feature optimization and stacked ensemble learning methods. →

Introduction Accurate monitoring of canopy nitrogen content is essential for sustainable nitrogen management, yield improvement, and environmental protection in industrial maize production. However, the high dimensionality of hyperspectral data and the limited accuracy and interpretability of existing models hinder practical applications. Methods This study was conducted in Heilongjiang Province, China, using the maize cultivar Jinboshi. Genetic Algorithm (GA), Successive Projections Algorithm (SPA), and their hybrid strategy were compared for spectral band optimization. Sensitive vegetation indices were selected using multiple evaluation criteria, and a 0-2 order fractional-order derivative (FOD) method was applied to construct optimal two-dimensional (2D) and three-dimensional (3D) spectral indices. A stacked ensemble learning model was developed using XGBoost, GBDT, and Ridge as base learners and Bayesian Ridge as the meta-learner. Interpretability techniques were applied to analyze feature contributions. Results The GA-SPA hybrid strategy effectively improved key spectral band selection. The 3D spectral index based on FOD achieved superior performance compared to vegetation indices and 2D indices (R 2 p = 0.801, RMSEP = 0.481). The optimized multi-source feature set combined with the stacked ensemble model yielded the best performance (R 2 p = 0.826, RMSEP = 0.450). Features from the red-edge and near-infrared regions, along with the 3D index, were the primary contributors to model predictions, consistent with plant nitrogen physiology. Discussion The proposed framework, integrating feature optimization, advanced modeling, and interpretability analysis, provides an effective tool for precise nitrogen management in industrial maize and supports improved production efficiency with reduced environmental impact.

Why it matches plant phenotyping methodsトウモロコシ群落の窒素含量という植物形質を、ハイパースペクトル特徴量最適化とアンサンブル学習で推定する手法が研究の中心であり、性能評価と解釈性分析も行っている。

abstractAccurate monitoring of canopy nitrogen content is essential

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.

Hybrid IGWO-Dingo optimized DeMoHybridNet model for multi-class leaf disease identification. →

To achieve efficient crop management, exact plant disease detection in leaves is required. This study proposes DeMoHybridNet (Bottleneck Reduction Fusion) for the automated classification of Corn, Apple, Citrus, and Mango crop diseases. Input images are processed through augmentation and resizing, and then features are learned using DenseNet-201 and MobileNetV2. Global Average Pooling is applied, which produces condensed features. The features are then compressed using bottleneck layers of 512 features. The features are concatenated and classified by Random Forest (RF) classifier. To further improve the performance, a hybrid meta-heuristic method called IGWO-DOA (Improved Grey Wolf Optimization-Dingo Optimization Algorithm) is used to optimize the hyperparameters of the model for better convergence and generalization. The proposed optimized model gives the classification accuracy is 98.56% for Corn, 98.99% for Apple, 97.83% for Citrus and 99.35% for Mango leaf dataset. Statistical analysis confirms its robustness and reliability, demonstrating its effectiveness for precision agriculture applications.

Why it matches plant phenotyping methods葉画像から植物の病害状態を自動分類する深層学習・特徴抽出・分類ワークフローが研究の中心であり、植物病害表現型の画像ベース推定手法に該当する。

abstractThis study proposes DeMoHybridNet (Bottleneck Reduction Fusion) for the automated classification of Corn, Apple, Citrus, and Mango crop diseases.

UAV-based multispectral imaging and machine learning for detecting and mapping maize leaf diseases in smallholder farms. →

Maize (Zea Mays) is one of the world's most important staple crops, providing food for humans and feed for livestock. However, its production is threatened by a range of stresses, including crop diseases, which significantly reduce yields, particularly in smallholder farming systems. Traditional disease detection methods, such as visual inspection, are often labour-intensive, subjective, and prone to error, leading to delayed interventions and widespread crop losses. This study uses unmanned aerial vehicle (UAV) remote sensing and machine learning (ML) to investigate the feasibility of detecting maize leaf diseases in a smallholder farm located in the Mopani District of Limpopo Province, South Africa. UAV-derived vegetation indices including NDVI, GNDVI, and NDRE were combined with UAV multispectral bands and the three ML algorithms, namely - support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), to first distinguish healthy from diseased plants and then to classify specific maize diseases. The SVM algorithm achieved the highest accuracy in both, distinguishing healthy and diseased crops from other land cover classes (91.73%) and in distinguishing specific diseases (89.41%). Among the diseases identified, Southern Corn Leaf Blight was classified with the highest user's accuracy, while phosphorus deficiency had the lowest user's classification accuracy. The results demonstrate the potential of integrating UAV-based multispectral imaging and ML for precision agriculture by providing timely, spatially detailed disease information that enables targeted management practices, reducing crop losses and enhancing food security for smallholder farmers.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を用いて、トウモロコシの健全・罹病状態および具体的な葉病害を推定する手法が研究の中心であり、植物病害状態のフェノタイピングに該当する。

abstractThis study uses unmanned aerial vehicle (UAV) remote sensing and machine learning (ML) to investigate the feasibility of detecting maize leaf diseases

OPTIMIZING PRE-PROCESSING OF NEAR INFRARED SPECTRA FOR PHENOMIC PREDICTION USING SINGULAR VALUE DECOMPOSITION →

Phenomic prediction (PP) is a genetic value prediction method based on near infrared spectroscopy (NIRS). Spectra pre-processing is a key step in the analysis pipeline of PP and generally involves chemometrics methods. However, the choice of pre-processing is usually done either arbitrarily or through a search of the optimal set of methods and associated parameters. In this study, we propose to implement a singular value decomposition (SVD) step in the pre-processing pipeline where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths. This way, estimations are based on a few informative, orthogonal and interpretable features of spectra instead of many correlated, uninformative wavelengths. We tested this pre-processing method on five datasets representing four plant species (maize, rice, sorghum and grapevine). Results show that estimating genetic values on components of raw spectra, that are not weighted by their eigenvalues, performs as well as doing it on spectra pre-processed with the best classical chemometrics methods in most cases, while requiring less parameter optimization. Moreover, this SVD step opens up possibilities for better understanding and selecting parts of the spectral information that are relevant for PP. Plain language summary Cultivated plants are the result of a breeding process during which their genetic values are used to select those to breed. Estimating these values requires heavy experimental means and is time consuming. Phenomic prediction is a low cost and high throughput method that is increasingly being used for this purpose. It often uses, as predictors, near infrared spectroscopy measurements that are easy to collect and thus routinely used in many species. However, near infrared spectra generally require pre-processing before being used in prediction. Currently used pre-processing methods arise from the chemometrics community, and still deserve a better in-depth appropriation by geneticists. In this study, we propose a pre-processing approach that performs as well as the best chemometrics pre-processing generally used, reduces computation time, and allows for a better understanding of what parts of spectral information are relevant for prediction. Core Ideas The SVD-based pre-processing performs as well as the best performing classical chemometrics pre-processing in most cases Using the SVD-based pre-processing reduces computing time of genetic value estimation and requires less parameter optimization than using classical chemometrics pre-processing Spectra are composed of chemical and physical information and classical pre-processing methods remove the physical part of the signal It is likely that chemical information is the most important for phenomic prediction even though physical information remains valuable Performance of the SVD-based pre-processing is likely due to a good estimation of the genetic part of spectra and the conservation of physical information of spectra

Why it matches plant phenotyping methods植物のNIRSスペクトルから遺伝的価値を推定するフェノミック予測について、SVDベースの前処理法を提案し、複数植物種のデータセットで既存法と比較検証しているため、フェノタイピング手法が中心である。

abstractIn this study, we propose to implement a singular value decomposition (SVD) step in the pre-processing pipeline where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths.

Deep convolutional models for robust multi-crop disease recognition in real-world conditions. →

Crop diseases significantly reduce agricultural output and are a serious problem, especially in the parts of the world where diagnostic experts are not readily available. Deep learning has recently shown us that it is possible for a computer to identify plant diseases directly from images of the leaves. Nevertheless, to make such solutions available on the web or mobile devices one has to really think about how heavy the calculations will be, how easy the user interface should be, and also the limit on the data used. Here is a paper on a web-based applied deep learning system for disease detection in multiple crops. The system detects disease in eight crops Apple, Banana, Grape, Mango, Cauliflower, Tomato, Potato, and Corn with each crop having several disease classes and healthy samples. Three transfer-learning-based CNN architectures MobileNetV3, EfficientNetB4, and ResNet50 were compared for classification performance on the public datasets collected from PlantVillage, Kaggle, and Mendeley. Considering class-wise accuracy, prediction time, and deployment scenarios, MobileNetV3 was picked as the main model to be integrated into the system. To compensate for the differences in image quality often found in pictures taken by users, an optional super-resolution preprocessing step with Real-ESRGAN is added and quantitatively assessed. Disease prediction with spectral activation maps (Grad-CAM) enhances the model's interpretability by highlighting image areas where the disease is detected. The resulting model is embedded in a multilingual Progressive Web Application (PWA). The platform enables users to submit their crop images and receive predicted disease names and treatment options, which are generated by a Large Language Model (LLM) using structured disease metadata. The research acknowledges dataset bias and limitations in extrapolating from curated datasets to the general real-world setting although it reports very good performance of the method on the test sets. In summary, the system proposed here is intended as a practical digital agriculture decision-support tool that demonstrates deployment feasibility and raises a few issues for future validation at the field level and improvement.

Why it matches plant phenotyping methods葉画像から植物病害を推定するCNN手法の比較・前処理評価・実装を中心とした研究であり、植物の病害状態を直接評価するため、植物フェノタイピング手法として中心的です。

abstractDeep learning has recently shown us that it is possible for a computer to identify plant diseases directly from images of the leaves.

Adaptive spectral band optimization for low-cost multispectral estimation of maize leaf chlorophyll using a LASSO-IARO strategy. →

Chlorophyll content in maize leaves is an important indicator of the physiological status and nutritional conditions of the crop. Rapid and cost-effective monitoring of chlorophyll is therefore essential for precision agriculture. However, spectral measurement instruments are expensive and difficult to deploy widely in field environments. In practical applications, low-cost multispectral sensors require the selection of a small number of informative spectral bands while maintaining prediction accuracy. In this study, an adaptive spectral band optimization strategy integrating the least absolute shrinkage and selection operator (LASSO) and an improved artificial rabbits optimization (IARO) was proposed to identify compact band subsets for chlorophyll estimation. The spectral data of maize leaves were preprocessed using Savitzky-Golay smoothing (SG) and Standard Normal Variate Transformation (SNV). Feature bands were selected using SPA, Pearson correlation, LASSO, and the proposed LASSO-IARO method, and predictive models were developed using partial least squares regression (PLSR) and support vector regression (SVR). Results showed that LASSO-IARO reduced the number of selected bands by 42.86-73.33% compared with conventional methods while maintaining comparable prediction accuracy. The LASSO-IARO-PLSR model achieved a coefficient of determination ( R 2 ) of 0.81 with a root mean square error (RMSE) value of 2.01 on the testing set. The optimized band subset (517 nm, 520 nm, 696 nm, and 730 nm) provides candidate wavelengths for designing low-cost multispectral sensors for in-field chlorophyll monitoring.

Why it matches plant phenotyping methodsトウモロコシ葉のクロロフィルという植物生理形質を対象に、低コスト multispectral センサー向けの波長選択・推定手法を開発し、予測性能を検証しているため、方法開発・検証が中心です。

abstractan adaptive spectral band optimization strategy integrating the least absolute shrinkage and selection operator (LASSO) and an improved artificial rabbits optimization (IARO) was proposed to identify compact band subsets for chlorophyll estimation.

Research on maize growth simulation and organ morphology co-modeling driven by multimodal data fusion. →

Introduction Addressing the core bottleneck in traditional crop models-the disconnect between morphology and physiological function at the organ scale and their limited dynamic response to environmental changes-this study aimed to construct a multi-source data fusion maize growth model for simultaneous organ-scale simulation. Methods We developed a closed-loop Environment-Driven-Functional Response-Morphological Feedback (EDFM) architecture. By integrating environmental time-series data, RGB images, and 3D point clouds, we created a multimodal fusion model based on a gated attention network. This approach adaptively weights multi-source features and pioneers a bidirectional morphology-physiology feedback loop based on physiological development time (PDT) and NURBS surfaces. The WOFOST moisture response function was also improved. Results The model significantly enhanced the simulation accuracy of organ-scale growth, reducing the root mean square error (RMSE) for plant height by 74.6% through a morphology-physiology dynamic weighting mechanism. More fundamentally, it resolved the disconnect between morphological and physiological processes. The improved plant height prediction validates the model's effectiveness at the organ scale. Discussion The pioneering "physiology-morphology" parallel simulation architecture provides an interpretable theoretical model and robust quantitative tools for designing high-photosynthetic-efficiency plant architecture and enabling precision water-fertilizer management.

Why it matches plant phenotyping methodsRGB画像・3D点群・環境データを統合し、器官スケールの形態と生長をシミュレーションする手法を開発しており、植物形質（草丈など）の推定が中心的な技術貢献である。

abstractWe developed a closed-loop Environment-Driven-Functional Response-Morphological Feedback (EDFM) architecture.

Real‐time monitoring of root dielectric properties for assessing crop plant damage caused by foliar application of glyphosate →

Abstract Premise There is a knowledge gap regarding how foliar injury and restricted water uptake can be detected by measuring root dielectric response. This pot study nondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying. Methods Root dielectric properties were recorded on a minute scale in control and glyphosate‐treated maize, cucumber, and pea. Chlorophyll, stomatal conductance, and biomass measurements were taken to interpret the dielectric changes. Results Electrical capacitance and conductance varied diurnally due to the circadian regulation of water uptake and hydraulic conductance. Glyphosate application reduced capacitance, indicating the impeded root growth and activity caused by impaired amino acid synthesis, foliar damage, and restricted transpiration. The dissipation factor decreased in response to glyphosate due to impeded apoplastic water flow, suppressed root lignification, and hampered water absorption. The enhanced leaf and root hydraulic resistance caused by glyphosate was manifested in sharply reduced electrical conductance. Changes in the species’ dielectric response were consistent with physiological symptoms and biomass loss. Discussion Real‐time dielectric measurement proved suitable for the nondestructive monitoring of plant responses to foliar stress through altered root traits. This method could be employed to evaluate herbicide tolerance in crops and to develop and determine dosage of herbicide ingredients.

Why it matches plant phenotyping methods植物の根の誘電特性をリアルタイム・非破壊で測定し、ストレス応答や根形質を評価する方法が研究の中心であるため。

abstractnondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying

Research on the Application of Agricultural Big Data in Plant Growth Prediction →

The intelligent transformation of agriculture places plant growth prediction as a critical component for ensuring food security, optimizing resource allocation, and enhancing sustainable productivity. Traditional methods reliant on empirical or simplified mechanistic models struggle with the nonlinearity, high dimensionality, and spatiotemporal heterogeneity inherent in agro-ecological systems. This study investigates the paradigm shift enabled by agricultural big data integrating multi-source, real-time streams from IoT sensors, satellites, UAVs, and farm management systems. We propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically couples process-based crop models with ensemble machine learning algorithms---including Transformer-based architectures and Physics-Informed Neural Networks---within a holistic pipeline encompassing multi-modal data fusion, hybrid modeling, and scalable deployment. Empirical validation across major crops (rice, wheat, maize, tomato) in diverse eco-regions of China (2023--2025) demonstrates significant improvements: the MDA-HI model achieved average RMSE reductions of 42.7% for yield prediction and 38.1% for key phenological stage prediction relative to best-in-class standalone models. A large-scale case study on rice-wheat rotation systems showed that data-driven prescriptions reduced nitrogen fertilizer use by 22.5% and irrigation water by 18.3% while increasing yield by 5.1%. The study further establishes a five-dimensional evaluation system covering accuracy, robustness, interpretability, scalability, and economic benefit. Remaining challenges include edge computing for real-time inference, federated learning for privacy-preserving collaboration, and explainability of complex ``black-box'' models. This research concludes that agricultural big data constitutes a foundational catalyst for predictive, precise, and proactive cognitive agriculture, with profound implications for global food system resilience.

Why it matches plant phenotyping methods農業ビッグデータを用いて生育・収量・フェノロジーを推定するMDA-HI手法を提案し、複数作物・地域で性能検証しており、植物形質推定手法が研究の中心である。

abstractWe propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically couples process-based crop models with ensemble machine learning algorithms---including Transformer-based architectures and Physics-Informed Neural Networks---within a holistic pipeline encompassing multi-modal data fusion, hybrid modeling, and scalable deployment.

Quantitative Imaging of RNA in Plants Using Hybridization Chain Reaction-RNA Fluorescence in Situ Hybridization (HCR RNA-FISH). →

Quantitative RNA imaging in large plant tissues has historically been challenging because of limited spatial resolution, low signal-to-noise ratios, and the largely qualitative nature of traditional RNA in situ hybridization methods. Hybridization chain reaction-RNA fluorescence in situ hybridization (HCR RNA-FISH) coupled with high-resolution microscopy enables sensitive detection of RNA molecules at cellular resolution. However, quantitative approaches that combine improved tissue accessibility with robust computational pipelines for single-cell transcript quantification in plants remain limited. Here, we present a quantitative HCR RNA-FISH protocol for the developing maize inflorescence. We describe a 4-day workflow that includes fixation, agarose immobilization, vibratome sectioning, probe hybridization, amplification, and mounting and enables multiplexed detection of transcripts at the cellular level in maize ear and tassel primordia. In addition, we provide a Python-based image analysis pipeline for (i) cell segmentation, (ii) RNA spot quantification, (iii) assignment of spots to cells, and (iv) data representation. The scripts can be easily run on Jupyter notebooks and are available on GitHub. Overall, this protocol highlights the importance of integrating robust imaging strategies with quantitative and reproducible data analysis frameworks to extract biologically meaningful insights from imaging data. © 2026 Wiley Periodicals LLC. Basic Protocol: Quantitative RNA imaging in sections of maize ear and tassel primordia using HCR RNA-FISH.

Why it matches plant phenotyping methods植物組織内RNAの細胞レベル画像化と、細胞分割・RNAスポット定量を行う再現可能な解析パイプラインを中心としたプロトコルであり、植物状態の定量的表現型取得法が主題である。

abstractHere, we present a quantitative HCR RNA-FISH protocol for the developing maize inflorescence.

QTL mapping of maize plant height based on a population of doubled haploid lines using UAV LiDAR high-throughput phenotyping data →

Maize ( Zea mays L.) is a globally significant crop that plays a crucial role in feeding the growing global population. Among its various traits, plant height is particularly important as it affects yield, lodging resistance, ecological adaptability, and other important factors. Traditional methods for measuring plant height often lack cost-efficiency and accuracy. In this study, we employed a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) to collect point cloud data from 270 doubled haploid (DH) lines. This innovative application of UAV-based LiDAR technology was explored for high-throughput phenotyping in maize breeding. We constructed high-density genetic maps and assessed plant height at both single-plant and row scales across multiple developmental stages and genetic backgrounds. Our findings revealed that for many varieties and small areas, single-plant-scale estimation accuracy was superior to row-scale estimation, with an R 2 of 0.67 versus 0.56 and an RMSE of 0.12 m vs . 0.17 m, respectively. Two high-density genetic maps were constructed based on SNP markers. In Sanya and Xinxiang, the F 1 DH and F 2 DH populations identified 12 and 20 QTLs (quantitative trait loci) for plant height, respectively. The study successfully identified and validated QTLs associated with plant height, revealing novel genetic loci and candidate genes. This research highlights the potential of UAV-based remote sensing to advance precision agriculture by enabling efficient, large-scale phenotyping and gene discovery in maize breeding programs.

Why it matches plant phenotyping methodsUAV搭載LiDARによるトウモロコシ草丈の高スループット推定を開発・評価し、単個体と列スケールの精度比較を行っているため、表現型取得法が研究の中心です。

abstractwe employed a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) to collect point cloud data from 270 doubled haploid (DH) lines

How to Monitor Growth and Identify Developmental Stages of Maize ( Zea mays ). →

Properly characterizing the stages of corn growth is critical to conducting successful experiments in maize genetics and breeding. Specifically, accurately identifying stages of growth is required to perform developmentally dependent sampling or data collection, to predict time to flowering and seed maturation, and to allow for comparisons between different lines and populations based on developmental time. In this protocol, we summarize previous knowledge about maize development and describe how to monitor these stages in the reference inbred line B73, a yellow dent corn.

Why it matches plant phenotyping methodsトウモロコシの発育段階という植物状態を、実験・育種で再現可能にモニタリングするプロトコルが中心であり、発育ステージ測定法として収録対象と判断します。

titleHow to Monitor Growth and Identify Developmental Stages of Maize ( Zea mays ).

An Integrated Digital Framework for Sustainable Crop Residue Management and AIBased Maize Leaf Disease Detection →

Two of the greatest agricultural sustainability, crop productivity, and environmental health problems are crop residue burning and maize leaf diseases. Poor management of residue causes wastage of resources and air pollution, whereas late diagnosis of diseases causes losses of huge yields. To solve these problems, this paper will suggest an integrated intelligent agricultural system, combining a web-based Crop Residue Management System (CRMS) with a maize leaf disease detection module, based on deep learning.

Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を深層学習で検出するモジュールが統合システムの中心的構成要素であり、植物病害フェノタイピング手法の開発に該当する。

titleAn Integrated Digital Framework for Sustainable Crop Residue Management and AIBased Maize Leaf Disease Detection

Application of unmanned aircraft remote sensing image processing method based on artificial intelligence algorithm in corn growth assessment →

This paper proposes a method based on UAV low-altitude photogrammetry and deep learning algorithms for corn crop growth monitoring. During the shooting process, a unified UAV photogrammetry strategy is set to ensure that the obtained images have high spatial resolution, and after pre-processing the original images, a convolutional neural network (CNN) model is utilized to extract features from the images and improve the accuracy of the CNN with the help of the idea of transfer learning. In addition, multi-scale feature fusion and attention mechanism are introduced to allow the model to focus on important location information, and weighted multi-task loss function is used to jointly optimize the multi-objective values such as plant height, leaf area index, and biomass. Experiments show that the method has good real-time performance and scalability while maintaining high prediction accuracy, providing an effective solution for crop monitoring in precision agriculture.

Why it matches plant phenotyping methodsUAV画像と深層学習を用いてトウモロコシの草丈、葉面積指数、バイオマスを推定する手法自体が研究の中心であり、植物形質推定の方法開発・応用に該当する。

abstractThis paper proposes a method based on UAV low-altitude photogrammetry and deep learning algorithms for corn crop growth monitoring.

Research and testing of a robot vision-based perception method for assessing corn sowing quality →

To address the low efficiency of manual inspection for corn sowing quality, which is labor-intensive and time-consuming, this study proposes an automatic seedling-stage plant-spacing measurement method based on three-dimensional machine vision. A high-clearance mobile platform equipped with a ZED 2i stereo camera and an industrial computer was developed to acquire RGB images and depth information of corn seedlings in the field in real time. Using the YOLOv11-Pose model, plant keypoints were detected and localized; combined with camera calibration and 3D reconstruction techniques, inter-plant distances were computed automatically. A sowing-quality evaluation framework was then established to enable automated analysis of the Quality of Feed Index (QFI), Multiple Index (MUL), Miss Index (MI), and coefficient of variation. Experimental results indicate that the system operates effectively under three preset plant spacings (15 cm, 20 cm, and 25 cm), achieving a keypoint-detection mAP@0.5 of 0.990 and an mAP@0.5:0.95 of 0.989. In sowing-quality evaluation, the qualified indices produced by the system were 77.83%, 80.36%, and 82.46%, respectively, and the Quality of Feed Index (QFI), Multiple Index (MUL), Miss Index (MI), and coefficient of variation showed trends consistent with manual measurements. The proposed method enables efficient, nondestructive detection of seedling-stage plant spacing and sowing quality, providing reliable technical support for precision sowing and field management.

Why it matches plant phenotyping methods3Dマシンビジョン、姿勢検出、校正、3D再構成を組み合わせ、トウモロコシ個体間距離を自動推定・検証する手法が研究の中心である。

abstractthis study proposes an automatic seedling-stage plant-spacing measurement method based on three-dimensional machine vision.

Mobile-assisted deep learning framework for identification of insect pests and diseases of maize from field images. →

Maize ( Zea mays L. ) production is severely affected by diseases and insect pests, leading to significant yield losses when timely diagnosis and management interventions are not implemented. Although automated image-based diagnostic systems have shown promising results, most existing studies address diseases or pests independently, rely on controlled datasets, and offer limited robustness under real field conditions. To address these limitations, this study proposes a unified deep learning-based framework for integrated identification of maize diseases and insect pests under natural field environments by combining object detection and image classification within a mobile-assisted diagnostic system. Four economically important diseases and insect pests were investigated: Maydis Leaf Blight (MLB), Turcicum Leaf Blight (TLB), Common Rust, and Fall Armyworm (FAW). MLB and TLB were addressed using YOLO-based object detection architectures, while Common Rust and FAW were treated as image-level classification tasks using lightweight deep learning models optimised for mobile inference. A self-collected dataset comprising 10,343 images across four classes was acquired under real field conditions to capture variability in background complexity, illumination, phenological stages, and symptom expression. Experimental results on an independent test set comprising original images demonstrate that MobileViT achieved the highest classification accuracy (99%) for image-level disease and pest recognition, whereas YOLOv11n outperformed other detection models, achieving the best performance for MLB and TLB lesion detection with mAP@0.5 of 0.875. Grad-CAM-based visual explanation analysis confirmed that the classification models focused on disease lesions and pest-infested regions, supporting interpretability. The framework was successfully deployed via a mobile application, enabling image acquisition, automated validation, diagnosis, and the generation of management recommendations. The results highlight the accuracy, robustness, and operational feasibility of the proposed system for in-field diagnosis of maize diseases and insect pests, supporting early detection and sustainable crop protection.

Why it matches plant phenotyping methodsトウモロコシ葉の病斑と害虫被害領域を圃場画像から検出・分類する画像解析手法と、モバイル診断プラットフォームを開発・評価しており、植物の病害状態の取得が中心的です。

abstractthis study proposes a unified deep learning-based framework for integrated identification of maize diseases and insect pests under natural field environments by combining object detection and image classification within a mobile-assisted diagnostic system.

SAB-DeepLabV3+: A Semantic Segmentation Framework for Mapping Maize Waterlogging from Single-Date Multispectral Imagery →

Rapid identification of maize waterlogging is essential for post-disaster agricultural assessment, but most existing methods rely on multi-temporal imagery that is often unavailable immediately after extreme rainfall events. This study proposes SAB-DeepLabV3+, a semantic segmentation model for mapping waterlogged maize from single-date multispectral imagery within pre-extracted maize planting areas. Built on DeepLabV3+, the model integrates three task-specific modules: a Spectral-Spatial Information Enhancement Module to improve feature discrimination under spectral mixing, an Adaptive Multi-Scale Pooling Module to capture heterogeneous patch sizes, and a Boundary Enhancement Module to refine transition zones. A pixel-level dataset containing 12,198 image patches was constructed from 62 multispectral scenes collected across five major maize-producing cities in Heilongjiang Province, China, during 2022–2024. On the test set, SAB-DeepLabV3+ achieved a waterlogged-class IoU of 68.30%, mIoU of 80.37%, mF1 of 88.62%, and OA of 93.49%, outperforming DeepLabV3+. Leave-one-city-out evaluation further produced an average mIoU of 76.56% and a waterlogged-class IoU of 63.45%. These results indicate that single-date high-resolution multispectral imagery can support rapid and reliable maize waterlogging mapping.

Why it matches plant phenotyping methodsマルチスペクトル画像からトウモロコシの水害状態を抽出するセマンティックセグメンテーション手法を開発し、データセットと都市間評価で検証しているため、植物フェノタイピング手法が中心である。

abstractThis study proposes SAB-DeepLabV3+, a semantic segmentation model for mapping waterlogged maize from single-date multispectral imagery within pre-extracted maize planting areas.

Deep learning technology in multimodal remote sensing data fusion to analyze crop growth and economic forecast →

This paper integrated multimodal remote sensing (RS) data with deep learning to develop a maize growth analysis and income prediction model based on CNN (Convolutional Neural Network)-Attention and Bi-LSTM (Bidirectional Long Short-Term Memory). Utilsing Landsat 8, Sentinel-2, and Sentinel-1 satellite data, the CNN-Attention network extracts vegetation features such as NDVI (Normalised Difference Vegetation Index), EVI (Enhanced Vegetation Index), and canopy density for corn growth stage identification and prediction. The study combined these features with meteorological, soil, and market data and fed them into a Bi-LSTM model for time series analysis to forecast corn income. The method achieved 98.3% accuracy in growth stage classification, with an RMSE (Root Mean Squared Error) of 0.04 and R² of 0.94 for canopy coverage prediction under 10-fold cross-validation, and an RMSE of 0.13 and R² of 0.94 for income prediction, showing high stability over time. Overall, this research supports data-driven precision agriculture through real-time monitoring and reliable economic forecasting.

Why it matches plant phenotyping methods衛星リモートセンシングと深層学習による作物生育段階・キャノピー密度の推定手法が中心的に開発・検証されており、植物状態の定量化を含むため。収益予測も扱うが、植物フェノタイプ推定部分が明示されている。

abstractintegrated multimodal remote sensing (RS) data with deep learning to develop a maize growth analysis and income prediction model

Evaluating SAR-Derived Phenological Metrics for Monsoon (Kharif) Crop Monitoring in Diversified Agricultural Systems: Insights from Central India →

Effective crop monitoring during monsoon growing seasons in Central India faces challenges from persistent cloud cover that limits optical remote sensing during critical agricultural periods. This study presents the first attempt to develop a novel set of SAR-derived phenological metrics organized into five thematic categories for monsoon crop discrimination in smallholder agricultural systems. Five major monsoon crops (cotton, rice, maize, soybean, and urad) were analyzed across five different agroclimatic zones in Central India using Sentinel-1 data for the 2021 growing season. Phenological features were extracted from VV, VH polarizations, and their ratio, including seasonal extrema, threshold crossings, duration measures, curve shape descriptors, and area under the curve. Distinct crop-specific signatures were observed, with cotton showing extended phenology and cereal–legume crops displaying compressed, overlapping growth patterns. VV polarization achieved the highest statistical discrimination for intensity-based metrics, with 75% thresholds (VV_HP75V: F = 1287) providing higher separability than other thresholds by capturing near-peak biomass differences. VH performed best for duration and integration-based metrics, while VH/VV provided limited additional separability across metric types. For area-under-the-curve metrics, AUC25 outperformed AUC50 and AUC75 by capturing cumulative backscatter across the broader growing season while remaining robust to soil- and residue-dominated backscatter variability at sowing and harvest. Multiclass classification achieved 48.3% overall accuracy with systematic cereal–legume confusion, reflecting fundamental phenological convergence among monsoon-aligned crops. Cotton achieved the highest performance (F1: 0.79), with VH polarization dominating feature importance (65% of top 20 features). Binary classification revealed crop-specific discrimination patterns: cotton was best separated using VV intensity metrics, maize using the VH/VV ratio, and rice using timing-based features. Cross-district transferability showed the highest mean overall accuracy for rice (74%) and cotton (72%), while the remaining crops showed lower accuracy due to their phenological similarity. These findings highlight both the potential and limitations of SAR phenological metrics for monsoon crop discrimination, with effective results for structurally distinct crops but persistent cereal–legume confusion, requiring further investigation with multi-sensor approaches.

Why it matches plant phenotyping methodsSAR時系列から作物のフェノロジー指標を抽出・評価し、識別性能や転移性を検証することが研究の中心であるため、植物フェノタイピング手法として含める。

abstractThis study presents the first attempt to develop a novel set of SAR-derived phenological metrics organized into five thematic categories for monsoon crop discrimination in smallholder agricultural systems.

Multi-class classification of plant leaf diseases using a hybrid deep neural transformer system and explainable AI techniques. →

An effective framework based on deep learning (DL) is developed in this study for reliable and accurate performance. The multi-class detection of crops such as corn, tomato and potato is accurate and reliable. The aim is to improve early disease detection which guarantees good classification accuracy, strong generalization across datasets, enhanced interpretability through XAI methods and enabling realistic agricultural applications. : The study uses two large publicly available datasets of plant leaf disease images of corn, tomato and potato. The first dataset designated as D-I consists of 39,203 images belonging to 18 classes of disease and the second dataset designated as D-II consists of 65,565 images also belonging to 18 classes of disease. The two datasets contain images showing different visual scenarios and variations of disease which will help form a multi-class classifier. In this study, five DL architectures were used; InceptionNetV3, ResNet152V2, ViT, BERT, and the proposed hybrid model (ResViT-152) that combined convolutional feature extraction with transformer-based global attention. Every model was trained, validated, and tested under the same experimental setup. Cross validation and multi-phase testing assessed their performances in their capacity to learn discriminative parameters in corn, tomato and potato disease classes. The hybrid model exhibited a better performance in all test conditions. In IntraTest1, the accuracies were 99.12%, 98.94% and 99.06% for corn, tomato and potato respectively. In IntraTest2, the model achieves accuracy of 99.23% for corn, 98.97% for tomato, and 98.98% for potato on D-II. The precise percentages for the cross-tests were 96.27% (corn), 95.14% (tomato), 95.06% (potato) for CrossTest1 and 95.77% (corn), 96.22% (tomato), 96.15% (potato) for CrossTest2. The performance across datasets for all three crops is good and generalization is robust. A study is conducted on an efficient, high-performing, and interpretable DL framework for plant leaf disease detection. The experimental results verify that the proposed work provides superior performance. Generalization outperforms standard architectures in effectiveness. In addition, there is also stable performance with varying datasets. With Explainability included, the model becomes more transparent and a strong candidate for further validation toward deployment in precision agriculture, pending evaluation on real-world field datasets.

Why it matches plant phenotyping methods植物葉の画像から病害状態を分類・検出する深層学習手法の開発と、複数データセットでの交差検証・比較評価が中心であり、植物フェノタイピング手法に該当する。

abstractA study is conducted on an efficient, high-performing, and interpretable DL framework for plant leaf disease detection.

A lightweight convolutional and vision transformer hybrid network for parameter efficient plant disease classification →

Abstract Accurate plant disease classification for edge deployment requires models that are both precise and efficient. Convolutional neural networks (CNNs) learn local lesion patterns effectively. Pure Transformers capture global dependencies more explicitly, but they typically incur a higher computational cost. We propose CViTLw, a lightweight CNN-Transformer hybrid comprising a MobileNetV2 branch, a compact Vision Transformer branch, and an attention-enhanced cross-fusion module. We further evaluate two lightweight attention mechanisms, SE and CBAM, within the same framework. Experiments on the PlantVillage and Maize Leaf Disease datasets are conducted under both controlled and field-acquired conditions. On the Maize Leaf Disease dataset, CViTLw-SE attains 94.85% accuracy with only 0.33M parameters. On PlantVillage, both CViTLw-SE and CViTLw-CB achieve 99.74% accuracy (as well as precision, recall, and F1-score) and an AUC of 100%. The most compact variants operate in less than 2 ms per image and exceed 500 FPS. Overall, CViTLw achieves a strong balance of accuracy, efficiency, and practical deployability. The source code is publicly available at \href{https://drive.google.com/file/d/1nHDZNomhAC7GNLEdAeMyH7qJq3yqh45W/view?usp=drive_link}{this link}.

Why it matches plant phenotyping methods植物病害の画像分類モデルを開発・評価しており、病害状態を観測画像から推定する計算的フェノタイピング手法が研究の中心です。

abstractWe propose CViTLw, a lightweight CNN-Transformer hybrid comprising a MobileNetV2 branch, a compact Vision Transformer branch, and an attention-enhanced cross-fusion module.

MIRAGE: Biomechanically interpretable 3D generation and reconstruction of maize plants →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsトウモロコシ植物の3D生成・再構成という、植物形態・構造を取得する計算的フェノタイピング手法の開発がタイトルで明示されており、方法が中心である。

titleMIRAGE: Biomechanically interpretable 3D generation and reconstruction of maize plants

EffcientNetB0-based Deep Learning Approach for Early Plant Disease Detection using Leaf Image Classification →

Plant diseases can damage crops and reduce their growth, which often results in economic problems in agriculture. Detecting these diseases at an early stage is essential to protect crop health and improve productivity. In this work, we developed an automated plant disease identification system using deep learning and leaf images from crops such as corn, grapes, and apples. Our introduced model is based on EfficientNetB0, which showed outstanding performance in classifying multiple disease categories. To make the system more reliable, data augmentation techniques were used to manage different lighting condition in lighting, angles, and backgrounds. The model obtained an accuracy of 97.92%, outperforming other architectures like InceptionV3, MobileNetV2, DenseNet121, Xception, and VGG16 with a lesser accuracy of 83.33%, 78.33%, 77.08%, 75%, 73.33% respectively. Performance measures like precision, recall, and F1-score confirmed its strong performance. The confusion matrix further showed that the model effectively distinguishes between healthy and diseased leaves. This approach provides a fast and accurate solution for real-time disease detection. Overall, the proposed system can support farmers in taking timely action and promoting sustainable agricultural practices.

Why it matches plant phenotyping methods葉画像から植物の病害状態を推定する深層学習手法を開発・比較評価しており、植物表現型の取得・分類が研究の中心であるため。

abstractwe developed an automated plant disease identification system using deep learning and leaf images

Drone‐based phenotyping of maize for multiple disease resistance and yield in breeding field trials →

Abstract Improving selection for multiple disease resistance (MDR) and yield in maize ( Zea mays L.) requires high‐throughput, objective phenotyping tools, particularly under field conditions where several foliar diseases co‐occur. We evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB; inoculated), northern leaf spot (NLS; natural), anthracnose top dieback (ATD; natural), and for predicting grain yield across 2 years in near‐isogenic inbreds, near‐isogenic hybrids, and a diverse hybrid panel. VIs showed lower coefficients of variation but broad‐sense heritability ranging from 0.09 to 0.95, compared with 0.30 to 0.99 for visual disease scores and 0.21 to 0.97 for yield. Correlations between VIs and ground traits were strongest in near‐isogenic hybrids, particularly for early‐season yield prediction ( r = 0.98–0.99 in 2018; r = 0.87–0.90 in 2019), and moderate for total disease severity (e.g., r = −0.61 to −0.68 in 2018). Associations were weaker and less consistent in the diverse hybrid panel (yield r = 0.11–0.28). Disease‐specific signals were temporally structured: NLS correlated most strongly with early‐season VIs ( r = −0.59 to −0.75), whereas ATD was best detected mid‐season ( r = −0.63 to −0.66) along with NLB ( r = −0.66 to −0.75). Overall, multispectral VIs captured meaningful canopy variation related to MDR and yield, with predictive performance depending on germplasm structure and flight timing. These findings highlight the potential of drone‐based temporal phenotyping to complement visual assessments and improve selection efficiency in maize breeding programs.

Why it matches plant phenotyping methodsドローン multispectral による作物キャノピー形質の取得・予測性能を検証し、病害抵抗性と収量予測への適用を評価しており、フェノタイピング手法が中心である。

abstractWe evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB; inoculated), northern leaf spot (NLS; natural), anthracnose top dieback (ATD; natural), and for predicting grain yield across 2 years

Exploring the optimal timing for detecting maize growth differences using unmanned aerial vehicle-derived crop surface models →

Unmanned aerial vehicle (UAV)-based remote sensing is useful to understand crop growth conditions or grain yield potential, and monitoring forage maize (Zea mays L.) is particularly advantageous because its tall canopy makes manual measurements time-consuming. This study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV. We conducted weekly aerial photography of a maize field under variable nitrogen conditions to produce artificial growth differences. The results showed that the crop surface model (CSM) could effectively visualize maize growth differences after exceeding approximately 1.0 m, which corresponded to the internode elongation stage. Moreover, the determination coefficient between temporal CSM values and grain yield reached a peak of 0.7 approximately one week before silking. These results suggest that approximately one week before silking is the optimal time for CSM-based observations and the early detection of within-field maize growth differences and yield variability.

Why it matches plant phenotyping methodsUAV空撮とフォトグラメトリによる作物表面モデル（CSM）を用いたトウモロコシ生育差・収量変動の検出時期を検証しており、表現型取得法が研究の中心である。

abstractThis study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV.

Exploring the optimal timing for detecting maize growth differences using unmanned aerial vehicle-derived crop surface models →

Unmanned aerial vehicle (UAV)-based remote sensing is useful to understand crop growth conditions or grain yield potential, and monitoring forage maize (Zea mays L.) is particularly advantageous because its tall canopy makes manual measurements time-consuming. This study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV. We conducted weekly aerial photography of a maize field under variable nitrogen conditions to produce artificial growth differences. The results showed that the crop surface model (CSM) could effectively visualize maize growth differences after exceeding approximately 1.0 m, which corresponded to the internode elongation stage. Moreover, the determination coefficient between temporal CSM values and grain yield reached a peak of 0.7 approximately one week before silking. These results suggest that approximately one week before silking is the optimal time for CSM-based observations and the early detection of within-field maize growth differences and yield variability.

Why it matches plant phenotyping methodsUAV航空写真から作成した作物表面モデル（CSM）によるトウモロコシの生育差・収量変動の検出時期を評価しており、植物表現型の取得方法の技術的適用と評価が中心である。

abstractThis study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV.

Research on acquiring maize aboveground structure data using NeRF-Based 3D reconstruction and point cloud segmentation →

Abstract Maize（Zea mays L.） is an important crop, and improving its productivity is required even under challenging conditions such as labor shortages and uncertain climate fluctuations. One approach to enhancing yield is utilizing crop data for cultivation management and yield prediction. However, efficient acquisition of such data remains constrained by various limitations. In this study, we developed a non-contact and labor-efficient method for crop data acquisition by generating 3D models of maize at the ripening stage using Neural Radiance Fields (NeRF). Segmentation was performed on the obtained point clouds to estimate plant height, leaf area, and leaf angle. The coefficients of determination (R2) were 0.903, 0.954, and -0.521, respectively, demonstrating high accuracy for plant height and leaf area even at the ripening stage, while reducing the time required for data acquisition by 93% compared to manual measurements. Nevertheless, some manual operations−such as removing kernels and separating overlapping leaves−were still necessary, and full automation was not achieved. The main sources of error were identified as reconstruction errors in the base during scale adjustment, excessive removal of leaf sheaths, and the curvature of individual plants. Furthermore, we examined how measurement accuracy was influenced by factors such as the time of day and cultivar. The proposed method is expected to contribute to the practical implementation of a labor -saving 3D measurement technique that supports yield prediction and growth diagnosis in maize.

Why it matches plant phenotyping methodsNeRFによる3D再構成と点群セグメンテーションを開発し、トウモロコシの草丈・葉面積・葉角度を推定して精度と誤差要因を検証しており、表現型取得法が研究の中心である。

abstractwe developed a non-contact and labor-efficient method for crop data acquisition by generating 3D models of maize at the ripening stage using Neural Radiance Fields (NeRF).

Scalable AI-Driven Maize Plant Disease Detection using Convolutional Neural Network →

Plant diseases have a significant impact on global food security, especially in staple crops like maize (Zea mays). Traditional disease detection systems depend on professional visual inspection, which is labor-intensive, time-consuming, and not scalable for large agricultural areas. Convolutional Neural Networks (CNNs) are used in this study's deep learning (DL) architecture to detect maize leaf diseases accurately and automatically. A curated dataset of approximately 7,000 high-resolution maize leaf photos was created, representing four classes: healthy, Common Rust (Puccinia sorghi), Northern Leaf Blight (Exserohilum turcicum), and Gray Leaf Spot (Cercospora zeae-maydis). Data were sourced from the Plant Village dataset, real-world field collections from Indian farms, and supplemented synthetically to simulate varied climatic circumstances. Advanced methods including as adaptive learning rate scheduling, gradient clipping, and significant data augmentation were used to train a bespoke CNN model that was improved by transfer learning with ResNet50 and VGG16 backbones. The model attained a test accuracy of 98.2%, beating classic machine learning algorithms like SVM (88.5%) and Random Forest (84.3%). Visualization approaches such as feature maps, Grad-CAM, and LIME improved interpretability and showed the model's capacity to locate disease-relevant features. Web-based user engagement is made possible by deployment-ready implementation, which enables farmers to upload leaf photos for immediate diagnosis. With the potential to cut maize crop losses by 20–30%, this research offers a scalable and affordable alternative to early disease detection in precision agriculture. Future research will investigate autonomous farm management with drone-based real-time surveillance and IoT system integration.

Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から推定するCNN手法を開発し、データセット、比較評価、精度検証、解釈性分析まで行っており、植物表現型取得が中心である。

abstractConvolutional Neural Networks (CNNs) are used in this study's deep learning (DL) architecture to detect maize leaf diseases accurately and automatically.

Unraveling the Dynamics of Tar Spot Epidemics in Corn Fields: A Data-Driven Framework for Disease Development and Surveillance. →

Tar spot, caused by Phyllachora maydis Maubl, has significantly impacted U.S. corn production since its first detection in 2015. The elusive nature of this fungus has hindered advancement in disease characterization, especially pertaining to its spatiotemporal development. This study provides an in-depth analysis of field-scale epidemics by considering both temporal and vertical dynamics to ascertain the spatiotemporal intensification of this disease. The analysis involved generating high-resolution severity data across four growing seasons (2021 to 2024) in two production-style corn fields in northwest Indiana. We then applied population growth models and Markov chains to parameterize the temporal and vertical dynamics of tar spot progression within the corn canopy. From this, we found three distinct epidemiological phases: an establishment phase characterized by sporadic onset with <0.5% severity, a lag phase with widespread low severity (0.5 to 1%), and an exponential phase with rapid severity increases exceeding 40% in some cases. Beyond the conventional "bottom-up" paradigm, we observed diverse infection-like patterns influenced by canopy position, onset timing, and corn growth stage. Exponential growth models described severity intensification with an average apparent infection rate of 0.20/day, which was applicable across canopy positions, locations, and years. An aggregated Markov chain model, built from the 2021-2023 data, accurately estimated the 2024 epidemic's vertical-temporal progression and thus validated the framework developed from this study. Ultimately, this approach supports improved surveillance for targeted management by establishing a foundation for real-time detection and probabilistic modeling to aid in the enhancement of crop production.

Why it matches plant phenotyping methodsトウモロコシ葉のタールスポット病 severity という植物病害状態を高解像度データで定量化し、時空間モデルとMarkov連鎖による進展推定・検証を行う枠組みが研究の中心であるため。

abstractThis study provides an in-depth analysis of field-scale epidemics by considering both temporal and vertical dynamics to ascertain the spatiotemporal intensification of this disease.

Using Multispectral Imaging and Artificial Intelligence to Detect Crop Diseases and Pests Early →

Abstract In sustainable agriculture, detecting pests and diseases early is critical. Recent technological advances in deep learning (DL) and multimodal imaging like multispectral and thermal data crop health monitoring is promising. Despite the progress, obtaining high accuracy across various crops with real-time performance is still a challenge. The hybrid convolutional neural network (CNN)-attention model integrating multispectral and thermal data for pest and disease detection has been introduced. A total of 1760 samples were collected from six crops (maize, rice, wheat, tomato and cassava), across different growth stages, labelled fungal, bacterial, viral and pest infections. The data was divided into 70% training, 15% validation, and 15% test sets. 3,500 samples were used for training. 750 samples were used for validation and test set. The hybrid CNN-attention model was contrasted with certain baseline models (SVM, Random Forest, CNN-RGB, CNN-Multispectral) and certain fusion methods (early, late, and hybrid fusion) based on accuracy, precision, recall, F1-score, and early detection sensitivity. The highest accuracy of 91.0% for rice at the vegetative stage was achieved by the hybrid model. It beats baseline and fusion models. The F1-score of the classification was reasonably high. Rice's sensitivity is 88.1%, and maize is 87.3%. The model fared well for all classes, getting 92.0 % for the healthy plant and 88.2 % for pest infestation. Future work can enhance the dataset with more crops and diseases and environmental factors and optimize detection time and early sensitivity for real-time deployment in agricultural decision support systems.

Why it matches plant phenotyping methodsマルチスペクトル・熱画像から植物の病害および害虫状態を推定するCNNモデルを開発し、複数モデルとの比較検証を行っており、表現型取得・判定手法が中心である。

titleUsing Multispectral Imaging and Artificial Intelligence to Detect Crop Diseases and Pests Early

Non-Destructive Determination of Moisture Content in Husk-On Fresh Corn Using Multichannel Visible-Near-Infrared Spectroscopy Combined with Deep Learning. →

Using spectroscopic technology for the accurate and non-destructive determination of moisture content (MC) in husk-on fresh corn ( Zea maize L. sinensis Kulesh) is crucial for optimizing harvesting periods, ensuring quality, and maintaining nutritional value. However, corn husks interfere with the propagation of incident photons within corn kernels, leading to acquired spectral signals that contain information unrelated to the kernels themselves, thereby decreasing the accuracy of moisture detection in the kernels. This study developed a multichannel visible and near-infrared (Vis-NIR) spectral acquisition system based on spatially resolved diffuse reflectance technology for MC detection in husk-on fresh corn. The developed system mitigates the interference of husks on the acquired spectral signals by collecting spectral information from multiple detection positions offset at specific distances from the incident light source. Meanwhile, three model building strategies based on deep learning frameworks, including feature-level fusion, data-level fusion, and decision-level fusion, were proposed and compared. Results showed that the decision-level fusion model with standard normal variate (SNV) preprocessing achieved the highest prediction accuracy, with a coefficient of determination (R 2 p ) of 0.897 and a root mean square error of prediction (RMSEP) of 4.13%. Furthermore, multichannel data relatively enhanced model performance, with the four-channel combination achieving the best performance. This study demonstrates the potential of deep learning and multichannel spectral data fusion in improving MC prediction accuracy, offering a practical solution for non-destructive moisture measurement in fresh corn.

Why it matches plant phenotyping methodsトウモロコシの水分含量という植物器官の状態を、非破壊分光計測と深層学習で推定する取得・解析手法を開発し、性能比較・検証しており、フェノタイピング手法が中心である。

abstractThis study developed a multichannel visible and near-infrared (Vis-NIR) spectral acquisition system based on spatially resolved diffuse reflectance technology for MC detection in husk-on fresh corn.

Research on multi modal corn seed vitality grading based on three branch cross attention fusion network. →

Maize is a globally significant crop for both food and feed, and its seed vigor directly impacts germination rate and yield. In the context of intelligent agriculture, there is an urgent need for rapid, non-destructive, and quantifiable methods for assessing seed vigor. This study proposes a novel multimodal fusion approach for maize seed vigor detection, integrating hyperspectral imaging (HSI), electronic nose (ENS), and machine vision (MV) technologies. Multisource data were systematically collected from seeds subjected to varying levels of artificial accelerated aging, thereby constructing a sample set encompassing five distinct vigor levels. Standard germination tests were employed as the ground truth for vigor labeling.Each modality was individually subjected to preprocessing procedures including calibration, denoising, feature extraction, and standardization. The processed data were then fused to construct a comprehensive dataset for model development. Among the unimodal models, classification accuracies of HSI, ENs, and MV reached 91.8%, 94.4%, and 92.0%, respectively. In contrast, the feature fusion network based on three-way cross attention (TCAF-Net) effectively utilizes the complementary information between spectral, olfactory, and morphological features. This model achieved an accuracy of 99.6%, demonstrating superior robustness and stability in distinguishing seed vigor levels.The results validate the efficacy of multimodal data fusion for rapid, non-invasive seed vigor assessment in maize, and provide a promising technical foundation for applications in smart agriculture and seed quality monitoring.

Why it matches plant phenotyping methodsトウモロコシ種子の活力という植物形質を、ハイパースペクトル画像・電子鼻・マシンビジョンと新規融合ネットワークで非破壊推定する方法研究であり、表現型取得・抽出が中心である。

abstractThis study proposes a novel multimodal fusion approach for maize seed vigor detection, integrating hyperspectral imaging (HSI), electronic nose (ENS), and machine vision (MV) technologies.

Exploring the optimal timing for detecting maize growth differences using unmanned aerial vehicle-derived crop surface models →

Unmanned aerial vehicle (UAV)-based remote sensing is useful to understand crop growth conditions or grain yield potential, and monitoring forage maize (Zea mays L.) is particularly advantageous because its tall canopy makes manual measurements time-consuming. This study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV. We conducted weekly aerial photography of a maize field under variable nitrogen conditions to produce artificial growth differences. The results showed that the crop surface model (CSM) could effectively visualize maize growth differences after exceeding approximately 1.0 m, which corresponded to the internode elongation stage. Moreover, the determination coefficient between temporal CSM values and grain yield reached a peak of 0.7 approximately one week before silking. These results suggest that approximately one week before silking is the optimal time for CSM-based observations and the early detection of within-field maize growth differences and yield variability.

Why it matches plant phenotyping methodsUAV航空写真から作成した作物表面モデル（CSM）によるトウモロコシの生育差・収量変動検出について、観測時期と技術性能を評価しており、植物表現型取得法が中心である。

abstractThis study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV.

Explainable artificial-intelligence-based hyperspectral image analysis for leaf disease detection in intercropping system. →

Introduction Intercropping regimes enhance the efficiency of land use and ecological sustainability but present serious problems to automated disease analysis since the overlapping canopy and the similarity of symptoms in crop species are visually indistinguishable. Methods This work presents an explainable artificial intelligence (XAI)-based hyperspectral analysis on leaf disease in intercropping systems. The framework combines the spectral-spatial feature generators that utilize transformers including vision transformer (ViT), Swin transformer, pyramid vision transformer (PVT), and detection transformer (DETR) to identify nuanced biochemical and structural changes in crop combinations for maize-soybean and pea-cucumber. In order to reduce spectral redundancy and high dimensionality, an enhanced greedy political optimization (EGPO) algorithm is used as a wrapper-based feature selection strategy. A capsule spatial shift neural network (CSSNet) is used to predict the classification of diseases. Explainable AI methods, such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) feature attribution analysis and gradient-weighted class activation mapping (Grad-CAM) visualization of disease-relevant regions, provide model transparency. The DETR + EGPO + CSSNet framework is tested on the conventional feature selection methods. Results and discussion The results or findings on publicly available hyperspectral datasets on intercropping show an average recall of 99.998% with high region consistency (Dice score: 99.997%) of activation maps and expert-marked disease regions. These findings affirm that the proposed framework is highly accurate, stable, and interpretable to identify subtle and overlapping disease in leaves in a complex system of intercropping.

Why it matches plant phenotyping methods植物葉の病害領域・病状をハイパースペクトル画像から推定する解析手法の開発と評価が中心であり、植物病害表現型の取得・抽出に直接関係する。

abstractThis work presents an explainable artificial intelligence (XAI)-based hyperspectral analysis on leaf disease in intercropping systems.

HTPRootSlides: A high-throughput phenotyping platform for crop root germination dynamic screening →

Root phenotyping is crucial for advancing our understanding of plant development and adaptation. However, existing platforms often face challenges in balancing high-throughput capacity with long-term, high-frequency monitoring. To overcome this limitation, we present HTPRootSlides, an integrated root phenotyping platform designed for dynamic and scalable trait analysis. Its design features a circulating zone that accommodates 141 specialized root boxes for high-throughput operation synchronously. Root boxes follow a continuous S-shaped trajectory step by step, facilitating repetitive imaging for high-throughput, time-series data acquisition. To address challenges such as water vapor condensation and fine root entanglement, we developed a dedicated segmentation algorithm, achieving 89.56 % accuracy in root isolation. Combining morphological and skeleton-based feature extraction techniques, the platform ensures comprehensive and efficient phenotypic trait quantification. We validated HTPRootSlides by dynamically monitoring root development in four staple crops (soybean, maize, wheat, and rice) during early-stage germination (<14 d). The results demonstrate the capability of HTPRootSlides for high-frequency, high-precision and large-scale root phenotyping (< 1h with 141 root boxes per run), offering researchers a powerful tool to investigate root dynamics and optimize crop performance through trait selection.

Why it matches plant phenotyping methods根の動態を高スループットで撮像・分割・特徴抽出し、形態・骨格形質を定量するプラットフォームの開発と検証が中心である。

abstractwe present HTPRootSlides, an integrated root phenotyping platform designed for dynamic and scalable trait analysis

Lightweight vision transformer and ResNet-9 models for real-time plant disease detection and pest classification with SHAP explainability. →

CONTEXT: The rapid advancement of digital technologies alongside increased global commitment to sustainability has intensified the need for efficient crop management solutions. Across agricultural systems, delayed detection and misclassification of plant diseases remain major contributors to reduced yields, threatening food security and undermining progress toward Sustainable Development Goals such as Zero Hunger, No Poverty, Good Health and Well-being, Climate Action, and Life on Land. Plant pests, diseases, and excessive chemical use further exacerbate these challenges. Early, automated visual detection offers a pathway to environmentally responsible and economically viable agricultural practices. OBJECTIVE: This study aims to develop and evaluate an automated, real-time plant disease classification framework using Vision Transformers (ViT) and hybrid ViT–CNN architectures, with the goal of supporting farmers and agronomists in early decision-making and sustainable crop protection. METHODS: The research employs deep learning techniques, including ViT and a combined ViT–CNN model built on ResNet-9, trained and evaluated using four publicly available datasets: the Turkey Plant Pests and Diseases (TPPD) dataset (15 classes), the Namibia Maize Image Dataset (3 classes), the Banana Image Dataset (3 classes), and the Tanzania Maize Dataset (3 classes). SHapley Additive exPlanations (SHAP) were applied to generate saliency maps for interpretability. Comparative analyses assessed performance, accuracy, and classification speed across attention-based and hybrid architectures. RESULTS AND CONCLUSIONS: The proposed model achieved strong performance with 97.4% accuracy, 96.4% precision, 97.09% recall, a 95.7% F1-score, and high agreement measured by Cohen’s Kappa, outperforming existing benchmark models. SHAP visualizations highlighted that the model leverages high-activation areas, edge features, color patterns, texture, shape, and contextual cues in its predictions. While attention-based models improved accuracy, they also caused reduced classification speed. However, integrating attention blocks with CNN layers effectively compensated for this slowdown, achieving both high accuracy and efficient inference. To evaluate the interpretability and deployment feasibility of the proposed model, we considered several parameters are considered to have an integration of faithfulness, localization quality, sparsity, latency, and energy of the models, including pointing accuracy, localization IoU, Centroid Localization Error (pixels), Attribution Sparsity (%), Insertion AUC, Deletion AUC, Time per Explanation (ms/image), Energy Consumption (J/image), Memory Footprint (MB). SIGNIFICANCE: This research provides a transparent and high-performing deep learning solution for plant disease classification, promoting sustainable agricultural development. By reducing reliance on excessive pesticide and herbicide use, enhancing early diagnosis, and improving decision-making, the model supports environmentally responsible farming practices and contributes to global efforts toward food security and ecological resilience. The significance of this evaluation is that it comprehensively assesses the model’s interpretability and real-world deployability by measuring explanation reliability (faithfulness), spatial precision (localization quality), efficiency (latency and memory), and sustainability (energy consumption), ensuring the model is not only accurate but also transparent, efficient, and practical for deployment.

Why it matches plant phenotyping methods植物病害を画像から直接分類する深層学習フレームワークの開発・評価が研究の中心であり、病害状態という植物表現型を推定するため、対象範囲に含める。

abstractThis study aims to develop and evaluate an automated, real-time plant disease classification framework using Vision Transformers (ViT) and hybrid ViT–CNN architectures

A physics-informed spectral-spatial unfolding network with fusion perception for maize spectral recovery from RGB images →

Given the substantial agronomic and economic significance of maize, the development of real-time and high-precision disease detection methodologies is essential for ensuring yield stability. While hyperspectral imaging excels at capturing fine-grained spectral signatures of infection, its higher detection precision comes with considerable high hardware and temporal costs compared to RGB imaging, posing significant challenges for scalable field applications. To bridge this gap, this article proposes a fusion perception unfolding network (FPUF-Net) for high-fidelity maize spectral reconstruction from RGB images. Distinct from conventional deep learning models, FPUF-Net unfolds the optimization problem via a half-quadratic splitting algorithm, solving the data subproblem and prior subproblem alternately during iterations. Specifically, a fusion feature learning network and a spectral-spatial joint attention network are designed within the data subproblem to explicitly exploit RGB spatial priors and mitigate spatial smoothing. Moreover, a spectral-spatial Transformer is utilized as the denoiser to capture long-range spectral dependencies in the prior subproblem. Experiments performed on a maize spectral recovery dataset comprehensively demonstrate that FPUF-Net can effectively reconstruct maize hyperspectral images with superior precision. The structural characteristics enable the network to perceive long-term spectral-spatial fusion features, significantly reducing reconstruction errors, particularly in the biologically critical red-edge region (620-700 nm). In downstream disease detection tasks, the overall accuracy of reconstructed HSIs improves over RGB by margins of 0.51% to 8.1% across different scenarios, while the average accuracy increases by 2.45% to 18.86%. These results indicate that the proposed model offers a viable, cost-effective solution for applying hyperspectral imaging in field settings, enabling its scalable use in agricultural robots.

Why it matches plant phenotyping methodsRGB画像からトウモロコシのハイパースペクトル情報を再構成する手法を開発し、データセットで性能評価している。植物のスペクトル状態および病害検出に直接関わる手法が研究の中心である。

abstractthis article proposes a fusion perception unfolding network (FPUF-Net) for high-fidelity maize spectral reconstruction from RGB images.

Maize yield estimation from Sentinel-2 multi-temporal imagery and CANbus data integration: a non-parametric regression approach →

In precision agriculture, the assessment and estimation of key crop parameters are crucial aspects for the optimisation of input usage and, as an ultimate goal, for the improvement of yield quality and quantity. In this context, a reliable prediction of yield by remotely sensed imagery is an enabling technology for optimisation. In this work, an innovative method for estimating yield in maize cultivation is presented, which exploits multi-temporal and multispectral Sentinel-2 satellite imagery with supervised Machine Learning (ML) techniques. For model training and validation, yield ground truth experimental data from combine harvesters was used, enabling the yield estimation at sub-field scale. The investigation, which was conducted on five case study plots, involved a preliminary comparison of four ML-based algorithms, trained with raw spectral bands. An assessment of the effect of the training dataset on the yield prediction accuracy was then performed. A set of Vegetation Indices (VIs) and Two Band Indices (TBIs) was also considered for this purpose. Finally, a multi-temporal analysis was conducted, in which the temporal evolution of crop spectral data over the maize growing season was exploited using imageries acquired in different epochs. The obtained results proved that an accurate estimation of maize yield can be reached using a Gaussian process regression model, exploiting multi-temporal features directly provided by the raw spectral bands. The model showed a high accuracy in the estimation of maize yield, even when fed with data acquired during only the maize vegetative phase, thus proving its capacity as a prediction tool.

Why it matches plant phenotyping methodsSentinel-2画像と機械学習を用いてトウモロコシ収量という植物形質を推定する手法を開発・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。

abstractan innovative method for estimating yield in maize cultivation is presented, which exploits multi-temporal and multispectral Sentinel-2 satellite imagery with supervised Machine Learning (ML) techniques.

Digital Platform for Crop Health and Agricultural Services →

Modern precision agriculture requires the incorporation of high-accuracy diagnostic instruments to guarantee food security for inexperienced practitioners. This paper introduces an AI-driven agricultural web architecture that connects deep learning-based diagnostics with real-world farm management. The main contribution is a Convolutional Neural Network (CNN) framework that can automatically find diseases in five common crops: Capsicum annuum, Vitis vinifera, Zea mays, Solanum tuberosum, and Solanum lycopersicum. The proposed model reached a final training accuracy of 98.30% and a validation accuracy of 90.12% over 10 epochs by using a sequential architecture with optimized convolutional layers and data augmentation. The platform has a localized marketplace, a government scheme eligibility engine, and a Crop Journal for long-term record-keeping to make it useful in the real world. Results demonstrate that this unified ecosystem provides a transparent and accessible framework for data-informed agricultural management, effectively lowering the technical barrier for new farmers.

Why it matches plant phenotyping methodsCNNによる作物病害の自動検出が中心的な技術貢献であり、植物の病害状態を画像ベースで推定するため、農業サービス部分を含んでも植物フェノタイピング手法として採用する。

abstractThis paper introduces an AI-driven agricultural web architecture that connects deep learning-based diagnostics with real-world farm management.

Automated Identification of Crop Diseases using Computer Vision →

Early and accurate identification of crop diseases is essential for ensuring agricultural productivity, food security, and sustainable farming practices. This study presents an automated computer vision based framework for multi-crop disease classification using a lightweight deep learning architecture. The proposed system employs the ReXNet-1.5 convolutional neural network as the core feature extractor, integrating efficient hierarchical feature learning with low computational complexity. A publicly available multi-crop dataset comprising 13,324 images across 17 disease and healthy classes covering corn, rice, potato, wheat, and sugarcane is used for model training and evaluation. Experimental results demonstrate strong performance, achieving 97.45% accuracy and a macro-F1 score of 96.26%, indicating reliable class-balanced prediction under dataset imbalance. Grad-CAM based visual explainability is incorporated to provide interpretable disease localization, enhancing transparency and trust in model predictions. Additionally, the model exhibits high computational efficiency, enabling real-time inference suitable for deployment on resource constrained platforms. The proposed framework offers an accurate, interpretable, and deployable solution for real world crop disease diagnosis, supporting intelligent decision-making and scalable agricultural monitoring systems.

Why it matches plant phenotyping methods植物画像から病害状態を分類・局在化するコンピュータビジョン手法が研究の中心であり、単なる病害測定ではなく、モデル開発と性能評価を実施している。

abstractThis study presents an automated computer vision based framework for multi-crop disease classification using a lightweight deep learning architecture.

DELP-YOLOv12: a lightweight deployable model for maize pest and disease detection. →

Background Maize is one of the world's most important cereal crops for both food and feed, yet its yield is severely threatened by pests and diseases. The complexity of field environments, the variability of illumination, and the diversity of pest and disease types make accurate detection a challenging task. Although existing maize pest and disease detection models have achieved substantial progress, they still face difficulties in small-object recognition, feature perception, and efficient deployment on edge devices. To address these limitations, this study proposes a deployable and lightweight detection framework, DELP-YOLOv12, based on the YOLOv12 architecture. Results A Dynamic RepConvBlock with NAM (DRN) module was designed and integrated into the C3k structure to form C3k_DRN, enabling multi-branch training and inference-time fusion to enhance both feature representation and computational efficiency. Additionally, a Lightweight Feature Enhancement Detection Head (LFEDH) was developed to refine feature extraction for small-scale pests and irregular lesions. To further improve feature discrimination, an Efficient Channel Attention (ECA) mechanism was incorporated to highlight lesion- and pest-related responses while suppressing background noise. Considering edge-device constraints, a Layer-Adaptive Sparsity for Magnitude-based Pruning (LAMP) strategy combined with fine-tuning was applied to compress the model while maintaining accuracy. Experimental results on the maize pest and disease dataset demonstrate that DELP-YOLOv12 achieved a precision of 94.1%, a recall of 89.6%, and an mAP50 of 94.2%, outperforming the baseline across all metrics. Meanwhile, the parameter count and computation cost were reduced by approximately 68% and 60%, respectively, while preserving real-time inference capability on embedded hardware such as the NVIDIA Jetson Orin NX. Conclusions The proposed DELP-YOLOv12 effectively balances accuracy, efficiency, and deployability for field-based maize pest and disease detection. Its integration of DRN, LFEDH, and LAMP modules enhances recognition of small and irregular targets while maintaining low computational demand, offering a practical solution for real-time agricultural monitoring and intelligent pest management.

Why it matches plant phenotyping methodsトウモロコシの病斑・病害状態を画像から検出する軽量モデルを開発し、精度、計算量、組込み機器での実時間性能を評価しており、植物病害フェノタイピング手法が中心である。

abstractthis study proposes a deployable and lightweight detection framework, DELP-YOLOv12, based on the YOLOv12 architecture.

Modified RefineNet with Attention-Based Fusion for Multi-Class Classification of Corn and Pepper Plant Diseases →

Early and precise detection of plant diseases is essential for safeguarding crop yield and ensuring sustainable agricultural practices. In this study, we propose the Modified RefineNet with Attention based Fusion (MoRefNet-AF), a Modified RefineNet architecture enhanced with attention-based fusion for multi-class classification of corn (maize) and Pepper leaf diseases. Unlike the original RefineNet, which was segmentation-oriented and computationally heavy, MoRefNet-AF is redesigned for lightweight and discriminative classification. The modifications include replacing standard convolutions with depthwise separable convolutions for efficiency, adopting the Mish activation function for smoother gradient flow, redesigning the multi-resolution fusion module with concatenation and shared convolution for richer cross-scale integration, and incorporating Squeeze-and-Excitation (SE) blocks for adaptive channel recalibration. Additionally, Chained Residual Pooling (CRP) with atrous convolutions enhances contextual representation, while global average pooling with dense layers improves classification readiness. When evaluated on a curated six-class dataset combining PlantVillage and Mendeley leaf disease repositories, MoRefNet-AF achieved 99.88% accuracy, 99.74% precision, 99.73% recall, 99.95% F1-score, and 99.73% specificity. These results outperform strong baselines including ResNet152V2, DenseNet201, EfficientNet-B0, and ConvNeXt-Tiny, while maintaining only 0.3 M parameters. With its compact design and TensorFlow Lite (v2.13) compatibility, MoRefNet-AF offers a robust, lightweight, and real-time deployable solution for precision agriculture and smart plant disease monitoring.

Why it matches plant phenotyping methods植物葉の病徴を画像から分類する軽量深層学習手法を開発し、複数データセットとベースラインで性能評価しているため、植物フェノタイピング手法が中心である。

abstractwe propose the Modified RefineNet with Attention based Fusion (MoRefNet-AF), a Modified RefineNet architecture enhanced with attention-based fusion for multi-class classification of corn (maize) and Pepper leaf diseases.

Hi MagicRing, tell me where I am: Toward affordable, physically reliable 3D plant phenotyping with MobilePheno3D →

3D plant phenotyping has garnered significant interest for its ability to quantify key structural traits such as plant volume and canopy architecture. However, standard monocular 3D reconstruction techniques suffer from inherent scale ambiguity, requiring an additional step to recover the true metric scale of the plants. Existing scale recovery methods, whether based on precisely fabricated 3D objects or planar patterns such as checkerboards, have been successfully applied in controlled environments but face practical constraints in certain real-world scenarios: some require costly fabrication or pre-reconstruction calibration, which can limit throughput in dynamic field environments. Here, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach that addresses these specific constraints and provides a complementary solution for high-throughput, mobile, and field-based phenotyping. MagicRing features a simple red ring printed on A4 paper with a known diameter. By leveraging color-based segmentation and geometric curve fitting, our approach automatically detects the ring within 3D point clouds, recovers the metric scale, and establishes a standardized world coordinate system without the need for pre-calibration. Its planar, isotropic design ensures robustness even under significant occlusion. We demonstrate the utility of MagicRing through MobilePheno3D, an integrated smartphone-based pipeline that performs fully automated 3D reconstruction, scale recovery, and phenotypic extraction from video sequences. This system, which was validated across multiple plant species, including vegetables, wheat, rice, and maize in both indoor and field settings, reliably reconstructs aboveground and root structures and supports continuous growth monitoring. MagicRing decouples data collection from data analysis, enabling a workflow transition from conventional step-by-step, scene-specific calibration toward more scalable, high-throughput 3D plant phenotyping.

Why it matches plant phenotyping methods植物の3D形態形質を抽出するためのスケール復元法とスマートフォン型フェノタイピング・パイプラインを開発し、複数植物種・環境で検証しており、手法が研究の中心である。

abstractHere, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach

Deep Hybrid Learning for Smart Agriculture using CNN-based Feature Extraction and LSTM-BiLSTM Sequence Modeling for Robust Plant Disease Detection →

Background: Plant diseases significantly reduce global crop productivity, creating an urgent demand for intelligent, automated diagnostic systems in agriculture. Traditional manual inspection is labor-intensive, subjective and often ineffective in detecting early or latent symptoms. This study presents a multi-class classification and severity estimation framework for ten plant disease categories: Maize brown spot, maize rust, maize healthy, potato early blight (Alternaria solani), potato late blight (Phytophthora infestans), potato healthy, soybean mosaic virus (SMV), soybean pod mottle virus (SPMV), soybean sudden death syndrome (SDS/SBS) and soybean healthy. The objective is to develop a robust hybrid deep learning model capable of accurate early detection and quantitative severity assessment to support precision agriculture. Methods: A hybrid architecture combining convolutional neural networks (CNN) with LSTM and BiLSTM networks was implemented. The preprocessing pipeline included leaf segmentation, binary masking, defect localization and edge detection to enhance lesion visibility. CNN layers extracted spatial and textural features, while recurrent layers modeled contextual dependencies within feature representations. Performance was evaluated using Precision, Recall, F1-score, defect percentage estimation, convergence analysis and t-SNE visualization. Result: Results demonstrated stable convergence with decreasing loss (0.8-1.2) and improved feature clustering. Defect severity ranged from 0.00% (Soybean healthy) to 87.93% (Maize brown spot). The framework enables early detection (0.29-5% infection), reduces yield loss, minimizes chemical overuse and promotes sustainable smart agriculture systems.

Why it matches plant phenotyping methodsCNN-LSTM/BiLSTMによる葉画像からの病害検出と病徴重症度推定手法の開発が中心であり、植物状態を直接推定している。

abstractThis study presents a multi-class classification and severity estimation framework for ten plant disease categories

Multi-source data assimilation of Sentinel-2 reflectance and SMAP soil moisture into APSIM for maize biomass estimation →

Abstract Purpose Crop growth models (CGM) are valuable tools for agricultural monitoring. However, the need for many input parameters, the uncertainties related to model parametrization and structure, and the lack of spatial information motivate the application of techniques such as data assimilation (DA). This paper proposes a DA framework to improve maize biomass estimation. Methods A particle filter (PF) was used to assimilate remotely sensed reflectance and soil moisture (SM) data, both independently and simultaneously, into the Agricultural Production Systems sIMulator (APSIM) model. Reflectance observations from Sentinel-2 were assimilated through coupling APSIM with the radiative transfer model (RTM) PROSAIL, while SMAP L-band SM products were directly assimilated into APSIM. Results The synthetic experiment, designed to evaluate the reliability of the proposed procedure, highlighted the strength of assimilating reflectance to constrain crop traits and of SM to reduce ensemble spread and improve robustness. Real-case results confirmed these findings. DA assimilation of SM especially contributed to improving overall biomass accuracy, particularly under data gaps and drought conditions. Although it did not consistently surpass single-source assimilation, the joint assimilation yielded consistent results. In 2022, it achieved a root-mean-square error (RMSE) of 2275.20 kg/ha, a normalized RMSE (nRMSE) of 44.99%, and a bias of 1081.90 kg/ha. In 2023, RMSE, nRMSE and bias were 1120.29 kg/ha, 14.79%, and 284.05 kg/ha, respectively. Furthermore, the joint assimilation led to a tighter ensemble spread than single source-assimilation. Conclusion The proposed framework demonstrates the potential of multi-source DA to enhance biomass estimation and support robust, spatially explicit crop monitoring.

Why it matches plant phenotyping methodsSentinel-2反射率とSMAP土壌水分をAPSIMへ同化し、トウモロコシのバイオマスを推定するデータ同化フレームワークが研究の中心であり、植物形質の取得・推定手法を提案・評価している。

abstractThis paper proposes a DA framework to improve maize biomass estimation.

Tree-based machine learning models to predict maize growth, nitrogen uptake, and yield using UAV remote sensing →

Unmanned aerial systems (UAS) equipped with multispectral sensors enable within-season crop phenotyping; however, conventional vegetation index–based approaches often lack accuracy in predicting crop growth and yield. This study evaluated the performance of three machine learning (ML) algorithms, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), for predicting maize leaf area index (LAI), relative chlorophyll content (SPAD), aboveground biomass (AGB), plant height, nitrogen (N) uptake, and grain yield, and compared their performance with stepwise multiple linear regression (SMLR). Multispectral imagery (five spectral bands) was collected across multiple growth stages during the 2021 and 2022 growing seasons near College Station, Texas, under varying split N-fertilizer applications. Band reflectance and eight vegetation indices were used as model inputs. All ML models outperformed SMLR in predicting LAI, SPAD, AGB, N uptake, and plant height. SVM, RF, and XGBoost showed comparable performance for LAI (R² = 0.82–0.83), AGB (R² = 0.86–0.92), and plant height (R² = 0.95–0.97). However, XGBoost exhibited overfitting, resulting in lower validation accuracy for SPAD (R² = 0.54) and N uptake (R² = 0.68) compared with SVM (R² = 0.73 and 0.80, respectively). Grain yield prediction accuracy increased with crop maturity, with reproductive-stage imagery producing the highest accuracy across models (R² = 0.88–0.98), where all ML models outperformed SMLR. However, at the V6 growth stage, SVM and RF did not perform as well as SMLR. Overall, integrating ML with UAS-based multispectral imagery improved prediction accuracy across key maize phenotypic and nutrient traits, providing a robust framework for non-destructive, high-throughput phenotyping and precision N management.

Why it matches plant phenotyping methodsUASマルチスペクトル画像と機械学習により、作物の形態・生理・収量関連形質を推定する方法を比較評価しており、フェノタイピング手法が研究の中心である。

abstractThis study evaluated the performance of three machine learning (ML) algorithms, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), for predicting maize leaf area index (LAI), relative chlorophyll content (SPAD), aboveground biomass (AGB), plant height, nitrogen (N) uptake, and grain yield

OpenAlea.HydroRoot: A modelling framework to dissect, predict and phenotype branched root hydraulic architecture →

Drought is a significant factor in agricultural losses, making it imperative to understand how root system architecture (RSA) adapts to environmental condition like water deficit. HydroRoot is a functional-structural plant model (FSPM) aimed at analyzing and simulating hydraulic and solute transport of RSA. The model integrates a static hydraulic solver, a coupled water-solute transport solver, a statistical generator of RSA based on Markov model, and a dynamic hydraulic model accounting for root growth. This paper presents the model, the mathematical description of the formalism of solvers, and use cases with their associated tutorials. Five use cases illustrate capabilities of HydroRoot, which has been successfully used for phenotyping root hydraulics across various species, including Arabidopsis, maize, and millet. The model-driven phenotyping method “cut and flow” is presented to characterize axial and radial conductivities on a given root genotype. Finally, three step-by-step tutorials provide a structured way to learn how to use HydroRoot 1) to simulate hydraulic on a given architecture, 2) to simulate water and solute transport on a maize root, and 3) to simulate hydraulic on two pearl millet genotypes with varying soil conditions. Hydroroot is an open-source package of the OpenAlea platform, with the code publicly available on Github. A comprehensive documentation is available with a reproducible gallery of examples.

Why it matches plant phenotyping methods根系の水理特性を解析・予測し、表現型化するモデルとオープンソースソフトウェアを開発・提示しており、植物フェノタイピング手法が中心である。

abstractHydroRoot is a functional-structural plant model (FSPM) aimed at analyzing and simulating hydraulic and solute transport of RSA.

Screening Methods for Downy Mildew Resistance in Maize: A Systematic Review. →

Background/Objectives: Downy mildew, caused by Peronosclerospora and Sclerophthora species, is a major constraint to maize production in tropical and subtropical regions, with yield losses of 30-100%. This systematic review synthesised evidence on methods used to screen maize for downy mildew resistance and assessed their effectiveness, reliability, and associated markers. Methods: PubMed, Google Scholar, ScienceDirect, and CAB Abstracts were searched (last searched 22 October 2025) for English-language studies (1990-2025) evaluating phenotypic or molecular screening methods. Risk of bias was assessed using the RoB 2 framework. Narrative synthesis was conducted following a protocol registered on the Open Science Framework. Results: Twelve studies met the inclusion criteria, predominantly from India and Cambodia. Spreader row systems (seven studies) and conidial spray inoculation (six studies) were the most common field methods, while the glasshouse sandwich technique generated the highest disease pressure. Cross-method correlations were strong (r = 0.92-0.99), and heritability estimates ranged from 0.50 to 0.97. QTL mapping identified resistance loci on chromosomes 2, 3, and 6, with chromosome 6 stable across multiple pathogen species. Evidence certainty was moderate for method effectiveness and low for molecular markers. Conclusions: Established phenotypic screening methods reliably discriminate resistant germplasm; however, standardised protocols, broader geographic validation, and independent molecular marker confirmation are needed.

Why it matches plant phenotyping methodsトウモロコシのべと病抵抗性という植物状態を評価する表現型スクリーニング法を体系的にレビューし、方法の有効性・信頼性を比較しているため、手法レビューとして収載する。

titleScreening Methods for Downy Mildew Resistance in Maize: A Systematic Review.

Coupling WOFOST and HYDRUS-1D to simulate daily maize growth, water– salt dynamics and stress responses under salinity gradients in salinized farmland →

Abstract Soil salinization constrains agricultural productivity across approximately 950 million hectares worldwide. In the Yellow River irrigation district of Ningxia, secondary salinization severely depresses maize yields. Existing crop–water–salt models lack day-by-day bidirectional coupling between salt transport and crop growth, over-simplify salt stress representation, and amplify stress through multiplicative integration. To address these gaps, we developed a fully coupled WOFOST–HYDRUS-1D model linking the Richards equation and convection–dispersion equation with crop photosynthesis, transpiration, and assimilate partitioning through a modified Maas–Hoffman function. Salt stress is transmitted via three physiological pathways, and the combined stress factor is computed using Liebig’s law of the minimum. The model was calibrated with 43 sampling points spanning low-to-high salinity gradients (1.49–6.79 g kg⁻¹) in Huinong District during 2024, and independently validated with 30 points (1.29–7.75 g kg⁻¹) in 2025. Calibration yielded R² = 0.883, RMSE = 0.744 t ha⁻¹, NRMSE = 10.96%, and NSE = 0.795; validation gave R² = 0.824, RMSE = 0.753 t ha⁻¹, NRMSE = 11.13%, and NSE = 0.810, confirming strong inter-annual parameter stability. Under high salinity (> 4 g kg⁻¹), simulated mean yield declined to 3.75 t ha⁻¹, a 53% reduction compared with low-salinity conditions. Compared with the standard WOFOST model (R² = 0.450, RMSE = 1.399 t ha⁻¹), the coupled model substantially improved accuracy.These results show that the coupled model can improve yield prediction under salinity stress and provide a useful tool for irrigation scheduling and water–salt management in salinized farmland.

Why it matches plant phenotyping methodsWOFOST–HYDRUS-1Dの結合モデルを開発し、トウモロコシの生育・塩ストレス・収量を推定する手法として独立検証しており、植物状態の推定手法が中心である。

abstractwe developed a fully coupled WOFOST–HYDRUS-1D model linking the Richards equation and convection–dispersion equation with crop photosynthesis, transpiration, and assimilate partitioning through a modified Maas–Hoffman function.

In situ detection of methyl jasmonate using plant-wearable sensors to quantify genotype-dependent herbivory resistance. →

Methyl jasmonate (MeJA) is a key phytohormone regulating plant responses to herbivory and environmental stress. While conventional analytical techniques, such as liquid chromatography-mass spectrometry, provide high accuracy, their application is often limited by labor-intensive workflows, high costs, and complex sample preparation requirements. In this study, we present a wearable electrochemical sensor for the in situ monitoring of MeJA in maize (Zea mays L.) under fall armyworm (FAW; Spodoptera frugiperda [J.E. Smith]) herbivory. The sensor employs an array of microneedles functionalized with a MeJA-specific molecularly imprinted polymer (MIP). This work presents the in situ monitoring of MeJA levels within intact plant tissues using a MeJA-specific MIP integrated with a microneedle-based sensor, and demonstrates, for the first time, the use of a plant-wearable sensor to quantify genotype-dependent herbivory resistance. The sensor exhibited considerable sensitivity and selectivity, with a detection limit of 0.18 μM. Sensor performance was validated in four maize genotypes with varying levels of resistance to FAW (Mp708, BS39:0043, Tx601, GEMN0131). Time-course measurements revealed that resistant genotypes exhibited earlier and stronger MeJA induction following infestation, whereas susceptible genotypes showed delayed and attenuated responses. Sensor measurements demonstrated a strong correlation with conventional measurement data. Statistical analysis using a randomized complete block design confirmed that genotype, detection methodology, and infestation status significantly influence MeJA variability. These findings highlight the potential of the present wearable sensor as a powerful tool for studying plant defense mechanisms and advancing precision agriculture through direct monitoring of phytohormonal signaling.

Why it matches plant phenotyping methods植物体内のホルモン状態を測定するウェアラブル電気化学センサーを開発し、複数遺伝子型で性能検証・従来法との相関評価を行っており、表現型取得手法が研究の中心である。

abstractwe present a wearable electrochemical sensor for the in situ monitoring of MeJA in maize (Zea mays L.) under fall armyworm (FAW; Spodoptera frugiperda [J.E. Smith]) herbivory.

Comparative Evaluation of Ground-based, Manual, and Remote Sensing Approaches for Crop Stress Detection: A Review between Malaysia and China →

This study presents a comparative evaluation of manual inspection, ground-based sensors, and UAV-based remote sensing for detecting crop stress in paddy, maize, and coconut fields across Malaysia and China. Although sensing technologies have advanced considerably, cross-country comparisons between regions with differing levels of technological maturity remain limited. China, recognised for its advanced adoption of UAV and sensor-based agriculture, provides a benchmark against Malaysia’s developing digital agriculture landscape. Each method was assessed based on accuracy, responsiveness, scalability, and cost-effectiveness under field conditions. UAV-based remote sensing achieved the highest overall accuracy (mean 92%) and demonstrated superior scalability, enabling rapid large-area monitoring using vegetation indices such as NDVI and NDRE. Ground-based sensors, including soil moisture probes and chlorophyll meters, showed moderate accuracy (mean 81%) and were suitable for plot-level monitoring with real-time feedback. Manual inspection recorded the lowest accuracy (mean 68%) and limited scalability due to labour dependency and subjective assessment. UAV methods were particularly effective in early stress detection, with thermal imaging identifying canopy temperature anomalies 3–5 days before visible symptoms, especially in maize and coconut fields. Integrating UAV and ground-based sensing provided more comprehensive and timely assessments than individual approaches. These findings support the development of scalable precision agriculture frameworks tailored to tropical and subtropical systems.

Why it matches plant phenotyping methods作物ストレスという植物状態を対象に、手動観察・地上センサー・UAVリモートセンシングを精度、応答性、拡張性、費用で比較評価しており、センシング手法の技術評価が中心である。

abstractThis study presents a comparative evaluation of manual inspection, ground-based sensors, and UAV-based remote sensing for detecting crop stress in paddy, maize, and coconut fields across Malaysia and China.

A three-tier deep learning framework with mobile application integration for multi-crop disease diagnosis →

Abstract Crop diseases remain a critical threat to global food security, contributing to substantial yield losses and reduced farmer incomes. Timely and accurate identification of these diseases is essential to mitigate their impact. Traditional diagnostic methods, dependent on expert visual inspection, are labour-intensive, time-consuming, and prone to judgment errors. Accurate and timely detection of crop diseases supports sustainable agricultural management and contributes to achieving global objectives under the United Nations Sustainable Development Goal 2 on Zero Hunger. This study proposes a three step framework that relies on pattern recognition and classification of visual disease symptoms to deliver reliable, field-applicable diagnostics. The approach combines image acquisition through smartphone camera with a structured processing pipeline that includes feature extraction, classification, and result delivery via a mobile application built on a three-tier architecture. Convolutional Neural Networks and an optimized VGG-16 model form the core classification engine, trained to recognize 19 leaf based diseases across wheat, rice, fodder, maize, and sugarcane. The models were trained and evaluated on a dataset comprising both field-collected and publicly available images using repeated stratified k-fold cross-validation. The framework achieves accuracies of 84.61% for wheat, 44.15% for rice, 85.71% for fodder, 95.23% for maize, and 64.28% for sugarcane (testing accuracy of the best-performing model per crop, where VGG-16 demonstrated superior generalization). The framework is able to support farmers, by integrating a technically robust backend with a simple and oriented interface, with diagnosis of multiple crops from a single platform, offering a scalable solution for precision agriculture and sustainable crop protection.

Why it matches plant phenotyping methods植物葉の病徴画像を対象に、画像取得・特徴抽出・分類・モバイルアプリ提供を一体化した診断手法を開発・評価しており、植物病害状態の表現型推定が中心です。

abstractThis study proposes a three step framework that relies on pattern recognition and classification of visual disease symptoms to deliver reliable, field-applicable diagnostics.

Early Detection and Classification of Gibberella Zeae Contamination in Maize Kernels Using SWIR Hyperspectral Imaging and Machine Learning. →

Early-stage fungal contamination in maize kernels is difficult to identify visually and it can cause severe quality and safety risks during storage and transportation. Short-wave infrared (SWIR) hyperspectral imaging offers a rapid, non-destructive approach by capturing chemical information related to water, proteins, and lipids. This study investigates the early detection and classification of Gibberella zeae contamination in maize kernels using SWIR hyperspectral imaging combined with machine learning. Two maize varieties were artificially inoculated and cultured under controlled conditions, followed by hyperspectral data collection over six contamination stages. Various preprocessing techniques including standard normal variate (SNV), second derivative (SD), multiplicative scatter correction (MSC), and derivatives were evaluated to enhance data quality. Feature wavelength selection was performed using successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE), significantly reducing redundancy and improving classification performance. Multiple models, including linear discriminant analysis (LDA), multilayer perceptron (MLP), support vector machine (SVM), a convolutional neural network (CNN), long short-term memory (LSTM) network, and a hybrid architecture Transformer that integrated a CNN, a LSTM network, and a Transformer (abbreviated as CLT), were constructed for both binary (healthy vs. contaminated) and multiclass classification tasks. Specifically, the multiclass task consisted of six contamination stages corresponding to contamination time from Day 0 to Day 5. The best binary classification task accuracy of 100% was achieved using SNV-preprocessed data with the MLP model. For multiclass classification task, the SD-preprocessed LDA model reached a test accuracy of 92.56%. Combined with appropriate preprocessing, feature selection and modeling, these results demonstrate that hyperspectral imaging is a powerful tool for the non-destructive, early-stage identification of fungal contamination in maize kernels, offering strong support for food safety and quality monitoring.

Why it matches plant phenotyping methodsSWIRハイパースペクトル画像と機械学習を用いて、トウモロコシ種子の真菌汚染状態・汚染段階を非破壊推定する手法が研究の中心であり、植物器官の病態の測定に該当する。

abstractThis study investigates the early detection and classification of Gibberella zeae contamination in maize kernels using SWIR hyperspectral imaging combined with machine learning.

Supporting agricultural statistics through multispectral UAV-based crop cover mapping in complex smallholder farming systems in Mozambique →

Reliable agricultural statistics support food security monitoring and evidence-based decision making. In Mozambique, official agricultural statistics are primarily derived from the Integrated Agricultural Survey (IAI), an enumerator-based field survey that provides essential contextual information on agricultural production but remains labour-intensive, costly and spatially and temporally constrained, particularly in remote rural areas. While satellite remote sensing offers complementary, wall-to-wall coverage, its spatial resolution is often insufficient to directly capture the fragmented fields, mixed and intercropping patterns, shifting cultivation and strong sub-field variability typical of smallholder farming systems. Consequently, consistent estimation of crop area and crop type derived from enumerator-based crop cover assessments remains challenging in these landscapes.This study investigates the potential of high-resolution multispectral data acquired with Uncrewed Aerial Vehicles (UAVs) to complement field surveys by providing spatially explicit and internally consistent crop cover and crop fraction estimates at the field and sub-field scale. By resolving individual crops and dominant intercropping systems, UAV-based observations support the interpretation of farmer-reported crop cover proportions, improve consistency across enumerators, and enable post-survey correction of crop area estimates, while providing a basis for future integration with coarser-resolution satellite remote sensing. High-resolution RGB and multispectral imagery (green, red, red edge, and near-infrared; ≤5 cm ground sampling distance) was collected using a DJI Mavic 3M with RTK over 30 sampling areas of 500 × 500 m in Manica Province during the 2025 agricultural season. In parallel, a field survey recorded standardized observations of agricultural activity, including crop type (of most field and tree crops), intercropping combinations and enumerator-based estimates of fractional crop cover. UAV images were processed using a workflow tailored to heterogeneous smallholder landscapes to produce orthomosaics, digital surface models (DSMs), and vegetation indices. These products were linked to field observations through segments representing relatively homogeneous land units, enabling direct comparison between UAV-derived and survey-based crop cover estimates.For crop classification, training polygons were delineated on RGB orthomosaics for single-crop fields (e.g. maize, beans, sorghum and cassava) and common intercropping combinations (e.g. maize–beans). Annotated mosaics were tiled and augmented and used to train convolutional neural network models (e.g. UNet++), incorporating multispectral vegetation indices and DSM-derived height information as additional input channels. Model performance was evaluated using Intersection over Union, Dice coefficients, and regression metrics for fractional cover accuracy.A comparison framework was implemented to relate UAV-derived crop type, crop combinations and fractional cover to field survey observations while explicitly accounting for measurement uncertainty. Model II regression quantified systematic bias and proportional differences between the two methods. Initial results indicate that UAV-derived estimates provide spatially consistent crop cover information in fields with complex intercropping structures. Ongoing work focuses on refining segmentation accuracy, analysing residual discrepancies and assessing how UAV-derived crop cover information can be integrated to expand the spatial coverage and reliability of agricultural statistics in smallholder landscapes.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物種・混植構造・作物被覆率を抽出し、CNN分類と分画被覆推定を検証する方法が研究の中心である。

abstractUAV-based observations support the interpretation of farmer-reported crop cover proportions, improve consistency across enumerators, and enable post-survey correction of crop area estimates

Hydro-Physiological Controls of Crop Water Stress Under Salinity and Deficit Irrigation: An ANN-Based Framework for Sustainable Irrigation Management in a Changing Climate →

Climate change is intensifying soil moisture variability, atmospheric evaporative demand, and salinity intrusion in agricultural landscapes, creating new challenges for sustainable food production. Understanding how soil hydrology and plant physiological stress interact under these conditions is essential for designing resilient irrigation strategies. This study presents a hydro-physiological assessment of wheat and maize grown under controlled combinations of soil salinity and deficit irrigation, and introduces an Artificial Neural Network (ANN) based Crop Water Stress Index (CWSI) model for real-time decision support in semi-arid farming systems of northern India.Field experiments (2023–2025) were conducted to measure canopy temperature, air temperature, relative humidity, vapor pressure deficit (VPD), and soil moisture under varying salinity (EC levels) and irrigation regimes. These data were used to develop whole-season and stage-specific ANN models capable of capturing non-linear interactions between soil hydrology, crop physiology, and atmospheric demand. The ANN-based CWSI successfully distinguished mild-to-severe stress transitions and detected early-stage water stress acceleration during periods of high VPD, indicating a propensity toward flash drought development under combined salinity–moisture constraints.Results show that salinity amplifies crop water stress by reducing effective root-zone moisture availability, leading to higher canopy–air temperature gradients and elevated CWSI values even under moderate irrigation. Stage-specific ANN models achieved strong performance (R² = 0.87–0.94), particularly during flowering and grain filling, where hydrological stress most affects yield. The framework demonstrates how data-driven CWSI modeling can translate complex soil–plant–atmosphere interactions into actionable irrigation insights for farmers.This work highlights a scalable approach to precision irrigation scheduling, enabling reduced water use without compromising crop health in regions vulnerable to hydrological extremes and sociohydrological pressures. By linking soil hydrology, irrigation management, and physiologically informed stress indicators, the study contributes to sustainable food production strategies in a global climate change context.

Why it matches plant phenotyping methodsANNによる作物水ストレス指標（CWSI）の開発と性能評価が中心で、キャノピー温度などから植物の生理的ストレス状態を推定している。

abstractintroduces an Artificial Neural Network (ANN) based Crop Water Stress Index (CWSI) model for real-time decision support

Mobile-Optimised Deep Learning Architecture for Multi-Crop Disease Detection Using CNN and SVM →

In this paper, we propose a deep learning pipeline for real-time crop disease classification on mobile devices. Our system employs a custom Convolutional Neural Network (CNN) trained on publicly available crop disease datasets (Maize, Tomato, Potato, Rice). In addition, two transfer-learning models; ResNet-50 and MobileNet are used as fixed feature extractors, with their output features classified by a multi-class Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. We compare the models’ performance across all crop datasets and evaluate inference latency and model size. Experimental results show that the ResNet50-SVM hybrid attains near-perfect accuracy (≈100% for Maize, Tomato, Potato; 99.96% for Rice) on plant disease classification, far exceeding both the custom CNN and MobileNet-SVM approaches. The MobileNet-SVM pipeline is notably faster (≈23–66 ms per image) and compact (~8.7 MB) than ResNet50+SVM (≈108–192 ms, ~90 MB), making it well-suited for on-device deployment. The final model is converted to TensorFlow Lite for mobile inference; on a typical smartphone CPU it processes an input image in ~0.15–0.19s on average, enabling practical field use. These results demonstrate an efficient mobile AI solution for crop disease detection that balances accuracy with resource constraints. The proposed system can empower farmers with timely, in-field disease diagnosis, helping to mitigate yield losses and improve crop management through accessible AI-driven tools.

Why it matches plant phenotyping methods植物画像から病害状態を分類する深層学習パイプラインを開発・比較し、精度、推論遅延、モデルサイズ、モバイル実装を評価しており、植物病害表現型の取得・抽出が中心です。

abstractwe propose a deep learning pipeline for real-time crop disease classification on mobile devices

Artificial intelligence-Driven detection and decision support system for precision management of maize downy mildew. →

Artificial intelligence (AI) enables rapid and precise plant disease detection, offering transformative potential for crop protection. Maize downy mildew (MDM), a destructive disease, causes substantial yield losses, making early detection critical. In this study, we evaluated the performance of thirteen machine-learning (ML) and deep-learning (DL) algorithms for classifying healthy and infected maize leaves using a curated field dataset. Model performance was assessed using multiple metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. Among the tested models, VGG16 achieved the highest performance, with 97% accuracy, 0.98 precision, 0.95 recall, 0.97 F1-score, and an AUC-ROC of 0.99. Training and validation curves indicated minimal overfitting, demonstrating robust generalization. Feature visualization using t-SNE revealed clear separability between healthy and diseased samples, while Grad-CAM analysis confirmed that VGG16 focused on biologically relevant symptomatic regions, such as chlorotic streaks and leaf discoloration. Confusion matrix analysis further validated near-perfect classification, with very few misclassifications. Furthermore, we developed a web-based application (https://maize-mdm.streamlit.app/) that not only classifies MDM but also provides farm-level advisory measures. Two-year field trials of DSS-guided fungicide applications effectively suppressed MDM, reducing disease severity (PDI 3.20-5.20; PROC 93-96%), increasing grain yield (75.6-80.2 q/ha; PIOC 195-289%), and improving economic returns (B:C ratio 3.36-3.57) compared to untreated controls. Overall, this study demonstrates that AI-driven models, integrated with web-based decision support, provide accurate, interpretable, and actionable solutions for precision management of maize diseases, contributing to improved yield, profitability, and sustainable agricultural practices.

Why it matches plant phenotyping methodsトウモロコシ葉の画像から病害症状を分類するAI手法を開発・比較し、性能検証と実装まで行っており、植物病害状態の表現型取得が中心である。

abstractwe evaluated the performance of thirteen machine-learning (ML) and deep-learning (DL) algorithms for classifying healthy and infected maize leaves using a curated field dataset.

Full-Spectrum Hyperspectral Modeling of Leaf Dry Matter Content Using a Stacked Ensemble Framework. →

The objective of this study was to assess the predictability of leaf dry matter content across a diverse range of plant species using hyperspectral reflectance data. The dataset encompassed leaves from multiple crops, including potatoes, beans, wheat, maize, peas, tomatoes, basil, and cucumbers, collected under varying growth conditions, cultivation systems, seasonal contexts, and developmental stages. As an initial benchmark, commonly used narrow-band spectral indices and their combinations were evaluated, but they exhibited limited predictive performance for dry matter content. Consequently, several full-spectrum machine learning models were trained and compared to assess their individual predictive ability. Given their complementary strengths, these models were integrated into a stacked ensemble framework to enhance overall accuracy. The resulting ensemble, combining the outputs of multiple base learners through a meta-learner, achieved a coefficient of determination of R2=0.896 on an independent test set, outperforming all individual models. The findings highlight the potential of a multi-model stacking approach to improve the accuracy and robustness of leaf biochemical property estimation from hyperspectral data.

Why it matches plant phenotyping methodsハイパースペクトル反射データから葉乾物含量を推定する機械学習手法を開発・比較・検証しており、植物形質の取得方法が研究の中心である。

abstractassess the predictability of leaf dry matter content across a diverse range of plant species using hyperspectral reflectance data

A Hybrid RTM-Informed Machine Learning Framework with Crop-Specific Canopy Structural Parameterization for Crop Fractional Vegetation Cover Estimation →

Fractional vegetation cover of crops (CropFVC) is a critical indicator for remote sensing-based crop monitoring. However, existing inversion models are largely developed for general vegetation types, limiting their effectiveness for crop-specific applications. Here, we developed a gap-fraction-refined hybrid CropFVC model that integrates crop-specific PROSAIL calibration, an ALA (averages of leaf angle) -based dynamic projection function, and a Random Forest model. The model was validated with 43343 CropFVC samples of four major crops (winter wheat, rice, maize, and soybean) across China during March to August 2024, spanning key phenological stages, and further compared against SNAP (10 m) and GEOV3 (300 m) products. Results showed that (1) the proposed model achieved stable performance across diverse canopy structures, with average RMSE

Why it matches plant phenotyping methods作物の葉面積被覆率という明示的な植物キャノピー形質を推定するハイブリッドモデルを開発し、多数のサンプルと既存プロダクトで検証しており、測定・推定手法が研究の中心である。

abstractHere, we developed a gap-fraction-refined hybrid CropFVC model that integrates crop-specific PROSAIL calibration, an ALA (averages of leaf angle) -based dynamic projection function, and a Random Forest model.

GLiMPSe: A low-cost, high-throughput and accurate field phenotyping system for maize architectural traits →

Maize phenotyping remains a major bottleneck in genetic analysis and breeding. Despite advances in drones, field robots, and gantry phenotyping systems, ultra-affordable, high-throughput, field-based maize phenotyping at single-plant resolution is still lacking, largely due to the high cost, complex deployment, and limited flexibility of existing platforms under heterogeneous field conditions. To address these challenges, we propose a novel paradigm that integrates DIY imaging devices with customized computer vision–based analytics, and present GLiMPSe ( G iraffe + Li zard M aize P henotyping S yst e m), including two end-to-end phenotyping modules for maize plant architecture (the Giraffe module) and leaf traits (the Lizard module). The imaging device in the Giraffe module are built from modular electronics and 3D-printed parts from local retailers to achieve high-quality image acquisition. The Giraffe and the Lizard modules operate at speeds of 15 seconds and 8 seconds per sample, with costs of $379.1 and $241.1, respectively. Both modules feature fine-tuned YOLOv11x segmentation models for reliable and robust target segmentation, followed by customized Python-based analytical pipelines that enable precise extraction and quantification of phenotypic traits. This methodology achieves high accuracies ( R² ) for five key traits, including plant height (0.928), heights of above-ear leaves (0.87∼0.958), ear height (0.925), above-ear leaf number (0.837), and leaf width (0.937). To enhance accessibility, we developed user-friendly graphical interfaces and publicly released manually annotated datasets and source code to support broader adoption and further innovation. This work provides a practical and accessible solution for high-throughput field phenotyping and offers new opportunities for democratizing crop phenomics through affordable, open-source technologies.

Why it matches plant phenotyping methods低コストな撮像装置、コンピュータビジョン解析、形質抽出パイプライン、GUI、データセットとコードを統合したトウモロコシ表現型測定システムの開発・検証が研究の中心である。

abstractwe propose a novel paradigm that integrates DIY imaging devices with customized computer vision–based analytics, and present GLiMPSe

TraitDiscover: An automated high-throughput platform for multimodal plant phenotyping with real-time trait analysis →

Plant phenotyping is essential for elucidating genotype–environment interactions, yet conventional methods remain labor-intensive and low-throughput. TraitDiscover transcends these constraints by uniting multimodal sensing with tightly coupled hardware-software orchestration in a single, end-to-end phenotyping platform. Aligned with the ”Plant Phenotyping Trinity” framework, the system comprises a millimetre-accurate triaxial automation unit, a modular sensor array–RGB imaging, three-dimension laser scanner or LiDAR (3D), infrad (IR) thermal imaging, hyperspectral imaging (HSI), and photosynthesis (PS) imaging–and the dedicated software TraitNavigator suite into one cohesive system. A unified spatiotemporal synchronization mechanism enables robust time-series analysis and fusion of multisource phenotypic data across the entire crop growth period, while the DepthCropSeg algorithm and a night-time imaging module enhance trait extraction under complex conditions, providing G × E × P-ready, multimodal phenotypic datasets. Validation across soybean, maize, and rice trials demonstrated high sensitivity—detecting drought stress four days before visible symptoms, identifying glyphosate injury 24 hours ahead of manual scoring, and quantifying local adaption patterns across ecological gradients. While challenges remain in scaling to complex open-field conditions, TraitDiscover offers a scalable, data-driven approach to accelerate stress phenotyping and breeding decisions and is readily poised for deeper integration with AI to advance sustainable agriculture.

Why it matches plant phenotyping methodsマルチモーダルセンシング、画像解析、同期機構、形質抽出アルゴリズムを統合した植物フェノタイピング基盤の開発と検証が中心であり、ストレス検出や形質定量も実証している。

abstractTraitDiscover transcends these constraints by uniting multimodal sensing with tightly coupled hardware-software orchestration in a single, end-to-end phenotyping platform.

A parametric maize leaf model quantifying morphology and geometry via 3D triangle mesh →

Maize leaf morphology is poorly investigated because quantifying maize leaf geometry is still an open question due to the complexity of the 3D curved shape. By utilization of geometric curves, maize leaf morphology can be effectively described parametrically and quantitatively. We divided maize leaf into three components: midrib, cross-section and blade contour. Each component is represented by parametric curves and controlled by a group of parameters. A 3D maize leaf model is generated by translation, rotation and scaling of the three components. We demonstrated the parametric maize leaf model allows the applications of leaf geometry analysis, leaf-level radiation capture simulation and dataset synthesis for phenotyping pipeline. The parametric maize leaf model is configurable, extensible and scalable, allowing it to be used in agricultural digital-twin and high-accuracy phenotyping. It also has potential to serve as a platform for maize biophysical and biomechanical studies. The code for 3D maize leaf model generation is available at https://github.com/xzcppm/parametric_maize_leaf.

Why it matches plant phenotyping methodsトウモロコシ葉の3D形態・幾何をパラメトリックにモデル化し、表現型解析用データ合成にも利用できる手法を開発しているため、植物表現型取得・解析手法が中心である。

abstractA 3D maize leaf model is generated by translation, rotation and scaling of the three components.

Spatial inheritance patterns across maize ears are associated with alleles that reduce pollen fitness. →

Often, more pollen grains land on recipient flowers than there are ovules to fertilize. Consequently, the haploid male gametophyte engages in post-pollination competition, one way that pollen genotype can influence inheritance. The maize (Zea mays subsp. mays L.) inflorescence (ear), with its elongated stigma and style structures (silks), has a conspicuous spatial heterogeneity, with longer silks at the base of the ear than at the apex. To evaluate the hypothesis that alleles with reduced pollen fitness influence the spatial distribution of progeny genotypes along the ear, we developed an updated phenotyping platform that maps fluorescently marked mutant (Ds-GFP) kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape). Our dataset (1384 ears) represents 58 Ds-GFP insertion alleles. None of the 48 alleles with Mendelian inheritance showed any significant spatial trend. In contrast, 50% of alleles with a pollen-specific transmission defect (5/10) exhibited significant spatial effects. An insertional mutant of the gene encoding a putative actin-binding protein, base-to-apex gradient1* (bag1*), is associated with decreased mutant transmission at the ear base relative to the apex. Surprisingly, a mutant allele of another pollen-expressed gene (Zm00001eb236740) generates the opposite trend, decreased mutant transmission toward the ear apex; and two mutant alleles of the sperm cell attachment factor gamete expressed2 (gex2) can produce ears with transmission highest at both base and apex. We conclude that pollen fitness mutants cause unexpectedly diverse spatial patterns of progeny genotypes.

Why it matches plant phenotyping methodsトウモロコシ穂上のカーネル表現型を画像認識でマッピングする更新版フェノタイピング基盤と統計解析パイプラインが中心的に開発・適用されているため。

abstractwe developed an updated phenotyping platform that maps fluorescently marked mutant (Ds-GFP) kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape).

Root system growth and function respond to soil temperature in maize ( Zea mays L.) →

Crop adaptation to the mixture of environments that defines the target population of environments is the result of balanced resource allocation between roots, shoots, and reproductive organs. Root growth plays a critical role in the determination of this delicate balance. The responses of root growth and function to temperature can determine the strength of roots as sinks but also influence a crop's ability to uptake water and nutrients. Surprisingly, this behavior has not been studied in maize (Zea mays) since the middle of the last century, and the genetic determinants are unknown. Low temperatures recorded frequently in deep soil layers limit root growth and soil exploration and may constitute a bottleneck for increasing drought tolerance, nitrogen recovery, sequestration of carbon, and productivity in maize. We developed high-throughput phenotyping systems to investigate these responses and to examine genetic variability therein across diverse maize germplasm. Here, we show that there is (i) genetic variation in root growth under low temperature below a previously set threshold of 10 °C and (ii) genotypic variation in water transport under low temperature. The trait set examined herein and the high-throughput phenotyping platform developed for its characterization provide a unique opportunity for removing a major bottleneck for crop improvement and adaptation to climate change.

Why it matches plant phenotyping methods根の成長と水輸送という植物形質を評価するためのハイスループット表現型解析システムの開発が中心的に記述されており、遺伝的変異の評価にも用いられているため。

abstractWe developed high-throughput phenotyping systems to investigate these responses and to examine genetic variability therein across diverse maize germplasm.

Multi-task geometric regression with agronomic priors for crop row and missing seedling detection in maize-soybean strip intercropping →

The strip intercropping of soybean and maize, characterized by planting the two crops alternately in adjacent rows, has been widely promoted in several regions of China due to its potential to enhance resource utilization efficiency and overall yield. Accurate detection of crop rows and missing seedlings is essential for enabling precision field operations such as variable fertilization and targeted spraying. Monocular vision has emerged as a core sensing modality owing to its low cost and high resolution. However, the significant differences in row and plant spacing between maize and soybean, coupled with complex field conditions such as weed interference and uneven emergence, severely limit the effectiveness of traditional image processing techniques based on thresholding and geometric fitting. These methods struggle to accommodate the morphological variability of multiple crops, resulting in poor row detection precision and unreliable identification of missing seedlings. In recent years, deep learning has shown strong performance in crop row detection and object recognition tasks, particularly through multi-task networks that integrate segmentation and localization-related features. Nevertheless, most existing studies focus on single-crop scenarios and often neglect the integration of agronomic knowledge, thereby limiting their robustness and interpretability in real-world field environments. To address these issues, this study proposes a Multi-Task Geometric Regression Network for row extraction and missing seedling detection in maize-soybean intercropping systems, built upon an improved U-Net++ architecture and guided by agronomic priors. The proposed method simultaneously performs crop segmentation, row direction prediction, and generation of a missing seedling heatmap. The geometric features output by the network are subsequently processed using agronomic prior-informed post-processing and geometric fitting to finally achieve row extraction and missing seedling localization. Agronomic constraints, such as row spacing regularity, are embedded in the loss function as prior-informed regularization terms, which further enhance detection accuracy and robustness in intercropped fields. Experimental results demonstrate that the semantic segmentation achieves an average Intersection over Union (IoU) of 0.82, an F1-score of 0.86, and a pixel accuracy of 0.91. Row centerline detection attains an F1-score of 0.86 and a mean offset (MO) of 3.9 pixels. For missing seedling detection, the crop classification accuracy reaches 0.91, the average localization error (ALE) is only 2.5 pixels, and the composite detection score (CD-F1) is 0.89. Compared with single-task methods without agronomic priors, the proposed multi-task framework exhibits significant improvements in both stability and accuracy for row detection and missing seedling localization in intercropping scenarios. These results provide practical guidance for deploying intelligent visual systems in precision agriculture and intercropping management.

Why it matches plant phenotyping methods作物画像から畝構造と欠株状態を抽出するマルチタスク手法を開発し、精度評価も行っており、植物状態の取得・推定が研究の中心である。

abstractthis study proposes a Multi-Task Geometric Regression Network for row extraction and missing seedling detection in maize-soybean intercropping systems

A stem-leaf segmentation method of maize plant point cloud based on region growing and leaf phenotypic parameters measurement →

Maize leaf phenotypic parameters effectively reflect the photosynthesis and growth information of maize plants, which is crucial for breeding superior maize varieties. Current challenges include separating stems and leaves from a single maize plant and accurately measuring the phenotypic parameters of maize leaves. This study proposes a stem-leaf segmentation method based on region growing, incorporating adaptive cuboid region growing and slice region growing, alongside techniques for measuring phenotypic parameters of maize leaves. First, terrestrial laser scanning (TLS) was employed to obtain three-dimensional (3D) point cloud data of maize at the five-leaf (V5) and six-leaf (V6) stages. The point cloud data were then preprocessed to isolate single plant point clouds. Next, the maize point clouds were pre-segmented into three categories-central point clouds, partially expanded leaf point clouds, and unexpanded leaf point clouds-using center-edge segmentation, statistical filtering, and leaf classification. Adaptive cuboid region growing was applied to segment the unexpanded leaf point clouds, while slice region growing was used for partially expanded leaves, with Euclidean clustering optimizing the leaf point clouds, completing the segmentation process. Finally, various methods-including clustering counting, point-to-point distance accumulation, point-to-line distance, vector angle, point cloud triangulation, and triangle area accumulation-were utilized to automatically measure the number of maize leaves, leaf length, leaf width, leaf inclination angle, and leaf area. Compared with other point cloud stem-leaf segmentation methods based on geometric features and common 3D point cloud deep learning models (PointNet++, PointTransformer), the method proposed in this paper performs better. The segmentation results indicated that the Precision (P), Recall (R) and F₁-Score (F₁) for stem-leaf segmentation of all maize plants at the V5 stage exceeded 92.00%, with average values of 96.87%, 97.08%, and 96.97%, respectively. At the V6 stage, P, R, and F₁ exceeded 95.00%, with averages of 97.73%, 97.01%, and 97.67%, respectively. The algorithm accurately measured the number of leaves at the V5 stage, while a small error was noted at the V6 stage, yielding a percentage error (PE) of 0.93%. Measurement accuracy for leaf length, width, and area at both growth stages was greater than 93.80%, 92.80%, and 89.50%, respectively. Measurement accuracy for leaf inclination angle was lower, at 82.00% and 88.02% for the V5 and V6 stages, respectively. The proposed methods for stem-leaf segmentation and measurement of leaf phenotypic parameters are fast and accurate, providing technical support for high-quality breeding and intelligent management of maize. Our point cloud data of maize and source code is available from https://github.com/lmj-cau/stem-leaf-segmentation.git.

Why it matches plant phenotyping methodsトウモロコシの3D点群から茎葉を分割し、葉数・長さ・幅・面積・傾斜角を自動推定する手法を開発・比較検証しており、植物表現型取得が研究の中心である。

abstractThis study proposes a stem-leaf segmentation method based on region growing, incorporating adaptive cuboid region growing and slice region growing, alongside techniques for measuring phenotypic parameters of maize leaves.

Real‐time crop leaf disease detection and classification using a hybrid Morlet wavelet interactive attention neural network optimized by the Red‐Billed Blue Magpie algorithm →

Abstract Traditional disease classification is slow and lab‐dependent. Machine learning aids faster image‐based detection but faces challenges like lighting variations, complex leaf shapes, background noise, and limited labeled data. This research develops a robust image‐based method to automatically classify corn, rice, and wheat leaf diseases under diverse environmental and imaging conditions. A Hybrid Morlet Wavelet Interactive Attention Neural Network optimized by the red‐billed blue magpie optimizer (HMWIANN‐RBBMO) is proposed in this study for accurate classification corn ( Zea mays ), rice ( Oryza sativa ), and wheat ( Triticum aestivum ) leaf diseases. First, a modified square‐root SageHusa adaptive Kalman filter is used to remove noise and improve image quality by image preprocessing. The DeepLabV3+ is used to accurately segment disease‐prone areas, and then the Sharpbelly Fish Optimization is used to identify the most discriminative features in the images. The HMWIANN will combine Morlet wavelet transformation with interactive attention to exhaust the capabilities of the classifier to recognize Healthy (No pathogen), Common Rust ( Puccinia sorghi ), Blight ( Xanthomonas oryzae ), Gray Leaf Spot ( Cercospora zeae‐maydis ), BrownSpot ( Bipolaris oryzae ), Hispa ( Dicladispa armigera ), LeafBlast ( Magnaporthe oryzae ), Stripe rust ( Puccinia striiformis ), and septoria ( Zymoseptoria tritici ). Furthermore, the RBBMO will be used to improve convergence speed, generalization, and classification accuracy. A graph‐based hybrid recommendation system is also incorporated to assist disease management decisions. Experimental evaluation on corn, rice, and wheat leaf disease dataset demonstrates superior performance, achieving 99.70% accuracy, 99.80% precision, 99.50% recall, 99.40% F1‐score, and a low false positive rate of 0.8%, outperforming existing state‐of‐the‐art methods.

Why it matches plant phenotyping methods植物葉の病害症状を画像から分割・特徴抽出・分類する手法を開発し、病害状態という植物表現型を直接推定して性能評価しているため、方法が中心的である。

abstractThis research develops a robust image‐based method to automatically classify corn, rice, and wheat leaf diseases under diverse environmental and imaging conditions.

A robust estimation method for canopy chlorophyll content based on FOD and hierarchical weighting combination models →

Assessing canopy chlorophyll content (CCC) is crucial for evaluating light capture and photosynthetic capacity, as well as for diagnosing and managing maize health. This study aims to develop a robust CCC estimation model using in situ canopy spectral data collected from two regions over a three-year period. The model employs fractional order differential (FOD) and partial least squares regression (PLSR) at multiple spectral resolutions (1 nm, 5 nm, 10 nm, 20 nm, and Sentinel-2 broadband). To mitigate the uncertainties associated with single models and enhance estimation accuracy, a hierarchical weighted combination model integrating k-means clustering and genetic algorithm (GA) is proposed. The results indicate that the CCC estimation models constructed from differential spectra generally outperform those based on original spectra across most orders. The optimal estimation orders are typically within the range of 1.2–1.6 (step: 0.2). For each resolution, the root mean square error (RMSE) of the optimal order in the test set is reduced by 1.65–25.04 % compared to the original spectra. After constructing the hierarchical weighted combination prediction model, the R² and RMSE of the combined model for each resolution are superior to those of the single models. Specifically, the RMSE of the test set is further reduced by 0.38–9.87 % compared to the optimal FOD order. Moreover, we achieved better monitoring results when we migrated the method to remotely sensed images. These findings suggest that the hierarchical weighted combination prediction model driven by fractional order differential spectra can achieve more accurate CCC estimation in maize. This method provides a basis for applying FOD to multi-resolution sensors, and this achievement contributes to the precise regulation of fertilizer and water during maize growth, offering a new technical approach for improving crop yield and resource use efficiency.

Why it matches plant phenotyping methodsトウモロコシ群落クロロフィル量という植物形質を対象に、分光データ、FOD、PLSR、階層重み付けモデルによる推定手法を開発・検証しており、表現型取得・抽出が研究の中心である。

abstractThis study aims to develop a robust CCC estimation model using in situ canopy spectral data collected from two regions over a three-year period.

A stem-leaf segmentation method of maize plant point cloud based on region growing and leaf phenotypic parameters measurement →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsトウモロコシの点群から茎・葉を分割し、葉の表現型パラメータを測定する手法開発が主題であり、植物フェノタイピング手法が中心です。

titleA stem-leaf segmentation method of maize plant point cloud based on region growing and leaf phenotypic parameters measurement

UAV-based estimates of corn LAI using hyperspectral and EnMAP spectral resolutions →

Accurate estimation of the Leaf Area Index (LAI) is essential for assessing vegetation health and managing agricultural productivity. This study examines the application of Unmanned Aerial Vehicle (UAV)-based hyperspectral imaging and convolved EnMAP spectral data for estimating corn LAI, utilizing machine learning (ML) models to improve prediction accuracy. Various ML models, including k-nearest Neighbors (KNN), Support Vector Machines (SVM), Partial Least Squares Regression (PLS), and Random Forests (RF), were assessed to predict LAI from hyperspectral, EnMAP, and vegetation index features. Results demonstrate that PLS models consistently outperformed other ML approaches, achieving coefficients of determination (R²) ranging from 0.79 to 0.82. Notably, for the top two performing models (PLS and SVM) spectral indices such as NDRE, GNDVI, and NDVI proved more effective for LAI prediction than individual spectral bands. Interestingly, no matter the incorporation of hyperspectral wavelengths or EnMAP bands, the models predicting LAI were comparable. Feature importance analysis reinforced the dominance of vegetation indices as key predictors. The findings emphasize the benefits of high-resolution UAV hyperspectral imaging, convolved satellite spectral data, and machine learning, particularly PLS, for scalable and accurate LAI estimation in agroecosystems.

Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習を用いてトウモロコシのLAIという植物形質を推定し、複数モデルと特徴量の性能を比較・評価しているため、形質取得手法が中心である。

abstractThis study examines the application of Unmanned Aerial Vehicle (UAV)-based hyperspectral imaging and convolved EnMAP spectral data for estimating corn LAI, utilizing machine learning (ML) models to improve prediction accuracy.

Yield forecasting in maize: Performance and limits of unmanned aerial vehicle and PlanetScope remote sensing across multiple growth cycles →

This study addresses the challenge of forecasting maize yield in southeastern Quebec by comparing weekly Unmanned Aerial Vehicle (UAV) and PlanetScope satellite imagery throughout the cropping season and across diverse growing conditions. Using five nitrogen treatments over three years with two sowing windows each year to generate variability within the dataset, eleven vegetation indices were evaluated to identify the best-performing indices and the optimal forecasting window. Indices were interpolated using curve fitting to enable evaluation at any stage of the growing season. Cross-validation simulated real-world application by excluding entire sowing events during model testing. Using a linear regression approach, results demonstrate that indices combining green and near-infrared bands (Green Normalized Difference Vegetation Index [GNDVI] and Chlorophyll Index Green [CIG]) exhibit superior forecasting potential compared to red-near-infrared (like NDVI) and RGB-based indices (like NGRDI). The optimal forecast window occurs during early grain filling (R2-R3 stages, around 2300 Crop Heat Units [CHU]), achieving Root Mean Square Coefficient of Variation (RMSCV) values of 12.51 % for UAVs and 15.28 % for PlanetScope. While PlanetScope maximum performance approached UAV capabilities, results showed a CHU range between 200 and 400 in the effective forecasting period (RMSCV < 20 %) compared to 850 to 1450 for UAV. For PlanetScope, adding multiple indices marginally improved precision, slightly reduced forecasting window and reduced model transferability. The analysis revealed weak correlations between indices and yield during early vegetative and senescence phases, indicating limited potential for enabling timely in-season management interventions. This study established UAV-based models as a reference point for assessing the limitations of satellite-derived forecasts.

Why it matches plant phenotyping methodsUAV・衛星画像と植生指数によるトウモロコシ収量推定を比較・交差検証しており、植物収量という形質の取得・予測手法が中心である。

abstractcomparing weekly Unmanned Aerial Vehicle (UAV) and PlanetScope satellite imagery throughout the cropping season

Development and validation of a crop yield prediction framework accounting for regional heterogeneity: A case study of spring maize in Jilin Province →

Accurate and timely crop-yield prediction is essential for ensuring food security, managing agricultural risk, and supporting policy formulation. To address the respective limitations of traditional crop growth models and deep learning methods under complex environmental conditions, a hybrid modeling framework is proposed that integrates remote-sensing data assimilation, a process-based crop growth model, and deep learning techniques. Using spring maize in Jilin Province, China (2015–2020) as the case study, leaf area index (LAI) retrieval accuracy is first improved by coupling the PROSAIL model with machine-learning algorithms. A complete meteorological sequence for the target year is then constructed using a dynamic time warping (DTW) algorithm to overcome early-season prediction challenges caused by missing real-time weather data. Retrieved LAI is assimilated into the WOFOST crop growth model through an ensemble Kalman filter (ENKF) to calibrate state variables and enable dynamic yield prediction across growth stages. Finally, a deep learning model (Convolutional Neural Network–Attention Long Short-Term Memory with Multi-Task Learning, CNN-ALSTM-MTL) is developed to fuse assimilation outputs with multi-source heterogeneous data, leveraging multi-task learning to enhance adaptability to regional heterogeneity and improve yield prediction performance at the regional scale. Assimilation is found to substantially improve maize-yield estimation, increasing R² by 0.2 and reducing RMSE by 276 kg ha⁻¹. Compared with the assimilated crop growth model alone, the hybrid framework further increases R² by 35 % and decreases RMSE by 23 % by hierarchically capturing feature information relevant to maize-yield estimation. The best performance is achieved during the key growth stage (jointing to tasseling stage), with an R² of 0.75 and an RMSE of 592 kg ha⁻¹, enabling reliable yield prediction approximately two months before harvest. This framework demonstrates potential for cross-crop and cross-regional applications and provides robust methodological support for regional-scale yield forecasting and food-security early warning.

Why it matches plant phenotyping methodsLAIという植物形質の推定・同化と収量推定を中核とする統合的な計算フェノタイピング／予測フレームワークを開発・検証しており、単なる農業実験での routine 測定ではない。

abstracta hybrid modeling framework is proposed that integrates remote-sensing data assimilation, a process-based crop growth model, and deep learning techniques

LKCAFormer: a lightweight transformer with large-kernel cooperative attention for the segmentation of field maize leaf diseases. →

In smart agriculture, accurate segmentation of maize-leaf diseases in real field imagery supports timely intervention, but remains challenging under cluttered backgrounds, uneven illumination, occlusion, and diverse lesion morphology. We present LKCAFormer, a lightweight encoder–decoder segmentation network that integrates two key components: (i) a three-stage Large-Kernel Cooperative Attention encoder (LK-COAT) that progressively enlarges the effective receptive field via large-kernel depthwise convolutions while preserving fine boundaries using cooperative channel–spatial gating; and (ii) a cross-scale decoder (CSDecoder) that fuses shallow edge/detail cues with deep semantics to refine lesion boundaries at low computational cost. We evaluate LKCAFormer on CD&S and a controlled single-leaf variant derived from it (Single-CD&S), using disease IoU as the primary endpoint. Robustness is further assessed on a 266-image complex-case subset curated from held-out test data, together with paired two-sided tests. On Single-CD&S, LKCAFormer achieves 76.23 ± 2.25 disease IoU and 86.70 ± 1.96 Dice, yielding a modest + 0.58 IoU gain over the strongest lightweight baseline (SwiftFormer). On the more challenging CD&S benchmark, LKCAFormer reaches 69.09 ± 1.65 disease IoU and 78.87 ± 2.13 Dice, outperforming the strongest baseline (SegFormer) by + 4.05 IoU; gains on the complex-case subset are statistically significant. LKCAFormer remains compact (3.68 M parameters; 1.13G FLOPs), corresponding to approximately 12.7% of U-Net’s parameters and 1.47% of its FLOPs, while retaining practical end-to-end throughput under a unified profiling protocol. Limitations include fixed dataset splits, the lack of cross-device latency/energy benchmarking, and the absence of multi-seed variability analysis. Future work will extend validation across crops and sensors and provide deployment-oriented, hardware-aware latency and energy evaluations.

Why it matches plant phenotyping methodsトウモロコシ葉の病斑・病害状態を画像から分割・定量化する手法を開発し、複数ベンチマークと複雑事例で性能検証しており、植物表現型取得が中心である。

abstractaccurate segmentation of maize-leaf diseases in real field imagery supports timely intervention

A Data-Driven Image Extraction and Analysis Pipeline for Plant Phenotyping in Controlled Environments →

Abstract Advances in automation, imaging, and artificial intelligence have enabled large-scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Precision Automated Phenotyping Greenhouse and expanded Plant Growth and Phenotyping (PGP v2) data across maize, cotton, rice, and sorghum. The pipeline integrates pseudo-RGB generation, plant detection and segmentation, image stitching, vegetation-index analysis, texture analysis, morphological trait extraction, and temporal comparison of image-derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, SAM v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. SAM2Long maintained plant-instance associations across vertically stacked frames, while Scale-Invariant Feature Transform (SIFT)-based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863-dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment-level temporal analysis of mutagenized sorghum lines and cold-stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatmentor stress-related responses. The combined dataset and workflow provide structured, automated, and well-documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlledenvironment plant science and crop improvement. Plain Language Summary Temporal imaging of plants in controlled environments helps scientists better understand growth and biological processes. However, analyzing large volumes of images has been limited by a lack of automated tools. Multispectral imagery captures additional information about plant pigments, structure, and stress beyond standard color images. We developed an automated analysis pipeline that identifies individual plants, tracks their growth over time, and measures traits such as height, area, shape, texture, and vegetation indices. Using artificial intelligence, the system efficiently processes thousands of images to provide consistent and repeatable measurements. By integrating engineering and plant biology, this work supports data-driven decisions for crop improvement and agricultural research.

Why it matches plant phenotyping methods植物画像から形態・スペクトル・テクスチャ形質を抽出する統合パイプラインの開発と評価が中心であり、植物フェノタイピング手法として明確に該当する。

abstractWe developed an integrated multispectral phenotyping framework

ZamYOLO-maize: a YOLOv8n-based deep learning framework for automated detection and classification of maize leaf diseases in field conditions in Zambia. →

Maize, a critical staple crop in Zambia, faces persistent threats from foliar diseases such as Gray Leaf Spot, Northern Corn Leaf Blight, and Maize Streak Virus, significantly affecting smallholder productivity. Limited access to expert diagnostics, coupled with complex field conditions including occlusions and variable lighting, necessitates accessible, real-time disease detection systems tailored to local environments. To address this gap, this study first developed a novel field-captured dataset of Zambian maize leaf images, annotated with bounding boxes for disease lesions and labeled by disease type and severity to reflect real-world agri-ecological variability. Building on this dataset, we propose ZamYOLO-Maize, a multi-stage automated diagnostic framework integrating lesion detection, hierarchical disease classification, and severity assessment. A comparative evaluation was conducted using four state-of-the-art object detection models: YOLOv5n, YOLOv8s, YOLOv10s, and YOLOv8n, with performance assessed using precision, recall, F1-score, and inference speed. Experimental results demonstrate that YOLOv10s achieved the highest predictive performance (Precision = 0.997, Recall = 0.999, F1-score = 0.999), while YOLOv8n provided the optimal trade-off for edge deployment, achieving the fastest inference speed (4.65 ms/image) with a competitive F1-score of 0.995. The framework exhibited strong robustness under field variability, confirming its practical applicability. By integrating a locally representative dataset with an efficient deep learning pipeline, this study establishes a scalable foundation for mobile-based maize disease diagnostics, contributing to precision agriculture and supporting food security initiatives in Zambia and comparable agricultural regions.

Why it matches plant phenotyping methodsトウモロコシ葉画像から病斑、病害種、重症度を推定するデータセットと深層学習フレームワークを開発・比較評価しており、植物病害表現型の取得・抽出が中心です。

abstractthis study first developed a novel field-captured dataset of Zambian maize leaf images, annotated with bounding boxes for disease lesions and labeled by disease type and severity

An Efficient Depthwise Multiscale Feature Learning Convolutional Network for Plant Leaf Disease Classification in Agriculture →

Abstract Plant disease detection and early disease treatment are essential for sustainable crop production. Computer vision for crop science is growing with the advancement in deep learning. The proposed work systematically addresses these issues through three datasets as Plant Village Maize Dataset (D1), Paddy Doctor Dataset (D2), and Sugarcane Leaf Image Dataset (D3) with different classes. The dataset contains 4188, 16225, and 6748 images from dataset sets D1, D2, and D3, respectively. This work has used a Generative Adversarial Network (GAN) to generate a synthetic dataset. Further use data preprocessing, and the data has been resized to 224×224×3. The proposed model use Depth-wise Multiscale Feature Learning ConvoNet (DMFL-ConvoNet) model, which includes the Depth-Wise Convolutional Block (DCB ) block of DMFL-ConvoNet with 3 × 3 and 5 × 5, facilitates the extraction of multiscale plant disease characteristics. Furthermore, it has added 2.5 million parameters. The proposed DMFL-ConvoNet model offers state-of-the-art performance and decreases computational complexity at 33 frames per second, making it ideal for real-time applications. The proposed DMFL-ConvoNet model has been compared with several transfer learning models, including ResNet50V2, InceptionResNetV2, NASNetMobile, EfficientNetV2L, and EfficientNetV2B0 models, and the proposed model has achieved 99.52% data accuracy in the multiple datasets.

Why it matches plant phenotyping methods葉画像から植物病害を分類する深層学習手法の開発・比較が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採用する。

abstractThe proposed work systematically addresses these issues through three datasets as Plant Village Maize Dataset (D1), Paddy Doctor Dataset (D2), and Sugarcane Leaf Image Dataset (D3) with different classes.

Spatial analysis of cell patterning to aid genetic and phenotypic understanding of grass stomatal density: A case study in maize →

Abstract Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype‐to‐phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high‐throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio‐developmental processes. Data from 192 recombinant inbred lines of maize [ Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP‐derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.

Why it matches plant phenotyping methods新しいstomatal patterning phenotype（SPP）による空間解析・形質分解手法が中心で、HTPデータから気孔パターンの構成形質を抽出・解析している。

abstractexploited by a new spatial analysis approach

A two-tier strategy for developing water deficit stress tolerant maize: hydroponics-based root phenotyping followed by rainfed field validation. →

Maize productivity is increasingly constrained by water deficit stress (WDS), particularly under erratic rainfall conditions. Efficient early-stage phenotyping coupled with field validation is critical for breeding WDS-tolerant genotypes. In this study, we developed a two-tier screening strategy integrating hydroponics-based root trait evaluation at pre-reproductive stage with subsequent field validation of maize inbreds under managed WDS at CIMMYT, Hyderabad. A set of 50 diverse maize inbreds were evaluated for root architectural traits and plant growth stages including grain yield components. Hydroponic screening applied PEG6000-induced osmotic stress to assess root length, tips, forks, segments and diameter, whereas field trials imposed pre-reproductive WDS through cumulative growing degree day-based irrigation withdrawal. Significant genotypic variation and genotype × trait interactions were observed across both environments, reflecting trait and environment-specific responses. Key root traits, including root tips, total length, forks and segments, showed strong positive correlations (r ≥ 0.70) with yield components and Normalized difference vegetation index (NDVI), underscoring their importance in WDS resilience. Multivariate analysis further confirmed the alignment of root vigor with kernel traits and canopy health as critical determinants of yield stability. Among the evaluated lines, introgressed ILM23 and ILM24 emerged as the principal donor lines, while PML1249, PML1275, and PML1285 were identified as promising donor sources, all exhibiting robust root systems, stable anthesis-silking interval (ASI) and superior stress tolerance indices. Spearman's rank correlation (ρ = 0.988) between hydroponics and field rankings highlighted the predictive reliability of controlled root phenotyping for field performance under WDS. This integrated hydroponics-to-field approach provides a rapid, efficient and cost-effective framework for the early identification of WDS-tolerant or high water-use-efficiency (WUE) maize hybrids, facilitating the accelerated breeding of resilient cultivars.

Why it matches plant phenotyping methods水耕栽培による根系形態フェノタイピングを開発し、圃場条件で予測信頼性を検証する二段階スクリーニング手法が研究の中心である。

abstractwe developed a two-tier screening strategy integrating hydroponics-based root trait evaluation at pre-reproductive stage with subsequent field validation

Hybrid CNN–Transformer Model for Maize Leaf Blight Classification using Adaptive Genetic Optimization →

Abstract Maize leaf blight is a disastrous foliar disease in the world production of maize that causes significant losses in terms of yield annually. The classical machine learning and convolutional neural network (CNN) models are prone to poor generalization across the different conditions in the field because of differences in lighting, background, and leaf morphology. To counter these difficulties, this research suggests the use of Hybrid CNN-Transformer architecture that is trained using the Adaptive Genetic Optimization (AGO) to provide accurate classification of maize leaf blight. The hybrid model will utilize the spatial feature extraction and Global attention mechanism of Vision Transformer (ViT) to extract local and contextual patterns of diseases. AGO algorithm is dynamically adjusted with essential hyperparameters, such as the learning rate, filter size, and embedding, and adjusted according to the population diversity and fitness assessment, thus enhancing the convergence speed and classification accuracy. The experimental analysis of an augmented dataset of maize leaf disease proves that the developed model has a higher classification accuracy of 98.1, compared to the base models, including ResNet50 (96.7) and MobileNetV3 (97.2). The hybrid AGOCNN Transformer version demonstrates better solutions to the intelligent system on agricultural disease management in the real-field conditions.

Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から分類するCNN・Transformer手法の開発と比較評価が研究の中心であり、植物病害フェノタイプの取得・推定に該当する。

abstractthis research suggests the use of Hybrid CNN-Transformer architecture that is trained using the Adaptive Genetic Optimization (AGO) to provide accurate classification of maize leaf blight.

SCFM-DETR: an enhanced transformer-based method for automated maize disease detection in field environments. →

Maize is susceptible to various diseases throughout its growth cycle, which can significantly reduce yields. The accurate identification of maize diseases with similar symptomatic manifestations is particularly challenging under field conditions due to heterogeneous lighting and variable weather conditions. This paper proposes a novel detection model named SCFM-DETR, which is based on an improved Real-Time DEtection TRansformer (RT-DETR) to achieve robust identification of maize diseases in complex environments. SimAM-StarNet is employed as the backbone for feature extraction in this model, reducing the number of parameters and improving multiscale feature fusion, thereby diminishing the impact of background noise. Furthermore, the original RepC3 module is replaced with a newly designed CGLU-FasterBlock-MANet (CFM) module, which enhances adaptive feature fusion for finer discriminative capability. The experimental results demonstrate that the SCFM-DETR model achieves an average precision of 96.7% and a recall of 95.8% on a maize disease dataset, exceeding the corresponding metrics of the baseline RT-DETR-R18 model by 3.1% and 6.0%. Additionally, the model reduces the number of parameters and computational load by 47% and 49%, respectively, making it highly suitable for deployment in computationally limited agricultural settings. This work offers a high-accuracy, lightweight framework that facilitates intelligent crop disease monitoring and supports the advancement of smart agriculture.

Why it matches plant phenotyping methodsトウモロコシの病徴を画像から検出するモデルを開発・評価しており、植物の病害状態を推定する画像ベースの表現型計測手法が中心である。

abstractThe experimental results demonstrate that the SCFM-DETR model achieves an average precision of 96.7% and a recall of 95.8% on a maize disease dataset

Simultaneous triple staining for detecting cell-type specific spatio-temporal distribution of cell wall materials in monocot roots →

Summary Anatomical and histochemical imaging of grass root systems relies on tissue sectioning and cell wall staining dyes because molecular reporter lines are limited for most organisms. Distinct staining dyes require variable incubation time and concentration across different tissues and organisms. As a result, staining with multiple dyes becomes time consuming or challenging. Here, we report a rapid method to perform simultaneous triple staining on a glass slide. The entire protocol requires ∼4 hours and a smaller volume of stain than traditional methods. We tested this method using the roots of two economically important crops, Triticum aestivum (wheat) and Zea mays (maize), as proof of concept. We have also demonstrated the presence of exodermis in wheat roots. Additionally, we identified the formation of polar lignin caps in maize exodermis using our simultaneous triple staining method. This method empowers a quantitative approach to cell biology by elucidating cell-type specific spatio-temporal distribution of cell wall materials in monocot root systems.

Why it matches plant phenotyping methods単子葉植物の根における細胞壁物質の細胞型別・時空間分布を定量的に可視化する同時三重染色法の開発が中心であり、植物状態の取得・解析手法に該当する。

abstractHere, we report a rapid method to perform simultaneous triple staining on a glass slide.

Lightweight plant phenotypic feature extraction via transferable attention head pruning in Vision Transformers →

We propose a lightweight Multi-Head Self-Attention (MHSA) mechanism for plant phenotypic feature extraction, which integrates cross-species transfer learning with dynamic head pruning to improve efficiency without compromising accuracy. The primary challenge stems from minimizing redundant computations without compromising the model's capacity to generalize over varied plant species, an issue intensified by the substantial dimensionality of attention mechanisms in Vision Transformers. Our solution, the Transferable Attention Head Alignment (TAHA) framework, operates in three stages: pre-training on a source species, cross-species alignment via a Domain Alignment Loss (DAL), and head pruning based on a transferability score. The framework selects and keeps solely the attention heads with the highest transferability, thus diminishing model intricacy without compromising the ability to distinguish phenotypic traits. Furthermore, the pruned MHSA module is smoothly combined with standard Transformer backbones, which makes efficient deployment on edge devices possible. Experiments were conducted on real edge hardware (Raspberry Pi 4, NVIDIA Jetson Nano) and GPU platforms, showing our approach attains accuracy similar to full-head models yet cuts computational expenses by as much as 40% (14.1 ms inference latency on Raspberry Pi 4, 519 M parameters). The method holds special importance for scalable plant phenotyping, in situations where computational capacity is frequently constrained yet generalization across species is essential. Moreover, the repeated alignment and pruning procedure permits gradual adjustment to novel species without complete retraining, which increases feasibility for agricultural applications in practical settings. Supplementary experiments on phylogenetically distant species (Arabidopsis → pine) demonstrate the framework's generalization limits, with a 7.2% F1-score drop compared to close-species transfer (Arabidopsis → maize), highlighting the need for trait-specific head adaptation in distant transfers. The proposed method improves lightweight feature extraction by merging transfer learning and attention head optimization, achieving a balanced compromise between performance and efficiency.

Why it matches plant phenotyping methods植物表現型特徴抽出のためのVision Transformer剪定・転移学習手法を開発し、複数種およびエッジデバイスで精度と計算効率を検証しており、表現型取得・抽出法が研究の中心である。

abstractWe propose a lightweight Multi-Head Self-Attention (MHSA) mechanism for plant phenotypic feature extraction

Non-destructive estimation of maize carotenoids using reflectance-based spectral indices. →

This study investigates the relationship between maize leaf carotenoid content and spectral reflectance, evaluates existing carotenoid estimation indices, and develops new spectral indices and machine learning models for improved prediction. A strong positive correlation was observed between carotenoid and chlorophyll content, highlighting carotenoids' role in both light harvesting and photoprotection. Spectral analysis revealed that carotenoid concentration significantly affects leaf reflectance in the visible range, particularly between 500-650 nm. Existing carotenoid indices exhibited limited predictive performance for the studied samples, prompting the development of nine new indices based on principal component analysis. Among these, CAR 7 , CAR 8 , and CAR 9 demonstrated superior predictive ability across different training (2021-2022: R 2 = 0.72-0.76, NRMSE = 15-16%, 2021-2023: R 2 = 0.60-0.62, NRMSE = 11-12%, 2022-2023: R 2 = 0.42-0.49, NRMSE = 18.3-18.5%) and testing periods (2023: R 2 = 0.44-0.50, NRMSE = 14-19%, 2022: R 2 = 0.65-0.72, NRMSE = 13-16%, 2021: R 2 = 0.81-0.83, NRMSE = 18.28-24.65%). Machine learning models further improved carotenoid estimation, with REPTree providing the most reliable and balanced performance during testing (R 2 = 0.79, NRMSE = 13.84%). The findings suggest that the combination of targeted spectral indices and appropriate machine learning approaches enables accurate, non-destructive estimation of maize carotenoid content, offering potential for practical applications in crop monitoring and stress assessment.

Why it matches plant phenotyping methodsトウモロコシ葉のカロテノイドという植物形質を、反射スペクトル指標と機械学習で非破壊推定する手法の開発・評価が研究の中心であるため。

abstractdevelops new spectral indices and machine learning models for improved prediction

Digital decision support integrated with diagnostics and precision fungicide application for Southern Corn Leaf Blight in maize. →

Southern Corn Leaf Blight (SCLB, also called Maize Leaf Blight, MLB), caused by Bipolaris maydis (teleomorph: Cochliobolus heterostrophus), severely limits maize yield under favourable conditions. Rapid detection and precise interventions are essential for sustainable production. We present an AI-driven framework integrating deep learning diagnostics, precision fungicide application, and a digital decision support system (DSS) for field-level SCLB management. Thirteen machine learning (ML) and deep learning (DL) algorithms were evaluated, with VGG16 achieving the highest performance (accuracy 97.0%, precision 0.98, recall 0.96, F1-score ≥ 0.97, AUC-ROC = 1.00). Feature extraction analysis highlighted VGG16’s ability to capture hierarchical disease-specific patterns (score = 0.95), and error- and variance-based assessment confirmed minimal prediction errors (MAE = 0.06, RMSE = 0.16, Explained Variance = 0.90, MBD = − 0.02). Confusion matrix analysis revealed only a small number of misclassifications (4 false negatives and 9 false positives), demonstrating excellent generalization. Grad-CAM heatmaps, t-SNE visualization, and learning curves confirmed lesion-focused predictions and feature separability. Two-year field trials (2023 and 2024) validated precision fungicide application (Azoxystrobin 18.2% + Difenoconazole 11.4% SC), reducing disease severity to ≈ 10% PDI (86.2% reduction) and increasing grain yield to 83.7 q/ha (C: B ratio 1:2.41). The Streamlit-based DSS provides actionable, real-time advisories, offering a scalable AI platform for automated disease detection and precision agriculture in maize. The proposed framework can be extended to other foliar diseases and integrated with IoT-based sensing for region-wide advisory systems.

Why it matches plant phenotyping methodsトウモロコシ葉の病斑・病害状態を深層学習で検出・分類する方法と、その性能検証および現地試験での検証が中心であり、植物病害フェノタイピング手法に該当する。

abstractWe present an AI-driven framework integrating deep learning diagnostics, precision fungicide application, and a digital decision support system (DSS) for field-level SCLB management.

Root system architecture profiling for aluminium tolerance in maize seedlings using an optimized high-throughput phenotyping. →

Aluminium (Al) toxicity is a potential constraint to maize productivity in acidic soils, primarily due to its inhibitory effect on root growth during its early establishment. In the present study, a hydroponic screening protocol was standardized using Modified Magnavaca-II solution at the seedling stage and applied to 250 tropical maize inbred lines. Five root traits-total root length (TRL), root surface area (RSA), root volume (RV), average root diameter (AD), and number of root tips (NRT)-were quantified using WinRHIZO. To assess differential tolerance, the Relative Root Tolerance Index (RRTI)-a ratio-based metric comparing root performance under stress versus control-was calculated along with percent reduction for all traits. Protocol optimization with seven elite inbreds exposed to graded AlCl₃ concentrations (0-1500 µM) identified 300 µM AlCl₃ at 11 days post-germination as optimal for differentiating genotypic responses. Under this optimized condition, the 250 inbreds showed highly significant genotypic variation and genotype × treatment interactions. Stress significantly reduced most root traits by 10-40%, while improving the average root diameter, indicating compensatory thickening. Substantial variability was observed for both RRTI and percent reduction indices, ranging from 3.83 to 533.88. Principal component analysis and composite indices identified IMR292, IMR592, IMR463, IMR621, IMR546, IMR534, IMR629 and IMR395 as tolerant due to high TRL, RSA and NRT under stress, while IMR388, IMR33, IMR58, IMR349 and IMR446 were highly susceptible. The tolerant inbreds offer promising genetic resources for breeding Al-tolerant maize, while the optimized hydroponic system provides a robust, scalable framework for future phenotyping and genetic dissection studies.

Why it matches plant phenotyping methodsアルミニウム耐性評価のための高スループット根系表現型測定プロトコルを標準化・最適化し、250系統へ適用しているため、表現型取得法が研究の中心です。

abstracta hydroponic screening protocol was standardized

Root electrical capacitance method for the field monitoring of maize response to elevated carbon dioxide concentration →

Abstract Aims This study evaluated the suitability of root electrical capacitance measurements for nondestructive plant phenotyping in a free-air CO 2 enrichment (FACE) experiment. Methods A two-year FACE study was conducted with maize grown under ambient and elevated [CO 2 ], and low and high nitrogen supply in three replicate plots. The saturation root electrical capacitance (C R *) was monitored during the plant growth cycle. Aboveground plant parameters were measured in situ at flowering. Results Capacitance measurements revealed a seasonal pattern in root development with a peak at flowering, and the positive effect of higher nitrogen dose and [CO 2 ] enrichment on plant growth. At anthesis, C R * was significantly ( p < 0.001) and linearly correlated with stem basal area (R 2 : 0.51–0.68), aboveground biomass index (basal area × plant height; R 2 : 0.47–0.62) and leaf chlorophyll concentration (R 2 : 0.40–0.56). However, the best correlation (R 2 : 0.73 and 0.74) was found for plant leaf area, which is closely related to root water uptake, suggesting that the applied current signal penetrated the roots, and that the capacitance method directly measured root status in the field. In addition, C R * at flowering was a reasonable early predictor of maize grain yield (R 2 : 0.58 and 0.64) under our experimental conditions. Conclusions The electrical capacitance method proved to be a practical high-throughput tool for phenotyping not only the root but the whole plant in the field. Being noninvasive, it is particularly beneficial in FACE systems, where destructive sampling and soil disturbance should be minimized. It would also provide cost-effective support for breeding stress-tolerant and climate-resilient crops. Graphical Abstract

Why it matches plant phenotyping methods根の電気容量測定を非破壊・高スループットな植物フェノタイピング手法として評価し、圃場での相関および予測性能を検証しているため、方法が研究の中心である。

abstractThis study evaluated the suitability of root electrical capacitance measurements for nondestructive plant phenotyping in a free-air CO 2 enrichment (FACE) experiment.

Auto3DPheno: Automated 3D Maize Seedling Phenotyping via Topologically-Constrained Laplacian Contraction with NeRF →

Analyzing three-dimensional (3D) phenotypic parameters of maize seedlings is of significant importance for maize cultivation and selection. However, existing methods often struggle to balance cost, efficiency, and accuracy, particularly when capturing the complex morphology of seedlings characterized by slender stems. To address these issues, this study proposes a novel end-to-end automated framework for extracting phenotypes using only consumer-grade RGB cameras. The pipeline initiates with Instant-NGP to rapidly reconstruct dense point clouds, establishing the 3D data foundation for phenotypic extraction. Subsequently, we formulate a directed topological graph-based mechanism. By mathematically defining bifurcation constraints via vector analysis, this mechanism guides a depth-first traversal strategy to explicitly disentangle stem and leaf skeletons. Building upon these decoupled skeletons, organ-level point cloud segmentation is achieved through constraint-based expansion, followed by density-based spatial clustering (DBSCAN) to detect individual leaves. Algorithms combining point cloud geometry with 3D Euclidean distance are also implemented to calculate key phenotypes including plant height and stem width. Finally, single-leaf skeleton fitting is used to estimate leaf length, and principal component analysis (PCA) is adopted to determine the stem–leaf angle, realizing the comprehensive automatic extraction of maize seedling phenotypes. Experiments show that the proposed method achieves high accuracy in extracting key phenotypic parameters. The mean relative errors for plant height, stem width, leaf length, stem-leaf angle, and leaf area are 0.76%, 2.93%, 1.26%, 2.13%, and 3.33%, respectively. Compared with existing methods as far as we know, the proposed method significantly improves extraction efficiency by reducing the processing time per plant to within 5 min while maintaining such high accuracy.

Why it matches plant phenotyping methodsRGBカメラとNeRF・点群処理・骨格解析を統合し、トウモロコシ幼苗の形質を自動抽出する手法を開発・精度評価した研究であり、フェノタイピング手法が中心である。

abstractthis study proposes a novel end-to-end automated framework for extracting phenotypes using only consumer-grade RGB cameras.

Deep Learning-Based Classification of Water Stress in Maize Using Biospeckle Activity Maps →

Biospeckle imaging enables non-destructive observation of dynamic physiological activity in plant tissues; however, the relative sensitivity of different biospeckle activity maps to water stress and their implications for data-driven classification remain insufficiently understood. This study systematically evaluates multiple biospeckle activity mapping approaches for water stress analysis in maize (Zea mays L.) leaves and examines how their characteristics influence deep learning–based classification performance. Maize plants were subjected to three irrigation levels (0%, 50%, and 100%) over a 7-day experimental period. Stomatal conductance was measured as an independent physiological reference, and a microfluidic phantom experiment was conducted to verify the physical response behavior of the biospeckle imaging system. Temporal variations in biospeckle activity were statistically analyzed, followed by deep learning–based classification using representative two-dimensional convolutional neural network models. Statistical analysis revealed that biospeckle activity exhibited stress-dependent responses, with severe water stress (0%) being consistently distinguishable, whereas moderate and well-watered conditions (50% and 100%) showed partially overlapping patterns. These trends were consistent with stomatal conductance measurements. Deep learning models trained on different biospeckle activity maps achieved classification accuracies of up to 0.73 and macro-averaged F1 scores of 0.73, with notable differences in performance depending on the selected activity representation. These results suggest that while traditional statistical parameters show limited linearity, the proposed deep learning-based biospeckle analysis could serve as a useful tool for water stress classification. By capturing complex spatial-texture features, this study presents a potential data-driven approach for precision plant phenotyping.

Why it matches plant phenotyping methods植物の水ストレス状態を推定するバイオスペックル画像マッピングと深層学習分類を系統的に評価し、独立した生理指標およびファントム実験で検証しているため、フェノタイピング手法が中心です。

abstractThis study systematically evaluates multiple biospeckle activity mapping approaches for water stress analysis in maize (Zea mays L.) leaves and examines how their characteristics influence deep learning–based classification performance.

Comparative assessment of root exudation in maize: Influence of experimental setup, growth conditions and root hairs. →

Background and aims A major challenge in root exudation research is obtaining exudates samples that accurately reflect the exudation processes under natural soil growth conditions. Both growth environment and experimental setup can significantly influence root exudation dynamics. This study investigated how different experimental systems and growth conditions affect carbon exudation in maize ( Zea mays L.) roots and whether these factors could influence the detection of genotypic differences between the wild type (B73) and its hairless mutant, rth3. Methods Maize plants were grown under various experimental conditions, including soil-based and hydroponic systems. Root exudates were collected using a combination of traditional and innovative sampling approaches. Carbon exudation rates were compared across experimental setups and genotypes. Laboratory results were further compared with data from a separate field experiment. Results Exudation rates obtained from soil-based laboratory experiments were comparable to those observed in the field under similar growth temperatures. The contribution of root hairs to total carbon exudation was negligible compared to the effect of growth conditions and experimental setup. Large differences in root biomass introduced bias into exudation measurements, particularly when root to sampling volume ratio (RSVR) varied substantially. Conclusions Experimental setup and environmental conditions have a strong influence on root exudation measurement. Soil-based laboratory systems that closely replicate field conditions, particularly temperature, can serve as reliable proxies for field experiments, providing ecologically meaningful data. Maintaining a consistent RSVR is also essential for obtaining accurate and comparable results. These findings offer important methodological guidance for reliably quantifying root carbon exudation in maize. Supplementary information The online version contains supplementary material available at 10.1007/s11104-026-08324-x.

Why it matches plant phenotyping methodsトウモロコシ根からの炭素滲出量の測定について、実験系・環境条件・サンプリング法の影響を比較検証し、信頼性と再現性のための測定指針を提示している。測定法の評価が研究の中心である。

abstractA major challenge in root exudation research is obtaining exudates samples that accurately reflect the exudation processes under natural soil growth conditions.

Investigating soil-plant-atmosphere interactions by combining spectral electrical impedance tomography with environmental and physiological timeseries →

The soil-plant-atmosphere continuum ( SPAC ) plays a critical role in the distribution of water and nutrients in terrestrial ecosystems. To understand the complex and rapid dynamics within the SPAC , it is necessary to observe its components with sub-daily resolution. While measurements of above-ground processes are frequently employed, monitoring of the below-ground part remains scarce due to its inaccessibility. In this study, we monitored water and nutrient transport processes in a maize field over several months. The rhizosphere was monitored with spectral electrical impedance tomography ( sEIT ) to capture soil water content ( SWC ) dynamics, root structure, and activity. Stem water transport and photosynthetic activity were measured with sapflow sensors and a fluorescence sensor, respectively, while atmospheric conditions were measured with a weather station. Timeseries were analyzed using cross and coherence wavelet analysis. Electrical imaging results revealed spatially and temporally resolved daily variations in subsurface conductivity and polarization properties, suggesting a sensitivity to water and ion uptake processes. Conductivity development was strongly correlated with SWC dynamics controlled by evaporation and water uptake of plants. Wavelet power showed that belowground polarization diurnality was consistent with a typical growth pattern of maize, and disappeared shortly after harvest. Cross wavelet analysis of sun-induced fluorescence, sapflow density, photosynthetically active radiation, and vapor pressure deficit revealed lags caused by environmental conditions, highlighting the coupling of plant activity to the atmosphere. Our results show that sEIT is a valuable tool to study rhizosphere processes and may aid in the holistic modeling of the SPAC .

Why it matches plant phenotyping methodssEITを用いて根圏の水分動態・根構造・根の活動を時空間的に取得し、他センサーとの時系列解析で植物の水輸送・生理状態を評価しており、植物状態のセンシング手法の実質的な適用が中心です。

abstractThe rhizosphere was monitored with spectral electrical impedance tomography ( sEIT ) to capture soil water content ( SWC ) dynamics, root structure, and activity.

The Rolled Towel Method for Hormone Response Assays in Maize. →

The rolled towel assay (RTA) is a soil-free method to evaluate juvenile phenotypes in crops such as maize and soybean. Here, we provide an updated RTA-based protocol to phenotype maize seedling responses to chemicals of interest. We exemplify the protocol with two synthetic auxin herbicides (2,4-dichlorophenoxyacetic acid and picloram), an auxin precursor (indole-3-butyric acid), and an auxin inhibitor ( N -1-naphthylphthalamic acid), but the method can be used with other hormones or plant growth regulators that are soluble in growth media. We also include instructions on how to annotate root traits and analyze primary root length trait data. The protocol can be scaled up for use in genetic screens, preparing tissue for gene expression analyses, carrying out genome-wide association studies (GWASs), and quantitative trait locus (QTL) identification.

Why it matches plant phenotyping methods根の形態・ホルモン応答を取得するロールドタオル法の更新プロトコルであり、根形質のアノテーションと解析も中心的に扱うため、植物フェノタイピング手法として適格です。

abstractThe rolled towel assay (RTA) is a soil-free method to evaluate juvenile phenotypes in crops such as maize and soybean.

Research Advances in Maize Crop Disease Detection Using Machine Learning and Deep Learning Approaches →

Recent developments in machine learning (ML) and deep learning (DL) algorithms have introduced a new approach to the automatic detection of plant diseases. However, existing reviews of this field tend to be broader than maize-focused and do not offer a comprehensive synthesis of how ML and DL methods have been applied to image-based detection of maize leaf disease. Following the PRISMA guidelines, this systematic review of 102 peer-reviewed papers published between 2017 and 2025 examined methods and approaches used to classify leaf images for detecting disease in maize plants. The 102 papers were categorized by disease type, dataset, task, learning approach, architecture, and metrics used to evaluate performance. The analysis results indicate that traditional ML methods, when combined with effective feature engineering, can achieve classification accuracies of approximately 79–100%, while DL, especially CNNs, provide consistent, superior classification performance on controlled benchmark datasets (up to 99.9%). Yet in “real field” conditions, many of these improvements typically decrease or disappear due to dataset bias, environmental factors, and limited evaluation. The review provides a comprehensive overview of emerging trends, performance trade-offs, and ongoing gaps in developing field-ready, explainable, reliable, and scalable maize leaf disease detection systems.

Why it matches plant phenotyping methodsトウモロコシ葉の病害を画像から分類・検出する機械学習手法を体系的にレビューしており、植物の病害状態を推定するフェノタイピング手法が中心である。

abstractThe review provides a comprehensive overview of emerging trends, performance trade-offs, and ongoing gaps in developing field-ready, explainable, reliable, and scalable maize leaf disease detection systems.

RGB-to-synthetic-thermal image translation using generative AI to support crop water stress assessment →

Thermal imaging is becoming a valuable tool for monitoring plant canopy temperature, which can serve as an indicator of crop water stress. However, specialized thermal sensors are often cost-prohibitive. This study explored strategies for supplementing crop water stress monitoring by generating synthetic thermal images from standard Red-Green-Blue (RGB) imagery captured using an unmanned aerial vehicle system (UAVs) equipped with a Zenmuse XT2 sensor and leveraging deep learning models. UAV-based RGB and thermal images were collected from 32 experimental plots of sweet corn and green beans over three growing seasons from 2020 to 2023. Each crop was subjected to one full and three deficit irrigation treatments, replicated four times. A total of 3,400 UAV images were collected over three seasons. Image processing was done in Pix4D software, and orthomosaic RGB and thermal maps were spatially aligned using ground control points (GCPs). The UAV RGB and thermal map data were split into 80 % and 20 % for training and testing, respectively. Two image-to-image translation generative adversarial network (GAN) deep learning models, specifically Pix2PixGAN and CycleGAN, were used to generate synthetic thermal images from RGB inputs. Image quality evaluation metrics, i.e., correlation coefficients (r), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), were used to evaluate the models’ performance. Crop water stress index (CWSI) values were also computed from measured and generated thermal imageries to assess practical applicability. Generated thermal canopy temperature outputs from the Pix2PixGAN model showed a strong correlation with the measured data using a thermal camera (r >0.95). Moreover, Pix2PixGAN resulted in lower MSE (5.63) and higher PSNR (42.98) than CycleGAN (MSE = 7.09, PSNR = 40.56), whereas CycleGAN had a slightly higher SSIM (0.44) than Pix2PixGAN (0.31). CWSI values derived from the generated thermal images reflected the expected gradients of water stress across irrigation treatments, with the highest CSWI observed from deficit irrigation treatments compared to the full irrigation. These results demonstrate that RGB-to-synthetic-thermal image translation using GAN models could be used to support crop water stress assessment and irrigation scheduling.

Why it matches plant phenotyping methodsRGB画像から合成熱画像を生成し、作物キャノピー温度と水ストレス指標を推定するGAN手法の開発・比較・性能評価が中心であり、植物状態の表現型取得に直接関係する。

abstractTwo image-to-image translation generative adversarial network (GAN) deep learning models, specifically Pix2PixGAN and CycleGAN, were used to generate synthetic thermal images from RGB inputs.

Hyperspectral image-based leaf-level spatial and spectral feature mining for phosphorus deficiency symptom differentiation in corn plants at early vegetative stage →

Phosphorus (P) is a vital macronutrient necessary for synthesizing essential plant biomolecules. Accurate identification of plant P deficiency symptoms is critical for effective crop management and optimizing crop yield. Hyperspectral sensing provides a real-time, non-destructive avenue for assessing crop nutrient status, while its performance largely depends on the representativeness of the extracted features. In this study, a handheld proximal transmittance hyperspectral imager, LeafSpec, was utilized to collect leaf-level hyperspectral images at corn V6 vegetative stage. A novel feature mining algorithm was proposed to extract and combine the spatial and spectral features in visible and near-infrared range, enabling effective differentiation of P deficiency. The correlation coefficient between the P content and the selected spatial-spectral features reached 0.77. Compared with spectral indices, the combined spatial-spectral feature showed a more significant differences among corn plants under different P treatments, especially between the medium and sufficient P levels. Feature visualization heatmaps, highlighting leaf venation variations with spatial-spectral calculations, provided direct evidences of effectiveness. This study shows the potential of integrating handheld proximal transmittance hyperspectral imaging with feature mining algorithm for early-stage differentiation of P levels in corn plants.

Why it matches plant phenotyping methodsトウモロコシ葉のリン欠乏状態を対象に、ハイパースペクトル画像取得と空間・スペクトル特徴マイニング手法を開発・評価しており、表現型状態の推定法が研究の中心である。

abstractA novel feature mining algorithm was proposed to extract and combine the spatial and spectral features in visible and near-infrared range, enabling effective differentiation of P deficiency.

Reverse temporal spectral signal extraction and deep spectral signal decoupling fusion for detection of SCLB infection process →

Close-range spectral imaging provides technical support for leaf detection during the early infection process of SCLB (southern corn leaf blight). However, due to the randomness of pathogen infection and the low visibility of early lesions, the temporal spectral signals obtained using this technique have poor continuity and low sensitivity. To improve the ability of temporal spectral signals to detect early-stage infection, this study proposes a signal extraction method based on reverse temporal spectral image matching, and a signal decoupling method based on DSO-CWT (decomposition of scale-optimized continuous wavelet transform) is also proposed to improve signal sensitivity. First, the spectral images were preprocessed. Image entropy was used to quantify the changing patterns of symptoms in early SCLB infection. Second, a temporal spectral signal extraction method based on ASpanFormer (adaptive span transformer) reverse temporal spectral image matching is proposed. The calculation results of LPIPS (learned perceptual image patch similarity) indicates that the average matching error between adjacent periods is less than 0.2, which suggests that this method can enhance the extraction accuracy of weak temporal spectral signals in early infection. Third, the T-test was used to evaluate the detection sensitivity of temporal spectral signals at different early stages of infection. The results showed that the temporal spectral signal still had low sensitivity for detecting different stages of infection. Therefore, a temporal signal decoupling method based on DSO-CWT is proposed, which enhances the detection sensitivity of temporal spectral signals by performing time–frequency domain conversion. Finally, a diagnostic model for the early SCLB infection was established by fusing fluorescence and reflectance spectral signals. After DSO-CWT processing, the accuracy of the modelling set improved from 47.62% to 94.22%, and the accuracy of the validation set was improved from 47.62% to 91.27%. This study improves the extraction accuracy and detection sensitivity of temporal spectral signals for early SCLB infection by using signal extraction based on reverse temporal spectral image matching and deep signal decoupling based on DSO-CWT, providing new insights for early detection of SCLB infection.

Why it matches plant phenotyping methods植物病害の感染状態を対象に、時系列スペクトル信号の抽出・分離手法を開発し、検出感度と診断精度を検証しているため、病徴状態のフェノタイピング手法が中心です。

abstractthis study proposes a signal extraction method based on reverse temporal spectral image matching, and a signal decoupling method based on DSO-CWT

Precision detection and geolocation of missed pre-tassels in hybrid maize seed production using UAV-based deep learning →

Hybrid maize seed production relies on detasseling, a critical process to ensure genetic purity by removing male pre-tassels from female plants. However, missed pre-tassels, which are immature tassels partially enclosed by leaves and similar in color to maize foliage, remain difficult to detect and typically require labor-intensive manual inspection. This study proposes an improved UAV-based detection framework, YOLO for Missed Pre-Tassel (YOLO-MPT), built upon YOLOv7 for precise identification and geolocation of missed pre-tassels in hybrid maize fields. YOLO-MPT integrates deformable convolutions (DCNv2) for adaptive feature extraction, the S²-MLPv2 attention mechanism for enhanced spatial representation, and an additional small-object detection head to increase sensitivity to tiny or occluded targets. A comprehensive UAV-derived pre-tassel dataset was constructed under diverse agronomic and lighting conditions to support model training and validation. The impact of input image size on detection performance was systematically analyzed to identify the optimal training resolution. Experimental results show that YOLO-MPT achieved an average precision (AP) of 93.8 %, precision (P) of 93.3 %, recall (R) of 90.2 %, and an F1-score of 91.7 %, outperforming baseline models. Furthermore, a geographic coordinate extraction method was developed and integrated into a standalone “Missed Pre-Tassel Detection and Localization Software,” enabling automatic conversion of pixel detections into precise geospatial locations. Field experiments verified the workflow’s robustness and positioning accuracy, demonstrating the system’s potential to improve post-detasseling efficiency and quality assurance in hybrid maize seed production.

Why it matches plant phenotyping methodsUAV画像からトウモロコシの未抽苔を検出・地理定位する手法を開発し、データセット、性能検証、ソフトウェア化まで行っており、植物状態の取得・抽出が研究の中心である。

abstractThis study proposes an improved UAV-based detection framework, YOLO for Missed Pre-Tassel (YOLO-MPT), built upon YOLOv7 for precise identification and geolocation of missed pre-tassels in hybrid maize fields.

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

Optimizing in-season nitrogen management through satellite-guided fertigation in field-scale maize production →

Improving nitrogen use efficiency (NUE) in commercial maize production remains a persistent challenge. A major barrier is the lack of simple, remote sensing–based decision-support frameworks that enable broad adoption of in-season, site-specific nitrogen (N) management. This study developed and evaluated a practical framework that contextualizes the Holland–Schepers sensor algorithm using PlanetScope (PS) satellite imagery to guide multiple in-season, variable-rate fertigation delivered through a flow-proportional injection system integrated with a center pivot system equipped with variable-rate irrigation. Field implementation was carried out during the 2023 and 2024 seasons across four N rates (0-N, Low-N, Fertigation, Full-N) and three irrigation treatments: full (BMP), deficit (50 %BMP), and rainfed. Normalized Difference Red Edge (NDRE)-derived sufficiency index (SI) values informed the amount, timing, and spatial distribution of N applications. In 2024, PS-guided fertigation achieved yields statistically comparable to Full-N while reducing total N input by 23 %. Significant improvements in NUE were observed, with Fertigation outperforming Full-N by 12 % in agronomic efficiency (AE) and 26 % in partial factor productivity of N (PFPN). Satellite-derived SI values were strongly correlated with UAV benchmarks from the MicaSense Altum and RedEdge-3 sensors (ρ = 0.82–0.95). However, PS consistently overestimated NDRE relative to UAV data, particularly under N-deficient conditions, underscoring the need for local calibration and bias correction. To improve diagnostic specificity, a biologically informed, rule-based stress-classification framework was developed to differentiate nitrogen stress from water stress using NDRE and soil water depletion (SWD) as diagnostic variables. Retrospective yield and management data were used to establish physiologically meaningful NDRE–SWD thresholds for stress diagnosis during the critical in-season fertigation window (V10–R2). Full-Yield plots achieved approximately 12,000 kg/ha at NDRE = 0.78 and SWD = 64 mm. The resulting NDRE–SWD–yield patterns highlight the feasibility of disentangling stress types under commercial field conditions. However, further validation across seasons and environments, along with integration of canopy water or temperature indices, is needed to improve water-stress detection and enable real-time decision-making. Overall, these results offer actionable guidance for implementing satellite-guided fertigation at commercial scale. The developed framework delivers scalable, data-driven N recommendations that enhance profitability and support environmentally responsible maize production. It also provides a reference for future research and extension programs aiming to turn satellite remote sensing into practical tools for site-specific N management.

Why it matches plant phenotyping methods衛星・UAVリモートセンシングによるNDRE指標を用いて植物の窒素・水ストレスを診断し、センサー間の検証とルールベース分類法の開発を行っているため、植物状態の取得・推定手法が中心的である。

abstractThis study developed and evaluated a practical framework that contextualizes the Holland–Schepers sensor algorithm using PlanetScope (PS) satellite imagery to guide multiple in-season, variable-rate fertigation

Spatiotemporal assessment of maize evapotranspiration and surface energy fluxes under varying irrigation regimes using UAV based METRIC. →

Evapotranspiration (ET) is a key component of the hydrological cycle and is critical for determining crop water requirements. Accurate ET estimation is essential for improving irrigation efficiency, particularly under increasing water scarcity and climate variability. Conventional approaches such as the soil water balance, empirical formulations, the FAO Penman-Monteith method, eddy covariance flux towers, lysimeters, and scintillometers each have limitations related to spatial representativeness, accuracy, or operational cost. Unmanned aerial vehicles (UAVs) equipped with multispectral and thermal sensors offer a high spatial resolution and cost-effective alternative for field-scale assessment of surface energy balance components and ET. In this study, a field experiment was conducted on maize during rabi season of 2022-23 under two irrigation regimes based on depletion of available soil moisture (20% DASM and 40% DASM). UAV-based multispectral (0.05 m) and thermal imagery (0.33 m) were acquired at five crop growth stages and processed using the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model to estimate actual evapotranspiration (ETa) and surface energy fluxes. Spatiotemporal analysis showed that the 20% DASM treatment (400 mm) resulted in a 1.7 °C lower land surface temperature, a 16.5% higher NDVI, and an 11% increase in daily ETa compared with the 40% DASM treatment (316 mm), which experienced water stress and a 20% reduction in seasonal ETa. The UAV-based METRIC estimates of daily ETa showed strong agreement with that of Penman-Monteith (PM) combination approach (R² = 0.84; RMSE = 0.22 mm day⁻¹; MAPE = 6.1%), with a slight underestimation of seasonal ETa (-7%). Agreement with the soil water balance method ranged from - 3% to + 3%, demonstrating the capability of the approach to capture irrigation-induced variability in ETa and surface energy fluxes. Overall, the results highlight the potential of UAV-based METRIC for spatiotemporal assessment of crop evapotranspiration and surface energy dynamics to support precision irrigation management.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像とMETRICモデルにより、トウモロコシの蒸発散量・表面エネルギーフラックスを取得し、複数手法との一致性を検証している。植物キャノピーの生理状態の定量が研究の中心であり、単なる灌漑試験のルーチン測定ではない。

abstractUAV-based multispectral (0.05 m) and thermal imagery (0.33 m) were acquired at five crop growth stages and processed using the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model to estimate actual evapotranspiration (ETa) and surface energy fluxes.

A new evolution-based genomic prediction model forecasts yield performance across environments and future climates and identifies adapted maize landraces →

Forecasting vulnerability of cultivated and wild species to climate changes is highly challenging. Evolutionary genomic models enable the prediction of mal-adaptation (genomic offset - GO) across environments and future climates under the assumption that populations are currently locally adapted but do not predict but the resulting phenotypic changes. To do so, we developed a new genomic prediction model (GP) integrating both genomic offset (GO) and within-population gene diversity (Hs) to capture genotype by environment interaction and inbreeding effects, respectively (GP-HO-Hs). As proof of concept, we applied this GP-GO-Hs model to a collection of 397 maize populations (landraces) evaluated across 25 environments in Europe using high-throughput DNA pool genotyping. GP-GO-Hs model accurately predicted yield, plant height and flowering time. It increased by 13% the predictive abilities of GP model for predicting yield of new landraces in new environments. GP-GO-Hs model also predicted that the more diverse the landrace, the more stable its agronomic performance across environments. GP-GO-Hs model generated phenotypic adaptive landscapes for each landrace in future climatic scenarios, enabling the identification of landraces with enhanced potential to adapt to future or emerging cultivation conditions. This GP-GO-Hs model could be easily applied to other wild and cultivated species. Teaser Identify promising landrace adapted to new and future environments by combining genomic selection and offset

Why it matches plant phenotyping methodsゲノム情報と環境適応指標を統合した新規予測モデルを開発し、収量・草丈・開花期という植物形質を予測することが研究の中心であるため、計算的フェノタイピング手法として収録する。

abstractwe developed a new genomic prediction model (GP) integrating both genomic offset (GO) and within-population gene diversity (Hs)

CropHealthyNet: A Lightweight Hybrid Network for Efficient Crop Disease Detection →

Deploying high-precision deep learning models on resource-constrained edge devices remains a challenge for agricultural disease detection. This study introduces CropHealthyNet, a lightweight hybrid architecture optimized for both accuracy and computational efficiency. The architecture incorporates three key components: the ExGhostConv module, which integrates FReLU and SimAM attention for enhanced feature utilization; a Universal Position Encoding mechanism that adaptively captures spatial information to address variable lesion scales; and a MemoryEfficientTransformer employing chunked attention to mitigate global modeling memory overhead. Experiments on CDC, AGD_256, and CornLeafDisease datasets indicate that CropHealthyNet achieves a weighted average accuracy of 90.55% with 0.47 million parameters. The model outperforms several state-of-the-art lightweight architectures and achieves accuracy comparable to DenseNet121, with approximately 15 times fewer parameters. These results position CropHealthyNet as a viable solution for real-world deployment in resource-limited agricultural environments.

Why it matches plant phenotyping methods植物病害（病斑）を画像から検出する深層学習モデルの開発・評価が研究の中心であり、植物の病害状態を推定するフェノタイピング手法に該当する。

abstractThis study introduces CropHealthyNet, a lightweight hybrid architecture optimized for both accuracy and computational efficiency.

Dynamic Quantification and Prediction of Salt Tolerance Threshold in Summer Maize Under Different Regimes of Brackish Water Irrigation →

To investigate how different training modes of salt stress priming affect the dynamic variation of the salt tolerance threshold (STT) in summer maize, and to enable the accurate quantification and prediction of STT, a micro-plot experiment utilizing diverse regimes of brackish water irrigation was conducted. Utilizing physiological, shoot, and root indicators, a comprehensive evaluation framework was developed to define a dynamic salt tolerance coefficient (αSTT), enabling the precise quantification of STT across growth stages. Building on this, the study established a unified predictive framework to systematically evaluate the performance of diverse modeling pathways and machine learning algorithms. The results revealed a distinct two-stage stress response pattern of summer maize to salt stress, characterized by an initial physiological adaptation phase dominated by regulatory adjustments, followed by a phenotypic adaptation phase associated primarily with improvements in growth performance. Different training modes led to distinct salt stress memory effects by modulating the coordination between these two adaptive stages. Among all modes, the S1-2-3 training regime exhibited the most favorable adaptive outcome, with the αSTT gradually recovering to 1.0 during later growth stages, indicating full adaptation to saline stress, and concomitantly exhibiting a relatively high STT. Regarding predictive performance, the PCR-STP modeling pathway incorporating process constraints outperformed purely data-driven pathways, and its combination with CatBoost achieved the highest accuracy (R² = 0.910, RMSE = 0.241). Overall, our study elucidates the dynamic nature of salt tolerance in summer maize, while the proposed STT quantification and prediction method provides a scientific basis for refining salt stress modules in crop models, optimizing brackish water irrigation regimes, and improving precision water resource management in arid and semi-arid regions.

Why it matches plant phenotyping methods塩耐性閾値（STT）の動的定量・予測フレームワークと機械学習モデルの構築が研究の中心であり、植物の生理・生育状態を抽出する方法論的貢献が明確である。

abstracta comprehensive evaluation framework was developed to define a dynamic salt tolerance coefficient (αSTT), enabling the precise quantification of STT across growth stages.

STN-MobileNetV2: A Hybrid Lightweight Deep Learning Model for Maize Disease Classification →

Abstract Agriculture occupies an essential role because of the demand for food, and it is a crucial source of human income in many countries, especially in developing countries. In the ‎world, China is a large agricultural country and many people ‎rely on agricultural production for a ‎living. Maize is widely cultivated as a kind of the major dominant crop, while the diseases of ‎maize ‎not only influence the maize plantation but also the economic development. Severe maize diseases ‎may even result in no ‎harvest of grains. Thereupon, looking for an accurate, fast, automatic, and ‎low-cost approach to conduct maize disease recognition is ‎of great realistic importance. In this study, we put forward a novel image-based network architecture for maize disease identification. The proposed network integrates a Spatial Transformer Network (STN) with MobileNetV2, forming a hybrid architecture termed STN-MobileNetV2. This model leverages MobileNetV2’s pre-trained efficiency while enhancing spatial invariance through STN, enabling robust recognition of maize disease types.We also improved the Focal-Loss (FL) function to enable it to handle multi-class problems and keep more attention on minor lesion characteristics. When benchmarked against other state-of-the-art (SOTA) techniques, the presented approach exhibits superior efficacy. Specifically, it attains an average recognition accuracy of 88.00% and a specificity of 92.00% using the publicly available dataset.Even when eight disease types are considered, the proposed approach realizes a mean accuracy and specificity of 92.84% and95.85% on the ‎locally captured maize disease images. Results derived from the experiments demonstrate that the proposed model is highly effective in the identification of maize-related diseases.

Why it matches plant phenotyping methodsトウモロコシ病害の病斑画像から植物の病害状態を推定する画像ベース手法を提案・ベンチマークしており、分類モデルの開発が研究の中心である。

abstractIn this study, we put forward a novel image-based network architecture for maize disease identification.

Prediction of Total Anthocyanin Content in Single-Kernel Maize Using Spectral and Color Space Data Coupled with AutoML. →

The non-destructive and chemical-free determination of anthocyanin content in single maize kernels is of great importance for plant-breeding programs. Previous studies have mainly relied on Near-Infrared Reflectance (NIR) spectroscopy and color-based approaches, often using conventional or randomly selected modeling techniques. In this study, an Automated Machine Learning (AutoML) framework was employed to predict anthocyanin content using spectral and digital image data obtained from individual maize kernels measured in two orientations (embryo-up and embryo-down). Forty colored maize genotypes representing diverse phenotypic characteristics were analyzed. Digital images were acquired in RGB, HSV, and LAB color spaces, together with NIR spectral data, from a total of 200 kernels. Reference anthocyanin content was determined using a colorimetric method. Ten datasets were constructed by combining different color space and spectral features and were grouped according to kernel orientation. AutoML was used to evaluate nine machine learning algorithms, while Partial Least Squares Regression (PLSR) served as a classical benchmark method, resulting in the development of 1918 predictive models. Kernel orientation had a notable effect on model performance and outlier detection. The best predictions were obtained from the RGB dataset for embryo-up kernels and from the combined RGB+HSV+LAB+NIR dataset for embryo-down kernels. Overall, AutoML outperformed conventional modeling by automatically identifying optimal algorithms for specific data structures, demonstrating its potential as an efficient screening tool for anthocyanin content at the single-kernel level.

Why it matches plant phenotyping methods単粒トウモロコシの画像・NIRデータからアントシアニン含量を非破壊推定するAutoML手法を開発・比較しており、植物形質の取得・抽出が中心である。

abstractThe non-destructive and chemical-free determination of anthocyanin content in single maize kernels is of great importance for plant-breeding programs.

Photogrammetric software performance for estimating maize above-ground biomass using UAVs →