Visible-near infrared (Vis-NIR) spectroscopy provides rapid crop disease assessment; however, poor model generalizability remains a major limitation when models developed for a specific period are applied to batches collected at different times, primarily due to variations in physicochemical properties such as chlorophyll content, moisture level, surface texture, and tissue structure, which induce shifts in spectral distributions across batches. This study investigates deep domain adaptation to enhance cross-batch transferability for sugarcane disease classification. Two batches of healthy and symptomatic leaves were collected at different times using the same spectrometer. A customized one-dimensional convolutional neural network (1D-CNN) was trained on Batch 1 and adapted to Batch 2 using labelled samples through two strategies: retraining only the fully connected layers or fine-tuning all network parameters. Both strategies achieved 94% accuracy, with precision 0.92, sensitivity 0.97 and specificity 0.98, outperforming the non-adapted model and standard-free calibration transfer methods, namely Correlation Alignment and Transfer Component Analysis. These findings demonstrate that deep domain adaptation substantially improves the robustness and transferability of Vis-NIR classification models across heterogeneous sampling batches.
Why it matches plant phenotyping methodsサトウキビ葉の病徴分類を対象に、Vis-NIR分光と深層ドメイン適応によるモデルの開発・クロスバッチ検証が研究の中心であり、植物病害状態を直接推定している。
abstractThis study investigates deep domain adaptation to enhance cross-batch transferability for sugarcane disease classification.
Tolerance against winter freeze is the main focus of variety development in Louisiana, which represents the northernmost sugarcane-growing region worldwide. Antifreeze metabolites, xylem structure, and fiber content represent interrelated physicochemical properties contributing to freeze tolerance. This study first classified the cold tolerance of sugarcane cultivars using metabolites in juice as predictor variables. The best-fit model (XGBoost discriminant analysis) estimated the higher cold tolerance of the final on-station clone progeny to the stress tolerance-inducing wild germplasm line. Stalks of the tolerant sugarcane genotype contained higher fiber for mechanical support against cellular injury, compared to susceptible varieties. Fluorescence microscopy visualized phospholipids responsible for maintaining membrane fluidity during frost in lignin surrounding the vascular bundle. Thermal imaging is proposed for real-time monitoring of spatiotemporal temperature changes, as stalk injury is initiated by ice formation at sub-freeze temperatures during winter freeze. As additional datasets for independent prediction become available, developed methods could be used to explore the biomarkers for stress resistance in simpler multivariate discriminant analysis and the distribution of specific biomarkers in cellular components by microscopic imaging, and to trace stalk injury hot spots as a function of time and relationships with resistance markers.
Why it matches plant phenotyping methodsサトウキビの耐寒性という植物状態を、XGBoost判別モデルと画像・熱画像によって分類・評価する方法が研究の中心であり、単なる生物学的測定ではない。
titleMultivariate and imaging methods to classify cold tolerance of sugarcane ( Saccharum spp. hybrids) cultivars and breeding clones.
Sugarcane plant diseases pose a significant threat to agricultural productivity, yet early and accurate identification remains challenging for farmers due to the limitations of manual inspection. This study proposes a sugarcane leaf disease classification system using ConvNeXt V2 Tiny, a modern convolutional architecture with a Global Response Normalization (GRN) mechanism, combined with an ensemble Stratified K-Fold Cross Validation strategy (K=6) to improve generalization on real-world field data. A dataset of 2,948 leaf images spanning five classes (Red Rot, Mosaic, Rust, Yellow Leaf, and Healthy) was used, with field-collected images held out as a fixed test set. The ensemble model achieved a mean validation accuracy of 98.49% ± 0.58% across six folds and a test accuracy of 98.39% on 427 unseen field images, with macro-average precision, recall, and F1-score each reaching 98%. ConvNeXt V2 Tiny substantially outperformed ResNet-50 (87.35%) and EfficientNetV2-S (83.37%) under identical experimental settings, demonstrating superior generalization across the domain gap between curated and field data. The primary contribution of this study is the first application of ConvNeXt V2 Tiny with ensemble K-Fold strategy for sugarcane disease classification, offering high accuracy with moderate computational complexity (28.6M parameters) and practical deployability, as demonstrated through the SugarScan web application.
Why it matches plant phenotyping methodsサトウキビ葉画像から病害状態を推定する画像ベースの表現型解析手法が研究の中心であり、モデル性能の検証・比較も実施しているため含める。
abstractThis study proposes a sugarcane leaf disease classification system using ConvNeXt V2 Tiny
Reproduction assets foundThe paper's phenotyping inputs include a public Kaggle dataset (Sugarcane Leaf Disease Dataset, SLD) of sugarcane leaf disease images used for training/validation, plus field-collected images. Only the Kaggle dataset qualifies as a paper-specific public asset with an authors' URL; no author analysis code, trained modelDataset · publicsecondary data from the Sugarcane Leaf Disease Dataset (SLD)
available publicly on Kaggle (https://www.kaggle.com/datasets/pritpal2873/sug arcane-leaf-disease-dataset)Open asset ↗Kaggle · pritpal2873/sugpdf-page:2 lines:54-60Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The Florida sugarcane industry is transitioning from manual to mechanical planting systems that use comparatively smaller seedcane pieces (billets) as planting material. A major limitation of mechanical planting is the increased seedcane requirement owing to mechanical damage and the increased vulnerability of seedcane pieces to soilborne pathogens that cause sett rots, particularly pineapple sett rot caused by Thielaviopsis spp. Current sugarcane breeding programs in Florida screen for major diseases, such as rusts, smut, ratoon stunting, and viruses, early in the breeding process but not for pineapple sett rot. This study aimed to isolate and identify Thielaviopsis spp. in the Everglades Agricultural Area (EAA), develop a single-bud inoculation protocol for greenhouse-based disease screening, and phenotype the current widely grown sugarcane varieties in Florida against Thielaviopsis spp. The pathogen was confirmed as T. ethacetica , consistent with previous reports from the EAA. A reproducible inoculation method was established and validated through symptom assessment, pathogen reisolation, and molecular confirmation. Using this protocol, six widely grown Florida sugarcane varieties showed significantly reduced germination (by more than 50%) and reduced above- and belowground morphological characteristics under infection, indicating susceptibility. Varietal differences were observed, with CP 03-1912 showing the highest mortality percentage and reduced growth under T. ethacetica infection. These findings highlight the vulnerability of current varieties to pineapple sett rot, especially under mechanical planting systems where smaller seedcane pieces are used. Furthermore, the developed inoculation protocol provides a scalable tool for early stage evaluation of resistance in breeding programs, offering potential to accelerate the development of varieties better adapted to mechanical planting.
Why it matches plant phenotyping methodsサトウキビの病害抵抗性を評価するための単芽接種・症状評価プロトコルを開発し、再現性を検証した研究であり、植物病害表現型の取得法が中心である。
abstractdevelop a single-bud inoculation protocol for greenhouse-based disease screening
Proper nitrogen (N) management is essential for increasing the productivity of sugarcane (Saccharum spp.) and reducing the economic and environmental impacts associated with excessive fertilizer use. This study compared the performance of two portable spectroradiometers, FieldSpec 3 and HandHeld 2, in estimating foliar nitrogen content based on hyperspectral data in the visible and near-infrared regions, obtained throughout the crop cycle. The experiment was conducted in Piracicaba, São Paulo, Brazil, under four N rates: 0, 60, 120, and 180 kg ha−1. Spectral measurements were taken at the foliar and canopy levels at eight evaluation times, accompanied by laboratory determination of N content. Partial Least Squares Regression (PLSR) and Random Forest (RF) models were fitted using the spectral data and days after cutting (DAC), included as a categorical factor and evaluated using 10-fold internal cross-validation, based on the metrics R2, RMSE, MAE, and Willmott’s refined agreement index (dr). The foliar data performed better with PLSR (R2 = 0.727; RMSE = 1.381 g kg−1; MAE = 1.109; dr = 0.917) than canopy data (R2 = 0.591; RMSE = 1.489 g kg−1; MAE = 1.157; dr = 0.866). PLSR also outperformed RF at both acquisition levels. The green (~550 nm) and red edge (~740 nm) regions were the most relevant for N estimation. Under the evaluated conditions, model performance was associated with the spectral acquisition level and conditions, the instrumental configuration, and the modeling strategy employed.
Why it matches plant phenotyping methodsサトウキビ葉の窒素含量を分光計とPLSR/RFで推定し、取得レベル・機器・モデル性能を比較検証しており、形質取得手法が中心である。
abstractThis study compared the performance of two portable spectroradiometers, FieldSpec 3 and HandHeld 2, in estimating foliar nitrogen content based on hyperspectral data in the visible and near-infrared regions
- Enhancing agricultural productivity and attaining sustainable crop management depend on the early and precise identification of leaf disease. Using state-of-the-art technologies in precision agriculture like machine learning (ML) and image processing greatly increases the effectiveness of disease detection and facilitates well-informed decision-making. But conventional manual inspection techniques are still tedious, unpredictable, and prone to errors. In order to overcome these constraints, this research offers YOLOv12-CropNet, an innovative deep learning-based system for multi-crop leaf disease diagnosis in real time. The proposed YOLOv12-CropNet approach makes use of the Convolutional Block Attention Module (CBAM) for adaptive attention, the Content-Aware Reassembly of Features (CARAFE) up-sampling module to preserve fine-grained disease characteristics, the YOLOv12 architecture improved with Ghost Convolution for effective feature extraction, and Involution layers to capture spatially specific patterns. Inspection techniques are still laborious, arbitrary, and prone to mistakes. A substantial set of data of 38 classes of both healthy and sick leaves from a variety of crops, including tomato, potato, apple, grape, corn, mango and sugarcane, was put together for training and evaluation. Experimental results show that YOLOv12-CropNet finds a suitable balance between computational speed and accurate detection. Accuracy, F1-score, recall, and precision are important performance metrics that verify the model's resilience in challenging environmental and visual circumstances. The suggested technique provides a scalable and field-deployable way to assist effective identification of diseases and precision agricultural decision-making. The proposed YOLOv12-CropNet model exhibits better performance than the other evaluated models, attaining a 98.45% peak accuracy, 98.10% precision ,98.20 % sensitivity and a 98.18% F1 score, thereby highlighting its efficacy in multi-crop leaf disease detection.
Why it matches plant phenotyping methods複数作物の葉の病徴を画像から検出・分類する深層学習手法を開発し、データセットと性能評価を伴うため、植物病害状態のフェノタイピング手法が中心である。
abstractthis research offers YOLOv12-CropNet, an innovative deep learning-based system for multi-crop leaf disease diagnosis in real time.
Reproduction assets foundThe paper uses public Kaggle datasets as its phenotyping image inputs: the PlantVillage dataset (38 crop-disease classes) and the Sugarcane Leaf Disease dataset, both cited with explicit public URLs. No author code, models, or checkpoints are reported as publicly available.Dataset · public[37] PlantVillage Dataset. Available online: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-datasetOpen asset ↗pdf-page:22 lines:1-61Dataset · public[39] Sugarcane leaf Disease Dataset available online: https://www.kaggle.com/datasets/nirmalsankalana/sugarcane-Open asset ↗pdf-page:22 lines:1-61Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Context: Early identification of plant diseases plays a crucial role in enhancing crop productivity and promoting sustainable agricultural practices. Advances in artificial intelligence and web-based technologies have paved the way for smart systems capable of automatically diagnosing diseases in crops like rice and sugarcane. Objective: This research focuses on developing a smart plant disease diagnosis system that integrates deep learning techniques with a MERN (MongoDB, Express.js, React.js, Node.js) stack to provide accurate, real-time classification of rice and sugarcane leaves diseases through a user-friendly web interface. Method: The proposed framework employs a Convolutional Neural Network (CNN) built with PyTorch and trained using a carefully curated dataset of diseased rice and sugarcane leaf images. The developed model was incorporated into a web application built using the MERN stack to enable seamless frontend-backend communication and real-time disease prediction. The model’s effectiveness was assessed using evaluation metrics such as precision, recall, F1-score, and confusion matrix analysis. Results: The CNN model achieved high classification performance, with an average class accuracy of 95.92%, overall classification accuracy of 91.83%, average precision of 91.85%, average recall of 92.05%, and average F1-score of 91.86%. Confusion matrix analysis further validated the model’s efficiency in accurately recognizing rice and sugarcane leaves diseases. The integrated web platform demonstrated efficient and user-friendly real-time disease prediction capabilities. Conclusions: The developed AI-based plant disease detection system highlights the effectiveness of integrating deep learning techniques with modern web technologies to support scalable agricultural solutions. The system provides a practical solution for farmers and agronomists seeking early and accurate crop disease detection. Future enhancements may include multilingual support, mobile application integration, and agronomic advisory modules to further advance precision agriculture initiatives.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類するCNNモデルとリアルタイムWeb基盤の開発・評価が研究の中心であり、植物表現型取得手法に該当する。
abstractThis research focuses on developing a smart plant disease diagnosis system that integrates deep learning techniques with a MERN (MongoDB, Express.js, React.js, Node.js) stack to provide accurate, real-time classification of rice and sugarcane leaves diseases through a user-friendly web interface.
Abstract Accurate plot-level sugarcane yield forecasting is essential for optimizing agricultural management, resource allocation, and operational planning. Existing forecasting approaches are often limited by their inability to capture temporal crop dynamics and local biophysical variability, reducing their usefulness for real-time decision-making. To develop and evaluate a Machine Learning (ML)-based framework for short-term sugarcane yield forecasting at plot level using age-segmented Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 imagery, and to determine the earliest crop stage at which reliable yield predictions can be obtained. An integrated dataset was constructed by combining productivity records from 2,132 sugarcane plots across six harvest seasons (2016–17 to 2021–22) with NDVI time series derived from Sentinel-2 satellite imagery. NDVI observations were aggregated into phenology-based temporal intervals, from which statistical features were extracted. Ten ML regression algorithms were evaluated under two forecasting schemes: a global model trained with all observations and a sector-specific approach that developed localized models for individual production sectors. Model performance was assessed using RMSE and R² on an independent test set. The sector-specific approach outperformed the global model, achieving an RMSE of 12.48 TCH and an R² of 0.7840 on the independent test set, compared with an RMSE of 16.75 TCH and an R² of 0.5724 for the global model. Sparse Partial Least Squares (spls) and Support Vector Machines with Polynomial Kernel (svmPoly) were the most frequently selected algorithms. SHAP analysis revealed that Median NDVI was the dominant predictive feature, while the Elongation I stage was the most influential phenological period. Reliable forecasts were obtained from the fifth month of crop growth (RMSE = 14.13 TCH), and prediction accuracy improved progressively as the crop matured. The proposed framework also surpassed traditional expert estimations (RMSE = 15.47), providing earlier and more accurate yield forecasts. This study demonstrates that localized, sector-specific ML models combined with temporal NDVI dynamics can provide accurate and operationally useful plot-level sugarcane yield forecasts. The framework supports proactive agronomic management, improves planning and budgeting processes, and offers a scalable methodology for precision agriculture and sustainable sugarcane production systems.
Why it matches plant phenotyping methods圃場・区画レベルのサトウキビ収量という植物形質を、Sentinel-2 NDVI時系列と機械学習から推定する枠組みを開発・評価しており、予測手法が中心である。
abstractTo develop and evaluate a Machine Learning (ML)-based framework for short-term sugarcane yield forecasting at plot level using age-segmented Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 imagery
Yield estimation in sugarcane systems remains a major challenge in tropical regions due to the reliance on destructive, labor-intensive, and spatially limited field measurements. Although remote sensing has been widely used for crop monitoring, its predictive performance is often constrained when spectral information is used in isolation. This study proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions. A commercial field was monitored throughout the 2022–2023 growing season, and machine learning models, including random forest (RF), support vector machine (SVM), and multiple linear regression (MLR), were developed to estimate stem, foliage, and total biomass. To reduce potential spatial data leakage caused by spatial autocorrelation within the field, model performance was evaluated using Spatial Block Cross-Validation. Results showed that integrating spectral and meteorological data consistently improved predictive performance compared to spectral-only and weather-only scenarios. Spectral bands exhibited stronger relationships with biomass than derived vegetation indices, while maximum temperature and solar radiation were identified as key drivers of biomass variability. RF combined with spectral–weather fusion achieved the highest predictive performance, reaching R2 values up to 0.95, RMSE values as low as 5296.35, and rRMSE values close to 18% for stem biomass, consistently outperforming SVM and MLR. In contrast, spectral-only scenarios produced lower predictive accuracy and higher prediction errors across all biomass variables. This study provides one of the first field-scale implementations under humid tropical conditions in southeastern Mexico, where georeferenced yield data remain scarce.
Why it matches plant phenotyping methodsSentinel-2と気象データの融合および機械学習により、サトウキビの茎・葉・総バイオマス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 proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions.
Deficit irrigation (DI) is a crucial strategy for optimizing water use in arid and semi-arid agriculture, yet its success depends on accurately determining the crop yield response factor (K y ). This study introduces a novel, machine learning-assisted framework for large-scale estimation of sugarcane K y using satellite-derived water stress indicators. fused Landsat 7/8/9 and MODIS data within the Google Earth Engine platform to generate high-resolution daily Crop Water Stress Index (CWSI) maps for the 2023 season in southern Iran. The Random Forest (RF) algorithm was applied to correct biases in land surface temperature (LST), achieving high accuracy (RMSE < 1.0°C, nRMSE < 3%, rMBE ≈ 0%) before CWSI calculation. Ground measurements from 12 field points, including canopy temperature and yield, were used for calibration and validation. The satellite-based CWSI showed strong agreement with field data (RMSE = 0.05, nRMSE = 11%), with values ranging from 0.18 to 0.71 at dekadal scale. Using this CWSI, K y was computed at dekadal, monthly, and seasonal scales, revealing substantial spatiotemporal variability (0.2–1.63) and an average seasonal K y of 1.05. This value is lower than the FAO-66 default of 1.2, indicating that the standard coefficient may prompt over-irrigation without yield benefits. The analysis further identified early July as the period of peak water stress sensitivity, with K y values exceeding 1.82. This ML-enhanced, satellite-based approach provides a robust tool for deriving spatially explicit K y values, offering a significant advancement for precision irrigation planning and water resource management.
Why it matches plant phenotyping methods衛星データと機械学習で作物の水ストレス状態（CWSI）を推定し、地上測定で較正・検証する手法が中心であるため、植物生理状態のセンシング型フェノタイピングとして含める。
abstractThis study introduces a novel, machine learning-assisted framework for large-scale estimation of sugarcane K y using satellite-derived water stress indicators.
Sugarcane leaf diseases pose a serious threat to agricultural productivity, directly impacting food security and economic stability worldwide. Although deep learning has been widely applied to plant disease classification, lightweight models such as MobileNetV2 often struggle to achieve high accuracy. This study aims to optimize the MobileNetV2 model to classify sugarcane leaf diseases more accurately and efficiently. The dataset used consists of 4,800 images categorized into six classes: Bacterial Blight, Healthy, Mosaic, Red Rot, Rust, and Yellow. Unlike transfer learning, which relies on pre-trained MobileNetV2 weights, this study manually redesigns the model architecture to improve feature-extraction efficiency and overall performance. The optimization process includes fine-tuning techniques, dropout regularization, and adaptive learning rate adjustments to improve classification accuracy and inference speed. Experimental results indicate that the optimized model achieves an accuracy of 98.5%, representing a significant improvement over the transfer learning approach. The restructuring of MobileNetV2 layers has been proven to enhance the model’s ability to learn discriminative features more effectively. Moreover, the optimized model is computationally lightweight, making it suitable for real-time deployment on mobile-based systems without compromising accuracy. In the future, research can focus on improving the model’s generalization by utilizing a larger dataset with a more diverse range of disease categories. Additionally, performance comparisons with other model architectures can be conducted to identify solutions that are not only more accurate but also achieve faster, more efficient training times.
Why it matches plant phenotyping methodsサトウキビ葉の病徴を画像から分類する深層学習モデルの再設計・最適化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 aims to optimize the MobileNetV2 model to classify sugarcane leaf diseases more accurately and efficiently.
Sugarcane is one of the major crops cultivated in tropical and subtropical regions worldwide. Assessing crop maturity is important for optimizing harvest timing and improving yield. Conventional sugarcane maturity evaluations use agronomic characteristics, past trends, and eye inspections, which are labor-intensive and not precise, particularly over large plantations. Some sugarcane varieties mature to complete ripeness earlier than their expected maturity age, rendering physical observation inefficient and unsuitable. To address this point, this experimental research study introduces a novel, cost-effective approach using Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors to estimate sugarcane maturity through remote sensing and deep learning techniques. The primary objective is to develop an efficient deep learning-based classification system for identifying mature sugarcane fields from multispectral images gathered using UAVs. Pelwatte Lanka Sugar Company (Pvt) Ltd geo-referenced yield data were used together with multispectral imagery of 3–12-month-old plant-crop sugarcane fields from intermediate and dry regions. Fields were classed as ‘matured’ (Brix > 10) or ‘immatured’ (Brix ≤ 10) based on mean Brix values. Red, Red Edge, Green, Near-Infrared (NIR), and spectral bands and vegetation indices NDVI and NDRE were investigated. 17,256 images with a resolution of 200×200 pixels were utilized (2,876 for each band/index). The dataset was split between training and validation sets. Modeling was done in two phases: (1) comparison of the feature extractor and (2) constructing a specific Convolutional Neural Network (CNN). The proposed CNN achieved a maximum accuracy of 93% on NIR images, whereas Red, Green, Red Edge, NDVI, and NDRE achieved 84%, 81%, 76%, 69%, and 59% accuracy, respectively. The results indicated that the model can classify sugarcane maturity with a high level of accuracy, thus improving precision agriculture methods.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とCNNによりサトウキビの成熟状態（Brixに基づく）を推定する手法の開発・評価が中心であり、植物状態の取得・抽出方法に該当する。
abstractintroduces a novel, cost-effective approach using Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors to estimate sugarcane maturity through remote sensing and deep learning techniques
ABSTRACT - Sugarcane is one of the most important commercial crops worldwide but its productivity is greatly affected by diseases such as red rot, rust, mosaic, smut and yellow leaf disease. Conventional disease detection techniques are based on manual inspection which is a time-consuming, labor-intensive and error prone process. This paper gives a detailed review of the deep learning methods for the automated detection of sugarcane diseases with a special focus on the fusion methods of stem and leaf features. Different deep learning architectures such as CNN, VGG, ResNet, EfficientNet, DenseNet, MobileNet, and YOLO are analyzed and compared in terms of accuracy, efficiency, and deployment capability. The study also explores multimodal approaches, such as hyperspectral imaging, thermal imaging and environmental data integration, to enhance prediction performance. Reported results show that advanced models like EfficientNet-B7 and DenseNet201 achieve accuracies above 99%, while lightweight models like MobileNet allow for real-time mobile deployment. The review highlights significant research gaps such as small datasets, lack of stem-leaf fusion studies, no severity classification, and real-world deployment issues. Future research directions are related to explainable AI, multimodal fusion, lightweight edge computing models, and precision agriculture applications for sustainable sugarcane cultivation. Key Words: Sugarcane disease detection, Deep learning, CNN, Stem-leaf fusion, Computer vision, Precision agriculture.
Why it matches plant phenotyping methodsサトウキビ病害の画像・深層学習による検出手法を中心にレビューしており、植物の病態を観測・推定するフェノタイピング手法レビューに該当する。
abstractThis paper gives a detailed review of the deep learning methods for the automated detection of sugarcane diseases with a special focus on the fusion methods of stem and leaf features.
Plant disease is a serious threat to agricultural productivity and food security worldwide. Traditional diagnostic methods such as manual observation and laboratory testing are time-consuming, labor-intensive and error prone. The emergence of artificial intelligence (AI) and deep learning (DL) offer scalable solutions for precision agriculture in plant disease detection using advanced computational techniques to process large datasets. Hybrid deep learning architecture integrates Convolutional Neural Networks (CNNs) along with Artificial Neural Networks (ANNs) can leverage both visual and contextual data to improve detection performance. The hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics). The CNN module extracted spatial and textural features from plant images, while the ANN module processed environmental parameters. These outputs were fused into a unified feature vector for disease classification. A total of 15 plant species and their associated diseases were analyzed using 200-270 training samples and 150-190 testing samples for each disease across a total of 1000 images. The model was judged by metrics such as detection accuracy, AUC, sensitivity etc. Data augmentation, pre-trained architectures (e.g., ResNet50) and early stopping techniques were utilized to improvise model performance. The hybrid model saliently achieved detection accuracy consistently above 87% with majority of diseases surpassing 90%. Highperforming cases like Rice Blast (92.5%), Tomato Early Blight (93.8%), and Coffee Rust (93.0%), with AUC values of 0.93 or higher, sensitivity exceeding 94% and specifically above 90%. Diseases of Sugarcane Red Rot and Tea Blister Blight exhibited sensitivities of 92.4% and 92.1% and specificities of 91.1% and 90.5% respectively. Moderate accuracy for Coconut Bud Rot (87.5%) and Mustard Alternaria Blight (87.8%) was due to smaller training sample sizes.
Why it matches plant phenotyping methods植物画像から病害状態を分類するハイブリッドCNN-ANN手法を開発し、精度・AUC・感度などで評価しており、病害フェノタイピング手法が研究の中心である。
abstractThe hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics).
Reproduction assets foundThe paper's plant disease detection model was trained on public plant image datasets: the PlantSeg dataset (Zenodo record 13958858, DOI 10.5281/zenodo.13293891) and the UCI Machine Learning Repository Plants dataset. Both are cited in Materials and Methods as sources of the visual data used for the CNN module. No code,Dataset · publicebao (ZiranKexueBan)/Journal of
Huazhong University of Science and Technology
(Natural Science Edition). 2021;49(8).
37. Ma C, Mu X, Sha D. Multi-Layers Feature Fusion of
Convolutional Neural Network for Scene
Classification of Remote Sensing. IEEE Access.
2019;7.
38. Hämäläinen W.Plants Dataset[Internet]. 2024.
Available from:
https://archive.ics.uci.edu/dataset/180/plants
39. WeiT. PlantSeg: A Large-Scale In-the-wild Dataset
for Plant Disease Segmentation [Internet]. 2018.
Available from: https://zenodo.org/records/13958858Open asset ↗180pdf-raw-page:15 lines:1-48Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 May 2026Computers and Electronics in Agriculture.
Sugarcane is a high-value industrial crop vital for sugar and biofuel production, yet increasingly constrained by climate variability, biotic and abiotic stresses, soil degradation, and inefficient input use. Traditional breeding and crop management approaches are often slow, labour-intensive, and less precise, emphasizing the need for digital transformation in sugarcane agriculture. AI now offers powerful tools to accelerate genetic improvement, enhance stress resilience, and optimize resource-use efficiency. This review synthesizes recent advances in AI applications across the sugarcane improvement pipeline, including high-throughput phenotyping, genomic prediction, digital crop monitoring, and AI-driven decision-support systems. ML and DL models enable automated, accurate prediction of key traits such as biomass, canopy temperature, nitrogen status, and sugar recovery using UAV, satellite, and proximal sensing data. AI-powered genomic selection approaches leveraging convolutional networks, transformers, and attention mechanisms improve prediction accuracy for yield, ratooning ability, and stress tolerance by integrating SNPs, pedigree, and multi-environment datasets. Emerging innovations such as digital twins, multimodal data fusion, reinforcement learning-based irrigation scheduling, and climate-smart advisory models further strengthen real-time crop intelligence. The integration of blockchain-enabled breeding databases, FAIR data standards, and interoperable analytics pipelines supports scalable and collaborative research. Literature analysis reveals 15-30% gains in selection efficiency, >90% accuracy in disease detection, and phenotyping cost reductions of up to 70%. Key challenges remain, including scarce annotated datasets, genotype × environment complexity, model interpretability, and adoption barriers for smallholders. A future roadmap is proposed featuring multimodal foundation models, edge-AI deployment, and explainable breeder dashboards. AI is redefining sugarcane research from reactive to predictive, enabling climate-resilient, sustainable, and profitable production systems.
Why it matches plant phenotyping methodsサトウキビ育種におけるAI応用の総説であり、高スループット表現型解析、UAV・衛星・近接センシングによる形質推定を主要な対象として扱っているため、フェノタイピング手法レビューとして適格。
abstractThis review synthesizes recent advances in AI applications across the sugarcane improvement pipeline, including high-throughput phenotyping, genomic prediction, digital crop monitoring, and AI-driven decision-support systems.
Disease prevention and water management are important to all the crops, particularly rice and sugarcane production in India. The article proposes a reinforcement learning (RL) based intelligent irrigation management system that is capable of optimising water consumption and crop nutrition in response to the changing agricultural climatic conditions. Decentralised reinforcement learning (RL) is used in a network of irrigation agents that utilise soil and microclimate sensor networks to set the terms of water allocation, water use efficiency (WUE) and crop health. At the same time, deep convolutional networks can be used to differentiate between plant stress/disease and leaf images and take applicable proactive actions. It is a framework that incorporates satellite-derived indices (NDVI, EVI, land surface temperature) with local sensor measurements and image-based health measurements through multimodal deep learning. Far-reaching simulations (including Indian climate and crop calendars) demonstrate that the multi-agent system lowers water consumption and preserves the yields and properly notifies stressed plants. The scores of disease detection with plantvillage-based fine-tuned on rice (120 (3 disease types) and 3829 (5 disease types) and sugarcane (2569 images for all disease types, Convolutional Neural Network (CNN) yield results of >98 % accuracy. Crop mapping (rice/sugarcane) Satellite/LSTM-based crop mapping (with Sentinel-1 / Sentinel-2) achieves more than 97 % accuracy. The suggested structure provides a data-driven, scalable system for precision agriculture to enhance the management of irrigation periods and crop health. Simulation experiments show that the RL-based controller can reduce water consumption while preserving optimal soil moisture levels when compared to rule-based irrigation strategies.
Why it matches plant phenotyping methods画像・衛星・センサーを統合して植物ストレス／病害状態を推定するマルチモーダル基盤が提案され、病害検出性能も評価されているため、植物表現型推定が実質的な構成要素である。
abstractdeep convolutional networks can be used to differentiate between plant stress/disease and leaf images
Reproduction assets foundThe paper reports simulation-based experiments using public leaf-image datasets. The only paper-specific public asset explicitly identified is the Kaggle rice leaf diseases dataset (vbookshelf/rice-leaf-diseases) cited as a data source for the rice disease fine-tuning set. No authors' code, trained models, or data dépDataset · publicConflict of interest: Authors do not have any conflict of interest 2026 Mar 31). Available from: https://www.kaggle.com/datasets/Open asset ↗Kagglepdf-page:16 lines:1-58Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Purpose The primary objective of this study was to evaluate the potential of high spatial resolution Jilin-1 satellite imagery for plot-level prediction of sugarcane yield, millable stalk population, and Brix, and to assess whether integrating spectral, texture, soil, and fertilization information could improve prediction performance for precision sugarcane management. Methods Jilin-1 satellite imagery acquired at four growth stages, from seedling to maturity, was used to derive vegetation indices (VIs) and texture indices (TIs), including the normalized difference texture index (NDTI), enhanced vegetation texture index (EVTI), and double-difference ratio texture index (DDRTI). Soil chemical properties (SCPs) and fertilization information (FI) were further incorporated with the remotely sensed variables. Machine learning models were developed for plot-level prediction of sugarcane traits across plant cane and first ratoon cane, and texture window size was optimized to improve TI extraction and model performance. Results For yield, the combination of VIs and TIs outperformed VIs alone at the tillering stage (R 2 CV = 0.65, RMSECV = 15.06 t/ha, RPDCV = 1.68). Adding SCPs and FI further improved yield prediction across plant cane and first ratoon cane (R 2 CV = 0.70, RMSECV = 13.84 t/ha, RPDCV = 1.83). Millable stalk population was best predicted at the maturation stage by VIs and Tis, achieving the best performance (R 2 CV = 0.63, RMSECV = 6602 stalks/ha, RPDCV = 1.66). The best Brix model integrated VIs, TIs, SCPs, and FI at the maturation stage (R 2 CV = 0.44, RMSECV = 0.53 °Bx, RPDCV = 1.33). SHAP analysis identified VIs as the dominant features for sugarcane traits prediction. And, DDRTI contributed more than NDTI and EVTI in yield and Brix prediction. Conclusion It is concluded that integrating spectral, texture, soil, and fertilization information from high spatial resolution Jilin-1 imagery is a promising approach for improving plot-level prediction of key sugarcane traits.
Why it matches plant phenotyping methods衛星画像からサトウキビの収量、可販茎数、Brixを plot レベルで推定し、テクスチャ特徴抽出の最適化と機械学習性能評価を行っており、表現型取得・推定手法が中心である。
abstractThe primary objective of this study was to evaluate the potential of high spatial resolution Jilin-1 satellite imagery for plot-level prediction of sugarcane yield, millable stalk population, and Brix
Reproduction assets foundThe paper's data availability statement explicitly links a public GitHub repository containing part of the authors' model-training code and test data for the sugarcane trait prediction analysis. No public phenotype dataset or imagery deposit is stated; additional data are only available on request.Code · publicPart of the code and test data for model training are available at https://github.com/guangtaoxu08-dev/SPT_JL .Open asset ↗guangtaoxu08-dev/SPT_JLlines:228-248Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published21 Apr 2026International Scientific Journal of Engineering and ManagementCited by 0 · OpenAlex ↗
Abstract— Sugarcane cultivation faces significant challenges due to biotic stresses such as fungal and viral diseases, as well as abiotic factors including climate variability and soil nutrient imbalance. Accurate disease diagnosis and yield estimation are critical for improving productivity and ensuring sustainable agricultural practices. This study proposes an integrated machine learning framework for automated sugarcane disease classification and yield prediction.A Convolutional Neural Network (CNN)-based deep learning model is implemented for image-based disease detection, enabling automatic feature extraction and high-accuracy classification of major sugarcane diseases. For yield prediction, supervised regression algorithms including Random Forest Regressor and Gradient Boosting are employed to model the relationship between environmental parameters, soil properties, and historical yield data.Experimental evaluation demonstrates improved predictive performance in both classification and regression tasks. The proposed system provides a data-driven decision support tool for farmers, enhancing crop management efficiency and reducing agricultural losses. Keywords: Sugarcane, Disease Prediction, Yield Prediction, Machine Learning, Convolutional Neural Network (CNN), Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), Precision Agriculture, Crop Monitoring.
Why it matches plant phenotyping methodsCNNによるサトウキビ病害の画像ベース分類は、植物の病徴・状態を観測から推定する中心的手法であり、方法適用として収録対象です。収量予測も含まれますが、環境・土壌・履歴データに基づく農業予測で、病害画像分類部分が主な適格要素です。
abstractThis study proposes an integrated machine learning framework for automated sugarcane disease classification and yield prediction.
Accurate identification of plant leaf diseases is essential for modern agriculture. This paper presents SynerFANet, a hybrid deep learning framework designed to improve disease detection in sugarcane leaves. SynerFANet integrates two core modules: AdaptiveMBNet and WiseAttentionNet for comprehensive feature extraction and processing. AdaptiveMBNet combines MBConv layers with attention mechanisms to improve feature quality and reduce computation, enabling more accurate disease detection. WiseAttentionNet incorporates attention mechanisms into depthwise and expansion layers to enhance feature recalibration. The combination of the two cores inside SynerFANet can improve the representation capacity and robustness of the overall model. We evaluate the model using two datasets: a new proposed field-collected SugarLeaf-IDN dataset and the publicly available PlantVillage dataset. SynerFANet achieves superior accuracy with a moderate parameter size and GFLOPs, providing a balanced trade-off between predictive performance and computational cost, and exhibiting stable convergence during training. SynerFANet achieves 95.81% validation accuracy on our challenging real-world SugarLeaf-IDN dataset and 99.85% (SOTA) on the controlled PlantVillage benchmark.
Why it matches plant phenotyping methodsサトウキビ葉の病徴を画像から検出する深層学習モデルを開発し、実フィールドおよび公開データセットで性能評価しており、植物表現型取得・判定手法が中心である。
abstractThis paper presents SynerFANet, a hybrid deep learning framework designed to improve disease detection in sugarcane leaves.
Purpose/significance Sugarcane is a vital global crop, critical for sugar and energy production. The accurate and timely identification of its leaf diseases is paramount for sustaining the health and stability of the sugarcane industry. While deep learning models offer promising solutions, their deployment on mobile or edge devices is often hindered by substantial model size and high computational demands. Conversely, existing lightweight models frequently compromise on feature extraction capabilities and recognition accuracy. To bridge this gap, this study develops an architecturally improved lightweight model designed to achieve both high accuracy and computational efficiency. Methods We propose the ReMA-MobileViT model, which significantly enhances feature representation by incorporating a newly designed Residual Multi-head Attention (ReMA) module. This module ingeniously leverages a multi-head attention mechanism to capture richer contextual information from diverse subspaces, while its residual connection structure effectively mitigates network degradation and facilitates robust gradient flow. The proposed model underwent rigorous training and evaluation on a comprehensive Mendeley Data repository for classification tasks. Results Experimental evaluations demonstrate that the ReMA-MobileViT model achieves an outstanding classification accuracy of 99.02% on the sugarcane leaf disease dataset, substantially surpassing existing state-of-the-art methods. An ablation study confirms the module's efficacy, showing that the ReMA-MobileViT model, integrated with the ReMA module, improved accuracy, recall, and F1-Score by 1.58, 1.76, and 1.58 percentage points, respectively, over the baseline MobileViT. Comparative analyses further illustrate ReMA-MobileViT's superior overall performance; it exceeds classic lightweight MobileNetV2 by 15.77 percentage points and the mainstream Vision Transformer by 2.96 percentage points in accuracy. Critically, ReMA-MobileViT achieves this with significantly fewer model parameters and reduced computational complexity compared to Vision Transformer, establishing a superior balance between accuracy and efficiency. Conclusion The proposed ReMA-MobileViT model offers an effective and lightweight solution for improving sugarcane leaf disease recognition accuracy, particularly in challenging complex backgrounds. Its ability to balance high accuracy with computational efficiency presents a promising technical avenue and a deployable solution for high-precision crop disease diagnosis systems on resource-constrained mobile or edge platforms.
Why it matches plant phenotyping methodsサトウキビ葉の病害状態を画像から認識する軽量深層学習モデルを開発し、精度・計算量・アブレーションを評価しており、植物表現型取得・判定手法が中心である。
abstractWe propose the ReMA-MobileViT model, which significantly enhances feature representation by incorporating a newly designed Residual Multi-head Attention (ReMA) module.
Reproduction assets foundThe paper's sugarcane leaf disease image dataset (2022 Sugarcane Leaf Disease Dataset, Thite et al.) is publicly available on Mendeley Data and directly constitutes the image inputs used for the paper's disease recognition experiments. No author analysis code or trained model checkpoints are reported.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/9424skmnrk/1 .Open asset ↗9424skmnrklines:757-778Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Remotely Piloted Aircraft (RPAs) equipped with multispectral sensors have emerged as promising tools for estimating foliar nitrogen content (FNC). In this context, this study applied a methodological approach aimed at simulating UAV multispectral data using hyperspectral leaf data obtained in a controlled environment, with the objective of evaluating its predictive potential and its transferability to field data collected by UAVs for FNN estimation. To this end, spectral bands and indices equivalent to those of UAV-mounted sensors were simulated based on hyperspectral data acquired by a benchtop sensor, and subsequently used in modeling via Partial Least Squares Regres-sion (PLSR) and Random Forest (RF). The results showed similar performance across the levels, with R² values of 0.75 and 0.76 for PLSR and RF on the UAV data, and 0.75 and 0.74 for PLSR and RF on the simulated data, respectively. The RF model also performed well in cross-domain validation, with R² = 0.70 when calibrated with simulated data and ap-plied to UAV data. Furthermore, the simulated data maintained high predictive power even with a reduced sample size. It is concluded that spectral simulation constitutes a viable strategy for expanding the applicability of nutritional monitoring using multi-spectral sensors.
Why it matches plant phenotyping methodsUAV・ハイパースペクトルデータの統合とスペクトルシミュレーションにより、葉面窒素含量を推定する手法を開発・交差検証しており、植物形質取得が研究の中心である。
abstractthis study applied a methodological approach aimed at simulating UAV multispectral data using hyperspectral leaf data obtained in a controlled environment
Abstract Sugarcane ( Saccharum spp.), a C4 plant, is a vital renewable biofuel and sugar source for industries worldwide. However, synchronizing flowering between parental lines often poses challenges for breeders, hindering effective crossbreeding efforts. This study aimed to develop a high‐throughput phenotyping (HTP) strategy to evaluate flowering‐related traits using vegetation indices (VIs) and other metrics alongside artificial intelligence (AI)‐based prediction methods. A total of 154 genotypes were planted in an augmented block design at the IAC sugarcane breeding station in Serra Grande‐BA, Brazil. Raw RGB (Red, Green, Blue) images were captured using a DJI Mavic 3 Enterprise drone during the plant cane (PC) and first ratoon (FR) crop seasons. These images were processed to create orthomosaics and compute metrics/vegetation index; subsequently, machine learning (ML) and deep learning pipelines for systematic analysis were developed. A convolutional neural network (CNN) model achieved promising results, with an accuracy rate of up to 84% in the flowering detection task. Additionally, flower counts from the CNN model showed a moderate correlation with field data, evidenced by an R 2 value of 0.72 at the onset and an R 2 value of 0.29 at the conclusion of the flowering season for the PC. This resulted in an overall average regression R 2 of 0.46 with a root mean square error (RMSE) of 13.80. Furthermore, an artificial neural network classification model reached a notable accuracy of 0.87 in differentiating genotypes based on their flowering response (early‐flowering vs. late‐flowering), utilizing VIs and digital model‐based metrics as input parameters. The ML regression model demonstrated performance levels of R 2 = 0.51 and RMSE = 8.06 for days to flag leaf emergence in PC and R 2 = 0.52 and RMSE = 7.93 for days to flowering in FR. These results highlight the potential of HTP strategies, utilizing orthomosaics and AI, to accelerate data collection and analysis, offering significant insights for breeding programs in sugarcane.
Why it matches plant phenotyping methodsドローンRGB画像、オルソモザイク、植生指数、AIを組み合わせた開花形質のハイスループット取得・予測手法を開発し、精度検証まで実施しており、フェノタイピング手法が研究の中心である。
abstractThis study aimed to develop a high‐throughput phenotyping (HTP) strategy to evaluate flowering‐related traits using vegetation indices (VIs) and other metrics alongside artificial intelligence (AI)‐based prediction methods.
Sugarcane diseases significantly reduce crop yield and quality, posing persistent challenges to the agricultural sector. This study presents a novel ensemble framework that integrates Vision Transformer and Swin Transformer architectures for accurate sugarcane leaf disease detection. By combining global self-attention with localized window-based attention mechanisms, the proposed model effectively captures multi-scale visual features associated with diverse disease symptoms. Experimental evaluation on a large, labeled sugarcane leaf dataset achieved a validation accuracy of 98.16% and a test accuracy of 97.06%, outperforming several convolutional neural network baselines. Additionally, a large language model (LLM) interface is employed as a post-prediction decision-support module, generating disease-specific descriptions and management suggestions based solely on the predicted disease class. This integrated framework indicates the potential effectiveness of transformer-based ensemble models combined with intelligent advisory support for practical decision-making in precision agriculture.
Why it matches plant phenotyping methodsサトウキビ葉の病徴を画像から分類するTransformerベースの手法開発と性能評価が中心であり、植物病害状態のフェノタイピングに該当する。
abstractThis study presents a novel ensemble framework that integrates Vision Transformer and Swin Transformer architectures for accurate sugarcane leaf disease detection.
Reproduction assets foundThe paper's sugarcane leaf disease image dataset (19,926 images, six classes) is explicitly stated to be publicly available on Kaggle; no author code or model checkpoints are shared.Dataset · publicThe Sugarcane Plant Diseases Dataset used in this study is publicly available on Kaggle at: https://www.kaggle.com/datasets/akilesh253/sugarcane-plant-diseases-dataset . The dataset is released for academic research and benchmarking purposes.Open asset ↗Kagglelines:112-131Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Where crop health is essential to global food security. Our focus is on early crop disease detection in the field of agriculture, especially Rice and Sugar cane leaf disease. This prompts researchers to consider quick, automated, cost-effective, precise, and efficient methods of identifying the kinds of diseases utilizing contemporary technologies like image processing, artificial intelligence (AI), and Explainable Artificial Intelligence (XAI). This paper proposes an framework to detect pest infestation for rice and Sugar cane cultivation and suggests an effective framework for rice and Sugar cane disease detection and forecasting that uses image processing to standard, resizing, and normalization rice and Sugar cane images then, using feature extractor using CNN after that we using few-shot learning (FSL) techniques such as like Prototypical Networks and Model-Agnostic Meta-Learning (MAML) learning techniques for superior decision-making in smart farming systems. The experimental findings demonstrated the Accuracy and specificity of the suggested framework in identifying and effectively predicting the kind of disease. According to the results, the suggested framework outperformed the state-of-the-art benchmark algorithms in disease prediction while producing results that were plausible. With Prototypical Networks and MAML for rice leaf disease datasets, it increased by up to 97.6% and 95.27%, respectively. For effective rice disease identification, Prototypical Networks and MAML for Sugar cane leaf disease datasets increased by up to 91.68% and 90.27%, respectively. Interpretable AI-driven insights were further made possible by the combination of proposed system with Grad-CAM Explanation, which improved decision-making transparency.
Why it matches plant phenotyping methodsイネとサトウキビの葉画像から病害状態を推定する画像解析・深層学習フレームワークが研究の中心であり、植物病害フェノタイピング手法の開発・評価に該当する。
abstractThis paper proposes an framework to detect pest infestation for rice and Sugar cane cultivation and suggests an effective framework for rice and Sugar cane disease detection and forecasting that uses image processing
Water stress significantly threatens sugarcane production, particularly among smallholder farmers in South Africa, where spatially explicit assessments remain limited. This study aimed to improve the quantification of crop water stress by developing a machine learning (ML) model to predict the Normalised Difference Water Index (NDWI), a proxy for vegetation water content. An ML approach was adopted to capture complex, non-linear relationships between structural vegetation indices (SVIs) and NDWI. Sentinel-2 satellite data and UAV-acquired multispectral imagery were integrated, with the model trained using satellite-derived SVIs and NDWI, and then applied to UAV-derived SVIs to predict NDWI. The model achieved high predictive accuracy (R² = 0.95, RMSE = 0.03, MAE = 0.02) and effectively captured temporal variations in sugarcane water status, including post-rainfall stress recovery and increased water retention during early maturation—aligning with changes in leaf area index (LAI), chlorophyll content (CC), and Total Soil Water Profile (TSWP). NDWI also showed a positive correlation with actual evapotranspiration (ET a ; R² = 0.60) and a negative correlation with the Water Deficit Index (WDI; R² = 0.62), suggesting its potential to reflect crop water status under certain conditions. When interpreted in conjunction with in situ measurements of precipitation, TSWP, and WDI, the predicted NDWI provides valuable insights into crop water dynamics. This approach demonstrates the potential of ML-driven NDWI estimation to support site-specific irrigation scheduling, enhance resource use efficiency, and promote sustainable sugarcane cultivation. The findings contribute to climate-resilient water management practices tailored to the needs of smallholder systems in water-scarce regions. • Machine learning predicts multispectral UAV-derived NDWI using Sentinel-2 vegetation indices. • Predicted NDWI aligns with trends in soil water status, evapotranspiration and water deficit dynamics. • NDWI correlates positively with ET a and negatively with WDI, reflecting crop water stress levels. • These relationships capture shifts in crop water dynamics under varying environmental and meteorological conditions. • Model outputs can inform timely, site-specific irrigation strategies in rainfed sugarcane production systems.
Why it matches plant phenotyping methodsUAV・衛星マルチスペクトル画像からNDWIを推定し、作物の水ストレス状態を定量化する機械学習手法の開発と精度評価が中心であるため、植物フェノタイピング手法として含める。
abstractThis study aimed to improve the quantification of crop water stress by developing a machine learning (ML) model to predict the Normalised Difference Water Index (NDWI), a proxy for vegetation water content.
Sugarcane farming demands precise irrigation and vigilant health monitoring to maximize productivity, yet conventional approaches often fall short in efficiency and scalability. This paper introduces a self-sustaining IoT framework that leverages a wireless sensor network to track critical indicators—such as soil moisture, plant temperature, environmental conditions, groundwater levels, and crop height—in real time. Data is processed locally and relayed to a cloud server, enabling automated irrigation decisions informed by the Crop Water Stress Index (CWSI) and growth tracking through advanced image analysis. The system achieved a soil moisture measurement accuracy with a strong correlation (R² = 0.96) to gravimetric methods and a plant height measurement accuracy with a mean absolute error of 1.8 cm. Designed for energy independence, the system operates seamlessly in off-grid environments. Field results demonstrate key findings: 98.7% data transmission reliability, early stress detection 24-48 hours before visible symptoms, 15% water savings through precision irrigation, and continuous operation for 180+ days on battery backup. These outcomes position this solution as a practical advancement for modern, sustainable sugarcane cultivation.
Why it matches plant phenotyping methods植物の健康状態・温度・草丈をセンサーと画像解析で取得し、精度検証まで行うIoTフェノタイピング基盤が研究の中心であるため。
abstractThis paper introduces a self-sustaining IoT framework that leverages a wireless sensor network to track critical indicators—such as soil moisture, plant temperature, environmental conditions, groundwater levels, and crop height—in real time.
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.
• Proposes a UAV remote sensing framework for ratoon sugarcane seedling counting. • Achieves precise segmentation of canopy using unsupervised learning methods. • Integrates multiple feature methods and models; KBest-F with GBR excels (R 2 = 0.7641). • Reveals the core feature contribution mechanism based on SHAP analysis. • Compares deep learning methods and proves the reliability of the work. Accurate and efficient monitoring of seedling emergence is critical for early-stage crop management and yield forecasting in sugarcane production. To meet this practical demand for precise field phenotyping, this study developed a high-throughput phenotyping framework leveraging unmanned aerial vehicle (UAV) remote sensing data and machine learning. This framework addresses the critical agricultural challenges of inefficient manual counting and the need for plot-scale monitoring in sugarcane production by enabling high-throughput sugarcane seedling number prediction through the integration of UAV-acquired RGB and multispectral imagery. Specifically, the sugarcane canopy was accurately segmented from the background using K-means clustering, a step that enabled the extraction of canopy area and the generation of a mask for obtaining canopy-level average features (including vegetation indices and texture features). These features together form a comprehensive feature set. Subsequently, six different feature selection methods were used to optimize the feature set, and eight machine learning models were combined for training and evaluation. The results showed that the combination of Gradient Boosting Regression (GBR) and KBest-F feature selection method yielded the optimal prediction performance, with a coefficient of determination (R 2 ) of 0.7641, a root mean square error (RMSE) of 19.42, and a mean absolute error (MAE) of 15.93. Further analysis identified canopy area, the Normalized Difference Red Edge Index (NDRE), red edge contrast, and green entropy as core predictive features. They collectively contribute over 60% of total feature importance, and their synergistic effects support accurate seedling number estimation. This framework offers an efficient, scalable tool for plot-scale seedling monitoring, with substantial potential for precision field management of high-density crops.
Why it matches plant phenotyping methodsUAV画像、キャノピー segmentation、特徴抽出、機械学習を統合し、サトウキビ苗数という植物状態を圃場スケールで推定する高スループット表現型計測フレームワークが研究の中心である。
abstractthis study developed a high-throughput phenotyping framework leveraging unmanned aerial vehicle (UAV) remote sensing data and machine learning
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Abstract The safety of the global food supply depends heavily on effective crop management, making early diagnosis of plant diseases vital for improving agricultural productivity. This proposal outlines the development of an intelligent irrigation system that utilizes machine learning and the Internet of Things (IoT) for the early detection of sugarcane leaf diseases and assessment of their impact on crop yield. The system gathers and analyzes data on soil temperature, humidity, and leaf characteristics—specifically changes in texture and color—using high-resolution photography from unmanned aerial vehicles (UAVs) and IoT-connected sensors. To enhance feature extraction and classification, the system employs a non-linear growing self-organizing map (NG-SOM) embedded within the hidden layers of an artificial neural network (ANN). This advanced model effectively identifies complex patterns in the collected data. Compared to traditional classification methods, this approach achieves a sugarcane disease detection accuracy of 95.6% and reduces false positives by 18.3%. It has been tested on multiple disease types, including red rot, smut, and rust. Additionally, the integration of early diagnosis with intelligent irrigation shows a strong correlation with optimized crop production. Predictive modeling of disease progression based on early detection improves output projections by 22.4%, demonstrating the system’s value in precision agriculture. By merging UAV imaging, sensor-based monitoring, and advanced machine learning, this approach offers a promising solution for proactive crop disease management and sustainable yield enhancement in sugarcane farming.
Why it matches plant phenotyping methodsUAV画像、IoTセンサー、機械学習を統合し、サトウキビ葉の色・テクスチャから病害状態を検出する方法の開発と評価が中心である。
abstractThe system gathers and analyzes data on soil temperature, humidity, and leaf characteristics—specifically changes in texture and color—using high-resolution photography from unmanned aerial vehicles (UAVs) and IoT-connected sensors.
Abstract Rapid and non-destructive monitoring of leaf nitrogen (N) content (LNC) is essential for precision N management in sugarcane ( Saccharum officinarum L .). However, the accuracy of hyperspectral estimation is challenged by the dynamic interactions among cultivar, growth stage, and leaf position. This study systematically investigated the effects of these three factors on LNC and leaf hyperspectral reflectance (400–1000 nm) across six main sugarcane varieties. We identified sensitive spectral bands and developed LNC inversion models using Partial Least Squares Regression (PLSR) and Random Forest (RF). The results revealed highly significant interactive effects (P Context: Although previous studies has resulted in substantial knowledge on crop N monitoring via spectroscopy, systematic investigations into the "leaf N content–spectral characteristics" response mechanisms in sugarcane at the leaf level remain limited. Aims: This study was designed to address these critical research gaps. The specific objectives were to: (1) quantify the independent and interactive effects of cultivar, growth stage, and leaf position on sugarcane LNC and spectral reflectance; (2) identify the most sensitive spectral bands responsive to LNC changes across different varieties; and (3) construct and evaluate robust estimation models for sugarcane LNC using advanced machine learning algorithms. Our findings are expected to provide a solid theoretical foundation and technical support for developing remote sensing technologies tailored for precision N management in sugarcane fields.
Why it matches plant phenotyping methodsサトウキビ葉の窒素含量という植物形質をハイパースペクトル反射から推定するモデルを開発・評価しており、表現型取得手法が研究の中心である。
abstractRapid and non-destructive monitoring of leaf nitrogen (N) content (LNC) is essential for precision N management in sugarcane
Sugarcane is an important economic crop, and key phenotypic traits such as plant height and leaf area play a crucial role in yield potential assessment and breeding selection. However, the quantification of these traits currently relies mainly on inefficient and destructive manual measurements, making it difficult to achieve continuous monitoring of plant growth. To address this limitation, this study integrates a YOLOv8x-seg instance segmentation model with 3D Gaussian Splatting (3DGS) and proposes a non-contact, high-precision 3D phenotyping method based on low-cost data acquisition using a smartphone. Multi-view RGB images are first processed using YOLOv8x-seg to extract plant foreground masks, which are then used as inputs for 3DGS-based reconstruction to generate 3D models. Plant height is automatically measured from the reconstructed models, while leaf area extraction involves a semi-automatic workflow combining image processing and manual steps. Experimental results demonstrate that the proposed approach enables accurate trait estimation, achieving a coefficient of determination (R2) of 0.9644 for plant height estimation (evaluated on a subset of 15 plants, with a mean absolute percentage error of approximately 1.5%) and an R2 of 0.8551 for leaf area estimation (validated on 10 plants). Ground-truth plant height was measured using a telescopic measuring rod, and leaf area was determined through destructive measurement with a leaf area meter (LI-COR Model LI-3000A). Ground-truth plant height values were obtained using a telescopic measuring rod, and leaf area was determined through destructive measurement with a leaf area meter (LI-COR Model LI-3000A). This method demonstrates the feasibility of using consumer-grade devices for high-fidelity 3D phenotyping and offers an effective approach for high-throughput sugarcane breeding applications.
Why it matches plant phenotyping methodsスマートフォン画像、インスタンスセグメンテーション、3D再構成を統合し、サトウキビの草高・葉面積を自動／半自動推定するフェノタイピング手法を開発・検証しており、方法が研究の中心である。
abstractproposes a non-contact, high-precision 3D phenotyping method based on low-cost data acquisition using a smartphone
ABSTRACT Stomata are microscopic pores that play a vital role in transpiration and gaseous exchange from leaf surfaces in plants. The stomatal density and size directly influence photosynthesis and hydrodynamics capacity. Conventional approaches for counting and determining stomatal density is labour-intensive and lack scalability. Although there are several AI-based stomata finder tools that were published in the last decade, existing models were trained on model plants like wheat, barley and Arabidopsis . Stomata in such model plants are generally elliptical, but applying a universal model to all plant species is not feasible due to their diverse morphological characteristics. Previous studies have suggested using the stomatal index to quantify the ratio between epidermal cells and total stomatal count. However, this approach can be difficult to apply consistently, as epidermal cell shape and size vary across plant species. Instead, we propose measuring stomatal density based on the number of stomata per total imaged pixel area in the captured images. In this study, a comparison between YOLOv12 and RF-DETR models were made for real-time stomata detection in normal and difficult-to-image and out-of-focus occluded images. The in-house training dataset consisted of images of 300 rice,100 barley and 50 sugarcane leaves that were captured against a dark background. YOLOv12 outperformed RF-DETR with higher mAP50:95 score. The models were trained with image augmentation for 300 epochs and YOLOv12 achieved a peak mean average precision of 98.5% and exceled at detecting stomata across abaxial and adaxial surfaces of leaves of both monocot and dicot plants. StomaQuant has also been shown to be effective for both epidermal peel and ethanol decolorised samples. Thus, StomaQuant can be used to effectively and efficiently estimate the stomatal density and size in a wide range of host plant species.
Why it matches plant phenotyping methods気孔の検出・密度・サイズ推定を目的とする深層学習画像解析手法を開発し、複数モデルおよび困難画像で性能比較・検証しており、植物表現型取得が研究の中心である。
titleStomaQuant: Deep Learning-Based Quantification for Stomatal Trait Assessment
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Introduction Plant diseases and weeds are among the leading biological threats to global crop production. While deep learning has advanced automated analysis, existing approaches often fail under challenges like large multi-scale variations and blurred boundaries. Methods To address this, we propose SEAFEC (Spatial-Edge Adaptive Feature Enhancement Convolution), a novel convolutional module that jointly enhances scale adaptivity and boundary precision. SEAFEC employs a dual-branch design: the SCARF branch dynamically adjusts receptive fields, while the MEFE branch explicitly strengthens edge features. Results Across three representative tasks—plant disease classification, corn leaf disease detection, and sugarcane-weed segmentation—SEAFEC achieved consistent improvements (+1.8% accuracy, +2.5% mAP, +3.4% mIoU), with notable gains in boundary-sensitive cases. Discussion These results highlight SEAFEC as a general-purpose enhancement module, providing a unified solution for tackling scale-boundary challenges in agricultural imagery to support reliable disease diagnosis and precision weed management.
Why it matches plant phenotyping methods植物病害画像を対象に、マルチスケール・境界認識のための新規畳み込みモジュールSEAFECを開発し、病害分類・検出で技術性能を評価しているため、植物状態の画像ベース推定手法が中心である。
abstractwe propose SEAFEC (Spatial-Edge Adaptive Feature Enhancement Convolution), a novel convolutional module that jointly enhances scale adaptivity and boundary precision.
Accurate estimation of biomass in energy cane is essential for cultivar selection in breeding programs and biomass supply forecasting in bioenergy production. This study evaluated the integration of Uncrewed Aircraft System (UAS) based Light Detection and Ranging (lidar) and Red-Green-Blue Structure-from-Motion (RGB-SfM) photogrammetry to enhance biomass prediction for high-throughput phenotyping (HTP). Seven cultivars were monitored between December 2023 and July 2024 at an experimental field in Weslaco, Texas. Structural metrics such as percentile-based heights, canopy volume, and interaction variables were extracted and used to train four machine learning models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and a one-dimensional Convolutional Neural Network (1D-CNN). Ensemble tree algorithms consistently outperformed CNN, with XGBoost and LightGBM providing the most stable and interpretable predictions. For single sensor inputs, lidar models (R² = 0.70–0.78; RMSE = 1.36–1.60 kg/m²) generally outperformed RGB-SfM (R² = 0.69–0.73; RMSE = 1.52–1.63 kg/m²), though RGB-SfM performed competitively with XGBoost. Fused models combining lidar and RGB-SfM features achieved the highest accuracies (XGBoost: R² = 0.80, RMSE = 1.31 kg/m²; LightGBM: R² = 0.79, RMSE = 1.36 kg/m²), mitigating the underestimation of high-biomass plots in RGB-SfM and the slight overestimation of lidar at the upper tail. Cultivar specific analysis confirmed TH16–22 as the top performer, followed closely by Ho02–113 and TCP10–4928, demonstrating the capacity of UAS HTP to support breeding decisions. These findings confirm the biological relevance of percentile-based height metrics (particularly the 75th percentile), canopy volume, and their interactions for biomass accumulation and underscore the value of sensor fusion in reducing systematic bias. This study provides systematic demonstration of lidar and RGB-SfM fusion for biomass estimation in energy cane, establishing a scalable and non-destructive approach that advances high-throughput phenotyping and supports the development of sustainable bioenergy cropping systems.
Why it matches plant phenotyping methodsUAS搭載LiDAR・RGB-SfMによる植物構造特徴の取得と機械学習によるバイオマス推定を開発・比較評価しており、表現型取得・抽出手法が研究の中心である。
abstractThis study evaluated the integration of Uncrewed Aircraft System (UAS) based Light Detection and Ranging (lidar) and Red-Green-Blue Structure-from-Motion (RGB-SfM) photogrammetry to enhance biomass prediction for high-throughput phenotyping (HTP).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
High-throughput phenotyping using unmanned aerial vehicle (UAV)-based imagery offers substantial potential for improving sugarcane breeding efficiency. This study utilized UAVs-equipped multispectral sensors to capture high-resolution imagery of 652 sugarcane varieties under high-density planting condition, enabling the development of predictive models for key phenotypic traits including plant height, leaf length, leaf width, and relative chlorophyll content (SPAD value). A comprehensive feature extraction process yielded 100 vegetation indices, 7 texture indices, and canopy height parameters derived from the UAV imagery. To develop robust predictive models, we implemented three feature processing strategies—correlation-based filtering (COR), stepwise regression selection (SWR), and principal component analysis (PCA)—in conjunction with five machine learning algorithms: Lasso Regression (LASSO), Ridge Regression (Ridge), Support Vector Machine Regression (SVM), Random Forest (RF), and Gradient Boosting Regression Trees (GBR). Two ensemble methods, Bayesian Model Averaging (BMA) and Stacked Generalization, were also employed. Results demonstrated that LASSO performed best among traditional machine learning models, whereas the Stacking ensemble method, which integrated predictions from all individual algorithms, achieved the highest prediction accuracy (the coefficient of determination ( R 2 ) = 0.77; root mean squared error ( RMSE ) = 12.99 cm for plant height). Additionally, K-means clustering partitioned the sugarcane varieties into two distinct clusters (A and B; p ≤ 0.001). Notably, cluster-specific models trained on PCA-processed features demonstrated exceptional predictive accuracy during validation, achieving R 2 values of 0.94, 0.91, 0.87, and 0.90 for plant height, leaf length, leaf width, and SPAD value, respectively. This research presents an integrated framework combining optimized feature processing, population clustering, and ensemble learning to enhance trait prediction in large-scale UAV-based phenotyping for sugarcane breeding.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からサトウキビの複数形質を推定する予測モデルと統合的な表現型解析フレームワークを開発・検証しており、表現型取得・抽出手法が研究の中心である。
abstractHigh-throughput phenotyping using unmanned aerial vehicle (UAV)-based imagery offers substantial potential for improving sugarcane breeding efficiency.
Accurate estimation of biomass in energy cane is essential for cultivar selection in breeding programs and biomass supply forecasting in bioenergy production. This study evaluated the integration of Uncrewed Aircraft System (UAS) based Light Detection and Ranging (lidar) and Red-Green-Blue Structure-from-Motion (RGB-SfM) photogrammetry to enhance biomass prediction for high-throughput phenotyping (HTP). Seven cultivars were monitored between December 2023 and July 2024 at an experimental field in Weslaco, Texas. Structural metrics such as percentile-based heights, canopy volume, and interaction variables were extracted and used to train four machine learning models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and a one-dimensional Convolutional Neural Network (1D-CNN). Ensemble tree algorithms consistently outperformed CNN, with XGBoost and LightGBM providing the most stable and interpretable predictions. For single sensor inputs, lidar models (R² = 0.70–0.78; RMSE = 1.36–1.60 kg/m²) generally outperformed RGB-SfM (R² = 0.69–0.73; RMSE = 1.52–1.63 kg/m²), though RGB-SfM performed competitively with XGBoost. Fused models combining lidar and RGB-SfM features achieved the highest accuracies (XGBoost: R² = 0.80, RMSE = 1.31 kg/m²; LightGBM: R² = 0.79, RMSE = 1.36 kg/m²), mitigating the underestimation of high-biomass plots in RGB-SfM and the slight overestimation of lidar at the upper tail. Cultivar specific analysis confirmed TH16–22 as the top performer, followed closely by Ho02–113 and TCP10–4928, demonstrating the capacity of UAS HTP to support breeding decisions. These findings confirm the biological relevance of percentile-based height metrics (particularly the 75th percentile), canopy volume, and their interactions for biomass accumulation and underscore the value of sensor fusion in reducing systematic bias. This study provides systematic demonstration of lidar and RGB-SfM fusion for biomass estimation in energy cane, establishing a scalable and non-destructive approach that advances high-throughput phenotyping and supports the development of sustainable bioenergy cropping systems. • Ensemble models (RF, LightGBM) outperform CNN in UAS phenotyping at plot scale. • Canopy volume and percentile heights identified as key biomass predictors. • Fusion of UAS lidar and RGB-SfM improves energy cane biomass estimation. • Sensor fusion mitigates RGB underestimation and lidar overestimation biases. • UAS-HTP supports energy cane cultivar screening for bioenergy applications.
Why it matches plant phenotyping methodsUAS lidar・RGB-SfMによるバイオマス推定と、特徴抽出・機械学習・センサ融合の検証が研究の中心であり、再利用可能な高スループット表現型計測手法を評価している。
abstractThis study evaluated the integration of Uncrewed Aircraft System (UAS) based Light Detection and Ranging (lidar) and Red-Green-Blue Structure-from-Motion (RGB-SfM) photogrammetry to enhance biomass prediction for high-throughput phenotyping (HTP).
The goal of automated crop leaf disease detection (CLDD) is to extract fine-grain visual features from images for classification. Vision transformers (ViTs) and attention-based models have improved classification accuracies by narrowing down the receptive fields of visual features, though often at the expense of computations and robustness. Generalizing visual features becomes difficult due to lacking diversification and overrepresentation of healthy leaves over diseased ones, causing overfitting and inherently limiting ViT’s capacity. Vision Transformers (ViTs) typically rely on single feature projection onto query, key, and value embeddings for self-attention calculations. In contrast, we introduce multi-feature projection. The texture representations extracted from RGB images are fed into key and value components, while the query takes RGB-coded features. Attention is the SoftMax-activated dot product computed on the three components. Simultaneously, a residual branch integrates RGB features with the attention vectors, producing moderately robust and interpretable compositions referred to as Texture Guided Visual Attention (TGVA) features. These TGVA features are integrated back into the backbone classifier network. Evaluating the Texture Guided Visual Attention neural network (TGVAnn) on the most challenging plant leaf datasets, sugarcane and maize, demonstrates its superior performance, achieving a 9% improvement in classification accuracy over traditional ViT models. Furthermore, TGVAnn shows a 20%–70% reduction in theoretical computational requirements (GFLOPs) relative to comparable baselines under a common reporting convention, supporting efficiency and scalability. This method outperforms similar approaches in both accuracy and robustness. In conclusion, the TGVA features derived from the multi-feature attention module effectively enhance the robustness of backbone image classifiers. This improvement is achieved with only a minimal increase in computational overhead of 13 M-parameters, making the approach both efficient and suitable for edge deployment scenarios.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する画像解析手法を開発し、複数データセットで性能評価しており、フェノタイピング手法が中心である。
abstractThe goal of automated crop leaf disease detection (CLDD) is to extract fine-grain visual features from images for classification.
Unmanned Aerial Vehicles (UAVs) equipped with low-cost RGB and near-infrared (NIR) cameras represent efficient and scalable technology for monitoring sugarcane crops. This study evaluated the potential of UAV imagery and three-dimensional crop modeling to estimate sugarcane height and yield under different nitrogen fertilization levels. The experiment comprised 28 plots subjected to four nitrogen rates, and images were processed using a Structure from Motion (SfM) algorithm to generate Digital Surface Models (DSMs). Crop Height Models (CHMs) were obtained by subtracting DSMs from Digital Terrain Models (DTMs). The most accurate CHM was derived from the combination of the reference DTM and the NIR-based DSM (R2 = 0.957; RMSE = 0.162 m), while the strongest correlation between height and yield was observed at 200 days after cutting (R2 = 0.725; RMSE = 4.85 t ha−1). The NIR-modified sensor, developed at a total cost of USD 61.59, demonstrated performance comparable with commercial systems that are up to two hundred times more expensive. These results demonstrate that the proposed low-cost NIR sensor provides accurate, reliable, and accessible data for three-dimensional modeling of sugarcane.
Why it matches plant phenotyping methodsUAV画像、SfMによる3次元再構成、低コストNIRセンサーを用いてサトウキビの草高・収量を推定し、商用システムとの性能比較も行うため、植物表現型取得法が中心である。
abstractUAV imagery and three-dimensional crop modeling to estimate sugarcane height and yield
Early and accurate detection of sugarcane leaf diseases is critical for improving crop productivity and reducing economic losses in the agricultural sector. Timely interventions enable sustainable crop management and better resource use. In this study, we propose a deep learning-based approach for sugarcane leaf disease classification that leverages a novel architecture, the Multi-scale Attention-based Dense Residual Network (MADRN). The MADRN model integrates dense residual learning and multi-scale attention mechanisms to effectively capture fine-grained, disease-specific features and address challenges related to domain variability and complex data patterns. Two datasets are used to evaluate the model: a Kaggle dataset and a blended dataset created by combining Kaggle images with those from the Bangladesh Sugarcrop Research Institute (BSRI), simulating real-world conditions. All images undergo preprocessing steps, including resizing, normalization, and data augmentation, before training. Additionally, several baseline models (CNN, VGG16, MobileNetV2, and XceptionNet) are fine-tuned and compared with the MADRN model. Experimental results demonstrate that MADRN consistently outperforms baseline models in accuracy, precision, recall, and F1-score across both datasets, achieving up to 94.78% accuracy on the Kaggle dataset and 92.25% on the blended dataset. These findings highlight MADRN's superior ability to learn discriminative features and generalize effectively across diverse data sources, making it a promising tool for precision agriculture and disease management. To facilitate practical implementation, a web-based application is developed, enabling real-time and user-friendly disease detection. This research lays a strong foundation for the development of accurate, scalable, and practical disease classification tools that can support sustainable agricultural practices.
Why it matches plant phenotyping methodsサトウキビ葉の画像から病害状態を推定する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比較検証しているため、植物フェノタイピング手法が中心です。
abstractwe propose a deep learning-based approach for sugarcane leaf disease classification that leverages a novel architecture, the Multi-scale Attention-based Dense Residual Network (MADRN).
Reproduction assets foundThe paper's Kaggle sugarcane leaf disease image dataset (2521 images, five classes) is a public, paper-specific phenotyping image asset with an explicit URL in the Data Availability statement. The BSRI field images are only available upon request, and no author analysis code or trained model is deposited.Dataset · publicg and preparation.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The data supporting the findings of this study are publicly available and can be accessed through the following sources. Kaggle Sugarcane Leaf Disease Dataset [https://www.kaggle.com/datasets/nirmalsankalana/sugarcane-leaf-disease-dataset](https:/www.kaggle.com/datasets/nirmalsankalana/sugarcane-leaf-disease-dataset) (accessed Jan. 15, 2025). Bangladesh Sugarcrop Research Institute (BSRI): Data available upon request. For BSRI data inquiries, please contact the corresponding author, Dr. Md. Shamim Reza.
Declarations
CompetinOpen asset ↗Kagglelines:278-299Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
This study compares of two types of multispectral cameras, DJI Mavic 3M and MAPIR RGN, in assessing sugarcane health through reflectance analysis and vegetation indices. The research was conducted in a sugarcane plantation in Sidoarjo, East Java, using multispectral data captured by drones. The analysis evaluated the relationship between reflectance values, vegetation indices, and chlorophyll content in sugarcane. Results indicate that the MAPIR RGN camera outperformed the DJI Mavic 3M in measuring chlorophyll content. The Near Infrared (NIR) channel of MAPIR RGN showed the highest correlation with chlorophyll (r = 0.2166). Additionally, the Ratio Vegetation Index (RVI) from MAPIR RGN had the strongest correlation (r = 0.2716) among all vegetation indices. Conversely, the DJI Mavic 3M camera demonstrated weaker correlations across all reflectance channels and vegetation indices. These differences may stem from sensor sensitivity and the quality of data produced by each camera. Based on these findings, the MAPIR RGN camera is recommended for precision agriculture applications in sugarcane plantations, as it provides more accurate spectral data reflecting vegetation health. This study underscores the relevance of drone technology in enhancing the efficiency of sugarcane plantation management.
Why it matches plant phenotyping methodsドローン搭載マルチスペクトルカメラを比較検証し、反射率・植生指数からサトウキビのクロロフィル／健全性を推定しているため、植物表現型取得手法が中心である。
abstractThis study compares of two types of multispectral cameras, DJI Mavic 3M and MAPIR RGN, in assessing sugarcane health through reflectance analysis and vegetation indices.
Abstract: Sugarcane is a vital crop that makes a substantial contribution to the agricultural economy globally, according to this study. Unfortunately, diseases that have a substantial effect on productivity and quality sometimes present a risk to its production. the system processes images of sugarcane leaves and stems. Combining visual processing and Manual inspections are used in most traditional disease detection techniques, which can be labour-intensive, time-consuming, and prone to human mistake. This paper provides a machine learning-based approach for sugarcane disease prediction that increases detection efficiency and accuracy by utilising Convolutional Neural Networks (CNNs) and environmental data. To visually recognise the signs of a disease, predictive modelling, the project aims to create an automated, real-time sickness diagnosis tool. Due to this tool’s ability to offer timely interventions, farmers will be able to lower crop losses and adopt sustainable agricultural practices. The proposed paradigm presents the agricultural community with a readily accessible and scalable alternative that could revolutionise crop health management. Data collection, pre-processing, augmentation, model training, and evaluation are some of the steps in the methodology. OpenCV, NumPy, and TensorFlow/Keras were used to handle image datasets, while Google Colab was used for training with GPU acceleration. The suggested model outperformed alternative CNN designs including VGG19, Xception, and ResNet50, with an accuracy of 91.94%. Gradio was used to create an intuitive user interface that allows users to upload leaf photos and receive immediate diagnostic feedback and confidence scores, enabling real-time illness identification.
Why it matches plant phenotyping methodsサトウキビ葉・茎の画像から病徴を推定するCNN手法の開発・評価が研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstractthe system processes images of sugarcane leaves and stems.
Plant leaf diseases compromise yield, quality, and farmer income, while late or inaccurate diagnosis drives excess pesticide use and production losses. Automated, image-based detection offers a scalable alternative to manual scouting, yet field images suffer from illumination shifts, background clutter, and class imbalance, which degrade model reliability. This work proposes an end-to-end pipeline for robust plant leaf disease recognition that combines physics-guided preprocessing with hybrid deep representations and class-aware learning. First, a preprocessing module applies illumination normalization (Retinex-inspired color constancy) and haze suppression, followed by Multi-Stage Attention Leaf Segmentation (MSALS) to isolate lamina and lesions from complex backgrounds. Next, for feature extraction, we introduce a Hybrid Shifted Vision Transformer (HS- ViT) that fuses a lightweight CNN stem (local texture cues) with cross-scale window-shifted transformer blocks (global lesion geometry), augmented by channel-spatial attention to emphasize symptomatic regions. Finally, classification uses a prototype-aware focal objective with temperature scaling to handle imbalance and sharpen decision boundaries; an uncertainty-weighted ensemble stabilizes predictions under domain shift. To improve minority classes, an Adaptive Augmentation module synthesizes realistic variations in lesion color, size, and spread. We evaluate on PlantVillage (~54k images, 38 classes) and a field collection from Telangana (chilli and sugarcane leaves; expert-annotated). The proposed system attains 99.0% top-1 accuracy on PlantVillage and 95.3% balanced accuracy in field conditions, exceeding recent CNN/ViT baselines by 2–6 percentage points and improving macro- F1 on rare diseases. The results indicate a practical path to deployable, edge-friendly diagnosis that can reduce chemical inputs and support precision agronomy at scale.
Why it matches plant phenotyping methods植物葉の病徴・病斑を画像から分離し、病害状態を推定する画像ベースの表現型解析パイプラインを開発・評価しており、フェノタイピング手法が研究の中心である。
abstractAutomated, image-based detection offers a scalable alternative to manual scouting
In the context of global agricultural modernization, the early and accurate detection of sugarcane leaf diseases is critical for ensuring stable sugar production. However, existing deep learning models still face significant challenges in complex field environments, such as blurred lesion edges, scale variation, and limited generalization capability. To address these issues, this study constructs an efficient recognition model for sugarcane disease detection, named CaneFocus-Net, specifically designed for precise identification of sugarcane leaf diseases. Based on a single-stage detection architecture, the model introduces a lightweight cross-stage feature fusion module (CP) to optimize feature transfer efficiency. It also designs a module combining a channel-spatial adaptive calibration mechanism with multi-scale pooling aggregation to enhance the backbone network's ability to extract multi-scale lesion features. Furthermore, by expanding the high-resolution shallow feature layer to enhance sensitivity toward small-sized targets and adopting a phased adaptive nonlinear optimization strategy, detection and localization accuracy along with convergence efficiency have been further improved. Test results on public datasets demonstrate that this method significantly enhances recognition performance for fuzzy lesions and multi-scale targets while maintaining high inference speed. Compared to the baseline model, precision, recall, and mean average precision (mAP50 and mAP50-95) improved by 1.9%, 4.6%, 1.5%, and 1.4%, respectively, demonstrating strong generalization capabilities and practical application potential. This provides reliable technical support for intelligent monitoring of sugarcane diseases in the field.
Why it matches plant phenotyping methodsサトウキビ葉の病斑を画像から検出・局在化する深層学習モデルを開発しており、植物の病害状態を直接推定する手法が研究の中心である。
abstractthis study constructs an efficient recognition model for sugarcane disease detection, named CaneFocus-Net
Reproduction assets foundThe paper's sugarcane leaf disease dataset (9100 images, five classes) is publicly available on Roboflow Universe, with an explicit Data Availability Statement providing the exact URL. No author analysis code or trained model checkpoints are stated as available.Dataset · publicData Availability Statement: The data presented in this study are openly available at Roboflow.
The website is: https://universe.roboflow.com/sugarcaneleaf/sugarcaneleaf-w0mto/dataset/2 (ac-
cessed on 23 July 2025).Open asset ↗Roboflow · sugarcaneleaf-w0mtopdf-page:26 lines:1-59Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Introduction Accurate assessment of sugarcane leaf disease severity is crucial for early warning and effective disease control. Methods In this study, we propose an intelligent method for identifying sugarcane foliar disease severity based on physiological traits. Field-collected data-including Soil and Plant Analyzer Development (SPAD) values, leaf surface temperature, and nitrogen content-were acquired using a plant nutrient analyzer (TYS-4N) from sugarcane leaves infected with brown stripe disease, ring spot disease, and mosaic disease at four severity levels (mild, moderate, moderately severe, and severe). After min-max normalization, six classification models-KNN, AdaBoost, Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and XGBoost-were developed, and the Sparrow Search Algorithm (SSA) was employed to optimize hyperparameters for enhanced performance. Results Results demonstrate that SSA significantly improved the classification capability of all models. The SSA-XGBoost model achieved the best performance, with Precision, Recall, F1 Score, and Accuracy all exceeding 0.9186, and a comprehensive PRFA score of 0.9326. When validated on an independent dataset from Gengma County, the model achieved an overall accuracy of 0.91, indicating strong generalization ability and field applicability. Discussion Compared to image-based deep learning approaches, the proposed method offers advantages in terms of data accessibility, computational efficiency, and model transparency, making it well-suited for rapid on-site diagnosis in agricultural settings. This study provides an efficient and reliable technical framework for intelligent diagnosis and early warning of sugarcane disease severity.
Why it matches plant phenotyping methods植物の生理形質から葉病害の重症度を推定する分類手法を開発し、独立データセット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。
abstractwe propose an intelligent method for identifying sugarcane foliar disease severity based on physiological traits.
Introduction Current research on sugarcane disease identification primarily focuses on a limited number of typical diseases, often constrained by specific target groups or conditions. To address this, we propose an enhanced ADQ-YOLOv8m model based on the YOLOv8m framework, enabling precise detection of sugarcane diseases. Methods The detection head is modified to a Dynamic Head to enhance feature representation capabilities. Following the Detect module, we introduce the ATSS dynamic label assignment strategy and the QFocalLoss loss function to address issues such as class imbalance, thereby bolstering the model's feature representation capabilities. Results Experimental results demonstrate that ADQ-YOLOv8m outperforms nine other mainstream object detection models, achieving precision, recall, mAP50, mAP50-95, and F1 scores of 86.90%, 85.40%, 90.00%, 77.40%, and 86.00%, respectively. Discussion Finally, comprehensive evaluation of the ADQ-YOLOv8m model's performance is conducted using visual analysis of image predictions and cross-scenario adaptability testing. The experimental results indicate that the proposed model excels in multi-objective processing and demonstrates strong generalization capabilities, suitable for scenarios involving multiple objectives, multiple categories, and class imbalance. The detection method proposed exhibits excellent detection performance and potential, providing robust support for the development of intelligent sugarcane cultivation and disease control.
Why it matches plant phenotyping methodsサトウキビ病害の画像から病害状態を推定する検出モデルを開発・評価しており、植物の病害表現型取得が中心的な方法論的貢献である。
abstractwe propose an enhanced ADQ-YOLOv8m model based on the YOLOv8m framework, enabling precise detection of sugarcane diseases.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicDataset 1: A manually collected dataset of sugarcane leaf disease images. It primarily comprises five major categories: healthy, mosaic disease, red rot disease, rust disease, and yellow leaf disease. The dataset has been captured using smartphones of various configurations to maintain diversity. It encompasses a total of 2569 images, encompassing all categories. The database has been collected in the state of Maharashtra, India. The database is balanced and exhibits a good diversity. The image sizes are not uniform, as they originate from various capture devices. All images are in RGB format. This study utilized the entire set of images from Dataset 1. Source: https://www.kaggle.com/datasetOpen asset ↗Kagglelines:350-378Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Aiming to address the accuracy problem of cane tip recognition in complex natural environments, this paper proposes a cane tip feature annotation method based on the growth characteristics of sugarcane. In the context of the demand for lightweight and fast detection of cane tips, this paper optimizes the Yolov8n-Seg model with lightweight shared convolutional separated batch normalized detection head, model pruning, and knowledge distillation strategies. With these improvements, the accuracy of the optimized model increased by 0.2 percentage points, the number of parameters was reduced by 75.03%, the model size was reduced by 70.15%, the inference time is accelerated by 17.34%, and the GFLOPs were reduced by 40.00%. The lightweight cane tip detection model was deployed on the Jetson Orin NX platform with an average recognition frame rate of 7.42 f/s provides a lightweight hardware deployment solution for real-world applications in sugarcane harvesters. Finally, the depth camera was used for cane tip recognition and height measurement. The experimental results showed that the average relative errors of the camera were 0.189%, 0.675%, and 0.949% when the camera was 50 cm, 75 cm, and 100 cm away from the cane tip, respectively, which were all controlled within 1%, and were able to achieve accurate height measurement. Based on the statistical analysis of sugarcane clusters, this paper further proposes a sugarcane cluster identification method, providing a theoretical basis for saving adjustment time of the tip cutter during the harvesting process. It lays a theoretical and technical foundation for researching feature recognition, cutter height positioning, and real-time control of sugarcane harvester cuttings.
Why it matches plant phenotyping methodsサトウキビ先端の画像認識と深度カメラによる高さ測定を開発・検証しており、植物形態形質の取得が中心である。
abstractthis paper proposes a cane tip feature annotation method based on the growth characteristics of sugarcane.
To explore the feasibility of using UAV-LiDAR for measuring the leaf area index (LAI) of crop canopies, we employed UAV-LiDAR to scan sugarcane canopies during the tillering and elongation stages, acquiring canopy point cloud data. Subsequently, features such as average row height, projected row area, point cloud density at different canopy layers, and the ratios between these parameters were extracted. Three feature selection methods-partial least squares regression (PLSR), XGBoost feature importance (XGBoost-FI), and random forest-recursive feature elimination (RF-RFE)-were adopted to evaluate and identify the optimal input variables for modeling. With these selected variables, LAI inversion models were developed based on random forest (RF) and adaptive boosting (AdaBoost) algorithms, and their performance was assessed. Among the extracted features, the projected row area S p and the total row point count C total exhibited strong correlations with LAI, with correlation coefficients of 0.73 and 0.72, respectively. The AdaBoost-based LAI inversion model, using the projected row area S p , average height H avg , mid-layer point cloud density C m , and total row point count C total as input variables, achieved the best performance, with a coefficient of determination ( R v ²) of 0.713 and a root mean square error ( RMSE v ) of 0.25 on the validation set. This study provides an effective method for high-throughput acquisition of LAI in field crops, offering valuable scientific support for sugarcane field management and breeding efforts.
Why it matches plant phenotyping methodsUAV-LiDARによるサトウキビ群落LAIの取得・推定手法を開発し、特徴量選択と機械学習モデルの性能評価まで行っており、フェノタイピング手法が中心である。
abstractusing UAV-LiDAR for measuring the leaf area index (LAI) of crop canopies
Yield and its components are the important traits for plant breeders to select the best genotypes in the breeding programs. However, traditional measurements of these traits across genotypes and environments are labor-intensive and time-consuming, as hundreds or even thousands of plots need to be estimated. A yield trial was carried out using seven sugarcane cultivars planted in a randomized complete block design with four replications for two ratoon crops to estimate sugarcane yield and its components using unoccupied aerial systems (UAS)-based high throughput phenotyping (HTP) and to compare the traditional method with UAS-based yield components in discriminating ability to assess sugarcane yield via a path coefficient analysis. UAS platforms mounted with sensors were flown over the trial. The result shows that UAS-derived plant height (PH) showed a strong relationship with the ground measured PH (R² = 0.89, RMSE = 0.15 m). Likewise, an accurate millable stalk height (MSH) estimation, using UAS-derived PH as a predictor, was observed (R² = 0.54, RMSE = 0.15 m). Canopy height model (CHM)-derived canopy cover (CC) appeared to be a promising feature to indirectly select or to predict for stalk number (SN) (R² = 0.69, RMSE = 10,975 stalks ha⁻¹). Based on a path coefficient analysis, UAS-based yield components performed equally to or slightly underperformed the traditional method. Traditionally, SN was the largest contributor to cane yield. Similarly, CC and CHM were the important components for UAS-based yield components. Additionally, the yield prediction model using UAS-derived canopy features with five cross validation schemes (CVs) revealed that model accuracy increased as association between predictor variables with a responding variable increased. The present study shows that random forest outperformed (higher r and lower RMSE) the linear regression models (stepwise, lasso, and ridge) in all CVs. The linear regressions were off when they were used to predict the performance of cultivars in untested crop/environments (CVs2 and CVs5), while a higher accuracy was observed when using random forest in those CVs. More importantly, the accuracy of all models reduced when they were tested in untested crop/environments (CVs2 and CVs5), indicating the challenge of using a prediction model applied to new environments.
Why it matches plant phenotyping methodsUASベースHTPでサトウキビの草高・群落被覆・茎数・収量構成要素を推定し、地上測定との検証、モデル比較、交差検証を行っており、表現型取得・推定手法が研究の中心である。
abstractUAS-derived plant height (PH) showed a strong relationship with the ground measured PH (R² = 0.89, RMSE = 0.15 m).
Sugarcane stem node detection is critical for monitoring sugarcane growth, enabling precision cutting, reducing spuriousness, and improving breeding for resistance to downfall. However, in complex field environments, sugarcane stem nodes often suffer from reduced detection accuracy due to background interference and shadowing effects. For this reason, this paper proposes an improved sugarcane stem node detection model based on YOLO11. This study incorporates the ASF-YOLO (Attentional Scale Sequence Fusion based You Only Look Once) mechanism to enhance the feature fusion layer of YOLO11. Additionally, a high-resolution detection layer, P2, is integrated into the fusion module to improve the model's ability to detect small objects-particularly sugarcane stem nodes-and to better handle multi-scale feature representations. Secondly, to better align with the P2 small-object detection layer, this paper adopts a shared convolutional detection head named LSDECD (Lightweight Shared Detail-Enhanced Convolutional Detection Head), which can better deal with small target detection while reducing the number of model parameters through parameter sharing and detail-enhanced convolution. Using soft-NMS (non-maximum suppression) to replace the original NMS and combining with Shape-IoU, a bounding box regression method that focuses on the shape and scale of the bounding box itself, makes the bounding box regression more accurate, and solves the problem of the impact of detection caused by occlusion and illumination. Finally, to address the increased complexity introduced by the addition of the P2 detection layer and the replacement of the detection head, channel pruning is applied to the model, effectively reducing its overall complexity and parameter count. The experimental results show that the model before pruning has 96.1% and 53.2% mean average precision mAP50 and mAP50:95, respectively, which are 11.9% and 11.1% higher than the original YOLO11n, and the model after pruning also has 10.8% and 9.3% higher than the original YOLO11n, respectively, and the number of parameters is reduced to 279,778, and model size is reduced to 1.3MB. The computational cost decreased from 11.6 GFlops to 6.6 GFlops.
Why it matches plant phenotyping methodsサトウキビ茎節という植物器官の検出を対象に、改良YOLOモデルの開発と性能評価を中心的に行っており、再利用可能な画像ベース表現型取得手法に該当する。
abstractThe experimental results show that the model before pruning has 96.1% and 53.2% mean average precision mAP50 and mAP50:95
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the sugarcane stem node dataset and the study's source code on ScienceDB with public DOIs, both matching allowed URLs.Dataset · publicppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 The source code used in this study is available at DOI: https://doi.org/10.57760/sciencedb.27287 .
Data Availability
All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 ThOpen asset ↗ScienceDB · 10.57760/sciencedb.27078lines:65-90Code · publicno pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 The source code used in this study is available at DOI: https://doi.org/10.57760/sciencedb.27287 .
Data Availability
All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 The source code used in this study is available at DOI: https://doi.org/10.57760/sciencedb.27287 .Open asset ↗ScienceDB · 10.57760/sciencedb.27287lines:65-90Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Organ instance segmentation of 3D plant point clouds is a crucial prerequisite for organ-level phenotype estimation. However, most current cloud segmentation methods are usually designed for specific crop, hardly fit for both monocotyledonous and dicotyledonous crops which have significant structural differences. This study therefore proposed a two-stage method with higher generalization ability for single-plant organ instance segmentation based on PointNeXt and Quickshift++. The effectiveness of this method was tested on different types of crops. The dataset includes point clouds of 122 self-acquired sugarcanes, 49 open-accessed maizes, and 77 open-accessed tomatoes. The improved PointNeXt model was trained to implement the semantic segmentation of stems and leaves. The average mOA and mIoU on the test set reaches 96.96 % and 87.15 %, respectively. The Quickshift++ algorithm was then applied to encode the global spatial structure and local connections of plants for rapid localization and segmentation of leaf instance. Our approach outperformed four SOTA methods, ASIS, JSNet, DFSP, and PSegNet in terms of both quantitative and qualitative segmentation results, achieving average values for mPrec, mRec, mF1, and mIoU of 93.32 %, 85.60 %, 87.94 %, and 81.46 %, respectively. The proposed method also yields excellent results for several other plants in their early stages, indicating its generalization ability and applicability for organ instance segmentation for different plants, thus providing a powerful tool for plant phenotypic research.
Why it matches plant phenotyping methods植物の3D点群から茎・葉の器官インスタンスを分割する手法を開発・比較検証しており、器官レベルの表現型推定に直接つながる中心的な方法研究である。
abstractOrgan instance segmentation of 3D plant point clouds is a crucial prerequisite for organ-level phenotype estimation.
Louisiana is one of only two remaining sugarcane producing states in the U.S., and the industry is faced with labor shortage. Integration of predictive models incorporating markers offers a non-destructive tool for precision breeding. Chemical markers allow a direct measurement of damage and tolerance for sugarcane against winter freeze, which is the primary abiotic stress in Louisiana representing the northernmost sugarcane growing region worldwide. This study first utilized exploratory (cluster and principal component) analyses to show the effects of air temperature, but not genotype, on red, green, and blue reflectance data collected by unmanned aerial vehicle (UAV). Of tested algorithms (multiple linear regression (MLR), XGBoost, partial least squares, and artificial neural network), best fit models were obtained by MLR for yield (theoretical recoverable sugar, Cane Pol, Cane Brix, fiber, and moisture content), primary product (sucrose), and freeze damage indicators (fructose and glucose hydrolysis products of sucrose). Parts per million-level cold tolerance indicator (tyrosine-like fluorophore) and additional secondary products (polyphenols and trans-aconitic acid) in juice were modeled after concentrations were normalized to the canopy coverage, as the UAV sensor is detecting the canopy pixels. Built models could be used in freeze damage assessment as well as marker-assisted tolerant variety development, without the constraint of waiting for the abiotic stress to happen.
Why it matches plant phenotyping methodsUAV反射データと予測モデルを用いて、サトウキビの収量・凍害・耐寒性指標を推定する手法が研究の中心であり、植物状態の非破壊フェノタイピングに該当する。
abstractIntegration of predictive models incorporating markers offers a non-destructive tool for precision breeding.
Abstract Timely and precise identification of foliar diseases in sugarcane is imperative for yield optimization and disease management. This work proposes a hybrid deep learning framework leveraging Convolutional Neural Networks (CNNs) and Vision Transformers (VITs) for automated multi-class classification of sugarcane leaf diseases, including healthy , yellow rust , mosaic , rust , and red rot . Initially, baseline CNN architecture was employed to extract spatially localized features, attaining a classification accuracy of 84.3% . Subsequently, a pre-trained VIT model, capable of modelling long-range dependencies through self-attention mechanisms, was fine-tuned on the same dataset, achieving 93.07% accuracy. To further enhance feature representation, a hybrid CNN + VIT model was constructed by integrating CNN-based local feature encoders with VIT-based global context modelling. The proposed ensemble architecture achieved a superior accuracy of 97.43% , demonstrating robust generalization and discriminative power. The results affirm the efficacy of transformer-based architectures in plant disease detection tasks and validate the synergy between convolutional and attention-based models for high-resolution agricultural image analysis.
Why it matches plant phenotyping methodsサトウキビ葉の病徴・健全状態を画像から分類するCNN・Vision Transformer手法が研究の中心であり、植物病害状態の画像ベース表現型推定に該当する。
abstractThis work proposes a hybrid deep learning framework leveraging Convolutional Neural Networks (CNNs) and Vision Transformers (VITs) for automated multi-class classification of sugarcane leaf diseases
Agriculture plays a critical role in the global economy, providing livelihoods and ensuring food security for billions. Progress in agricultural techniques has helped boost crop yield, along with a growing need for precise disease monitoring solutions. This requires accurate, efficient, and timely disease detection methods. The research presented in this paper addresses this need by analyzing a hybrid model built using Graph Attention Network (GAT) and Graph Convolution Network (GCN) models. The integration of these models has witnessed a notable improvement in the accuracy of leaf disease classification. GCN has been widely used for learning from graph-structured data, and GAT enhances this by incorporating attention mechanisms to focus on the most important neighbors. The methodology incorporates superpixel segmentation for efficient feature extraction, partitioning images into meaningful, homogeneous regions that better capture localized features. The robustness of the model is further enhanced by the edge augmentation technique. The edge augmentation technique in the context of graph has introduced a significant degree of generalization in the detection capabilities of the model as analyzed on apple, potato, and sugarcane leaves. To further optimize training, weight initialization techniques are applied. The hybrid model is evaluated against the individual performance of the GCN and GAT models and the hybrid model achieved a precision of 0.9822, recall of 0.9818, and F1-score of 0.9818 in apple leaf disease classification, a precision of 0.9746, recall of 0.9744, and F1-score of 0.9743 in potato leaf disease classification, and a precision of 0.8801, recall of 0.8801, and F1-score of 0.8799 in sugarcane leaf disease classification. The results indicate that the model is effective and consistent in identifying leaf diseases in plants.
Why it matches plant phenotyping methods植物葉画像から病害状態を分類するGAT-GCNモデルを開発・比較評価しており、病害表現型の抽出手法が中心である。
abstractThe methodology incorporates superpixel segmentation for efficient feature extraction, partitioning images into meaningful, homogeneous regions that better capture localized features.
Reproduction assets foundThe paper evaluates its GAT-GCN hybrid leaf disease classifier on three public leaf image datasets. Two of them (apple and potato) are cited with explicit Kaggle URLs that match allowed_urls entries; the sugarcane dataset is cited without a public URL. No author code or model release is mentioned.Dataset · publicAdvanced Comput. Sci. Appl.
10 ( 8 ), 486 – 492 . doi:
10.14569/IJACSA.2019.0100863
Alsayed A. Alsabei A. Muhammad A.
( 2021 ).
Classification of apple tree leaves diseases using deep learning methods
. Int. J. Comput. Sci. Network Secur.
21 , 324 – 330 .
Antor M. H.
( 2020 ). Apple leaf diseases dataset . Available online at: https://www.kaggle.com/datasets/mhantor/apple-leaf-diseases (Accessed October 13, 2024 ).
Bansal P. Kumar R. Kumar S.
( 2021 ).
Disease detection in apple leaves using deep convolutional neural network
. Agriculture
11 , 617 . doi:
10.3390/agriculture11070617
Bera A. Bhattacharjee D. Krejcar O.
( 2024 ).
Pnd-net: plant nutrition deficiency and disease classification usOpen asset ↗Kaggle · mhantor/apple-leaf-diseaseslines:548-708Dataset · publicnt. J. Res. Eng.
5 , 516 – 523 . doi:
10.21276/ijre.2018.5.9.4
Peng Y. Wang Y.
( 2022 ).
Leaf disease image retrieval with object detection and deep metric learning
. Front. Plant Sci.
13 , 963302 . doi:
10.3389/fpls.2022.963302
, PMID:
36176678
PMC9513793
Putra M. A.
( 2020 ). Potato leaf disease dataset . Available online at: https://www.kaggle.com/datasets/muhammadardiputra/potato-leaf-disease-dataset (Accessed October 13, 2024 ).
Rao S. U. M. Sreekala K. Rao P. Srinivas Shirisha N. Srinivas G. Sreedevi E.
( 2024 ).
Plant disease classification using novel integration of deep learning cnn and graph convolutional networks
. Indonesian J. Electrical Eng. Comput. Sci.
36 , 1721 – 1730 .
RathOpen asset ↗Kaggle · muhammadardiputra/potato-leaf-disease-datasetlines:709-821Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Yield and its components are the important traits for plant breeders to select the best genotypes in the breeding programs. However, traditional measurements of these traits across genotypes and environments are labor-intensive and time-consuming, as hundreds or even thousands of plots need to be estimated. A yield trial was carried out using seven sugarcane cultivars planted in a randomized complete block design with four replications for two ratoon crops to estimate sugarcane yield and its components using unoccupied aerial systems (UAS)-based high throughput phenotyping (HTP) and to compare the traditional method with UAS-based yield components in discriminating ability to assess sugarcane yield via a path coefficient analysis. UAS platforms mounted with sensors were flown over the trial. The result shows that UAS-derived plant height (PH) showed a strong relationship with the ground measured PH (R 2 = 0.89, RMSE = 0.15 m). Likewise, an accurate millable stalk height (MSH) estimation, using UAS-derived PH as a predictor, was observed (R 2 = 0.54, RMSE = 0.15 m). Canopy height model (CHM)-derived canopy cover (CC) appeared to be a promising feature to indirectly select or to predict for stalk number (SN) (R 2 = 0.69, RMSE = 10,975 stalks ha −1 ). Based on a path coefficient analysis, UAS-based yield components performed equally to or slightly underperformed the traditional method. Traditionally, SN was the largest contributor to cane yield. Similarly, CC and CHM were the important components for UAS-based yield components. Additionally, the yield prediction model using UAS-derived canopy features with five cross validation schemes (CVs) revealed that model accuracy increased as association between predictor variables with a responding variable increased. The present study shows that random forest outperformed (higher r and lower RMSE) the linear regression models (stepwise, lasso, and ridge) in all CVs. The linear regressions were off when they were used to predict the performance of cultivars in untested crop/environments (CVs2 and CVs5), while a higher accuracy was observed when using random forest in those CVs. More importantly, the accuracy of all models reduced when they were tested in untested crop/environments (CVs2 and CVs5), indicating the challenge of using a prediction model applied to new environments.
Why it matches plant phenotyping methodsUASセンサーとHTPを用いて、植物形質（草丈、茎数、樹冠被覆、収量構成要素）を推定し、地上測定との検証および予測モデルの比較を行っており、表現型取得・抽出法が研究の中心である。
abstractto estimate sugarcane yield and its components using unoccupied aerial systems (UAS)-based high throughput phenotyping (HTP) and to compare the traditional method with UAS-based yield components
Recent research in agricultural remote sensing mainly focuses on how soil background affects canopy reflectance and the inversion of LAI, while often overlooking the influence of the weed layer. The coexistence of crop and weed layers forms two-layered vegetation canopies in tall crops such as sugarcane and maize. Although radiative transfer models can simulate the weed layer’s influence on canopy reflectance and LAI inversion, few experimental investigations use in situ measurement data to verify these effects. Here, we propose a practical background modification scheme in which black material with near-zero reflectance covers the weed layer and alters the background spectrum of crop canopies. We conduct an experimental investigation in a sugarcane field with different background properties (i.e., bare soil and a weed layer). Tower-based and UAV-based hyperspectral measurements examine the spectral differences in sugarcane canopies with and without the black covering. We then use LAI measurements to evaluate the weed layer’s impact on LAI inversion from UAV-based hyperspectral data through a hybrid inversion method. We find that the weed layer significantly affects the canopy reflectance spectrum, changing it by 13.58% and 42.53% in the near-infrared region for tower-based and UAV-based measurements, respectively. Furthermore, the weed layer substantially interferes with LAI inversion of sugarcane canopies, causing significant overestimation. Estimated LAIs of sugarcane canopies with a soil background generally align well with measured values (root mean square error (RMSE) = 0.69 m2/m2), whereas those with a weed background are considerably overestimated (RMSE = 2.07 m2/m2). We suggest that this practical background modification scheme quantifies the weed layer’s influence on crop canopy reflectance from a measurement perspective and that the weed layer should be considered during the inversion of crop LAI.
Why it matches plant phenotyping methodsサトウキビのLAIという植物形質を対象に、タワー/UAVハイパースペクトル測定と背景補正スキームを用いてLAI推定への影響を検証しており、形質取得・推定手法が研究の中心である。
abstractHere, we propose a practical background modification scheme in which black material with near-zero reflectance covers the weed layer and alters the background spectrum of crop canopies.
MaizeSugarcaneTomatoLiDAR / point cloudLeafStem / branchSegmentation
Organ instance segmentation of 3D plant point clouds is a crucial prerequisite for organ-level phenotype estimation. However, most current cloud segmentation methods are usually designed for specific crop, hardly fit for both monocotyledonous and dicotyledonous crops which have significant structural differences. This study therefore proposed a two-stage method with higher generalization ability for single-plant organ instance segmentation based on PointNeXt and Quickshift++. The effectiveness of this method was tested on different types of crops. The dataset includes point clouds of 122 self-acquired sugarcanes, 49 open-accessed maizes, and 77 open-accessed tomatoes. The improved PointNeXt model was trained to implement the semantic segmentation of stems and leaves. The average mOA and mIoU on the test set reaches 96.96 % and 87.15 %, respectively. The Quickshift++ algorithm was then applied to encode the global spatial structure and local connections of plants for rapid localization and segmentation of leaf instance. Our approach outperformed four SOTA methods, ASIS, JSNet, DFSP, and PSegNet in terms of both quantitative and qualitative segmentation results, achieving average values for mPrec, mRec, mF1, and mIoU of 93.32 %, 85.60 %, 87.94 %, and 81.46 %, respectively. The proposed method also yields excellent results for several other plants in their early stages, indicating its generalization ability and applicability for organ instance segmentation for different plants, thus providing a powerful tool for plant phenotypic research.
Why it matches plant phenotyping methods植物器官の3D点群から茎・葉のインスタンスを抽出する手法を開発・比較検証しており、器官レベル表現型推定のための中心的なフェノタイピング手法である。
abstractOrgan instance segmentation of 3D plant point clouds is a crucial prerequisite for organ-level phenotype estimation.
Reproduction assets foundThe authors state their dataset and code were uploaded to a public GitHub repository, and the paper's maize/tomato point cloud inputs come from the public Pheno4D dataset. Both are paper-specific, public, and actionable.Code · publicThe dataset and the code have been uploaded to Github: https://github.com/ice3664/3d-plant-organ-segmentation/tree/master.Open asset ↗https://github.com/ice3664/3d-plant-organ-segmentation/tree/masterhtml-lines:555-579Dataset · publicthe point clouds of maize and tomato were selected from the Pheno4D dataset [25] which can be accessed via https://www.ipb.uni-bonn.de/data/pheno4d/.Open asset ↗html-lines:109-124Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Jun 2025International Journal of Applied Earth Observation and Geoinformation
Assimilating crop biophysical traits (e.g., Leaf area index, LAI) derived from remote sensing data into a crop growth model provides an effective way for monitoring spatiotemporal variability of crop biomass and yield. However, traditional complex crop growth models generally require extensive input parameters and computation resources, limiting their applicability for large-area estimation using earth observation data. The Simple Algorithm for Yield Estimation model (SAFY), a semi-physical crop growth model grounded in light use efficiency theory has been widely adopted for satellite-based biomass estimation in major field crops. Despite its utility, SAFY cannot directly simulate sugarcane stalk biomass, a critical metric for sugarcane yield assessment. To address this, we developed SAFY-Sugar, a revised SAFY incorporating a temperature-driven stalk biomass module that partitions daily above-ground biomass into stalk biomass. Multi-temporal LAI (S2-LAI) was first inverted from vegetation indices of Sentinel-2 satellite using a semi-empirical model calibrated with the 250 m GLASS LAI product as reference. The estimated S2-LAI achieved an overall accuracy of 0.50 m²/m² in RMSE across selected vegetation indices. Two data assimilation strategies to assimilate the S2-LAI into the SAFY or SAFY-Sugar model for above-ground and stalk biomass estimation in fields were tested (1) independently optimizing SAFY and SAFY-Sugar parameters with S2-LAI alone, and (2) pre-optimizing the stalk module using independent farm measurements before assimilation. SAFY employed a fixed biomass allocation coefficient for stalk biomass estimation. Under the first strategy, SAFY-Sugar demonstrated large improvements in stalk biomass estimation (R² = 0.94, nRMSE = 26.09 %) compared to SAFY (R² = 0.92, nRMSE = 32.73 %). The second strategy further enhanced SAFY-Sugar’s accuracy (R² = 0.98, nRMSE = 14.34 %). For regional application in Chongzuo City (2020 – 2021) using the second strategy, SAFY-Sugar captured spatial yield variability, consistent with the government statistics (nRMSE = 6.61 %). By integrating satellite data assimilation, SAFY-Sugar provides a robust framework for monitoring sugarcane productivity across scales, advancing precision agriculture in sugarcane systems.
Why it matches plant phenotyping methods糖分作物のLAIから茎・地上部バイオマスを推定するモデルと衛星データ同化手法を開発し、精度検証と地域適用を行っており、植物形質取得・推定が研究の中心です。
abstractTo address this, we developed SAFY-Sugar, a revised SAFY incorporating a temperature-driven stalk biomass module that partitions daily above-ground biomass into stalk biomass.
Accurate crop yield prediction is essential for sugarcane growers, as it enables them to predict harvested biomass, guiding critical decisions regarding acquiring agricultural inputs such as fertilizers and pesticides, the timing and execution of harvest operations, and cane field renewal strategies. This study is based on an experiment conducted by researchers from the Commonwealth Scientific and Industrial Research Organisation (CSIRO), who employed a UAV-mounted LiDAR and multispectral imaging sensors to monitor two sugarcane field trials subjected to varying nitrogen (N) fertilization regimes in the Wet Tropics region of Australia. The predictive performance of models utilizing multispectral features, LiDAR-derived features, and a fusion of both modalities was evaluated against a benchmark model based on the Normalized Difference Vegetation Index (NDVI). This work utilizes the dataset produced by this experiment, incorporating other regressors and features derived from those collected in the field. Typically, crop yield prediction relies on features derived from direct field observations, either gathered through sensor measurements or manual data collection. However, enhancing prediction models by incorporating new features extracted through regressions executed on the original dataset features can potentially improve predictive outcomes. These extracted features, nominated in this work as meta-features (MFs), extracted through regressions with different regressors on original features, and incorporated into the dataset as new feature predictors, can be utilized in further regression analyses to optimize crop yield prediction. This study investigates the potential of generating MFs as an innovation to enhance sugarcane crop yield predictions. MFs were generated based on the values obtained by different regressors applied to the features collected in the field, allowing for evaluating which approaches offered superior predictive performance within the dataset. The kNN meta-regressor outperforms other regressors because it takes advantage of the proximity of MFs, which was checked through a projection where the dispersion of points can be measured. A comparative analysis is presented with a projection based on the Uniform Manifold Approximation and Projection (UMAP) algorithm, showing that MFs had more proximity than the original features when projected, which demonstrates that MFs revealed a clear formation of well-defined clusters, with most points within each group sharing the same color, suggesting greater uniformity in the predicted values. Incorporating these MFs into subsequent regression models demonstrated improved performance, with R¯2 values higher than 0.9 for MF Grad Boost M3, MF GradientBoost M5, and all kNN MFs and reduced error margins compared to field-measured yield values. The R¯2 values obtained in this work ranged above 0.98 for the AdaBoost meta-regressor applied to MFs, which were obtained from kNN regression on five models created by the researchers of CSIRO, and around 0.99 for the kNN meta-regressor applied to MFs obtained from kNN regression on these five models.
Why it matches plant phenotyping methodsUAV LiDAR・マルチスペクトル画像から得た特徴量を用い、回帰ベースのメタ特徴量生成と収量推定ワークフローを技術的に評価しており、植物形質であるサトウキビ収量の推定手法が中心である。
abstractThe predictive performance of models utilizing multispectral features, LiDAR-derived features, and a fusion of both modalities was evaluated against a benchmark model based on the Normalized Difference Vegetation Index (NDVI).
Accurate classification of plant disease by farming robot cameras can increase crop yield and reduce unnecessary agricultural chemicals, which is a fundamental task in the field of sustainable and precision agriculture. However, until now, disease classification has mostly been performed by manual methods, such as visual inspection, which are labor-intensive and often lead to misclassification of disease types. Therefore, previous studies have proposed disease classification methods based on machine learning or deep learning techniques; however, most did not consider real-world plant images with complex backgrounds and incurred high computational costs. To address these issues, this study proposes a computationally effective residual convolutional attention network (RCA-Net) for the disease classification of plants in field images with complex backgrounds. RCA-Net leverages attention mechanisms and multiscale feature extraction strategies to enhance salient features while reducing background noises. In addition, we introduce fractal dimension estimation to analyze the complexity and irregularity of class activation maps for both healthy plants and their diseases, confirming that our model can extract important features for the correct classification of plant disease. The experiments utilized two publicly available datasets: the sugarcane leaf disease and potato leaf disease datasets. Furthermore, to improve the capability of our proposed system, we performed fractal dimension estimation to evaluate the structural complexity of healthy and diseased leaf patterns. The experimental results show that RCA-Net outperforms state-of-the-art methods with an accuracy of 93.81% on the first dataset and 78.14% on the second dataset. Furthermore, we confirm that our method can be operated on an embedded system for farming robots or mobile devices at fast processing speed (78.7 frames per second).
Why it matches plant phenotyping methods植物画像から病害状態を推定する画像解析手法を開発・評価しており、病害分類とモデル性能検証が研究の中心であるため。
abstractthis study proposes a computationally effective residual convolutional attention network (RCA-Net) for the disease classification of plants in field images with complex backgrounds.
Reproduction assets foundThe authors explicitly state their RCA-Net model and code are publicly available on GitHub, which constitutes the paper's computational analysis asset. The sugarcane and potato leaf disease datasets are cited third-party prior datasets, not paper-specific deposits.Code · publicData Availability Statement: Our model and code are made publicly available on GitHub site
(https://github.com/mhamza92/RCA-Net, accessed on 15 April 2025).Open asset ↗https://github.com/mhamza92/RCA-Netpdf-page:33 lines:1-57Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published14 Apr 2025International Journal on Advanced Computer Theory and EngineeringCited by 0 · OpenAlex ↗
Sugarcane is the crucial crop in the world, and the many diseases are impacted on this crop. Early disease detection of the crop is the important for the preventing losses of the yield. This research proposes a deep learning based approach for the detecting diseases of the sugarcane using the DenseNet and Sequential models. This pre-trained model uses the convolutional neural networks (CNNs) to extract features from the sugarcane images and classify them into the different diseases based on their features. The Sequential model achieves the high accuracy i.e. 94% while the DenseNet achieves the 75% accuracy. These result shows that this models can effectively detect the diseases of the sugarcane crop which is helpful for the preventing the disease spread and the reduce the yield losses. Sugarcane is a vital crop worldwide, and its production is severely impacted by various diseases. Early detection of these diseases is crucial for preventing significant yield losses. This research proposes a deep learning-based approach for detecting sugarcane crop diseases using DenseNet and Sequential models. The proposed models utilize convolutional neural networks (CNNs) to extract features from sugarcane images and classify them into different disease categories. The DenseNet model achieves a high accuracy of 75%, while the Sequential model attains an accuracy of 94%. The results demonstrate that the proposed models can effectively detect sugarcane crop diseases, enabling farmers and agricultural experts to take timely measures to prevent disease spread and reduce yield losses. This research contributes to the development of precision agriculture techniques, promoting sustainable and efficient sugarcane production.
Why it matches plant phenotyping methodsサトウキビ画像から病害状態を推定する深層学習手法が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThis research proposes a deep learning based approach for the detecting diseases of the sugarcane using the DenseNet and Sequential models.
Objective The objective is to develop a reliable deep learning (DL) based model that can accurately diagnose diseases. It seeks to address the challenges posed by the traditional approach of manually diagnosing diseases to enhance the control of disease and sugarcane production. Methods In order to identify the diseases in sugarcane leaves, this study used EfficientNet architectures along with other well-known convolutional neural network (ConvNet) models such as DenseNet201, ResNetV2, InceptionV4, MobileNetV3 and RegNetX. The models were trained and tested on the Sugarcane Leaf Dataset (SLD) which consists of 6748 images of healthy and diseased leaves, across 11 disease classes. To provide a valid evaluation for the proposed models, the dataset was additionally split into subsets for training (70%), validation (15%) and testing (15%). The models provided were also assessed inclusively in terms of accuracy, further evaluation also took into account level of model's complexity and its depth. Results EfficientNet-B7 and DenseNet201 achieved the highest classification accuracy rates of 99.79% and 99.50%, respectively, among 14 models tested. To ensure a robust evaluation and reduce potential biases, 5-fold cross-validation was used, further validating the consistency and reliability of the models across different dataset partitions. Analysis revealed no direct correlation between model complexity, depth, and accuracy for the 11-class sugarcane dataset, emphasizing that optimal performance is not solely dependent on the model's architecture or depth but also on its adaptability to the dataset. Discussion The study demonstrates the effectiveness of DL models, particularly EfficientNet-B7 and DenseNet201, for fast, accurate, and automatic disease detection in sugarcane leaves. These systems offer a significant improvement over traditional manual methods, enabling farmers and agricultural managers to make timely and informed decisions, ultimately reducing crop loss and enhancing overall sugarcane yield. This work highlights the transformative potential of DL in agriculture.
Why it matches plant phenotyping methodsサトウキビ葉の病害状態を画像から分類する深層学習手法の開発・比較・検証が研究の中心であり、植物病害表現型の取得方法に該当する。
abstractThe objective is to develop a reliable deep learning (DL) based model that can accurately diagnose diseases.
Effective disease management is essential for sustaining sugarcane yield and quality, and traditional methods, such as visual inspection and chemical analysis, are often costly and time-consuming. This study proposes an innovative solution that leverages artificial intelligence (AI) through Convolutional Neural Networks (CNNs) for advanced crop detection and management in sugarcane farming. The AI precision system aims to automate the detection of sugarcane pests and diseases by analyzing collected imagery using machine learning algorithms. This system processes various image parameters, including leaf color, pest types, damage areas, and texture, to accurately identify diseases. By integrating machine learning with image processing techniques, the system provides farmers with rapid and precise diagnoses, enhancing their ability to manage crops effectively and efficiently. The study employs a descriptive developmental approach, emphasizing the AI-driven system's design, development, and evaluation. The objectives include creating a CNN-based system to capture and analyze sugarcane imagery, facilitating timely pest and disease detection, and assessing the system's quality and usability. Integrating AI in sugarcane agriculture promises significant advancements in crop monitoring and management. This research aims to contribute to precision agriculture, enabling farmers to optimize sugarcane cultivation, reduce losses, and enhance productivity. The proposed system offers a scalable solution for real-time monitoring of extensive crop fields, promoting sustainable agricultural practices and supporting economic stability.
Why it matches plant phenotyping methodsサトウキビ画像から葉色・損傷領域・テクスチャ等を抽出し、病害状態をCNNで判定するシステムの設計・開発・評価が中心であり、植物病害フェノタイピング手法に該当する。
abstractThis study proposes an innovative solution that leverages artificial intelligence (AI) through Convolutional Neural Networks (CNNs) for advanced crop detection and management in sugarcane farming.
Sugarcane farming plays a vital role in India's economy, society, and culture, as the country is among the top producers and users of sugarcane globally. Plant parasitic nematodes (PPNs) is a major global threat to sugarcane crops, resulting in yield reductions and financial hardship for farmers. In order to minimize crop damage and implement efficient management strategies, the early detection of nematode infestations is imperative. Artificial Intelligence (AI) presents a viable approach for the early identification, tracking, and prevention of damage caused by nematodes through the implementation of cutting-edge machine learning algorithms, remote sensing technologies, and data analytics. This review focuses on the use of AI in sugarcane crop nematode infection detection and management. By integrating AI technologies in a complementary way with conventional agricultural practices, it is feasible to enhance the productivity and resistance of sugarcane crops to nematode infections.
Why it matches plant phenotyping methodsサトウキビの線虫感染という植物状態の検出を対象に、AI、リモートセンシング、データ解析による検出手法を中心に扱うレビューであり、方法論的役割が明確です。
abstractThis review focuses on the use of AI in sugarcane crop nematode infection detection and management.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Plant growth analysis provides insight regarding the variation behind yield differences in tested genotypes for plant breeders, but adopting this application solely for traditional plant phenotyping remains challenging. Here, we propose a procedure of using uncrewed aerial systems (UAS) to obtain successive phenotype data for growth analysis. The objectives of this study were to obtain high-temporal UAS-based phenotype data for growth analysis and investigate the correlation between the UAS-based phenotype and biomass yield. Seven different energy cane genotypes were grown in a random complete block design with four replications. Twenty-six UAS flight missions were flown throughout the growing season, and canopy cover (CC) and canopy height (CH) measurements were extracted. A five-parameter logistic (5PL) function was fitted through these temporal measurements of CC and CH. The first- and second-order derivatives of this function were calculated to obtain several growth parameters, which were then used to assess the growth of different genotypes with respect to weed competitiveness and biomass yield traits. The results show that CC and CH growth rates significantly differed among genotypes. TH16-16 was outstanding for its ground cover growth; therefore, it was identified as a weed-competitive genotype. Furthermore, TH16-22 had a higher CH maximum growth rate per day, yielding a higher biomass compared to other genotypes. The CH-based multi-temporal data as well as the growth parameters had a better relationship with biomass yield. This study highlights the application of UAS-based high-throughput phenotyping (HTP), along with growth analysis, for assisting plant breeders in decision-making.
Why it matches plant phenotyping methodsUAS画像から作物のキャノピー被覆率・高さを反復取得し、成長パラメータを抽出する高スループット表現型解析手法の適用が中心である。
abstractHere, we propose a procedure of using uncrewed aerial systems (UAS) to obtain successive phenotype data for growth analysis.
Research into large-scale crop monitoring has flourished due to increased accessibility to satellite imagery. This review delves into previously unexplored and under-explored areas in sugarcane health monitoring and disease/pest detection using satellite-based spectroscopy and Machine Learning (ML). It discusses key considerations in system development, including relevant satellites, vegetation indices, ML methods, factors influencing sugarcane reflectance, optimal growth conditions, common diseases, and traditional detection methods. Many studies highlight how factors like crop age, soil type, viewing angle, water content, recent weather patterns, and sugarcane variety can impact spectral reflectance, affecting the accuracy of health assessments via spectroscopy. However, these variables have not been fully considered in the literature. In addition, the current literature lacks comprehensive comparisons between ML techniques and vegetation indices. This review addresses these gaps and discusses that, while current findings suggest the potential for an ML-driven satellite spectroscopy system for monitoring sugarcane health, further research is essential. This paper offers a comprehensive analysis of previous research to aid in unlocking this potential and advancing the development of an effective sugarcane health monitoring system using satellite technology.
Why it matches plant phenotyping methods衛星分光と機械学習によるサトウキビの健康状態・病害評価手法を中心に扱うレビューであり、植物状態の取得・推定方法が主題である。
titleSugarcane health monitoring with satellite spectroscopy and machine learning: A review
Brazil is the world's largest sugarcane producer, meaning productivity monitoring is crucial to enable mills to plan for future seasons and make decisions during the harvest. This results in time, labor, and resource savings. This study aims to leverage machine learning models and spectral data from the Wide Swath Multispectral and Panchromatic Camera (WPM/CBERS-4A) and the Wide Field Imager (WFI/CBERS-4) to estimate sugarcane productivity at various stages of crop development. The study focuses on 6229 sugarcane fields in the Araçatuba region of São Paulo. Productivity data for these fields from the 2020 to 2022 harvest, were provided by a partnering sugarcane mill. A time series of spectral data (bands and the vegetation indices) from the two sensors were used, combined with meteorological, water balance and agronomic data. From these variables, two distinct datasets were created, one for each sensor (WPM and WFI). Sixteen empirical models were developed for each dataset, each representing different stages of sugarcane development, both for plant cane (PC) and ratoon cane (RC), giving 32 models. The models were created iteratively, where each model was developed from the input data set of the previous and current month, allowing estimates throughout crop development along with choosing the most important variables. Data processing included sensor cross-calibration, cloud removal, vegetation index calculation, and integration of meteorological data. The models were trained for different phenological stages of the crops, considering variables such as precipitation, solar radiation, and the number of cuts. The Random Forest model was chosen for its robustness in dealing with large volumes of data, ability to capture complex relationships between variables, and resistance to overfitting. Additionally, the Nemenyi post-hoc test for mean comparison was applied. Upon analyzing the results, it was observed that for both the WFI and WPM models, the optimal stage to begin productivity estimations is from the 6th/7th month for plant cane and the 3.5th/4th month for ratoon cane. Before these time points, the predicted data statistically differed from those observed. Models using data from the complete crop development cycle yielded the best results, which achieved a Coefficient of determination (R²) of 0.63, a modified Willmott index (dₘₒd) of 0.70, and a root mean square error (RMSE) of 10.34 t.ha⁻¹. Thus, data from the WPM/CBERS-4A and WFI/CBERS-4 sensors integrated with additional data demonstrated a promising potential for predicting sugarcane productivity.
Why it matches plant phenotyping methods衛星センサー時系列と機械学習を用いてサトウキビ圃場の生産性（収量形質）を推定し、複数モデルを開発・評価しているため、単なる農業実験の routine 測定ではなく、植物形質推定ワークフローが中心である。
abstractThis study aims to leverage machine learning models and spectral data from the Wide Swath Multispectral and Panchromatic Camera (WPM/CBERS-4A) and the Wide Field Imager (WFI/CBERS-4) to estimate sugarcane productivity at various stages of crop development.
The efficacy of generalized sugarcane yield prediction models holds significant implications for global food security. Given that machine learning algorithms often surpass the precision of remote sensing technology, further exploration of machine learning algorithms in the development of sugarcane yield prediction models is imperative. In this study, we employed six key phenotypic traits of sugarcane, specifically plant height, stem diameter, third-node length (internode length), leaf length, leaf width, and field brix, along with eight machine learning methods: logistic regression, linear regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Backpropagation Neural Network (BPNN), Decision Tree, Random Forest, and the XGBoost algorithm. The aim was to establish an intelligent model ensemble for predicting two crucial phenotypic characteristics-stem diameter and plant height-that determine sugarcane yield, ultimately enhancing the overall yield.The experimental findings indicate that the XGBoost algorithm outperforms the other seven algorithms in predicting these significant phenotypic traits of sugarcane. Furthermore, an analysis of the sugarcane intelligent prediction model's performance under a specialized data environment, incorporating self-prepared data, reveals that the XGBoost algorithm exhibits greater stability. Notably, the data pertaining to these crucial phenotypic traits have a profound impact on the efficacy of the intelligent models. The research demonstrates that a sugarcane yield prediction model ensemble, incorporating multiple intelligent algorithms, can accurately forecast stem diameter and plant height, thereby predicting sugarcane yield. Additionally, this approach, combined with the principles of sugarcane cross-breeding, provides a valuable reference for the artificial breeding of new sugarcane varieties that excel in stem diameter and plant height, bridging a research gap in indirect yield prediction through sugarcane phenotypic traits.
Why it matches plant phenotyping methodsサトウキビの茎径・草丈という植物形質を、複数の機械学習手法で予測・比較する方法論が研究の中心であるため。
abstractThe aim was to establish an intelligent model ensemble for predicting two crucial phenotypic characteristics-stem diameter and plant height
In the agricultural sector, sugarcane farming is one of the most organized forms of cultivation. India is the second-largest producer of sugarcane in the world. However, sugarcane crops are highly affected by diseases, which significantly affect crop production. Despite development in deep learning techniques, disease detection remains a challenging and time-consuming task. This paper presents a novel Hybrid Shifted-Vision Transformer approach for the automated classification of sugarcane leaf diseases. The model integrates the Vision Transformer architecture with Hybrid Shifted Windows to effectively capture both local and global features, which is crucial for accurately identifying disease patterns at different spatial scales. To improve feature representation and model performance, self-supervised learning is employed using data augmentation techniques like random rotation, flipping, and occlusion, combined with a jigsaw puzzle task that helps the model learn spatial relationships in images. The method addresses class imbalances in the dataset through stratified sampling, ensuring balanced training and testing sets. The approach is fine-tuned on sugarcane leaf disease datasets using categorical cross-entropy loss, minimizing dissimilarity between predicted probabilities and real labels. Experimental results demonstrate that the Hybrid Shifted-Vision Transformer outperforms traditional models, achieving higher accuracy in disease detection of 98.5%, making it crucial for reliable disease diagnosis and decision-making in agriculture. This architecture enables efficient, large-scale automated sugarcane disease monitoring.
Why it matches plant phenotyping methodsサトウキビ葉の病害状態を画像から自動分類する新規モデルを開発・評価しており、植物病害表現型の取得・推定が研究の中心である。
abstractThis paper presents a novel Hybrid Shifted-Vision Transformer approach for the automated classification of sugarcane leaf diseases.
Reproduction assets foundThe paper's sugarcane leaf disease classification experiments use three public Kaggle image datasets (SLI, RRSDL, SLDC). The SLDC dataset is cited with a Kaggle URL matching an allowed URL, making it a paper-specific, publicly actionable phenotyping image asset. No author analysis code, trained model checkpoints, or onDataset · publicare collected
from three different datasets: Sugarcane Leaf Image
(SLI) Dataset (Thite Sandip et al., 2023), the Red Rot
Sugarcane Disease Leaf (RRSDL) Dataset (Aakash
Kumar et al., 2023; https://www.kaggle.com/datas
ets/alihussainkhan24/red-rot-sugarcane-disease-leaf-
dataset), and the SLDC Dataset (Aakash Kumar et al.,
2023; https://www.kaggle.com/datasets/pungliyavi
thika/sugarcane-leaf-disease-classification).
The SLI dataset contains a total of 6748 high-
resolution sugarcane leaf images. The images were
captured using Samsung Galaxy F 23 5 G Android
Mobile. The size of the image is 768×1024 pixels and
it includes twelve different disease classes: Banded
Chlorosis (471), Brown Spot (172Open asset ↗pungliyavipdf-raw-page:4 lines:1-92Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published30 Nov 2024Logistic and Operation Management Research (LOMR)Cited by 0 · OpenAlex ↗
Traditional sugarcane growth monitoring methods are time-consuming and error-prone. This study investigated the use of deep learning to automate and enhance the accuracy of sugarcane growth stage classification. The study develops deep learning-based system that leverages high-resolution drone imagery for precise sugarcane age classification, thereby enabling accurate identification of the growth stages. High-resolution drone images were captured at various stages of sugarcane growth and were stitched together to form a comprehensive dataset. Segmentation of isolated areas of interest for analysis. The ResNet-50 deep learning model, enhanced with an additional fully connected layer, was used to classify the growth stages. The model was trained on cropped image sections, and its performance was compared to other deep learning architectures, such as GoogLeNet and VGG, to evaluate its accuracy. The ResNet-50 model outperformed other architectures, achieving 91% accuracy in classifying growth stages, demonstrating its effectiveness in agricultural image analysis and its potential to advance precision agriculture. This study is the first to apply deep learning to sugarcane age classification using high-resolution drone imagery, and it sets a new benchmark for agricultural image analysis. The dataset containing drone images from specific sugarcane fields may limit the model’s generalizability to different regions and environmental conditions.
Why it matches plant phenotyping methodsドローン画像からサトウキビの生育段階を推定する深層学習手法を開発し、複数モデルとの精度比較で検証しており、植物フェノタイピング手法が中心である。
abstractThis study investigated the use of deep learning to automate and enhance the accuracy of sugarcane growth stage classification.
Accurate and rapid discrimination between nodes and internodes in sugarcane is vital for automating planting processes, particularly for minimizing bud damage and optimizing planting material quality. This study investigates the potential of visible-shortwave near-infrared (Vis-SWNIR) spectroscopy (400-1000 nm) combined with machine learning for this classification task. Spectral data were acquired from the sugarcane cultivar Khon Kaen 3 at multiple orientations, and various preprocessing techniques were employed to enhance spectral features. Three machine learning algorithms, linear discriminant analysis (LDA), K-Nearest Neighbors (KNNs), and artificial neural networks (ANNs), were evaluated for their classification performance. The results demonstrated high accuracy across all models, with ANN coupled with derivative preprocessing achieving an F1-score of 0.93 on both calibration and validation datasets, and 0.92 on an independent test set. This study underscores the feasibility of Vis-SWNIR spectroscopy and machine learning for rapid and precise node/internode classification, paving the way for automation in sugarcane billet preparation and other precision agriculture applications.
Why it matches plant phenotyping methodsサトウキビの節・節間という植物器官形態を、近赤外分光と機械学習で分類する取得・解析手法が研究の中心であり、精度検証も行っている。
abstractThis study investigates the potential of visible-shortwave near-infrared (Vis-SWNIR) spectroscopy (400-1000 nm) combined with machine learning for this classification task.
Magnetic resonance imaging (MRI) is a versatile technique in the biomedical field, but its application to the study of plant metabolism in vivo remains challenging because of magnetic susceptibility problems. In this study, we report the establishment of chemical exchange saturation transfer (CEST) for plant MRI. This method enables noninvasive access to the metabolism of sugars and amino acids in complex sink organs (seeds, fruits, taproots, and tubers) of major crops (maize, barley, pea, potato, sugar beet, and sugarcane). Because of its high signal detection sensitivity and low susceptibility to magnetic field inhomogeneities, CEST analyzes heterogeneous botanical samples inaccessible to conventional magnetic resonance spectroscopy. The approach provides unprecedented insight into the dynamics and distribution of sugars and amino acids in intact, living plant tissue. The method is validated by chemical shift imaging, infrared microscopy, chromatography, and mass spectrometry. CEST is a versatile and promising tool for studying plant metabolism in vivo, with many applications in plant science and crop improvement.
Why it matches plant phenotyping methods植物の生体内代謝を非侵襲的に測定するCEST-MRI法を確立し、複数手法で検証しており、植物表現型取得法が研究の中心である。
abstractIn this study, we report the establishment of chemical exchange saturation transfer (CEST) for plant MRI.
Crop monitoring with unmanned aerial vehicles (UAVs) has the potential to reduce field monitoring costs while increasing monitoring frequency and improving efficiency. However, the utilization of RGB-based UAV imagery for crop-specific monitoring, especially for sugarcane, remains limited. This work proposes a UAV platform with an RGB camera as a low-cost solution to monitor sugarcane fields, complementing the commonly used multi-spectral methods. This new approach optimizes the RGB vegetation indices for accurate prediction of sugarcane growth, providing many improvements in scalable crop-management methods. The images were captured by a DJI Mavic Pro drone. Four RGB vegetation indices (VIs) (GLI, VARI, GRVI, and MGRVI) and the crop surface model plant height (CSM_PH) were derived from the images. The fractional vegetation cover (FVC) values were compared by image classification. Sugarcane plant height predictions were generated using two machine learning (ML) algorithms—multiple linear regression (MLR) and random forest (RF)—which were compared across five predictor combinations (CSM_PH and four VIs). At the early stage, all VIs showed significantly lower values than later stages (p
Why it matches plant phenotyping methodsRGB UAV画像から植生指数・作物表面モデルを抽出し、サトウキビの草丈や植生被覆を機械学習で推定・比較する手法が研究の中心であるため。
abstractThis work proposes a UAV platform with an RGB camera as a low-cost solution to monitor sugarcane fields
Predicting saccharine and bioenergy feedstocks in sugarcane enables growers and industries to determine the precise time and location for harvesting a better-quality product in the field. On one hand, Brix, Purity, and total recoverable sugars (TRS) can provide meaningful and reliable indicators of high-quality raw materials for first-generation (1 G) bioethanol. Conversely, Cellulose, Hemicellulose, and Lignin are the primary constituents of straw, directly contributing to second-generation (2G) bioethanol. However, analyzing these materials in the laboratory is a time-consuming and non-scalable task. Therefore, we propose an approach based on a multi-sensor framework, which includes multispectral unmanned aerial vehicle (UAV) imagery, thermal, photosynthetic active radiation (PAR), and chlorophyll fluorescence (ChlF) data, along with machine learning (ML) algorithms namely random forest (RF), multiple linear regression (MLR), decision tree (DT), and support vector machine (SVM), to develop a non-invasive and predictive framework for mapping sugarcane feedstocks. We collected samples of stalks and leaves/straw during the maturity stage while simultaneously collecting remote sensing data. The ML models played a crucial role in predicting 1 G (R² = 0.88–0.93) and 2 G (R² = 0.56–0.82) feedstocks. Notably, remote sensing data could serve as important features for the models, mainly through the spectral bands (Blue, Green, and RedEdge), DTemp and ChlF. Hence, the best features can be further implemented within a framework to predict sugarcane feedstocks. Our study marks a significant advancement in the industrial-scale prediction of sugarcane feedstocks, providing stakeholders with invaluable prescriptive harvesting strategies for both primary products and by-products.
Why it matches plant phenotyping methodsサトウキビの茎・葉由来の飼料成分を、複数センサーと機械学習で非侵襲的に推定・マッピングする方法を中心に開発しており、植物形質の取得・推定が主題である。
abstractwe propose an approach based on a multi-sensor framework, which includes multispectral unmanned aerial vehicle (UAV) imagery, thermal, photosynthetic active radiation (PAR), and chlorophyll fluorescence (ChlF) data, along with machine learning (ML) algorithms namely random forest (RF), multiple linear regression (MLR), decision tree (DT), and support vector machine (SVM), to develop a non-invasive and predictive framework for mapping sugarcane feedstocks.
Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380-1400 nm) to improve the accuracy of sugarcane diseases recognition. Using 130 sugarcane leaf samples, the obtained one-dimensional CWT coefficients from Vis-NIR spectra were transformed into two-dimensional spectrograms. Employing CNN, spectrogram features were extracted and incorporated into decision tree, K-nearest neighbour, partial least squares discriminant analysis, and random forest (RF) calibration models. The RF model, integrating spectrogram-derived features, demonstrated the best performance with an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487. This study may offer a non-destructive, rapid, and accurate means to detect sugarcane diseases, enabling farmers to receive timely and actionable insights on the crops' health, thus minimizing crop loss and optimizing yields.
Why it matches plant phenotyping methodsサトウキビ葉の病害状態を対象に、Vis-NIR分光、CWTスペクトログラム、CNNおよび分類モデルを組み合わせた非破壊的な病害認識手法を開発・評価しており、植物表現型の取得・抽出が中心である。
abstractThis study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380-1400 nm)
In the current times, the number of viruses and infections in the sugarcane plants is widespread. If we want to properly correct these infections, we need to use artificial intelligence such as CNN and RNN. Therefore, in this study, how to prevent the disease in sugarcane using the CNN and RNN system is taken as a test. This model is trained on a diverse dataset of automatic diagnosis images, with a focus on addressing the inherent class in detection. The neural network architecture is designed to capture intricate patterns indicative of sugarcane manifestations in automatic diagnosis images. Through an iterative training process, the model learns to discern subtle features associated with automatic diagnosis, achieving remarkable accuracy. The experimental results confirm the efficacy of our proposed methodology. It explores the many CNN architectures used for plant disease detection, including AlexNet, VGGNet, ResNet, InceptionNet, and DenseNet, as well as their pros and limitations. The survey also discusses the importance of RNNs in plant disease detection, specifically in time-series data analysis, where RNNs have been shown to be useful in forecasting the spread of plant diseases over time. This report also provides a successful outcome for researchers working on the creation of a recognition system for sugarcane diseases. Keywords: convolutional neural networks (cnn), recurrent neural networks (rnn), Sugarcane plants disease detection Accuracy, Genetic algorithm
Why it matches plant phenotyping methodsサトウキビの病徴を画像からCNN/RNNで検出する手法が研究の中心であり、植物病害状態の画像ベース表現型推定に該当する。
abstractthe number of viruses and infections in the sugarcane plants is widespread. If we want to properly correct these infections, we need to use artificial intelligence such as CNN and RNN.
Sugarcane tip cutting is essential to reducing the rate of impurities in the harvest. To achieve adaptive regulation of the tip-cutting position by a sugarcane harvester, we propose a method for estimating the height of the tip-cutting position of sugarcane. The RGB and Binocular depth cameras are aligned to process the sugarcane tip region image. This involves threshold segmentation, morphological operations, and contour detection to identify the tip-cutting position and upper boundary contours. The depth image is segmented using contour pixel information and merged to form a colour depth image of the sugarcane's tip. This image is then transformed using depth data and triangular parallax principles to determine the height of the sugarcane tip-cutting position. The proposed method was evaluated in various sugarcane plantation environments. Comparative analysis between the proposed method and manual measurements of actual cutting position heights revealed that the RMSE ranged from 1.22 cm to 1.78 cm, and R² varied between 0.79 and 0.86. These results demonstrate the effectiveness of the proposed method in accurately extracting the height information of sugarcane tip-cutting positions, which has a specific application value for the adaptive adjustment of the tip-cutting device of the sugarcane harvester.
Why it matches plant phenotyping methodsRGB・深度画像の融合、画像処理、三角測量によりサトウキビ先端の切断位置高さを推定し、実測値と比較検証しているため、植物形質取得法が研究の中心です。
abstractwe propose a method for estimating the height of the tip-cutting position of sugarcane
The world’s most significant yield by production quantity is sugarcane. It is the primary source for sugar, ethanol, chipboards, paper, barrages, and confectionery. Many people are affiliated with sugarcane production and their products around the globe. The sugarcane industries make an agreement with farmers before the tillering phase of plants. Industries are keen on knowing the sugarcane field’s pre-harvest estimation for planning their production and purchases. The proposed research contribution is twofold: by publishing our newly developed dataset, we also present a methodology to estimate the number of sugarcane plants in the tillering phase. The dataset has been obtained from sugarcane fields in the fall season. In this work, a modified architecture of Faster R-CNN with feature extraction using VGG-16 with Inception-v3 modules and sigmoid threshold function has been proposed for the detection and classification of sugarcane plants. Significantly promising results with 82.10% accuracy have been obtained with the proposed architecture, showing the viability of the developed methodology.
Why it matches plant phenotyping methodsサトウキビ個体数という植物形態・群落状態を画像から推定する手法を開発し、データセット公開と精度評価も行っており、表現型取得が研究の中心である。
abstractby publishing our newly developed dataset, we also present a methodology to estimate the number of sugarcane plants in the tillering phase
Reproduction assets foundThe authors publicly deposited their sugarcane tillering-phase video-derived image dataset and the annotated (bounding-box) dataset on Mendeley Data, both explicitly cited in the Data Availability Statement. No analysis code or trained model checkpoint is reported as available.Dataset · publicuthors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Dataset is available at the following: Ubaid, Talha; Javaid, Sameena (2024), “Sugarcane Plant in Tillering Phase”, Mendeley Data, V1, https://doi.org/10.17632/m5zxyznvgz.1 . Ubaid, Talha; Javaid, Sameena (2024), “Annotated Sugarcane Plants”, Mendeley Data, V1, https://doi.org/10.17632/ydr8vgg64w.1 .
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This research received no external funding.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and daOpen asset ↗Mendeley Data · 10.17632/m5zxyznvgz.1lines:147-178Dataset · publicformed Consent Statement
Not applicable.
Data Availability Statement
Dataset is available at the following: Ubaid, Talha; Javaid, Sameena (2024), “Sugarcane Plant in Tillering Phase”, Mendeley Data, V1, https://doi.org/10.17632/m5zxyznvgz.1 . Ubaid, Talha; Javaid, Sameena (2024), “Annotated Sugarcane Plants”, Mendeley Data, V1, https://doi.org/10.17632/ydr8vgg64w.1 .
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This research received no external funding.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the ediOpen asset ↗Mendeley Data · 10.17632/ydr8vgg64w.1lines:147-178Plant phenotyping relevance match · UnverifiedOpenAlex · checked 7 Sept 2026
Sugarcane breeding for drought tolerance is a sustainable strategy to cope with drought. In addition to biotechnology, high-throughput phenotyping has become an emerging tool for plant breeders. The objectives of the present study were to (1) identify drought-tolerant cultivars using vegetation indices (VIs), compared to the traditional method and (2) assess the accuracy of VIs-based prediction model estimating stomatal conductance (Gs) and chlorophyll content (Chl). A field trial was arranged in a randomized complete block design, consisting of seven cultivars of sugarcane. At the tillering and elongation stages, irrigation was withheld, and then furrow irrigation was applied to relieve sugarcane from stress. The physiological assessment measuring Gs and Chl using a handheld device and VIs were recorded under stress and recovery periods. The results showed that the same cultivars were identified as drought-tolerant cultivars when VIs and traditional methods were used for identification. Likewise, the results derived from genotype by trait biplot and heatmap were comparable, in which TCP93-4245 and CP72-1210 cultivars were classified as tolerant cultivars, while sensitive cultivars were CP06-2400 and CP89-2143 for both physiological parameters and VIs-based identification. In the prediction model, the random forest outperformed linear models in predicting the performance of cultivars in untested crops/environments for both Gs and Chl. In contrast, it underperformed linear models in the tested crops/environments. The identification of tolerant cultivars through prediction models revealed that at least two out of three cultivars had consistent rankings in both measured and predicted outcomes for both traits. This study shows the possibility of using UAS mounted with sensors to assist plant breeders in their decision-making.
Why it matches plant phenotyping methodsUAS搭載センサーによる植生指数で糖濃度?ではなく、糖? ではなくサトウキビの乾燥ストレス、生理形質（気孔コンダクタンス・クロロフィル）を推定し、従来法との比較と予測モデルの精度評価を行っており、フェノタイピング手法が中心的である。
abstractThe objectives of the present study were to (1) identify drought-tolerant cultivars using vegetation indices (VIs), compared to the traditional method and (2) assess the accuracy of VIs-based prediction model estimating stomatal conductance (Gs) and chlorophyll content (Chl).
With an emphasis on classifying diseases of sugarcane leaves, this research suggests an attention-based multilevel deep learning architecture for reliably classifying plant diseases. The suggested architecture comprises spatial and channel attention for saliency detection and blends features from lower to higher levels. On a self-created database, the model outperformed cutting-edge models like VGG19, ResNet50, XceptionNet, and EfficientNet_B7 with an accuracy of 86.53%. The findings show how essential all-level characteristics are for categorizing images and how they can improve efficiency even with tiny databases. The suggested architecture has the potential to support the early detection and diagnosis of plant diseases, enabling fast crop damage mitigation. Additionally, the implementation of the proposed AMRCNN model in the Android phone-based application gives an opportunity for the widespread use of mobile phones in the classification of sugarcane diseases.
Why it matches plant phenotyping methodsサトウキビ葉画像から病害状態を推定する深層学習手法を開発・比較し、モバイル実装まで行っており、植物表現型（病害状態）の取得・推定が中心である。
abstractthis research suggests an attention-based multilevel deep learning architecture for reliably classifying plant diseases
The food industry has led the agricultural economy of the state all India to prosperity. India has historically been the largest producing nation having identity of Agricultural Land. Grains , fruits , Vegetables , such as potatoes, oranges, Tomato ,sugarcane and other specially grains and cottons are the chief crops of the India. Citrus and cotton industries have been a driving force behind Maharashtra's impressive economic growth.. The situation has created job opportunities for many people, boosting the state's economic potential. To maintain the prosperity of citrus and cotton industries, Government has been concerned about disease control, labour cost, and global market. During the recent past, citrus canker and citrus greening, Black spot-n cotton has become serious threats to citrus in Maharashtra. Infection by these diseases weakens trees, leading to decline, mortality, lower yields, and decreased commercial value. Likewise, the farmers are concerned about costs from tree loss, scouting, and chemicals used in an attempt to control the disease. An automated detection system may help in prevention and, thus reduce the serious loss to the industries, farmers and Economy of country. This research aims to the development of disease detection with pattern recognition approaches for these diseases in crop. The detection approach consists of three major sub-systems, namely, image acquisition, image processing and pattern recognition. The imaging processing sub-system includes image preprocessing for background noise removal, leaf boundary detection and image feature extraction. Pattern recognition approaches will be use to classify samples among several different conditions on crops. In order to evaluate the classification approaches, results will be compared between classification methods for the different induvial fruits, vegetable, grains disease detection. Obtained results will help in demonstration of classification accuracy which is targeted as better than existing for proposed model as high as 97.00%. This study aimed to assess the potential of identifying plant diseases by examining visible signs on fruits and leaves. These data collection and initial knowledge acquisition is plan in offline approaches. By implementing this simple model, we can achieve a more favourable cost-to-production ratio compared to complex solutions.
Why it matches plant phenotyping methods植物の葉・果実の可視症状から病害状態を画像取得・画像処理・パターン認識で推定する手法の開発が中心であり、植物病害フェノタイピングに該当する。
abstractThis research aims to the development of disease detection with pattern recognition approaches for these diseases in crop.
The agriculture sector is the most important contributor to expanding economies and people because of the vital role it plays in providing high-quality food. It is possible for plant diseases to cause significant decreases in food production as well as the extinction of endangered species. Improving food production quality and minimizing economic losses can be achieved by early identification of plant diseases utilizing reliable or automated detection techniques. Deep learning has recently made great strides in improving the accuracy of object detection and picture categorization systems. The authors of this research proposed a new method for detecting plant diseases; they called it the Learning Network for Disease Identification (LNDI), and they cross-validated it with the Convolutional Neural Network (CNN). Differentiating between diseases in different plant species is a key goal of this research. Several plant kinds, including as fruits and vegetables, wheat, raisins, sugarcane, and lettuce have been incorporated into the system's development process. A wide variety of herbal ailments can also be diagnosed by the computer. Using a large dataset consisting of photos of diseased and healthy plant leaves, the specialists trained deep learning models to detect and distinguish between various plant illnesses and those that went unnoticed. Biological research and agricultural institutes are only two of the many potential uses for plant leaf disease detection. Research into plant leaf disease detection is necessary because it has the potential to improve crop monitoring by automatically identifying disease signs on plant leaves as they emerge.
Why it matches plant phenotyping methods植物葉の病徴を画像から識別する深層学習手法を開発し、CNNとの交差検証を行っており、病害状態の表現型取得が研究の中心です。
abstractThe authors of this research proposed a new method for detecting plant diseases; they called it the Learning Network for Disease Identification (LNDI), and they cross-validated it with the Convolutional Neural Network (CNN).
The rapid and accurate identification of sugarcane internodes is of great significance for tasks such as field operations and precision management in the sugarcane industry, and it is also a fundamental task for the intelligence of the sugarcane industry. However, in complex field environments, traditional image processing techniques have low accuracy, efficiency, and are mainly limited to server-side processing. Meanwhile, the sugarcane industry requires a large amount of manual involvement, leading to high labor costs. In response to the aforementioned issues, this paper employed YOLOv5s as the original model algorithm, incorporated the K-means clustering algorithm, and added the CBAM attention module and VarifocalNet mechanism to the algorithm. The improved model is referred to as YOLOv5s-KCV. We implemented the YOLOv5s-KCV algorithm on Jetson TX2 edge computing devices with a well-configured runtime environment, completing the design and development of a real-time sugarcane internode identification system. Through ablation experiments, comparative experiments of various mainstream visual recognition network models, and performance experiments conducted in the field, the effectiveness of the proposed improvement method and the developed real-time sugarcane internode identification system were verified. The experimental results demonstrate that the improvement method of YOLOv5s-KCV is effective, with an algorithm recognition accuracy of 89.89%, a recall rate of 89.95%, and an mAP value of 92.16%, which respectively increased by 6.66%, 5.92%, and 7.44% compared to YOLOv5s. The system underwent performance testing in various weather conditions and at different times in the field, achieving a minimum recognition accuracy of sugarcane internodes of 93.5%. Therefore, the developed system in this paper can achieve real-time and accurate identification of sugarcane internodes in field environments, providing new insights for related work in sugarcane field industries.
Why it matches plant phenotyping methodsサトウキビ節間という植物器官の画像認識手法とリアルタイムシステムを開発し、アブレーション・比較・圃場性能試験で検証しており、表現型取得が中心である。
abstractcompleting the design and development of a real-time sugarcane internode identification system
Generative Adversarial Networks (GAN) were applied to provide methodological support for efficient sample expansion of crop disease features. Accurate extraction of leaf foreground scenes is crucial for generating high-quality disease features. However, the reported GAN models, such as LeafGAN and STA-GAN, mainly use Grad-CAM to achieve leaf segmentation, which is only suitable for circular-like leaves under simple backgrounds, and perform unsatisfactory for striped sugarcane leaves under complex backgrounds. To address these problems, we have established a novel data augmentation model SugarcaneGAN with a proposed lightweight U-RSwinT as its leaf extraction module and generator. The proposed U-RSwinT combines the advantages of CNN and Swin Transformer. Two datasets of real sugarcane diseased leaves and healthy leaves were collected and several corresponding GAN-generated disease datasets were generated to train classification models in the downstream task. Experimental results show that U-RSwinT outperforms other modules, such as DeepLabV3, Swin-unet, etc., in leaf extraction accuracy as well as in lesion generation quality under various conditions. The mean FID score of the data generated by SugarcaneGAN was 24% and 34% lower than that of LeafGAN and CycleGAN, respectively, indicating much higher quality of the generated data of SugarcaneGAN. Moreover, SugarcaneGAN only required 51.1% of the training time of LeafGAN. Three classification models (ResNet50, SLViT, and ViT/B16) training from different GAN-generated datasets were further tested in the real sugarcane disease dataset, SugarcaneGAN brings significantly higher test accuracies for all three classification models. The ResNet50 model trained by the SugarcaneGAN-generated dataset has its test accuracy, precision, recall, specificity, and F1 score improved by 12.8%, 0.49%, 26.26%, 2.99%, and 0.1554, respectively, compared to that based on the second best LeafGAN-generated dataset. All the results show that SugarcaneGAN brings great improvement in the upstream task of data generation as well as in the downstream task of disease classification compared to the state-of-art GAN models, indicating great potential for leaf-diseased leaf image augmentation and in-situ leaf disease diagnosis.
Why it matches plant phenotyping methods糖蔗病叶图像中的叶片提取、病斑生成和疾病分类工作流是核心方法贡献，直接从植物图像提取叶片与病害表型，并进行了方法比较和验证。
abstractwe have established a novel data augmentation model SugarcaneGAN with a proposed lightweight U-RSwinT as its leaf extraction module and generator
Drought is a major abiotic stress that affects the productivity of sugarcane worldwide. Water deficiency during sugarcane growth will lead to a reduction in leaf pigment content, such as chlorophyll, known as chlorosis. Although changes in spectral reflectance signature were identified a conspicuous sign of chlorophyll content changes caused by drought stress, the quantitative relationships between leaf chlorophyll content and spectral reflection signatures are still poorly explored. In this study, we present our contribution in systematically establishing a model for estimating leaf chlorophyll content in drought-affected sugarcane using VIS/NIR reflectance spectroscopy and characteristic band extraction techniques. Leaves of sugarcane plants at early elongation stage under different controlled irrigation conditions were used for spectra data collection, and the chlorophyll contents were collected with standard analytical methods. Different characteristic band extraction techniques and regression models were compared and discussed to obtain a chlorophyll content estimation model with the best performance. As the quantitative results, the combination of characteristic bands extracted by the successive projection algorithm (SPA) with a Stacking regression model achieved a high chlorophyll content estimation performance (R² = 0.9834, RMSE = 0.0544 mg/cm²) with only 4.3% of original spectral variables as inputs. This study provides a theoretical basis for accurate and non-invasive drought stress level estimation in large-scale cultivation.
Why it matches plant phenotyping methodsVIS/NIR分光と特徴波長抽出・回帰モデルにより、干ばつ下のサトウキビ葉クロロフィル量を非破壊推定する手法を開発・比較しており、植物形質取得が中心である。
abstractwe present our contribution in systematically establishing a model for estimating leaf chlorophyll content in drought-affected sugarcane using VIS/NIR reflectance spectroscopy and characteristic band extraction techniques.
Lodging is a critical factor that impedes sugarcane growth and restricts the production of sucrose and refined ethanol. Considering bending properties of the stalk are highly associated with lodging resistance, there is currently no method for assessing them in a rapid and accurate manner, which limits the development of lodging-resistant varieties. In this study, a high-throughput phenotyping assay for sugarcane stalk bending strength (SBS) and flexural rigidity (EI) characterization was developed by combining partial least squares (PLS) and artificial neural networks (ANN) approaches via near-infrared spectrum calibration. Both strategies demonstrated high coefficient of determination (R²) and ratio performance deviation (RPD) values during calibration, external validation, and internal cross-validation. Compared with the PSL, the ANN algorithm exhibited better performance for both two types of bending properties calibration, with the R²cv and RPD values as high as 0.94 and 4.18, respectively. Most importantly, these models exhibited consistently excellent predictive performance in successive multi-year phenotypic analyses, where the extreme genotypes could be successfully screened out from a large-scale sugarcane population. In conclusion, this study refines a high-throughput phenotyping approach for the determination of mechanical strength in sugarcane stalks that could be applied to the breeding of lodging resistant sugarcane varieties.
Why it matches plant phenotyping methodsサトウキビ茎の曲げ強度・曲げ剛性を近赤外分光とPLS/ANNで推定する高速・高スループット表現型解析法を開発し、外部検証・交差検証・複数年評価を行っており、方法が研究の中心である。
abstracta high-throughput phenotyping assay for sugarcane stalk bending strength (SBS) and flexural rigidity (EI) characterization was developed by combining partial least squares (PLS) and artificial neural networks (ANN) approaches via near-infrared spectrum calibration.
Sugarcane breeding is resource-intensive and time-consuming, and could benefit substantially from the integration of aerial phenotyping (AP) for rapidly identifying genotypes with superior yield traits. The study aimed to assess the feasibility of using AP to enhance sugarcane breeding by rapidly identifying genotypes with superior yield traits. The specific objectives of the study were to: (1) assess the impacts of canopy cover and stomatal conductance on stalk dry mass yield (SDM); (2) assess the feasibility of estimating these traits with aerially sensed normalized difference vegetation index (NDVI) and canopy temperature (Tc); (3) evaluate the potential for predicting SDM from NDVI and Tc; (4) formulate best AP procedures. The study comprised a replicated field trial near Komatipoort, South Africa, with 54 genotypes grown under well-watered and water deficit conditions. Traits were measured on the ground (canopy cover and stomatal conductance) and remotely sensed from the air with a drone (NDVI and Tc) throughout the plant and first ratoon crops, and SDM was measured at harvest. Measurements were categorized by crop water status and extent of canopy cover, and phenotypic trait correlations were analyzed for these different categories. The study confirmed canopy cover and stomatal conductance as influential traits for determining SDM. Canopy cover could be used as a proxy for identifying high- and low-yielding genotypes early on in water stress-free crops. Findings suggest that high stomatal conductance benefits well-watered crops, while relatively low conductance could be advantageous in dry environments, though further investigation is needed. Canopy cover was predicted well from NDVI at partial canopy for well-watered crops, while the prediction of stomatal conductance from Tc lacked reliability. It was concluded that NDVI and Tc could be used to identify high- and low-yielding genotypes when measured earlier on in the growth cycle for well-watered crops. Results also showed potential for using water treatment differences in Tc and SDM to identify drought tolerant genotypes. Lastly, the study highlighted methodological challenges and insights for future agronomic trait prediction using AP techniques. The findings of this study will be used in further testing in the early stages of the breeding programme along with the breeding populations, ultimately helping to manage breeding strategies for target environments. This has the potential to enhance breeding efficiency and ultimately genetic gains towards productive sugarcane cultivars for the future.
Why it matches plant phenotyping methods航空センシングによるNDVI・群落温度から植物形質を推定し、予測性能と育種利用性を評価することが中心であるため。
abstractassess the feasibility of estimating these traits with aerially sensed normalized difference vegetation index (NDVI) and canopy temperature (Tc)
Sugarcane, a vital crop for the global sugar industry, is susceptible to various diseases that significantly impact its yield and quality. Accurate and timely disease detection is crucial for effective management and prevention strategies. We persent the "Sugarcane Leaf Dataset" consisting of 6748 high-resolution leaf images classified into nine disease categories, a healthy leaves category, and a dried leaves category. The dataset covers diseases such as smut, yellow leaf disease, pokkah boeng, mosale, grassy shoot, brown spot, brown rust, banded cholorsis, and sett rot. The dataset's potential for reuse is significant. The provided dataset serves as a valuable resource for researchers and practitioners interested in developing machine learning algorithms for disease detection and classification in sugarcane leaves. By leveraging this dataset, various machine learning techniques can be applied, including deep learning, feature extraction, and pattern recognition, to enhance the accuracy and efficiency of automated sugarcane disease identification systems. The open availability of this dataset encourages collaboration within the scientific community, expediting research on disease control strategies and improving sugarcane production. By leveraging the "Sugarcane Leaf Dataset," we can advance disease detection, monitoring, and management in sugarcane cultivation, leading to enhanced agricultural practices and higher crop yields.
Why it matches plant phenotyping methodsサトウキビ葉の病徴画像を用いた疾患分類データセット自体が中心であり、植物の病害状態を観測・分類する再利用可能な資源である。
abstractWe persent the "Sugarcane Leaf Dataset" consisting of 6748 high-resolution leaf images classified into nine disease categories, a healthy leaves category, and a dried leaves category.
Reproduction assets foundThe paper is a data descriptor for the authors' own Sugarcane Leaf Dataset (6748 leaf images across 11 classes), publicly deposited on Mendeley Data with DOI 10.17632/355y629ynj.1 and a direct URL matching an allowed URL. This is a paper-specific, publicly available plant image dataset directly reproducing the paper's酚Dataset · publicmprehensive dataset that reflects real-world scenarios.
Data source location
Kendur, Taluka- Shirur, District -Pune
Pin - 412403.
Maharashtra, Country- India.
Latitude- 18.785097, Longitude- 74.022090
Data accessibility
Repository name: Sugarcane Leaf Dataset
Data identification number: 10.17632/355y629ynj.1
Direct URL to data: https://data.mendeley.com/drafts/355y629ynj
1
Value of the Data
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Comprehensive and Diverse: The dataset comprises 6748 high-resolution images, serving as a valuable resource for studying sugarcane leaf diseases and healthy leaves. It enables effective disease detection and classification in sugarcane.
•Open asset ↗Mendeley · 10.17632/355y629ynj.1lines:1-56Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Nitrogen is one of the essential nutrients for the production of agricultural crops, participating in a complex interaction among soil, plant and the atmosphere. Therefore, its monitoring is important both economically and environmentally. The aim of this work was to estimate the leaf nitrogen contents in sugarcane from hyperspectral reflectance data during different vegetative stages of the plant. The assessments were performed from an experiment designed in completely randomized blocks, with increasing nitrogen doses (0, 60, 120 and 180 kg ha -1 ). The acquisition of the spectral data occurred at different stages of crop development (67, 99, 144, 164, 200, 228, 255 and 313 days after cutting; DAC). In the laboratory, the hyperspectral responses of the leaves and the Leaf Nitrogen Contents (LNC) were obtained. The hyperspectral data and the LNC values were used to generate spectral models employing the technique of Partial Least Squares Regression (PLSR) Analysis, also with the calculation of the spectral bands of greatest relevance, by the Variable Importance in Projection (VIP). In general, the increase in LNC promoted a smaller reflectance in all wavelengths in the visible (400-680 nm). Acceptable models were obtained (R 2 > 0.70 and RMSE -1 ), the most robust of which were those generated from spectra in the visible (400-680 nm) and red-edge (680-750 nm), with values of R 2 > 0.81 and RMSE -1 . An independent validation, leave-one-date-out cross validation (LOOCV), was performed using data from other collections, which confirmed the robustness and the possibility of LNC prediction in new data sets, derived, for instance, from samplings subsequent to the period of study.
Why it matches plant phenotyping methodsハイパースペクトル反射データからサトウキビ葉の窒素含量を推定する分光計測・PLSRモデルを開発し、独立交差検証で性能を評価しており、表現型取得手法が研究の中心である。
abstractThe aim of this work was to estimate the leaf nitrogen contents in sugarcane from hyperspectral reflectance data during different vegetative stages of the plant.
Developing accurate models of sugarcane yield mapping at fine-scale is of paramount importance and will benefit many aspects of managing growth and harvest of sugarcane crops. Here, we combined high-spatial-resolution multi-sensor (optical and microwave) remote sensing data to estimate sugarcane yield using two approaches. First, we retrieved the 10m resolution vegetation optical depth (VOD) using C-band Sentinel-1 synthetic aperture radar data via the water cloud model, then derived a combined VOD and Sentinel-2 derived green-red vegetation index (GRVI, a traditional vegetation index) time series. Second, we adopted the eXtreme Gradient Boost (XGBoost) machine learning algorithm to estimate total sugarcane production based on the aggregated mean monthly VOD and GRVI time series at the county scale. County-scale sugarcane yield data from 81 counties across Guangxi, China (2018-2020) were used for model training and testing. We then kept the trained best-performance model unchanged to re-predict pixel-scale (10-m resolution) yield using VOD and GRVI images. A SHapley Additive exPlanations (SHAP) approach was employed to test XGBoost knowledge regarding the mechanisms affecting yield. The main outcomes from this study were as follows. (1) The XGBoost model and VOD and GRVI time series unequivocally provided reliable and precise estimates of sugarcane yield, with a low relative error both at the county scale and at the pixel scale. (2) Predictions of sugarcane yield in the growing season using combined VOD and GRVI data yields high accuracy (R² = 0.815) with specific spatial patterns of sugarcane yields for Guangxi province, and were feasible up to two months prior to harvest. (3) The SHAP model revealed that Sentinel-1 derived VOD data are important for predicting yield. This research portrays the benefits of combining VOD and GRVI data in automatic machine learning models for estimating sugarcane yield at high spatial and temporal resolutions.
Why it matches plant phenotyping methodsSentinel-1/2データからサトウキビ収量を推定するセンサ・機械学習ワークフローを開発・検証しており、植物形質である収量推定が研究の中心である。
abstractwe combined high-spatial-resolution multi-sensor (optical and microwave) remote sensing data to estimate sugarcane yield using two approaches
Identification of sugarcane stem nodes is generally dependent on high-performance recognition equipment in sugarcane seed pre-cutting machines and inefficient. Accordingly, this study proposes a novel lightweight architecture for the detection of sugarcane stem nodes based on the YOLOv5 framework, named G-YOLOv5s-SS. Firstly, the study removes the CBS and C3 structures at the end of the backbone network to fully utilize shallow-level feature information. This enhances the detection performance of sugarcane stem nodes. Simultaneously, it eliminates the 32 times down-sampled branches in the neck structure and the 20x20 detection heads at the prediction end, reducing model complexity. Secondly, a Ghost lightweight module is introduced to replace the conventional convolution module in the BottleNeck structure, further reducing the model's complexity. Finally, the study incorporates the SimAM attention mechanism to enhance the extraction of sugarcane stem node features without introducing additional parameters. This improvement aims to enhance recognition accuracy, compensating for any loss in precision due to lightweight modifications. The experimental results showed that the average precision of the improved network for sugarcane stem node identification reached 97.6%, which was 0.6% higher than that of the YOLOv5 baseline network. Meanwhile, a model size of 2.6MB, 1,129,340 parameters, and 7.2G FLOPs, representing respective reductions of 82%, 84%, and 54.4%. Compared with mainstream one-stage target detection algorithms such as YOLOv4-tiny, YOLOv4, YOLOv5n, YOLOv6n, YOLOv6s, YOLOv7-tiny, and YOLOv7, G-YOLOv5s-SS achieved respective average precision improvements of 12.9%, 5.07%, 3.6%, 2.1%, 1.2%, 3%, and 0.4% in sugarcane stem nodes recognition. Meanwhile, the model size was compressed by 88.9%, 98.9%, 33.3%, 72%, 92.9%, 78.8% and 96.3%, respectively. Compared with similar studies, G-YOLOv5s-SS not only enhanced recognition accuracy but also considered model size, demonstrating an overall excellent performance that aligns with the requirements of sugarcane seed pre-cutting machines.
Why it matches plant phenotyping methodsサトウキビ茎節を画像から検出する軽量YOLOv5手法を開発・評価しており、植物器官の認識手法が研究の中心である。
abstractthis study proposes a novel lightweight architecture for the detection of sugarcane stem nodes based on the YOLOv5 framework
The objective of this study is to estimate the canopy height of three sugarcane varieties at different growth stages, with UAV data and to evaluate its relationship with two vegetation indices (VIs) (NDVI and EVI) at different spatial resolutions (3m, 10m and 30m). The indices were calculated using images from the PlanetScope, Sentinel-2, and Landsat 8 satellites, acquired as close as possible to the UAV imaging date. The estimated canopy height for each field was obtained by subtracting the Digital Surface Model (DSM) from the Digital Terrain Model (DTM), built by the Structure from Motion (SfM) technique with UAV RGB images as input. The average from each estimated height was compared with the average measured in the field, to verify the accuracy of the model. Both Pearson’s correlation and the Determination Coefficient (R²) were calculated between the estimated heights and the VIs. The average estimated canopy height and measurements in the field were different (p<0.05), with the model generally underestimating the height. However, the plantation’s surface models portrayed the spatial variability within the field. The use of GCPs is mandatory to reduce errors in estimation. Regarding the indices, the spatial resolution did not influence the correlation analysis, with NDVI showing higher values than EVI, except for area A. However, all values, for both coefficients, were below 0.5 for all areas. Despite that, a temporal analysis is necessary to improve the relationship between the canopy height and VIs. The potential of UAV data as a proxy to zonal management should be addressed in future studies.
Why it matches plant phenotyping methodsUAVのSfM画像からサトウキビの群落高を推定し、圃場測定値で精度検証しており、植物形質の取得手法と技術評価が中心です。
abstractThe estimated canopy height for each field was obtained by subtracting the Digital Surface Model (DSM) from the Digital Terrain Model (DTM), built by the Structure from Motion (SfM) technique with UAV RGB images as input.
Broken cane and impurities such as top, leaf in harvested raw sugarcane significantly influence the yield of the sugar manufacturing process. It is crucial to determine the breakage and impurity ratios for assessing the quality and price of raw sugarcane in sugar refineries. However, the traditional manual sampling approach for detecting breakage and impurity ratios suffers from subjectivity, low efficiency, and result discrepancies. To address this problem, a novel approach combining an estimation model and semantic segmentation method for breakage and impurity ratios detection was developed. A machine vision-based image acquisition platform was designed, and custom image and mass datasets of cane, broken cane, top, and leaf were created. For cane, broken cane, top, and leaf, normal fitting of mean surface densities based on pixel information and measured mass was conducted. An estimation model for the mass of each class and the breakage and impurity ratios was established using the mean surface density and pixels. Furthermore, the MDSC-DeepLabv3+ model was developed to accurately and efficiently segment pixels of the four classes of objects. This model integrates improved MobileNetv2, atrous spatial pyramid pooling with deepwise separable convolution and strip pooling module, and coordinate attention mechanism to achieve high segmentation accuracy, deployability, and efficiency simultaneously. Experimental results based on the custom image and mass datasets showed that the estimation model achieved high accuracy for breakage and impurity ratios between estimated and measured value with R 2 values of 0.976 and 0.968, respectively. MDSC-DeepLabv3+ outperformed the compared models with mPA and mIoU of 97.55% and 94.84%, respectively. Compared to the baseline DeepLabv3+, MDSC-DeepLabv3+ demonstrated significant improvements in mPA and mIoU and reduced Params, FLOPs, and inference time, making it suitable for deployment on edge devices and real-time inference. The average relative errors of breakage and impurity ratios between estimated and measured values were 11.3% and 6.5%, respectively. Overall, this novel approach enables high-precision, efficient, and intelligent detection of breakage and impurity ratios for raw sugarcane.
Why it matches plant phenotyping methods収穫サトウキビの破損率・異物率という植物材料の状態を、画像取得、データセット、質量推定モデル、セマンティックセグメンテーションで定量化する方法が研究の中心である。
abstracta novel approach combining an estimation model and semantic segmentation method for breakage and impurity ratios detection was developed.
Sugarcane field re-seeding robot is a promising yield-enhancing technology proposed to solve the seedling absences in sugarcane fields. In this study, in combination with developing the sugarcane field re-seeding robot, an improved YOLOv5s model was proposed to detect sugarcane seedlings and predict seed replenishment positions. Firstly, field images of one-month-old sugarcane seedlings were taken at different light conditions as a dataset. Secondly, the Slim-Neck was introduced to replace the Neck network, which can reduce the complexity of the model while maintaining sufficient accuracy. Thirdly, the Efficient Channel Attention (ECA) module was added to the Backbone network to enhance the model's attention on critical feature information of sugarcane seedlings. Fourthly, the SCYLLA-IoU (SIoU) loss function was introduced to speed up the convergence of the proposed model. Lastly, a method for predicting seed replenishment positions was proposed and verified by the field tests. The experimental results showed that the mean average precision (mAP), precision, and recall of the improved YOLOv5s model were 93.1 %, 92.1 %, and 89.9 %, respectively, and the detection speed was 82 frames per second (FPS), which increased the mAP by 1.5 % and the detection speed by 12.3 % compared to the original YOLOv5s model. In addition, compared with Faster R-CNN, SSD, and YOLOv4-tiny models, the improved YOLOv5s model had a higher accuracy, faster detection speed, and less memory consumption. The field test showed that the real-time detection speed of the improved YOLOv5s model was 23 FPS in Nvidia Jetson TX2. The real-time detection precision of sugarcane seedlings was 97.2 %, and the recall was 86.7 %. The mean relative error between the numbers of seed replenishment positions predicted by the robot and that predicted by the human was 18.7 %. Consequently, the improved YOLOv5s model can efficiently and accurately detect sugarcane seedlings and predict seed replenishment positions. This technology provides valuable visual detection support for the sugarcane field re-seeding robot.
Why it matches plant phenotyping methodsサトウキビ苗の画像検出と補植位置推定のための改良YOLOv5s手法を開発・検証しており、植物状態の取得がロボット応用の中心的技術である。
abstractan improved YOLOv5s model was proposed to detect sugarcane seedlings and predict seed replenishment positions
Determination of the commercial cane sugar (CCS) content in sugarcane stalk is an important process in the factory. A near infrared hyperspectral imaging system was investigated to develop a CCS prediction model directly from the stalk without peeling. Two scanning techniques and the presence of wax, or not, on the stalk surface were compared for prediction accuracy of the developed models based on laboratory analysis. The model was developed from the spectral data based on an axis rotation scan, which enabled rotation of the stalk around a stationary axis while being scanned. This model performed better than the model based on the data from a normal linear translation scan, especially for wax-covered samples. The axis rotation scan produced a pixel-wise CCS map that could be visualised across the whole surface of the stalk, whereas the pixel-wise CCS map based on the translation scan was able to show only the CCS values for a projected surface area and the opposite projected surface area of the stalk, with error at the curved edge of the stalk. In addition, the single colour CCS maps of each portion of the stalk, which were created by the model based on a translation scan of the wax-covered samples, could be used for visually detecting the right maturity of the sugarcane in the field.
Why it matches plant phenotyping methodsサトウキビ茎の糖含量を推定する近赤外ハイパースペクトル画像法を開発し、走査方式を比較・検証しており、植物形質取得が中心である。
abstractA near infrared hyperspectral imaging system was investigated to develop a CCS prediction model directly from the stalk without peeling.
The near-infrared spectroscopy (NIRS) has been used for efficient characterization and rapid assay of biomass saccharification in other energy plants, but its application in sugarcane is not reported yet. The current study collected a total of 541 sugarcane accessions to take an online NIRS assay. Among these sugarcane collections, we observed large variations in biomass digestibility, particularly for fermentable hexose and total sugar yield from fresh sugarcane stalks, which were detected ranging from 69.88–239.86kg t⁻¹ and 66.56-228.55kg t⁻¹ respectively. Using the modified partial least squares method, six reliable NIRS models were obtained with a high coefficient of determination (R²) and the ratio of prediction to deviation (RPD) values during calibration, internal-cross validation and external validation. Notably, the equation for fermentable hexose exhibited the most consistently high R² (0.98) and RPD (6.62) values, as well as retaining relatively low root mean square error during calibration (3.74kg t⁻¹) and validation (4.19kg t⁻¹), indicating excellent predictive capacity. All models demonstrated accurate and stable prediction performance in the two-year large-scale germplasm resources evaluation, and the optima accessions with high or low biomass digestibility can be screened out consistently. Therefore, this study provides a precise and consistent NIRS assay for high throughput scanning of biomass digestibility in sugarcane.
Why it matches plant phenotyping methodsサトウキビ茎のバイオマス消化性という植物形質をNIRSで高スループット推定する測定法を開発・検証しており、校正・交差検証・外部検証と大規模適用が中心である。
abstractUsing the modified partial least squares method, six reliable NIRS models were obtained with a high coefficient of determination (R²) and the ratio of prediction to deviation (RPD) values during calibration, internal-cross validation and external validation.
Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis. In this study, a novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves. Experimental data comprising spectral reflectance measurements (220-1400 nm) were collected from sugarcane leaf samples at different growth stages, including seedling, tillering, and jointing, and the corresponding chlorophyll contents were measured. The proposed MRC method was employed to select optimal wavelengths for analysis, and subsequent partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed to establish the relationship between the selected wavelengths and the measured chlorophyll contents. In comparison to full-spectrum modelling and other commonly employed wavelength selection techniques, the proposed simplified MRC-GPR model, utilizing a subset of 291 selected wavelengths, demonstrated superior performance. The MRC-GPR model achieved higher coefficient of determination of 0.9665 and 0.8659, and lower root mean squared error of 1.7624 and 3.2029, for calibration set and prediction set, respectively. Results showed that the GPR model, a nonlinear regression approach, outperformed the PLSR model.
Why it matches plant phenotyping methodsサトウキビ葉の分光計測と波長選択・回帰モデルによりクロロフィル含量を推定する手法を開発・比較しており、植物形質取得が研究の中心である。
abstracta novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Abstract Background Sugarcane ( Saccharum spp. ) is the core crop for sugar and bioethanol production over the world. A major problem in sugarcane production is stalk lodging due to weak mechanical strength. Rind penetrometer resistance (RPR) and breaking force are two kinds of regular parameters for mechanical strength characterization. However, due to the lack of efficient methods for determining RPR and breaking force in sugarcane, genetic approaches for improving these traits are generally limited. This study was designed to use near-infrared spectroscopy (NIRS) calibration assay to accurately assess mechanical strength on a high-throughput basis for the first time. Results Based on well-established laboratory measurements of sugarcane stalk internodes collected in the years 2019 and 2020, considerable variations in RPR and breaking force were observed in the stalk internodes. Following a standard NIRS calibration process, two online models were obtained with a high coefficient of determination ( R 2 ) and the ratio of prediction to deviation (RPD) values during calibration, internal cross-validation, and external validation. Remarkably, the equation for RPR exhibited R 2 and RPD values as high as 0.997 and 17.70, as well as showing relatively low root mean square error values at 0.44 N mm −2 during global modeling, demonstrating excellent predictive performance. Conclusions This study delivered a successful attempt for rapid and precise prediction of rind penetrometer resistance and breaking force in sugarcane stalk by NIRS assay. These established models can be used to improve phenotyping jobs for sugarcane germplasm on a large scale.
Why it matches plant phenotyping methodsサトウキビ茎の機械的強度という植物形質をNIRSで高スループット推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractThis study was designed to use near-infrared spectroscopy (NIRS) calibration assay to accurately assess mechanical strength on a high-throughput basis for the first time.
Quantity of seed needed for sugarcane planting can be reduced by employing the single bud planting method. This approach involves utilizing the stem node or bud extracted from harvested sugarcane for replanting. Currently, this process is manually done using traditional planting techniques. To automate node/bud planting, this study developed a computer vision system based on artificial intelligence. The purpose of this system is to automate the recognition of the stem node, where the sugarcane buds are naturally located. By automating the recognition process, this system can facilitate the automation of node cutting equipment and prevent bud damage during the cutting phase. In this study, a transfer learning-based approach was employed, harnessing the capabilities of a pre-trained convolutional neural network to adapt to the specific application of stem node detection. Transfer learning was chosen to avoid the need for developing and training a new model from scratch, thereby saving time in model development, and enhancing accuracy. Transfer learning allows us to leverage the knowledge acquired from a previously trained machine learning model and apply it to a different but related problem. The goal is to exploit the learned knowledge from one task to improve generalization in another. Specifically, the weights learned by a network during "task A" are transferred to a new "task B." Four different transfer learning models were trained and tested in this work: VGG16, ResNet-50, EfficientNetb7, and MobileNet. Practical tests demonstrated that VGG16 exhibited the best performance in stem node detection accuracy, achieving approximately 98%, with an average image processing time of 0.182 seconds per detection. The developed model and methodology hold broad applicability for automating stem node/bud-based sugarcane replanting.
Why it matches plant phenotyping methodsサトウキビ茎節という植物器官の画像認識を自動化するコンピュータビジョン手法の開発が中心であり、単なる生物学的実験の routine 測定ではない。
abstractThe purpose of this system is to automate the recognition of the stem node, where the sugarcane buds are naturally located.
The color and texture characteristics of crops can reflect their nitrogen (N) nutrient status and help optimize N fertilizer management. This study conducted a one-year field experiment to collect sugarcane leaf images at tillering and elongation stages using a commercial digital camera and extract leaf image color feature (CF) and texture feature (TF) parameters using digital image processing techniques. By analyzing the correlation between leaf N content and feature parameters, feature dimensionality reduction was performed using principal component analysis (PCA), and three regression methods (multiple linear regression; MLR, random forest regression; RF, stacking fusion model; SFM) were used to construct N content estimation models based on different image feature parameters. All models were built using five-fold cross-validation and grid search to verify the model performance and stability. The results showed that the models based on color-texture integrated principal component features (C-T-PCA) outperformed the single-feature models based on CF or TF. Among them, SFM had the highest accuracy for the validation dataset with the model coefficient of determination (R 2 ) of 0.9264 for the tillering stage and 0.9111 for the elongation stage, with the maximum improvement of 9.85% and 8.91%, respectively, compared with the other tested models. In conclusion, the SFM framework based on C-T-PCA combines the advantages of multiple models to enhance the model performance while enhancing the anti-interference and generalization capabilities. Combining digital image processing techniques and machine learning facilitates fast and nondestructive estimation of crop N-substance nutrition.
Why it matches plant phenotyping methodsデジタル画像から葉の色・テクスチャ特徴を抽出し、機械学習でサトウキビ葉の窒素状態を推定する手法を構築・交差検証しており、表現型取得・推定が研究の中心である。
abstractextract leaf image color feature (CF) and texture feature (TF) parameters using digital image processing techniques
Abstract Rapid and accurate identification of sugarcane diseases is an important way to improve sugarcane yield. Therefore, this study proposes an improved model based on ShuffleNetV2 network (Im-ShuffleNetV2) for sugarcane disease identification. Firstly, we incorporated the ECA (Enhanced Channel Attention) attention mechanism into ShuffleNetV2, enhancing the network's ability to extract features and detect sugarcane lesion areas. Secondly, a new multi-scale feature extraction branch and Transformer module have been introduced, further improving the independent learning ability of the network. Finally, a large number of numerical results have demonstrated the advantages of the proposed model in terms of parameter size and sugarcane disease identification accuracy. Just as Im-ShuffleNetV2 only has a parameter of 0.4MB, it has significant advantages over parameters such as EfficientV2-S (55.6MB), MobileNetV2 (8.73MB), MobileViT XX small (3.76MB), FasterNetT2 (52.4MB), AlexNet (55.6MB), and MobileNetV3 Large (16.2MB). In addition, compared with the ShuffleNetV2 network, the accuracy has improved by 3.4%. This model not only improves the accuracy of sugarcane leaf disease detection, but also demonstrates the advantage of lightweight, providing valuable reference for future research in the field of sugarcane.
Why it matches plant phenotyping methodsサトウキビ葉の病斑領域・病害を画像から識別する軽量深層学習モデルを開発し、既存モデルと精度・パラメータ数を比較検証しているため、植物病害表現型の取得手法が中心である。
abstractTherefore, this study proposes an improved model based on ShuffleNetV2 network (Im-ShuffleNetV2) for sugarcane disease identification.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Sugarcane is a major industrial crop around the world. Lodging due to weak mechanical strength is one of the main problems leading to huge yield losses in sugarcane. However, due to the lack of high efficiency phenotyping methods for stalk mechanical strength characterization, genetic approaches for lodging-resistant improvement are severely restricted. This study attempted to apply near-infrared spectroscopy high-throughput assays for the first time to estimate the crushing strength of sugarcane stalks. A total of 335 sugarcane samples with huge variation in stalk crushing strength were collected for online NIRS modeling. A comprehensive analysis demonstrated that the calibration and validation sets were comparable. By applying a modified partial least squares method, we obtained high-performance equations that had large coefficients of determination (R2 > 0.80) and high ratio performance deviations (RPD > 2.4). Particularly, when the calibration and external validation sets combined for an integrative modeling, we obtained the final equation with a coefficient of determination (R2) and ratio performance deviation (RPD) above 0.9 and 3.0, respectively, demonstrating excellent prediction capacity. Additionally, the obtained model was applied for characterization of stalk crushing strength in large-scale sugarcane germplasm. In a three-year study, the genetic characteristics of stalk crushing strength were found to remain stable, and the optimal sugarcane genotypes were screened out consistently. In conclusion, this study offers a feasible option for a high-throughput analysis of sugarcane mechanical strength, which can be used for the breeding of lodging resistant sugarcane and beyond.
Why it matches plant phenotyping methodsサトウキビ茎の破砕強度という植物形質を、近赤外分光法と高スループット予測モデルで測定・検証した方法開発および大規模適用が中心である。
titleA high-throughput phenotyping assay for precisely determining stalk crushing strength in large-scale sugarcane germplasm
In sugarcane breeding, dense experiments have been considered in the initial phase (T1), such as the Simplified System (SS) of genotype selection. In this method, the seedlings of each family are transplanted directly from the seed box to the field, forming a kind of carpet. Despite the practical aspect of the method, selection problems are common, as stalks from the same individual within the family are subject to being taken to later evaluation stages, to the detriment of stalks from different individuals. To facilitate the discrimination of stalks of the same family in SS, we evaluated using RGB images (red:green:blue) and NIR (near infrared) spectra. We applied Euclidean distance (D) and Mahalanobis distance (D2) dissimilarity measures to the image and spectral data to distinguish stalks with different genotypes. RGB and NIR data were taken from type +1 leaf samples collected from two experimental blocks, totaling 31 evaluated families. The analyzes were carried out in two stages. In the first stage, we sought to evaluate the classification capacity using RGB images and NIR spectra, using D as a measure of dissimilarity. In the second step, we developed and validated a protocol using RGB images to classify clones, with D2 as a dissimilarity measure. Preliminary results, with distance D, allowed to discriminate clones based on the distance of the evaluated attributes and their combinations. In addition, with the analyzes using the D distance, it was identified that only the use of the R attribute (red band) would give satisfactory results for the second stage, which was the proposed analysis protocol, applying the D2 distance. The D2 statistic and associated p-value confirmed the protocol's usefulness in discriminating stalks in SS, especially stalks from the same families.
Why it matches plant phenotyping methodsサトウキビのRGB画像・NIRデータからクローンを識別する解析手法を開発し、距離指標と分類プロトコルを検証しており、表現型取得・抽出手法が研究の中心である。
abstractWe applied Euclidean distance (D) and Mahalanobis distance (D2) dissimilarity measures to the image and spectral data to distinguish stalks with different genotypes.
Deep learning practices in the agriculture sector can address many challenges faced by the farmers such as disease detection, yield estimation, soil profile estimation, etc. In this paper, disease classification for the sugarcane plant and the experimentation involved thereby is thoroughly discussed. Experimental results include the performances of the well-known existing transfer learning techniques and proposed ensemble deep learning based architecture that incorporates stack ensemble of two networks with one having level-wise spatial attention helping to provide better generalization. A Self-created database of sugarcane leaf diseases is introduced to the research community through this paper. It involves 5 categories with a total of 2569 images. Here, it is observed that best performing transfer learning method, MobileNet-V2 shows an accuracy of around 84% with the lowest number of parameters whereas ensemble model reaching to 86.53% with less epochs and with acceptable number of parameters.
Why it matches plant phenotyping methodsサトウキビ葉の病害状態を画像から分類する深層学習手法を比較・提案し、独自画像データセットも構築しているため、植物表現型取得・解析手法が中心である。
abstractdisease classification for the sugarcane plant and the experimentation involved thereby is thoroughly discussed.
The present study was conducted to identify the association of non-destructive rapid estimation of stalk volume by calculation method with water displacement method and to predict the single cane weight by stalk volume. The stalk volume by water displacement method (SVWM) and stalk volume by calculation method (SVCM) were compared for the efficacy of stalk weight determination in sugarcane clones. Results from both methods were similar and a highly significant relationship was found between the two methods (r2 = 0.9092, P 0.88***) with the original single cane weight by both the studied methods, thus, measurements of stalk volume based on calculation method which provide simple, rapid, non-destructive field phenotyping of single cane weight in sugarcane crop may be recommended for the sugarcane research.
Why it matches plant phenotyping methodsサトウキビの茎体積を用いた非破壊的な単茎重量推定法を開発・比較検証しており、植物形質取得が研究の中心である。
abstractnon-destructive rapid estimation of stalk volume by calculation method
The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380-1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition. The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). The developed simplified SVM model, which utilized the MFPA wavelength selection method yielded the best performances, achieving a precision value of 0.9753, a sensitivity value of 0.9259, a specificity value of 0.9524, and an accuracy of 0.9487. These results outperformed those obtained by other wavelength selection approaches, including the selectivity ratio, variable importance in projection, and the baseline method of the flower pollination algorithm.
Why it matches plant phenotyping methods可視・近赤外分光でサトウキビの病害状態を直接推定し、波長選択法と機械学習モデルを開発・比較検証しているため、植物フェノタイピング手法が中心である。
abstractvisible and near-infrared spectroscopy (380-1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition.
Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet-visible (UV-VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R 2 values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C 3 and C 4 plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, β-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants.
Why it matches plant phenotyping methodsハイパースペクトル反射分光と機械学習により、複数作物の色素を非破壊・同時評価する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractThis study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments
Unmanned aerial vehicles (UAVs) equipped with a global real-time kinematic navigation satellite system (GNSS RTK) could be a state-of-the-art solution to measuring gaps in sugarcane fields and enable site-specific management. Recent studies recommend the use of UAVs to map these gaps. However, low-accuracy GNSS provides incomplete or inaccurate photogrammetric reconstructions, which could easily generate an error in the gap measurement and constrain the applicability of these techniques. Therefore, in this study, we evaluated the potential of UAV RTK imagery for mapping gaps in sugarcane. To compare this solution with conventional UAV approaches, the precision and accuracy of RTK and non-RTK flights were evaluated. To increase the robustness of the research, flights were performed to map gaps found naturally in the field and with plants at different stages of development. Our results showed that the lengths of gaps identified by both RTK and non-RTK UAV imagery were similar, with differences in precision and accuracy of about 1% for both systems. In contrast, RTK was much more efficient and provides stakeholders with guidelines for accurate and precise mapping gaps, allowing them to make confident decisions on site-specific management.
Why it matches plant phenotyping methodsサトウキビ圃場の植生ギャップをUAV-RTK画像で抽出・測定し、RTKと非RTKの精度・正確度を比較検証しており、植物状態の取得手法が研究の中心である。
abstractTherefore, in this study, we evaluated the potential of UAV RTK imagery for mapping gaps in sugarcane.
Developing artificial intelligence applications continue to make our lives easier. Imageprocessing technology has been developed for field-useful studies in medical science, education, finance,agriculture, industry, security, and many other sectors. For agricultural products, good works are done withartificial intelligence to detect plant diseases and take precautions accordingly. In our study, a comparisonwas made with deep learning methods on the images of the sugar cane plant in different categories. TheVGG-19 architecture, which was classified separately from the 5 pre-trained architectures AlexNet,DarkNet-53, GoogLeNet, ResNet-50, and VGG-19, reached the highest accuracy with 92.2%.
Why it matches plant phenotyping methodsサトウキビ葉の画像から病害を分類する深層学習手法を比較・評価しており、植物の病害状態を画像ベースで推定する方法が研究の中心です。
abstractIn our study, a comparisonwas made with deep learning methods on the images of the sugar cane plant in different categories.
Reproduction assets foundThe paper's phenotyping input is a publicly available sugarcane leaf disease image dataset (2,569 images; Healthy, Mosaic, Redrot, Rust, Jaundice) deposited on Mendeley Data by the cited authors (Daphal & Koli, 2022, doi:10.17632/9424skmnrk.1). No author analysis code, trained models, or supplementary assets are statedDataset · public[16] S. Daphal ve S. Koli, «Sugarcane Leaf Disease Dataset,
Mendeley Data, V1,,» 30 April 2022. [Çevrimiçi].
Available: doi: 10.17632/9424skmnrk.1.Mendeley Data · 10.17632/9424skmnrk.1pdf-raw-page:7 lines:1-46Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Summary Volume and surface area of chloroplasts and surface area of plasmodesmata pit fields are presented for two C 4 species, maize and sugarcane, with respect to cell surface area and cell volume. Serial block face scanning electron microscopy (SBF‐SEM) and confocal laser scanning microscopy with the Airyscan system (LSM) were used. Chloroplast size estimates were much faster and easier using LSM than with SBF‐SEM; however, the results were more variable than SBF‐SEM. Mesophyll cells were lobed where chloroplasts were located, facilitating cell‐to‐cell connections while allowing for greater intercellular airspace exposure. Bundle sheath cells were cylindrical with chloroplasts arranged centrifugally. Chloroplasts occupied c. 30–50% of mesophyll cell volume, and 60–70% of bundle sheath cell volume. Roughly 2–3% of each cell surface area was covered by plasmodesmata pit fields for both bundle sheath and mesophyll cells. This work will aid future research to develop SBF‐SEM methodologies with the aim to better understand the effect of cell structure on C 4 photosynthesis.
Why it matches plant phenotyping methodsSBF-SEMと共焦点顕微鏡を用いて葉細胞・葉緑体・原形質連絡の3D形態形質を定量し、手法の速度と変動性も比較しているため、植物表現型取得法が中心的です。
abstractSerial block face scanning electron microscopy (SBF‐SEM) and confocal laser scanning microscopy with the Airyscan system (LSM) were used.
Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds.
Why it matches plant phenotyping methodsトウモロコシ葉の病害領域を画像から検出・分類し、病斑をセグメンテーションして追跡する深層学習・モバイル手法が研究の中心であり、植物病害状態の表現型推定に該当する。
abstractprovides an application for the detection and classification of diseases in maize crop using deep learning models
The Grassy Shoot Disease is a severe problem in sugarcane crops, affecting their productivity and causing significant economic losses. The research aims to introduce a model that utilizes both CNN and SVM techniques to make precise predictions about the severity levels of Grassy Shoot Disease in sugarcane cultivation. The methodology involves data preprocessing, CNN-based feature extraction, SVM-based classification, and model evaluation. The data preprocessing phase involved data cleaning, normalization, and augmentation, followed by the extraction of features using a three-layer CNN model. Following feature extraction, the extracted features were fed into an SVM-based classifier with regularisation to avoid overfitting. The classifier's overall accuracy was 81.53%, and its precision, recall, F1-score, and support values ranged from 65.71% to 85.37% depending on the severity level. These results show that the suggested method is a solid method for accurately estimating the degrees of Grassy Shoot Disease severity in sugarcane crops.
Why it matches plant phenotyping methodsサトウキビ個体の病害重症度を画像由来のCNN特徴抽出とSVM分類で推定する手法を開発・評価しており、植物の状態（病害重症度）の取得・推定が中心である。
abstractThe research aims to introduce a model that utilizes both CNN and SVM techniques to make precise predictions about the severity levels of Grassy Shoot Disease in sugarcane cultivation.
The color and texture characteristics of crops can reflect their nitrogen (N) nutrient status and help optimize N fertilizer management. This study conducted a one-year field experiment to collect sugarcane leaf images at tillering and elongation stages using a commercial digital camera and extract leaf image color feature (CF) and texture feature (TF) parameters using digital image processing techniques. By analyzing the correlation between leaf N content and feature parameters, feature dimensionality reduction was performed using principal component analysis (PCA), and three regression methods (multiple linear regression; MLR, random forest regression; RF, stacking fusion model; SFM) were used to construct N content estimation models based on different image feature parameters. All models were built using five-fold cross-validation and grid search to verify the model performance and stability. The results showed that the models based on color-texture integrated principal component features (C-T-PCA) outperformed the single-feature models based on CF or TF. Among them, SFM had the highest accuracy for the validation dataset with the model coefficient of determination (R²) of 0.9264 for the tillering stage and 0.9111 for the elongation stage, with the maximum improvement of 9.85% and 8.91%, respectively, compared with the other tested models. In conclusion, the SFM framework based on C-T-PCA combines the advantages of multiple models to enhance the model performance while enhancing the anti-interference and generalization capabilities. Combining digital image processing techniques and machine learning facilitates fast and nondestructive estimation of crop N-substance nutrition.
Why it matches plant phenotyping methodsデジタル画像からサトウキビ葉の窒素栄養状態を推定する画像処理・機械学習手法を構築し、交差検証で性能を評価しており、フェノタイピング手法が中心である。
abstractextract leaf image color feature (CF) and texture feature (TF) parameters using digital image processing techniques
Sugarcane crop lodging is an agronomic condition that critically affects the cane yield and sugar quality. Lodging also impedes intercultural management and harvest operations. Currently, no approaches known exist to non-invasively assess sugarcane lodging. This study is therefore focused on expedited and autonomous sugarcane lodging detection using state-of-the-art computer vision techniques. Total 1600 digital red-green-blue (RGB) images of lodged and non-lodged sugarcane were acquired for two cultivars and two years (2020, and 2021) of growing seasons. These images were augmented to obtain a total of 6400 images. Based on tested proportions for minimum model overfittings, 80 % of the 6400 images were used for training, 10 % for validation, and remaining 10 % for testing of seven state-of-the-art deep learning (DL) models; ResNet50, GoogLeNet, DarkNet53, Inception V3, Xception, AlexNet, and MobileNetV2. When validated, amongst all, ResNet50 demonstrated the highest lodging prediction accuracy of 98.5 %, followed by GoogLeNet (98.0 %), DarkNet53 (97.6 %), InceptionV3 (97.6 %), Xception (97.1 %), AlexNet (96.5 %), and MobileNetV2 (93.6 %) and respective model precisions of 98.6 %, 98.6 %, 97.5 %, 97.2 %, 96.9 %, 96.1 %, and 93.1 %. Maximum accuracies and minimum model overfitting were observed for batch size of 16 and 30 epochs for all the models. The overall error rate for MobileNetV2, AlexNet, Xception, DarkNet53, InceptionV3, GoogLeNet, and ResNet50 models were 6.4 %, 3.5 %, 2.9 %, 2.4 %, 2.4 %, 2.0 % and 1.5 %, respectively. Best performing ResNet50 model was again tested on 50 independent images from real field conditions from both years and a net accuracy of 94 % was obtained. The residual blocks and skip connection features of ResNet50 help optimizing training parameters and therefore achieved better performance compared to other DL models. Autonomous lodging assessments with AI models could help guide supervised harvest operations without compromising the lodged crop, yield potentials, and site-specific management of other intercultural operations.
Why it matches plant phenotyping methodsサトウキビの倒伏状態という植物状態をRGB画像から自動推定するコンピュータビジョン手法を開発・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 is therefore focused on expedited and autonomous sugarcane lodging detection using state-of-the-art computer vision techniques.
Predicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of full-scale production. °Brix and Purity can offer significant and reliable indicators of high-quality raw material for industrial processing for food and fuel. However, their analysis in a relevant laboratory can be costly, time-consuming, and not scalable. We, therefore, analyzed whether merging multispectral images and machine learning (ML) algorithms can develop a non-invasive, predictive framework to map canopy reflectance to °Brix and Purity. We acquired multispectral images data of a sugarcane-producing area via unmanned aerial vehicle (UAV) while determining °Brix and analytical Purity from juice in a routine laboratory. We then tested a suite of ML algorithms, namely multiple linear regression (MLR), random forest (RF), decision tree (DT), and support vector machine (SVM) for adequacy and complexity in predicting °Brix and Purity upon single spectral bands, vegetation indices (VIs), and growing degree days (GDD). We obtained evidence for biophysical functions accurately predicting °Brix and Purity. Those can bring at least 80% of adequacy to the modeling. Therefore, our study represents progress in assessing and monitoring sugarcane on an industrial scale. Our insights can offer stakeholders possibilities to develop prescriptive harvesting and resource-effective, high-performance manufacturing lines for by-products.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習により、サトウキビの品質形質（°BrixおよびPurity）を非侵襲的に推定・マッピングする方法が研究の中心であり、複数アルゴリズムの性能評価も行っている。
abstractWe, therefore, analyzed whether merging multispectral images and machine learning (ML) algorithms can develop a non-invasive, predictive framework to map canopy reflectance to °Brix and Purity.
Latest improvements in precision agriculture through machine learning, deep learning, remote sensing has helped to come up with different methods to detect crop diseases. One of the main reasons for yield loss of a crop is non detection of disease early in time. This paper reviews the various methods and techniques that can be used to detect diseases in sugarcane crop. Firstly, we provide a review on the different types of input data w.r.t imagery -RGB, multispectral and hyperspectral. Then we highlight the different techniques applied for disease detection-machine learning, deep learning, transfer learning and spectral information divergence. We also give an overview of the results achieved by using the different techniques.
Why it matches plant phenotyping methodsサトウキビの病害を画像・リモートセンシングと機械学習で検出する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方法が中心である。
abstractThis paper reviews the various methods and techniques that can be used to detect diseases in sugarcane crop.
A sensor mounted on an unmanned aerial system (UAS) may enable breeding programs’ selection efficiency. The objectives of this study were to investigate the use of UAS with red, green, and blue (RGB) camera to assess sugarcane yield and to investigate the direct and indirect influences of canopy features on cane yield of sugarcane. A trial was conducted from 2019 to 2021 at the Texas A&M AgriLife Research and Extension Center in Weslaco, Texas, and arranged in a complete randomized block design, consisting of 7 genotypes with 4 replications. Seven UAS image acquisitions were performed at the plant cane stage using an RGB sensor. At the first ratoon stage, five flights were conducted using RGB and multispectral sensors. Ground measurements were obtained, including pol, tons of sugar per hectare (TSH), and tons of cane per hectare (TCH). The results showed that the correlation of canopy features with pol at the elongation phase was poor, compared to those acquired at the maturity phase. Likewise, the most significant correlations between canopy features with TSH and TCH were found in the late elongation to maturity phase. In addition, a good agreement of the validation dataset between the observed and predicted pol (R2 0.58, RMSE 0.77%), TSH (R2 0.68, RMSE 2.24 Mg ha−1), and TCH (R2 0.66, RMSE 13.64 Mg ha−1) was observed. The canopy height model (CHM) and Normalized Difference Vegetation Index (NDVI) had the highest direct effect on TCH. Therefore, assessing sugarcane yield using UAS-based imagery looks promising for High Throughput Phenotyping (HTP) in sugarcane breeding programs.
Why it matches plant phenotyping methodsUASのRGB・マルチスペクトル画像からサトウキビの収量を推定し、観測値と予測値を検証しており、植物表現型取得・推定手法が研究の中心である。
titleThe use of UAS-based high throughput phenotyping (HTP) to assess sugarcane yield
The main bottleneck to accelerating the development of new sugarcane varieties with desirable traits to meet the demands of the sugar-energy sector and adaptation to climate change is the absence of high-throughput phenotyping methods for evaluating varieties in the field. Traditional methods of field phenotyping depend on trained specialists for visual evaluations that are slow, laborious, and subjective. In this study, we investigated UAV-based multispectral data and machine learning algorithms to improve efficiency in the evaluation of field phenotyping of sugarcane varieties regarding the resistance to infection by orange and brown rusts. Spectral data from five bands (Blue, Green, Red, Red-edge, and NIR) and 14 vegetation indices were tested in direct correlations with infection scores collected in the field for the two types of rust. Sugarcane varieties were classified according to their resistance to rusts using three machine learning algorithms (Random Forest, radial SVM, and KNN). Correlations between the Red band data and infection scores of the two types of rust were significant (r = 0.67) for evaluations made at 165 days after planting (DAP). Conversely, regarding the varietal classification into three resistance classes, a high level of overall (88.1%) and balanced (Resistant = 90.3, Moderately resistant = 88.6, and Susceptible = 82.1) accuracy was reached at 195 DAP with the radial SVM model. UAV-based multispectral data is able to assist in the phenotyping of new sugarcane varieties regarding the resistance to these diseases.
Why it matches plant phenotyping methodsUAVマルチスペクトル計測と機械学習によるサトウキビのさび病抵抗性表現型評価が研究の中心であり、感染スコアに基づく分類性能も検証している。
abstractwe investigated UAV-based multispectral data and machine learning algorithms to improve efficiency in the evaluation of field phenotyping of sugarcane varieties regarding the resistance to infection by orange and brown rusts.
Abstract Crop monitoring is a process that involves regular field visits that seem to be difficult since it needs a huge amount of time and manpower. Thus, in modern agriculture, with an extensive range of satellite data such as Landsat, Sentinel-2, Modis, and Palsar, data are readily available. Sugarcane is a tall perennial grass belonging to the genus Saccharum, utilized for producing sugar. These plants were generally 2–6 m tall with fibrous, stout, jointed stalks, rich in sucrose, that will be accumulated in the stalk internodes. Sugarcanes have a different growth pattern and phenology than many other crops; thus, the spectral and temporal features of satellite data are examined by utilizing statistical and machine learning (ML) techniques for optimal discrimination of sugarcane fields with other crops. In this study, we propose an Optimal Deep Learning Driven Smart Sugarcane Crop Monitoring (ODLD-SSCM) model on Remote Sensing Images. The presented ODLD-SSCM model mainly intends to estimate the crop yield of sugarcanes using RSIs. In the presented ODLD-SSCM technique, the sugarcane yield mapping can be derived by the use of the self-attentive deep learning (SADL) model. Besides, an oppositional spider colony optimization (OSCO) algorithm is used for the hyperparameter tuning of the ODLD-SSCM model. A detailed set of experimentations were performed to demonstrate the enhanced outcomes of the ODLDSSCM model. A comprehensive comparison study pointed out the enhancements of the ODLD-SSCM model over other recent approaches.
Why it matches plant phenotyping methodsリモートセンシング画像からサトウキビ収量を推定する深層学習モデルとハイパーパラメータ最適化を中心に開発・比較しており、植物の収量形質を抽出する方法が主要貢献である。
abstractThe presented ODLD-SSCM model mainly intends to estimate the crop yield of sugarcanes using RSIs.
Background: Sugarcane ( Saccharum officinarum L .) is the core crop for sugar and bioethanol production over the world. A major problem in sugarcane production is stalk lodging due to weak mechanical strength. Since there are no efficient methods for determining stalk mechanical strength in sugarcane, genetic approaches for improving stalk lodging resistance are largely limited. This study was designed to use near-infrared spectroscopy (NIRS) calibration assay to accurately assess mechanical strength on a high-throughput basis for the first time. Results: : Hundreds of sugarcane germplasms were harvested at the mature stage in the year of 2019 and 2020. In terms of determining rind penetrometer resistance (RPR) and breaking force, large variations of mechanical strength were found in the sugarcane stalk internodes, based on well-established laboratory measurements. Through partial least square regression analysis, two online NIRS models were established with a high coefficient of determination ( R 2 ) and the ratio of prediction to deviation (RPD) values during calibration, internal cross-validation, and external validation. Remarkably, the equation for RPR exhibited R 2 and RPD values as high as 1.00 and 17.7, as well as showing relatively low root mean square error values at 0.44 N mm -2 during global modeling, demonstrating excellent predictive performance. Conclusions: : This study delivered a successful attempt for rapid and precise prediction of mechanical strength in sugarcane stalk by NIRS assay. By using these established models, genetic improvements could be made to phenotyping jobs for large-scale sugarcane germplasm.
Why it matches plant phenotyping methodsサトウキビ茎の機械的強度という植物形質を、NIRSと回帰モデルで高スループット推定する手法を開発・検証しており、フェノタイピング手法が中心である。
abstractThis study was designed to use near-infrared spectroscopy (NIRS) calibration assay to accurately assess mechanical strength on a high-throughput basis for the first time.
Sugarcane stem node identification is the core technology required for the intelligence and mechanization of the sugarcane industry. However, detecting stem nodes quickly and accurately is still a significant challenge. In this paper, in order to solve this problem, a new algorithm combining YOLOv3 and traditional methods of computer vision is proposed, which can improve the identification rate during automated cutting. First, the input image is preprocessed, during which affine transformation is used to correct the posture of the sugarcane and a rotation matrix is established to obtain the region of interest of the sugarcane. Then, a dataset is built to train the YOLOv3 network model and the position of the stem nodes is initially determined using the YOLOv3 model. Finally, the position of the stem nodes is further located accurately. In this step, a new gradient operator is proposed to extract the edge of the image after YOLOv3 recognition. Then, a local threshold determination method is proposed, which is used to binarize the image after edge extraction. Finally, a localization algorithm for stem nodes is designed to accurately determine the number and location of the stem nodes. The experimental results show that the precision rate, recall rate, and harmonic mean of the stem node recognition algorithm in this paper are 99.68%, 100%, and 99.84%, respectively. Compared to the YOLOv3 network, the precision rate and the harmonic mean are improved by 2.28% and 1.13%, respectively. Compared to other methods introduced in this paper, this algorithm has the highest recognition rate.
Why it matches plant phenotyping methodsサトウキビ茎節の画像認識・位置推定アルゴリズム自体を開発し、茎節の数と位置という再利用可能な器官形質を抽出しているため、収穫用位置決めを超える植物フェノタイピング手法に該当する。
abstracta new algorithm combining YOLOv3 and traditional methods of computer vision is proposed
This study aimed to evaluate the use of proximal Vis-NIR-SWIR spectroscopy to estimate technological parameters and tons of sugarcane per hectare (TSH) with the application of rock powder to the soil. It was carried out in an Arenosol area in Paranavaí , Brazil. The treatments were arranged within a split-plot system, designed in randomized blocks with four repetitions. For the experimental plots, inputs supplying calcium, magnesium, and sulfur were applied, whereas inputs supplying potassium were used for the subplots. The first and second cycles (sugarcane) were completed at 14 and 26 months, respectively, after the inputs application. During both growth cycles, technological parameters of the crop and of the TSH were determined. In addition, the stem spectrum of the crop was collected with a Vis-NIR-SWIR proximal spectroradiometer for later prediction of the parameters and TSH through the Partial Least Square Regression technique. It was possible to adjust models in the prediction phase with R²ₚ > 0.50 and RPDₚ > 1.50 for all evaluated attributes, with emphasis on three variables, namely purity, reducing sugars, and brix, which had R²ₚ and RPDₚ values above 0.86 and 2.75, respectively. The results show that proximal Vis-NIR-SWIR spectroscopy can be used to predict technological parameters and TSH of sugarcane with the application of rock powder. It offers advantages in relation to usual methods, since it is less time-consuming and low-cost, moreover it does not involve the use of toxic reagents.
Why it matches plant phenotyping methodsサトウキビの品質形質と収量を近接Vis-NIR-SWIR分光で推定する手法を開発・評価しており、形質取得と予測モデルが研究の中心である。
abstractthe use of proximal Vis-NIR-SWIR spectroscopy to estimate technological parameters and tons of sugarcane per hectare (TSH)
Nitrogen is an essential factor for assessing the quality of sugarcane during the growing season, as deficiency of this component significantly reduces crop yield . The Kjeldahl method is the most common approach to measuring sugarcane nitrogen, but it is a laborious, costly, and time-consuming process. Conversely, multispectral satellite imagery can provide timely, cost-effective, and large-scale information on the nitrogen content in sugarcane fields. The current study applied random forest (RF) and support vector regression (SVR) models to estimate sugarcane leaf nitrogen using vegetation indices and spectral bands of Sentinel-2. In-situ data was taken from 45 farms (1125 ha) in a sugarcane production agro-industrial complex in southwest Iran. The global environmental monitoringindex (GEMI), chlorophyllindexgreen (Clgreen), and Sentinel-2 red-edge position index (S2REP) were found to be the most important variables related to sugarcane nitrogen. The coefficient of determination (R²) for RF and SVR was 0.59 and 0.58, respectively, and the corresponding root mean square error (RMSE) was 0.08 and 0.09, respectively. Despite the similar performances of the two models, RF showed higher accuracy; however, to improve the results, the use of multi-temporal data for model calibration is recommended.
Why it matches plant phenotyping methodsSentinel-2マルチスペクトルデータとRF/SVRにより、サトウキビ葉窒素という植物形質を推定し、モデル性能を比較・評価する方法中心の研究である。
abstractThe current study applied random forest (RF) and support vector regression (SVR) models to estimate sugarcane leaf nitrogen using vegetation indices and spectral bands of Sentinel-2.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
Marker-assisted selection (MAS) has been widely used in the last few decades in plant breeding programs for the mapping and introgression of genes for economically important traits, which has enabled the development of a number of superior cultivars in different crops. In sugarcane, which is the most important source for sugar and bioethanol, marker development work was initiated long ago; however, marker-assisted breeding in sugarcane has been lagging, mainly due to its large complex genome, high levels of polyploidy and heterozygosity, varied number of chromosomes, and use of low/medium-density markers. Genomic selection (GS) is a proven technology in animal breeding and has recently been incorporated in plant breeding programs. GS is a potential tool for the rapid selection of superior genotypes and accelerating breeding cycle. However, its full potential could be realized by an integrated approach combining high-throughput phenotyping, genotyping, machine learning, and speed breeding with genomic selection. For better understanding of GS integration, we comprehensively discuss the concept of genetic gain through the breeder's equation, GS methodology, prediction models, current status of GS in sugarcane, challenges of prediction accuracy, challenges of GS in sugarcane, integrated GS, high-throughput phenotyping (HTP), high-throughput genotyping (HTG), machine learning, and speed breeding followed by its prospective applications in sugarcane improvement.
Why it matches plant phenotyping methodsサトウキビ育種におけるゲノム選抜と統合される高スループット表現型解析を、方法論・応用の一部として包括的に論じるレビューであり、植物フェノタイピングが明示的かつ実質的な主題です。
abstractwe comprehensively discuss the concept of genetic gain through the breeder's equation, GS methodology, prediction models, current status of GS in sugarcane, challenges of prediction accuracy, challenges of GS in sugarcane, integrated GS, high-throughput phenotyping (HTP), high-throughput genotyping (HTG), machine learning, and speed breeding followed by its prospective applications in sugarcane improvement.
Crop leaf area index (LAI) mapping from remote sensing observations is highly demanded for regional agricultural applications, such as crop health monitoring and crop yield prediction. However, the popularly used model inversion method inevitably presents an ill-posed problem, which leads to unstable and inaccurate retrieval results. An agricultural parcel is a relatively homogeneous object due to uniform agricultural practices and similar environmental conditions. Crops inside a parcel generally present the same growth stage and similar growth status. In this study, a new method was proposed by adopting spatiotemporal constraints at the parcel level in the model inversion process for LAI retrieval. Firstly, phenology information of parcels was utilized to constrain the LAI ranges based on the established prior knowledge, which described the temporal variation of LAI during the life cycle of crop. Subsequently, spatial constraint was adopted in the model inversion through a proposed novel cost function, which assumed the spatial autocorrelation of parameters inside a parcel according to the first law of geography. Sugarcane was taken as an example to evaluate the proposed method. The method was applied to Sentinel-2 data and validated using ground-measured LAI data. The retrieved model parameters exhibited smoother spatial patterns and lower intra-parcel spatial variations through the proposed parcel-level inversion method, compared to the conventional pixel-level inversion method. Evaluations of LAI retrieval accuracy showed that the parcel-level inversion method yielded more accurate results (root mean square error (RMSE): 0.34 m²/m²; relative root mean square error (RRMSE): 20.89%), compared to the pixel-level inversion method (RMSE: 0.56 m²/m²; RRMSE: 34.53%). The spatiotemporal constraint strategy presented the ability to prevent severe overestimation and underestimation. Among the validation data set, the accuracy of the severely overestimated samples of the pixel-level method (RMSE: 0.93 m²/m²) was improved via the new method (RMSE: 0.43 m²/m²); the accuracy of the severely underestimated samples of the pixel-level method (RMSE: 0.66 m²/m²) was improved via the new method (RMSE: 0.26 m²/m²). Finally, the proposed method was applied to obtain sugarcane LAI mapping. The study demonstrates that the proposed parcel-level spatiotemporal constraint strategy improves the accuracy of LAI retrieval, has the capability of producing a highly reliable crop LAI mapping, and shows good potential for agricultural applications at regional scales.
Why it matches plant phenotyping methods作物LAIという植物形態形質のリモートセンシング推定手法を開発し、実測LAIおよび従来法と比較検証しているため、方法が中心的である。
abstractIn this study, a new method was proposed by adopting spatiotemporal constraints at the parcel level in the model inversion process for LAI retrieval.
Sugarcane (Saccharum spp. interspecific hybrids), a high biomass perennial crop, in which manual data collection for early yield prediction, through its growth cycle (∼12 mo long), is labor intensive and time consuming. Alternately, aerial imagery can be explored to predict yield‐related components and high‐throughput phenotyping for genetic selection. In this study, aerial imagery and ground data were collected in Stage IV (final stage of genotype selection) of the Florida sugarcane cultivar development program to evaluate the use of unmanned aerial vehicles in yield prediction (tons of cane per hectare [TCH], sucrose concentration, and tons of sugar per hectare [TSH]) in multiple new genotypes (13 in plant cane crop, nine in first ratoon crop). Aerial imagery data were collected using hyperspectral sensor, and yield data were collected through manual sampling of sugarcane stalks at harvest. The gradient‐boosting regression tree model was selected based on low mean absolute percentage error on multiple dates (April, July, and September) to determine the best timing of yield predictions. Results showed that yield was predicted with greater accuracy in July in plant crop and April in the first ratoon crop. Also, sucrose percentage was predicted with greater accuracy (94% in plant crop and 93% in first ratoon crop) than TCH and TSH. Although only two out of the top five genotypes were common in both selection methods (measured vs. predicted yields) in Stage IV, high accuracy in TCH and sucrose percentage shows that aerial imagery may be useful in making genotype selection in early stages when actual yield estimation is not feasible.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習により、サトウキビの収量・糖度を推定し、遺伝子型選抜に利用する高スループット表現型解析手法を評価しており、表現型取得・推定が研究の中心です。
abstractaerial imagery can be explored to predict yield‐related components and high‐throughput phenotyping for genetic selection
Early prediction of sugarcane crop yield would benefit sugarcane growers and policymakers by allowing for timely decisions. The primary objective of this study was to reduce reliance on satellite images and improve early prediction of sugarcane yield at row level by using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery. To our knowledge, no previous study has evaluated the performance of multispectral UAV-derived vegetation indices in sugarcane crops at the crop row level. In this study, we used UAV mapping on 48 rows of sugarcane at three main growth stages (early, middle, and mature) over three growing seasons. A secondary objective was to predict future sugarcane yields at the earliest possible stage of growth. The results showed that the optimal growth stage for all 23 VIs varied, but the middle stage, from mid-March to early May, was the most prevalent. Further detailed analysis in the middle stage revealed that March was the best month for predicting future sugarcane yields when compared to April and May. This result is approximately a month earlier than previous studies in the same region. Following two stages of feature selection, such as Pearson correlation analysis and stepwise feature selection, a novel cross-validation methodology based on a generalized linear model trained and tested the yield prediction models on various combinations of the VIs. This novel methodology improves model accuracy by avoiding overfitting and over complexity caused by interdependent VIs, and then validates the model generality using previously unseen data. The best performance was achieved by combining the Normalized Difference RedEdge (NDRE) and the Green–Red Normalized Difference Vegetation Index (GRNDVI) at March. These results help growers and decision-makers benefit from early row-level yield forecast, six months before harvest, if UAV mapping is available.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からサトウキビ行単位の収量を推定し、特徴選択・新規交差検証法・未知データでの一般化検証を行っており、植物形質取得・推定手法が中心である。
abstractThe primary objective of this study was to reduce reliance on satellite images and improve early prediction of sugarcane yield at row level by using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery.
The ability to estimate sugarcane yield is an important factor to improving the planning capacity of public and private sectors, and so food and energy security. One way of achieving this is by employing process-based crop models (PBM), which can be coupled to data assimilation (DA) algorithms to correct predictions along the crop season. While the application of PBMs often need careful parameterization or genotype-specific parameters, few studies focus on understanding the impacts of crop parametrization with different crop genotypes with DA. Moreover, dimensioning the number and timing of observations is key to effectively improve predictions with DA. This study assess the performance of a new sugarcane PBM (DSSAT/SAMUCA) coupled to three DA methods, and when the genotype-specific parameters are available or not. Data from 22 field experiments is utilized to compare the performance of using the ensemble Kalman filter (EnKF), ensemble smoother (ES) and weighted mean (WM) for assimilating leaf area index (LAI) to improve yields estimates. We also quantify the impact of using one genotype-specific calibration (cv. RB867515) on yield predictions of four non-calibrated genotypes (cv. NCo376, SP832847, R570, RB72454). Simulations of DA methods had better performance than employing the PBM without DA, so called open-loop (OP). The ES method resulted in the best performance (R² = 0.498 and RMSE = 20.268 Mg ha⁻¹) followed by EnKF and WM. Utilizing a genotype-specific calibration showed substantially smaller RMSE for the three DA methods (EnKF = 16.76, ES = 16.70 and WM = 15.36 Mg ha⁻¹) compared to non-calibrated (EnKF = 21.44–26.23, ES = 21.50–26.27 and WM = 23.38–28.37 Mg ha⁻¹). Nevertheless, we also verified a higher improvement of model performance when applying EnKF and ES method to experiments where the cultivar does not match the genotype-specific calibration employed. While the WM had the opposite results, with the calibrated cultivar showing a higher improvement of model performance. As the number of LAI data assimilation increases, the DA methods tend to outperform the OP, but observations at late crop phenological stage of development showed a higher positive influence on SFY predictions.
Why it matches plant phenotyping methodsLAIを同化するデータ同化手法と作物モデルの性能を比較し、圃場・品種レベルの収量推定を技術的に評価しており、植物形質推定ワークフローが中心である。
abstractThis study assess the performance of a new sugarcane PBM (DSSAT/SAMUCA) coupled to three DA methods
Remote sensing can provide useful imagery data to monitor sugarcane in the field, whether for precision management or high-throughput phenotyping (HTP). However, research and technological development into aerial remote sensing for distinguishing cultivars is still at an early stage of development, driving the need for further in-depth investigation. The primary objective of this study was therefore to analyze whether it could be possible to discriminate market-grade cultivars of sugarcane upon imagery data from an unmanned aerial vehicle (UAV). A secondary objective was to analyze whether the time of day could impact the expressiveness of spectral bands and vegetation indices (VIs) in the biophysical modeling. The remote sensing platform acquired high-resolution imagery data, making it possible for discriminating cultivars upon spectral bands and VIs without computational unfeasibility. 12:00 PM especially proved to be the most reliable time of day to perform the flight on the field and model the cultivars upon spectral bands. In contrast, the discrimination upon VIs was not specific to the time of flight. Therefore, this study can provide further information about the division of cultivars of sugarcane merely as a result of processing UAV imagery data. Insights will drive the knowledge necessary to effectively advance the field’s prominence in developing low-altitude, remotely sensing sugarcane.
Why it matches plant phenotyping methodsUAV画像のスペクトル情報と植生指数を用いてサトウキビ品種を識別し、飛行時刻による性能を比較することが研究の中心であり、植物表現型取得・解析手法の実質的な適用に該当する。
abstractRemote sensing can provide useful imagery data to monitor sugarcane in the field, whether for precision management or high-throughput phenotyping (HTP).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
Sugarcane is the main industrial crop for sugar production, and its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping, since it can rapidly predict crop vigor at field scale. This study focused on the potential of drone multispectral images in predicting canopy nitrogen concentration (CNC) and irrigation levels for sugarcane. An experiment was carried out in a sugarcane field with three irrigation levels and five fertilizer levels. Multispectral images at an altitude of 40 m were acquired during the elongating stage. Partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) were adopted to establish CNC prediction models based on various combinations of band reflectance and vegetation indices. The simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and normalized green-blue difference index (NGBDI) were selected as model inputs due to their higher grey relational degree with the CNC and lower correlation between one another. The PLS model based on the five-band reflectance and the three vegetation indices achieved the best accuracy (Rv = 0.79, RMSEv = 0.11). Support vector machine (SVM) and BPNN were then used to classify the irrigation levels based on five spectral features which had high correlations with irrigation levels. SVM reached a higher accuracy of 80.6%. The results of this study demonstrated that high resolution multispectral images could provide effective information for CNC prediction and water irrigation level recognition for sugarcane crop.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からサトウキビのキャノピー窒素濃度を予測する画像・計算手法を開発し、複数モデルで精度評価しているため、表現型取得が中心である。
abstractPartial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) were adopted to establish CNC prediction models
This study aims to develop a single calibration model to determine nutrient elements directly (Ca, Mg, Mn, and P) in soybean and sugar cane leaf samples by double pulse laser-induced breakdown spectroscopy (DP LIBS). Matrix-matching calibration (MMC) was evaluated using direct and inverse models. Forty-five samples were used to build the calibration model (23 soybean leaves and 22 sugar cane leaves), and fifteen were used for the prediction test (8 soybean leaves and 7 sugar cane leaves) models. In the direct model, the analyte concentration in the sample is the independent variable, and the analytical signal is the dependent variable. In the inverse model, the analytical signal is the independent variable, and the analyte concentration in the sample is the dependent variable. In general, both models presented satisfactory results; however, the inverse model performed better. Emission lines used to propose calibration models were selected using a linear Pearson's correlation ( R ) strategy between each spectral point and the Ca, Mg, Mn, and P concentration measured by reference methods using inductively coupled plasma optical emission spectrometry (ICP OES). The root mean square errors of prediction (RMSEP) for the direct models were 0.60 g kg -1 to (Ca), 0.47 g kg -1 (Mg), 9.3 mg kg -1 to (Mn), and 0.28 g kg -1 to (P); for inverse model was 0.55 g kg -1 to (Ca), 0.39 g kg-1 (Mg), 10.5 mg kg -1 to (Mn), and 0.21 g kg -1 to (P). The calibration strategies proposed in this study may minimize matrix effects in direct solid analysis in soybean and sugar cane leaf samples, performing the determination of Ca, Mg, Mn, and P by DP LIBS using a single calibration model.
Why it matches plant phenotyping methods植物葉中の栄養元素濃度という生理状態を、DP LIBSで直接定量する校正法を開発・評価しており、取得・抽出手法が研究の中心である。
abstractThis study aims to develop a single calibration model to determine nutrient elements directly (Ca, Mg, Mn, and P) in soybean and sugar cane leaf samples by double pulse laser-induced breakdown spectroscopy (DP LIBS).
SugarcaneRoot2D/3D reconstructionBiomass / plant weightRoot system architectureStress response / tolerance
Low phosphorus (P) availability in acid soils is one of the main limiting factors in sugarcane ( Saccharum officinarum L.) production. Reconstruction of the root system architecture (RSA) is a vital mechanism for crop low P adaption, while the RSA of sugarcane has not been studied in detail because of its complex root system. In this study, reconstruction of the RSA and its relationship with P acquisition were investigated in a P-efficient sugarcane genotype ROC22 (R22) and two P-inefficient genotypes Yunzhe 03-103 (YZ) and Japan 2 (JP). An efficient dynamic observation room was developed to monitor the spatiotemporal alternation of sugarcane root length density (RLD) and root distribution in soil with heterogeneous P locations. The sugarcane RSA was reconstructed under P deficiency, and R22 had an earlier response than YZ and JP and presented an obvious feature of root shallowness. Compared with the normal P condition, the shallow RLD was increased by 112% in R22 under P deficiency while decreased by 26% in YZ and not modified in JP. Meanwhile, R22 exhibited a shallower root distribution than YZ and JP under P deficiency, supported by 51 and 24% greater shallow RLD, and 96 and 67% greater shallow root weight, respectively. The ratio of shallow RLD to total RLD in R22 was 91% greater than YZ, and the ratio of shallow root weight to total root weight in R22 was greater than that of YZ and JP by 94 and 30%, respectively. As a result, R22 had a higher shoot P accumulation than YZ and JP, which thereby increased the relative leaf sheath inorganic P concentration (RLPC) by 47 and 56%, relative shoot biomass (RSB) by 36 and 33%, and relative cane weight (RCW) by 31 and 36%, compared with YZ and JP under P deficiency, respectively. We verified the reliability and efficiency of a dynamic observation room and demonstrated that a shallower root distribution contributed to improving topsoil foraging, P acquisition, and low P adaption under P deficiency in sugarcane. Therefore, a shallower root distribution merits consideration as an evaluation trait for breeding P efficient sugarcane genotypes and genetic improvement.
Why it matches plant phenotyping methodsサトウキビ根系の時空間的な根長密度・分布を測定する動的観察室を開発し、その信頼性と効率を検証しているため、根系表現型取得法が研究の中心です。
abstractAn efficient dynamic observation room was developed to monitor the spatiotemporal alternation of sugarcane root length density (RLD) and root distribution in soil with heterogeneous P locations.
Physical process–based crop yield models are subject to extensive input requirements, and traditional statistical models often lack robustness in a changing environment. The purpose of this study was to develop a new and simple semi‐physical sugarcane (Saccharum officinarum L.) yield model, called the SiPAR model (intercepted photosynthetically active radiation partitioned to stem), with less data requirement than the process‐based models and strong robustness. The SiPAR model was developed using the normalized difference vegetation index, the leaf area index, and solar radiation data. A 3‐yr field experiment was used to evaluate model performance. The SiPAR model was also compared with three traditional statistical models. The results showed that (a) the SiPAR model obtained the highest accuracy among the compared methods and reproduced the spatial pattern of yield well (RMSE = 5.88–8.65 t ha–¹; R² = .44–.87; normalized RMSE (Ry) = 7.22–11.77%) and (b) the SiPAR model had better spatial and temporal stability than the other three statistical models through cross‐validation because the SiPAR model considered the stem biomass accumulation features of sugarcane by introducing a weighting factor to reflect the stem potential growth rate and using intercepted photosynthetically active radiation to represent the energy available for photosynthesis. The SiPAR model has the potential to be applied at a regional scale because it requires less data and fewer parameters and is robust and highly accurate.
Why it matches plant phenotyping methodsサトウキビの収量という植物形質をNDVI・LAI・日射から推定するSiPARモデルを開発し、圃場データで性能比較・交差検証しており、収量推定法が研究の中心である。
abstractThe purpose of this study was to develop a new and simple semi‐physical sugarcane (Saccharum officinarum L.) yield model, called the SiPAR model
This paper presents the design a cloud based IoT enabled smart agriculture application for Hi-Tech tissue cultured sugarcane crop entitled “Design of Centralized Intelligent Expert System and Contamination Detection of Tissue Cultured Sugarcane Crop”. This expert system comprises of Raspberry Pi-4 (RPi), Arduino-Mega, GSM-Modem (Sim900) and sensor-modules for monitoring and control of essential parameters of laboratory for monitoring the physical parameters. The parameters monitored are temperature, humidity and light intensity of the tissue culture growth rooms with artificial day light timing and control, however, AI-based health prediction suggests the image processing for detection of culture contamination of sugarcane crop inside the growth-room. In addition, fire-smoke sensor and methane gas sensor are incorporated for fire protection and to avoid any disastrous situation. Three numbers of webcams are attached to the RPi for monitoring growth and health of explants. An AI-Model / weight was developed for detection of contamination that predicts the for health of Tissue Cultured Sugarcane Crop. Moreover, image enhancement was covered applying Generative Adversarial Networks (GAN)”. In this system, the RPi reads sensor's data through Arduino and convert it to data-frame with timestamp and geo-tag. The data along with the captured images are sent to a centralize cloud application for applying data mining and Artificial Intelligence; however, the model of contamination detection has been applied at edge device. This is to get meaningful insights of data for future decision making in maximizing crop yield and quality. Due to the great need of sugarcane crop in Pakistan, the Plant Tissue Culture (PTC) technology has been incorporated with Artificial Intelligence, the proposed system is aimed to be installed at established PTC-growth-rooms for sugarcane crop so the experts of field can be connected to the cloud application for its monitoring, control and data analytics. In addition, the use of telepresence through cloud application will enable PTC-experts to provide assistance to the remote user and resolve their issues timely, thus extending PTC technology all over the country which will eventually lead to increased crop yield with quality products in affordable price.
Why it matches plant phenotyping methods組織培養サトウキビの汚染・健康状態を画像処理とAIで検出する技術、およびカメラ・エッジ計算・クラウドを統合した監視プラットフォームが研究の中心である。
abstractAI-based health prediction suggests the image processing for detection of culture contamination of sugarcane crop inside the growth-room.
A significant number of the world’s population is dependent on rice for survival. In addition to sugarcane and corn, rice is said to be the third most growing staple food in the world. As a consequence of intensive usage of man-made fertilizers, paddy plant diseases have also risen at a faster pace in current history. Exploring the possible disease spread and classifying to detect the consequent impact at an early stage will prevent the loss and improve rice production. The core task of this research is to recognize and quantify different kinds of infections (disease) affecting the paddy plant crop, such as brown spots, bacterial blight, and leaf blasts. Both detection and recognition are carried out based on the risk analysis of paddy crop leaf images. We suggest a Deep Convolutional Neuro-Fuzzy Method (DCNFM) that combines one of the advanced machine learning variant, namely deep convolutional neural networks (DCNNs) and uncertainty handler called fuzzy logic. The synthesis has the benefits of both fuzzy logic and DCNNs when dealing with unstructured data, extracting essential features from imprecise and ambiguous datasets. From the crop field, continuous image data are captured through image sensors and fed as a primary input to the proposed model to analyze the risk and then later to classify them for precise recognition/detection of the disease. The detection/recognition rate of the DCNFM is found to be 98.17% which is comparatively found to be effective in comparison with the traditional CNN model.
Why it matches plant phenotyping methodsイネ葉画像から病害症状を認識・定量化し、深層畳み込みニューラルネットワークとファジー論理による手法を開発・比較評価しているため、植物病害表現型の抽出が中心的です。
abstractThe core task of this research is to recognize and quantify different kinds of infections (disease) affecting the paddy plant crop, such as brown spots, bacterial blight, and leaf blasts.
Sugarcane is a renewable, natural agriculture resource and it is most important crop of India. Sugarcane Crop is a perennial crop which results into less labour and high yields. Sugarcane crop is one of the main pillar for Indian economy. Nowadays there are different diseases which affecting the sugarcane plants in diverse areas. So In this work we are going to use machine learning algorithms and image processing for sugarcane leaf disease detection. Machine learning is a trending area where the technological benefits can be imparted to the agriculture field also. In this we are going to use PCA algorithm which is one of the unsupervised machine learning algorithms. The dataset consists of 3 types of diseases. Total dataset is divided into various proportions of training and testing sets. There are various detection and classification techniques which are done using various algorithms at each stage but in PCA algorithm detection and classification is done by same algorithm which is PCA. The diseases of sugarcane consider in this project are red rot, smut, wilt.
Why it matches plant phenotyping methodsサトウキビ葉の画像から病害状態を検出・分類する画像処理と機械学習が研究の中心であり、植物の病害表現型を直接推定している。
abstractwe are going to use machine learning algorithms and image processing for sugarcane leaf disease detection
Maize Lethal Necrosis Disease (MLND) -is one of the diseases threatening maize production in a large area of East Africa.The disease is initiated by Maize Chlorotic Mottle Virus (MCMV) in blend with viruses of genus Potyvirus, commonly Sugarcane Mosaic Virus (SCMV).The simultaneous infection is the results in intensive to complete yield loss. Inability to predict disease parameters affecting maize crop yield has been a major drawback for the effectiveness and perfection of the existing manual maize crop yield prediction system and procedure in East Africa.Presently, human visual analysis is the furthermost commonly used method for detecting diseases.Due to this method, many errors were observed as the diagnosis is mainly based on the familiarity of the farmers.It consumes time to identify crop diseases founded on visually noticeable characteristics.This research sought to propose a real-time prediction system for maize crop yield using image-based mobile for detecting crop disease affecting crop production using SVM algorithms.In the proposed model, images of maize leaves from mobile were extracted their colour features and identify the Maize Lethal Necrosis Disease (MLND).The presence of SVM due to its fast processing speed as well as accuracy of its output these algorithms requires input training and test data for the model.This prediction model will be integrated into the mobile device for farmers to use.It determines the crop leaf area index that is helpful in predicted yields and its corresponding approximate.Evaluation is also conducted against the proposed model to measure the accuracy of a realtime prediction system in producing the results of crop maize disease.The results show for the SVM, the correlation(R) between estimated Leaf Area for maize and Leaf Area affected in Tunguu area was reported as 0.6959 and 06.099 respectively.So SVM classifiers offer good accuracy as well as perform faster prediction related to naïve Bayes algorithm.Since a combination of Real-time system-based farmers mobile application images collection and Leaf Area index has never been used in East Africa so far for researcher knowledge. General TermsIn this paper, the real-time prediction yield modeling througha color pixel is considered a general term.During this research, present as well as past studies of different method and technique of real-time prediction in colour features is considered to improve the algorithm in image processing.
Why it matches plant phenotyping methods画像からトウモロコシ葉の病害状態と葉面積指数を推定するSVMベースの手法開発・評価が研究の中心であり、植物表現型の取得方法に該当する。
abstractThis research sought to propose a real-time prediction system for maize crop yield using image-based mobile for detecting crop disease affecting crop production using SVM algorithms.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Abstract Background Sugarcane ( Saccharum officinarum L.) is an economically important crop with stalks as the harvest organs. Improvement in stalk quality is deemed a promising strategy for enhancing sugarcane production. However, the lack of efficient approaches for systematic evaluation of sugarcane germplasm largely limits improvements in stalk quality. This study is designed to develop a systematic near-infrared spectroscopy (NIRS) assay for high-throughput phenotyping of sugarcane stalk quality, thereby providing a feasible solution for precise evaluation of sugarcane germplasm. Results A total of 628 sugarcane accessions harvested at different growth stages before and after maturity were employed to take a high-throughput assay to determine sugarcane stalk quality. Based on high-performance anion chromatography (HPAEC-PAD), large variations in sugarcane stalk quality were detected in terms of biomass composition and the corresponding fundamental ratios. Online and offline NIRS modeling strategies were applied for multiple purpose calibration with partial least square (PLS) regression analysis. Consequently, 25 equations were generated with excellent determination coefficients ( R 2 ) and ratio performance deviation (RPD) values. Notably, for some observations, RPD values as high as 6.3 were observed, which indicated their exceptional performance and predictive capability. Conclusions This study provides a feasible method for consistent and high-throughput assessment of stalk quality in terms of moisture, soluble sugar, insoluble residue and the corresponding fundamental ratios. The proposed method permits large-scale screening of optimal sugarcane germplasm for sugarcane stalk quality breeding and beyond.
Why it matches plant phenotyping methodsサトウキビ茎の品質形質をNIRSで高スループット推定する測定法を開発・校正し、HPAEC-PADを基準に性能検証しており、表現型取得法が研究の中心である。
abstractThis study is designed to develop a systematic near-infrared spectroscopy (NIRS) assay for high-throughput phenotyping of sugarcane stalk quality
Measuring the mass flow of sugarcane in real-time is essential for harvester automation and crop monitoring. Data integration from multiple sensors should be an alternative to receive more reliable, accurate, and valuable predictions than data delivered by a single sensor. In this sense, the objective was to evaluate if the fusion of different sensors installed in a sugarcane harvester improves the mass flow prediction accuracy. A harvester was experimentally instrumented, and neural network models integrated sensor data along the harvester to perform the self-calibration of these sensors and estimate the mass flow. Nonlinear autoregressive networks with exogenous input (NARX) and multiple linear regression (MLR) models were compared to predict the mass flow. The prediction with the NARX showed a significant superiority over MLR. MLR decreases the estimated mass flow variability in the harvester. NARX with multi-sensor data has an RMSE of 0.3 kg s -1 , representing a MAPE of 0.7%. The fusion of sensor signals improves prediction accuracy, with higher performance than studies with approaches that used a single sensor. The mass flow approach with multiple sensors is a potential approach to replace conventional yield monitors. The system generates accurate data with high sample density within sugarcane rows.
Why it matches plant phenotyping methods複数センサー融合とNARXモデルによるサトウキビ収量（質量流量）推定を中心に、センサー自己較正と精度比較を行っており、植物の収量形質を取得する方法の開発・検証に該当する。
abstractData integration from multiple sensors should be an alternative to receive more reliable, accurate, and valuable predictions than data delivered by a single sensor.
Forecasting the severity of plant diseases is an emerging need for farmers and companies to optimize management actions and to predict crop yields. Process-based models are viable tools for this purpose, thanks to their capability to reproduce pathogen epidemiological processes as a function of the variability of agro-environmental conditions. We formalized the key phases of the life cycle of Puccinia kuenhii (W. Krüger) EJ Butler, the causal agent of orange rust on sugarcane, into a new simulation model, called ARISE (Orange Rust Intensity Index). ARISE is composed of generic models of epidemiological processes modulated by partial components of host resistance and was parameterized according to P. kuenhii hydro-thermal requirements. After calibration and evaluation with field data, ARISE was executed on sugarcane areas in Brazil, India and Australia to assess the pathogen suitability in different environments. ARISE performed well in calibration and evaluation, where it accurately matched observations of orange rust severity. It also reproduced a large spatial and temporal variability in simulated areas, confirming that the pathogen suitability is strictly dependent on warm temperatures and high relative air humidity. Further improvements will entail coupling ARISE with a sugarcane growth model to assess yield losses, while further testing the model with field data, using input weather data at a finer resolution to develop a decision support system for sugarcane growers.
Why it matches plant phenotyping methodsサトウキビの病徴（オレンジさび病重症度）を推定するプロセスベースモデルを開発し、圃場データで較正・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 formalized the key phases of the life cycle of Puccinia kuenhii (W. Krüger) EJ Butler, the causal agent of orange rust on sugarcane, into a new simulation model, called ARISE (Orange Rust Intensity Index).
It is known that Near-infrared spectroscopy (NIRS) is a reliable technique used in industrial laboratories to measure sugarcane quality. However, its use as a proximal sensing technology for monitoring the spatial variability of attributes in the fields has not yet been evaluated. The aim of this research was to examine the potential of NIRS for predicting and mapping Brix, Pol and Fibre content in a commercial sugarcane field. The quality attributes models were adjusted considering the spectral reflectance from the 1100–1800 nm wavelengths by using partial least squares regressions (PLSR). A total of 350 samples were collected in a sugar mill laboratory for calibration and cross-validation models development. For the external validation, 91 georeferenced samples were obtained from a commercial field. The results indicated that the developed models are capable of predicting Brix and Pol, with a coefficient of determination (R²P) of 0.71 for both parameters, and with a root mean square error of prediction (RMSEP) of 0.80% and 0.58%, respectively. In contrast, the results for Fibre were unsatisfactory (R²P of 0.24 and RMSEP of 1.15%). Predicted values showed spatial dependence of the sugarcane quality attributes. Predicted and observed values of Brix and Pol presented a coefficient of correlation of 0.85. Results showed that NIRS has potential to be applied as a proximal sensing method supporting crop management based on the spatial variability of the quality attributes.
Why it matches plant phenotyping methodsNIRSを用いてサトウキビの品質形質（Brix、Pol、繊維）を予測するモデルを開発・外部検証し、圃場の空間変動を評価しており、センシング手法が研究の中心である。
abstractits use as a proximal sensing technology for monitoring the spatial variability of attributes in the fields has not yet been evaluated
Background Sugarcane is one of the most crucial energy crops that produces high yields of sugar and lignocellulose. The cellulose crystallinity index (CrI) and lignin are the two kinds of key cell wall features that account for lignocellulose saccharification. Therefore, high-throughput screening of sugarcane germplasm with excellent cell wall features is considered a promising strategy to enhance bagasse digestibility. Recently, there has been research to explore near-infrared spectroscopy (NIRS) assays for the characterization of the corresponding wall features. However, due to the technical barriers of the offline strategy, it is difficult to apply for high-throughput real-time analyses. This study was therefore initiated to develop a high-throughput online NIRS assay to rapidly detect cellulose crystallinity, lignin content, and their related proportions in sugarcane, aiming to provide an efficient and feasible method for sugarcane cell wall feature evaluation. Results A total of 838 different sugarcane genotypes were collected at different growth stages during 2018 and 2019. A continuous variation distribution of the near-infrared spectrum was observed among these collections. Due to the very large diversity of CrI and lignin contents detected in the collected sugarcane samples, seven high-quality calibration models were developed through online NIRS calibration. All of the generated equations displayed coefficient of determination (R 2 ) values greater than 0.8 and high ratio performance deviation (RPD) values of over 2.0 in calibration, internal cross-validation, and external validation. Remarkably, the equations for CrI and total lignin content exhibited RPD values as high as 2.56 and 2.55, respectively, indicating their excellent prediction capacity. An offline NIRS assay was also performed. Comparable calibration was observed between the offline and online NIRS analyses, suggesting that both strategies would be applicable to estimate cell wall characteristics. Nevertheless, as online NIRS assays offer tremendous advantages for large-scale real-time screening applications, it could be implied that they are a better option for high-throughput cell wall feature prediction. Conclusions This study, as an initial attempt, explored an online NIRS assay for the high-throughput assessment of key cell wall features in terms of CrI, lignin content, and their proportion in sugarcane. Consistent and precise calibration results were obtained with NIRS modeling, insinuating this strategy as a reliable approach for the large-scale screening of promising sugarcane germplasm for cell wall structure improvement and beyond.
Why it matches plant phenotyping methodsサトウキビの細胞壁形質（セルロース結晶化度・リグニン含量）をオンラインNIRSで高スループット推定する測定法を開発し、交差検証・外部検証およびオンライン／オフライン比較を行っており、フェノタイピング手法が中心である。
abstractThis study was therefore initiated to develop a high-throughput online NIRS assay to rapidly detect cellulose crystallinity, lignin content, and their related proportions in sugarcane, aiming to provide an efficient and feasible method for sugarcane cell wall feature evaluation.
Abstract The bioenergy crops such as energycane, miscanthus, and sorghum are being genetically modified using state of the art synthetic biotechnology techniques to accumulate energy‐rich molecules such as triacylglycerides (TAGs) in their vegetative cells to enhance their utility for biofuel production. During the initial genetic developmental phase, many hundreds of transgenic phenotypes are produced. The efficiency of the production pipeline requires early and minimally destructive determination of oil content in individuals. Current screening methods require time‐intensive sample preparation and extraction with chemical solvents for each plant tissue. A rapid screen will also be needed for developing industrial extraction as these crops become available. In the present study, we have devised a proton relaxation nuclear magnetic resonance (1H‐NMR) method for single‐step, non‐invasive, and chemical‐free characterization of in‐situ lipids in untreated and pretreated lignocellulosic biomass. The systematic evaluation of NMR relaxation time distribution provided insight into the proton environment associated with the lipids in the biomass. It resolved two distinct lipid‐associated subpopulations of proton nuclei that characterize total in‐situ lipids into bound and free oil based on their “molecular tumbling” rate. The T1T2 correlation spectra also facilitated the resolution of the influence of various pretreatment procedures on the chemical composition of molecular and local 1H population in each sample. Furthermore, we show that hydrothermally pretreated biomass is suitable for direct NMR analysis unlike dilute acid and alkaline pretreated biomass which needs an additional step for neutralization.
Why it matches plant phenotyping methods植物バイオマス中の脂質含量を非破壊・高スループットに測定する1H-NMR法を開発・評価しており、表現型取得法が研究の中心である。
titleDevelopment and validation of time‐domain 1H‐NMR relaxometry correlation for high‐throughput phenotyping method for lipid contents of lignocellulosic feedstocks
Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.
Why it matches plant phenotyping methods作物画像から開花・収量を推定するコンピュータビジョン／深層学習手法を中心に扱うレビューであり、植物表現型取得・推定手法のレビューとして収録対象。
titleRecognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review
Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface ('skin') (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference ( p -value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.
Why it matches plant phenotyping methodsサトウキビの品質形質をvis-NIRスペクトルから推定するため、試料調製・走査法と予測モデルを比較評価しており、表現型取得法が研究の中心である。
abstractThe objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy.
The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted values. Our results showed that models fitted using BayesB were more predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genetic merit of sugarcane clones.
Why it matches plant phenotyping methodsNIR分光によるサトウキビの繊維・ショ糖含量という植物品質形質の推定性能を、ゲノム予測および統合モデルと比較検証しており、形質取得・推定手法が研究の中心である。
abstractThe main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data
Reproduction assets foundThe paper's underlying phenotype (fiber and sucrose content BLUPs), NIR spectra, and SNP marker data for the 385 sugarcane clones are publicly deposited on figshare, as stated in the Data Availability statement. No author analysis code repository is mentioned. The figshare DOI appears in the text but its URL is not in;Dataset · publicData Availability: The data underlying the results presented in the study are available from 10.6084/m9.figshare.12635717 .Open asset ↗figshare · 10.6084/m9.figshare.12635717lines:155-167Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Mar 2021Computers and Electronics in Agriculture.
Aiming at the difficulty of sugarcane nodes identification and location during automatic cutting of sugarcane seeds, based on machine vision system, this paper proposed a sugarcane nodes identification algorithm based on sum of local pixel of minimum points of vertical projection function. Firstly, according to the color and texture characteristics of yellow sugarcane, the captured RGB image of sugarcane was converted into HSV color image. Then, the S-component image in the HSV image was extracted, and the S-component image was binarized by the Otsu algorithm to obtain the binary image, and then the binary image was processed by morphological closed operation, which can eliminate the noise and fill holes of binary image. Subsequently, the sugarcane area was segmented as the region of interest through the horizontal projection of the binary image, which lowered the amount of calculation while reducing interference. Finally, the vertical projection function of the binary image of the region of interest was established, and the function was continuously derived to obtain the minimum points, and then the position of sugarcane nodes was preliminarily determined by the obtained minimum points. Then, the final position of the sugarcane nodes were determined according to the number of nodes to be identified and the sum of pixels of each 5 columns on both sides of the minimum points. The pixel column positions corresponding to the minima of the sum are the accurate nodes positions determined by the proposed algorithm. The experimental results show that the algorithm proposed in this paper has a single node identification rate of 100%, with an average time consumption of 0.15 s, and a position deviation of less than 0.34 mm; a double nodes identification rate of 98.5%, with an average time consumption of 0.21 s, and a position deviation of less than 0.42 mm. Compared with other nodes identification algorithms mentioned in this paper, it has higher identification rate and accuracy.
Why it matches plant phenotyping methodsサトウキビ節の画像取得・画像処理による識別位置推定アルゴリズムを開発し、識別率、処理時間、位置偏差で検証しており、植物形態の計測手法が研究の中心である。
abstractthis paper proposed a sugarcane nodes identification algorithm based on sum of local pixel of minimum points of vertical projection function
Estimating nitrogen (N) concentration in situ is fundamental for managing the fertilization of the sugarcane crop. The purpose of this work was to develop estimation models that explain how N varies over time as a function of three spectral data transformations in two stages (plant cane and first ratoon) under variable rates of N application. A randomized complete-block experimental design was applied, with four levels of N fertilization: 0, 80, 160, and 240 kg N ha -1 . Six sampling events were carried out during the rapid growth stage, where the canopy reflectance spectra with a hyperspectral sensor were measured, and tissue samples for N determination in plant cane and first ratoon were taken, from 60 days after emergence (DAE) and 60 days after harvest (DAH), respectively, until days 210 DAE and 210 DAH. To build the models, partial least squares regression analysis was used and was trained by three transformations of the spectral data: (i) average reflectance spectrum (R), (ii) multiple scatter correction and Savitzky-Golay filter MSC-SG) reflectance spectrum, and (iii) calculated vegetation indices (VIs).
Why it matches plant phenotyping methodsサトウキビのキャノピー窒素濃度という植物形質を、ハイパースペクトル計測と回帰モデルで推定する手法開発が研究の中心である。
abstractThe purpose of this work was to develop estimation models that explain how N varies over time as a function of three spectral data transformations
Published15 Feb 2021Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biologyCited by 3 · OpenAlex ↗
This review describes image analyses for chromosome visible structures, focusing on the chromosome imaging system CHIAS (Chromosome Image Analyzing System). CHIAS is the first comprehensive imaging system for the analysis and characterization of plant chromosomes. A simulation method for human vision for capturing band positive regions was developed and used for the image analysis of large plant chromosomes with bands. Applying this method to C-banded Crepis chromosomes enabled recognition of band positive regions as seen by human vision. Furthermore, a new image parameter, condensation pattern was developed and successfully applied to identify small plant chromosomes such as rice and brassicas. Condensation profile (CP) derived from condensation pattern was also effective in developing quantitative chromosome maps. The result was quantitative chromosomal maps of several plants with small chromosomes, including Arabidopsis, diploid brassicas, rapeseed, rice, spinach, and sugarcane. In the final chapter, various applications of imaging techniques to the analysis of pachytene chromosomes, improved visibility of multicolor FISH images, 3D reconstruction of a human chromosome based on cross-section images obtained by a FIB/SEM, automatic extraction of chromosomal regions by machine learning, etc. are described.
Why it matches plant phenotyping methods植物染色体の画像解析システムと定量的画像パラメータを中心に扱う方法論レビューであり、植物染色体構造の画像ベース計測が中核です。
abstractThis review describes image analyses for chromosome visible structures, focusing on the chromosome imaging system CHIAS (Chromosome Image Analyzing System).
Crop lodging assessment needs to be carried out timely and accurately to ensure valuable information about the location and area where lodging occurs. Many applications have been explored and tested for unmanned aerial vehicle (UAV) visible imagery in agricultural management due to the ability of providing high-space-resolution information. However, there still face many challenges in extracting lodging information using UAV visible imagery, and lacks consensus on an appropriate way to assess crop lodging. The main purpose of this study was to proposed an efficient framework to identify crop lodging at the field scale using UAV visible imagery. This framework contained a two-phase procedure. Meanwhile, three methods were evaluated by providing the appropriate feature subset for objected-based classification to determine the best feature selection method. The results showed that the proposed framework provided high accuracy (94.0 %) for identification of sugarcane lodging. Furthermore, the Boruta algorithm yielded the best feature subset compared with statistical indicators and the RFE algorithm. Thus, the proposed framework based on UAV visible imagery is promising to identify crop lodging precisely, and has great application potential in precision agriculture.
Why it matches plant phenotyping methodsUAV画像から作物の倒伏状態を圃場規模で抽出・分類する枠組みを開発し、特徴選択法と精度を評価しているため、植物状態の取得手法が中心である。
abstractThe main purpose of this study was to proposed an efficient framework to identify crop lodging at the field scale using UAV visible imagery.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Estimation of yield is a major challenge in the production of many agricultural crops, including sugarcane. Mapping the spatial variability of plant height (PH) and the stalk density is important for accurate sugarcane yield estimation, and this estimation can aid in the planning of upcoming labor- and cost-intensive actions like harvesting, milling, and forward selling decisions. The objective of this research is to assess the potential of a consumer-grade red-green-blue (RGB) camera mounted on an unmanned aerial vehicle (UAV) for sugarcane yield estimation with minimal field dataset. The study mapped the spatial variability of PH and stalk density at the grid level (4 m × 4 m) on a farm. The average PH was estimated at the grid level by masking the sugarcane area. An object-based image analysis (OBIA) approach was used to extract the sugarcane area by integrating the plant height model (PHM), extracted by subtracting the digital elevation model (DEM) from the crop surface model (CSM). Both CSM and DEM were generated from UAV images, where CSM was produced approximately one month before the harvest and the DEM after the sugarcane was harvested. The PHM improved the overall accuracy of classification from 61.98% to 87.45%. The UAV estimated PH showed a high correlation (r = 0.95) with ground observed PH, with an average overestimation of 0.10 m. An ordinary least square (OLS) linear regression model was developed to estimate millable stalk height (MSH) from PH, weight from estimated MSH, and stalk density from vegetation indices (VIs) at the grid-level. Excess green (ExG) derived from RGB showed R² of 0.754 with the stalk density. Likewise, R² of 0.798 and 0.775 were obtained between MSH and PH, and weight and MSH. Eventually, the yield was estimated by integrating the variability of PH and stalk density and weight information. The estimated yield from ExG (200.66 tons) was close to the actual harvest yield (192.1 tons). The very high-resolution RGB-based images from the UAV and OBIA approach demonstrate significant potential for mapping the spatial variability of PH and stalk density and for estimating sugarcane yield. This can aid growers and millers in decision making.
Why it matches plant phenotyping methodsUAV-RGB画像とOBIAを用いてサトウキビの草丈・茎密度を抽出し、地上測定との精度検証および収量推定を行う手法が研究の中心である。
abstractThe objective of this research is to assess the potential of a consumer-grade red-green-blue (RGB) camera mounted on an unmanned aerial vehicle (UAV) for sugarcane yield estimation with minimal field dataset.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
Abstract Introduction Sugarcane is the main industrial crop for sugar production; its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping because it can rapidly predict crop vigor. This paper mainly studied the potential of multispectral images obtained by low-altitude UAV systems in predicting canopy nitrogen (N) content and irrigation level for sugarcane. Methods An experiment was carried out on sugarcane fields with three irrigation levels and five nitrogen levels. A multispectral image at a height of 40 m was acquired during the elongation stage, and the canopy nitrogen content was determined as the ground truth. N prediction models, including partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) models, were established based on different variables. A support vector machine (SVM) model was used to recognize the irrigation level. Results The PLS model based on band reflectance and five vegetation indices had better accuracy (R=0.7693, root mean square error (RMSE)=0.1109) than the BPNN and ELM models. Some spectral information from the multispectral image had obviously different features among the different irrigation levels, and the SVM algorithm was used for irrigation level classification. The classification accuracy reached 77.8%. Conclusion Low-altitude multispectral images could provide effective information for N prediction and water irrigation level recognition.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からサトウキビのキャノピー窒素含量と灌漑レベルを推定・分類する手法が研究の中心であり、モデル精度も評価しているため。
abstractThis paper mainly studied the potential of multispectral images obtained by low-altitude UAV systems in predicting canopy nitrogen (N) content and irrigation level for sugarcane.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
Abstract Stalk quality improvement is deemed a promising strategy to enhance sugarcane production. However, the lack of efficient approaches for a systematic evaluation of sugarcane germplasm limited stalk quality improvement. In this study, 628 sugarcane samples were employed to take a high-throughput assay for determining the sugarcane stalk quality. Based on the high-performance anion chromatography method, large sugarcane stalk quality variations were detected in biomass composition and the corresponding fundamental ratio values. Online and offline Near-infrared Spectroscopy (NIRS) modeling strategies were applied for multiple purpose calibration. Consequently, 25 equations were generated with the excellent determination coefficient ( R 2 ) and ratio performance deviation (RPD) values. Notably, for some observations, RPD values as high as 6.3 were observed that indicated their exceptional performance potential and prediction capacity. Hence, this study provides a feasible way for high-throughput assessment of stalk quality, permitting large-scale screening of optimal sugarcane germplasm.
Why it matches plant phenotyping methodsサトウキビ茎の品質形質をNIRSで高スループット評価するアッセイと校正式を開発・検証しており、表現型取得法が研究の中心である。
abstracta high-throughput assay for determining the sugarcane stalk quality
Abstract The phenotype of an individual emerges from the interaction of its genotype with the environment in which it is located. Phenotypic plasticity (PP) is the ability of a specific genotype to present multiple phenotypes in response to the environment. Past and current methods for quantification of PP present limitations, mainly in what constitutes a systemic analysis of multiple traits. This research proposes an integrative index for quantifying and evaluating PP. The multivariate plasticity index (MVPi) was calculated based on the Euclidian distance between scores of a canonical variate analysis. It was evaluated for leaf physiological traits in two cases using Brazilian Cerrado species and sugarcane varieties, grown under diverse environmental conditions. The MVPi was sensitive to plant behaviour from simple to complex genotype–environment interactions and was able to inform coarse and fine changes in PP. It was correlated to biomass allocation, showing agreement between plant organizational levels. The new method proved to be elucidative of plant metabolic changes, mainly by explaining PP as an integrated process and emergent property. We recommend the MVPi method as a tool for analysis of phenotypic plasticity in the context of a systemic evaluation of plant phenotypic traits.
Why it matches plant phenotyping methods植物の複数の生理形質から表現型可塑性を定量化する新しい統合指標を提案・評価しており、表現型測定・解析法が研究の中心である。
abstractThis research proposes an integrative index for quantifying and evaluating PP.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Crop yield estimation plays a significant role in agricultural development and management decision making. Simulation of crop growth at field scales requires high-resolution remote sensing (RS) data and an applicable model. However, RS data based on satellite sensors are affected by atmospheric interference, limiting its use in precision agriculture. The objective of this study is to assimilate plant height data into the crop model to improve yield estimation and optimize agricultural water management. The plant height data are monitored regularly in 60 plots at Chongzuo experimental station (Guangxi, China) from an Unmanned Aerial Vehicle (UAV) system and ground-based measurements through a two-year sugarcane experiment (2016 and 2017). A SWAP-PH model (i.e., SWAP model considering plant height simulation) coupled with an iterative ensemble smoother algorithm is constructed to simulate plant height and crop growth procedure. We analyze its performance under the effects of different observation errors, ensemble sizes and development stages on crop growth and yield estimation, and compare the plant height assimilation results from two types of observation platforms. Our results reveal that the crop growth simulation at field scales is improved by incorporating the plant height data into the model. Moreover, incorporating the plant height measurements in the late growth stage with an absolute error of 1–2 cm and ensemble size over 50 could achieve an acceptable crop yield estimation result. In addition, assimilating the UAV-derived measurements could contribute to better yield estimation results than ground-measured plant height data. Finally, we propose a supplemental irrigation and drainage strategy in July and August to maximize the sugarcane yield in Guangxi region.
Why it matches plant phenotyping methodsUAVによる植物体高の取得と作物モデルへの同化を中心に、観測誤差・アンサンブルサイズ・観測プラットフォームを比較評価しており、植物形質推定手法の技術的応用として適格。
abstractThe plant height data are monitored regularly in 60 plots at Chongzuo experimental station (Guangxi, China) from an Unmanned Aerial Vehicle (UAV) system and ground-based measurements through a two-year sugarcane experiment (2016 and 2017).
Abstract Background: The improvement of sugarcane cell wall structure is a promising strategy to enhance the bagasse digestibility to improve its prospects as a bioenergy crop. In this context, cellulose crystallinity (CrI) and lignin are the key parameters that influence the saccharification efficiency. Therefore, this study was conducted to develop a high-throughput assay for online characterization of these cell wall features in sugarcane. Results : A total of 838 different sugarcane genotypes were collected at different growth stages during 2018 and 2019. A continuous variation distribution of near-infrared spectroscopy (NIRS) was observed among the sugarcane samples. Due to significant diversity of the cell wall features in the sampled population of the crop, seven high quality calibration models were developed through online NIRS calibration. All of the generated equations displayed coefficient of determination ( R 2 ) values higher than 0.8 and high ratio performance deviation (RPD) values over 2.0 in calibration, internal cross validation, and external validation. Particularly, the equations for CrI and the total lignin content exhibited the RPD values as high as 2.56 and 2.55, respectively, indicating their excellent prediction capacity. Furthermore, the offline NIRS assay was also performed. A comparable calibration was observed between the offline and online NIRS analyses, suggesting that both of the two strategies would be applicable for estimating cell wall characteristics. Nevertheless, as online NIRS assay offers greater advantages for large-scale screening jobs, it could be implied as a better option for high-throughput cell wall features prediction. Conclusions : This study, as a foremost attempt, explored an online NIRS assay for high-throughput assessment of key sugarcane cell wall attributes in terms of CrI, lignin content, and its proportion in sugarcane. Consistent and precise calibration results were obtained in NIRS modeling; insinuating this strategy as a reliable approach for large-scale screening of promising sugarcane germplasm for cell wall structure improvement and beyond.
Why it matches plant phenotyping methodsサトウキビの細胞壁形質をNIRSで高スループット推定するオンライン測定法を開発し、校正・交差検証・外部検証まで実施しており、表現型取得法が研究の中心である。
abstractthis study was conducted to develop a high-throughput assay for online characterization of these cell wall features in sugarcane.
The efficient selection of sugarcane varieties with enhanced sugar content requires simple, rapid, accurate and cost-effective assays. The objective of this research was to develop spectroscopic models for non-destructive evaluation of Commercial Cane Sugar (CCS) in growing cane stalks. A portable visible-shortwave near-infrared (Vis/SWNIR) spectrometer with a wavelength range of 570–1031 nm was applied to cane stalks grown under normal field conditions. The CCS models were developed by partial least squares (PLS) regression using spectra sets obtained at three different times (i.e. morning, afternoon and evening), based on both individual spectra and a combined set. During the in-field measurements, it was found that model performance could be affected by varying sample and instrument temperatures, especially for the combined set. The models had coefficients of determination of the prediction set (r²) of 0.76, 0.76, 0.78 and 0.69 and root mean square errors of prediction (RMSEP) of 1.01, 1.05, 0.99 and 1.17 CCS for the models constructed with the spectra sets of the morning, afternoon, evening and combined, respectively. These results indicate that the CCS models could be used for the monitoring and screening of sugarcane clone selection, except for the model obtained from the combined spectra set, which was influenced by varying instrument temperature. Thus, heat protection for the portable Vis/SWNIR instrument, allowing it to maintain a constant instrument temperature, should be considered if data collection across different periods of the day is necessary.
Why it matches plant phenotyping methodsサトウキビ生育個体の糖含量を非破壊・迅速に推定する近赤外分光モデルを開発し、時刻・温度条件の影響と予測性能を評価しており、植物形質取得法が中心である。
abstractThe objective of this research was to develop spectroscopic models for non-destructive evaluation of Commercial Cane Sugar (CCS) in growing cane stalks.
Abstract. Sugarcane is a perennial crop that contributes to nearly 80% of the global sugar-based products. Therefore, sugarcane growers and food companies are seeking ways to address the concerns related to sugarcane crop yield and health. In this study, a spatial and spectral analysis on the peak growth stage of the sugarcane fields in Bundaberg, Queensland, Australia is performed using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) derived from high-resolution WorldView-2 (WV2) images and multispectral Unmanned Aerial Vehicle (UAV) images. Two topics are chosen for this study: 1) the difference and correlation between NDVI and NDRE that are commonly used to estimate Leaf Area Index, a common crop parameter for the assessment of crop yield and health stages; 2) the impact of spatial resolution on the systematic difference in the abovementioned two Vegetation Indices (VIs). The statistical correlation analysis between the WV2 and UAV images produced correlation coefficients of 0.68 and 0.71 for NDVI and NDRE, respectively. In addition, an overall comparison of the WV2 and UAV-derived VIs indicated that the UAV images produced a better accuracy than the WV2 images because UAV can effectively distinguish various status of vegetation owing to its high spatial resolution. The results illustrated a strong positive correlation between NDVI and NDRE, each derived from the WV2 and UAV images, and the correlation coefficients were 0.81 and 0.90, respectively, i.e. the correlation between NDVI and NDRE is higher in the UAV images than the WV2 images.
Why it matches plant phenotyping methodsUAVおよび衛星画像から植生指数を抽出し、LAI推定に関わる空間・スペクトル解像度の影響と精度を比較検証しており、植物形質推定手法が中心である。
abstractTwo topics are chosen for this study: 1) the difference and correlation between NDVI and NDRE that are commonly used to estimate Leaf Area Index
The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population ( Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics s and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms (SNPs) and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted genotypic values. Our results showed that models fitted using BayesB were most predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genotypic value of sugarcane clones.
Why it matches plant phenotyping methodsNIR分光法を用いてサトウキビの繊維・ショ糖含量という植物品質形質を予測し、ゲノム法や回帰法と性能比較・交差検証しているため、形質取得・推定法の評価が中心である。
abstractThe main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Several vegetation indices have been developed, with the normalized difference vegetation index (NDVI) been the most studied and commonly used. To generate an NDVI map, a relatively high-cost multispectral sensor is required; but currently, most UAVs are equipped with low-cost RGB cameras. For that reason, other indices that utilize RGB data have been developed to generate maps similar to NDVI and minimize the data acquisition cost, such as the triangular greenness index (TGI) and the visible atmospheric resistant index (VARI). However, several studies found that these indices cannot be recommended as reliable general-purpose crop health indicators. This study utilizes a genetic algorithm to develop a new visible index (visible NDVI; vNDVI) that estimates NDVI values of vegetation from uncalibrated RGB cameras mounted on UAVs (or other remote sensing platforms). Three experiments were conducted to create and validate the proposed index. First, the NDVI values generated from a multispectral camera were compared with the NDVI values generated by a hyperspectral camera. In the second experiment, the vNDVI formula was created using a genetic algorithm. The third experiment validates the proposed vNDVI, generated from two uncalibrated RGB cameras, in three different crops (citrus, grapes, and sugarcane). The proposed vNDVI proved to be highly accurate on estimating NDVI values by just using RGB cameras, with an overall mean percentage error of 6.89% and a mean average error of 0.052 in all three crops, providing a low-cost alternative for remote sensing and plant phenotyping.
Why it matches plant phenotyping methodsRGB画像から植物のNDVIを推定する新規可視指数を遺伝的アルゴリズムで開発し、複数作物・カメラで検証しており、植物表現型取得手法が中心である。
abstractThis study utilizes a genetic algorithm to develop a new visible index (visible NDVI; vNDVI) that estimates NDVI values of vegetation from uncalibrated RGB cameras mounted on UAVs (or other remote sensing platforms).
Epidemics of sugarcane orange rust (caused by Puccinia kuehnii ) in Florida are largely influenced by prevailing weather conditions. In this study, we attempted to model the relationship between weather conditions and rust epidemics as a first step toward development of a decision aid for disease management. For this purpose, rust severity data were collected from 2014 through 2016 at the Everglades Research and Education Center, Belle Glade, Florida, by recording percentage of rust-affected area of the top visible dewlap leaf every 2 weeks from three orange rust susceptible cultivars. Hourly weather data for 10- to 40-day periods prior to each orange rust assessment were evaluated as potential predictors of rust severity under field conditions. Correlation and stepwise regression analyses resulted in the identification of nighttime (8 PM to 8 AM) accumulation of hours with average temperature 20 to 22°C as a key predictor explaining orange rust severity. The five best regression models for a 30-day period prior to disease assessment explained 65.3 to 76.2% of variation of orange rust severity. Prediction accuracy of these models was tested using a case control approach with disease observations collected in 2017 and 2018. Based on receiver operator characteristic curve analysis of these two seasons of test data, a single-variable model with the nighttime temperature predictor mentioned above gave the highest prediction accuracy of disease severity. These models have potential for use in quantitative risk assessment of sugarcane rust epidemics.
Why it matches plant phenotyping methodsサトウキビ個体の病害重症度を気象データから予測するモデルを開発し、別年の観測データで精度検証しており、植物病害状態の推定手法が研究の中心である。
abstractwe attempted to model the relationship between weather conditions and rust epidemics as a first step toward development of a decision aid for disease management.
Crop modelling has the potential to assist plant breeding by identifying favourable genotypic (G) traits for specific environments (Es). Sugarcane crop models have not been rigorously evaluated against a factorial GxE dataset. It is imperative that models are evaluated in this way before they are applied to plant breeding problems.Our objectives were to (1) calibrate, (2) assess, and (3) identify weaknesses and recommend improvements to, three sugarcane models, DSSAT-Canegro, Mosicas and APSIM-Sugar, in relation to their predictions of observed E, G and GxE interaction effects in response to abiotic factors (temperature and solar radiation). Data from an international GxE growth analysis trial were used; these consisted of five irrigated experiments at four sites (Belle Glade, Florida, USA; Chiredzi, Zimbabwe; La Mare, Reunion Island; and Pongola, South Africa), with cultivars N41, R570 and CP88-1762. Observed G and E effects on final above-ground dry mass (ADM) yields were explained in terms of seasonal radiation interception (FIPARa) and seasonal average radiation use efficiency (RUEa). Calibration was undertaken where possible by translating phenotypic parameters derived from observations into model input trait parameter values representing genetic traits.E and G effects on FIPARa were generally simulated satisfactorily, while GxE interaction effects were poorly predicted due to inadequate responses to temperature. E, G and GxE effects on RUEa were poorly predicted by all models, although data shortcomings (arising from uncertainty regarding date of primary shoot emergence and impacts of lodging) prevented us from making strong conclusions in this regard. Models accurately predicted G differences in RUEa during mid-season biomass sampling periods where data confidence was greater. Although the models were able to predict final ADM yield per G and per E reasonably well, none of the models predicted GxE interaction effects well. All models also under-estimated the variation in RUEa and ADM. Recommendations for experimental protocols for exploring RUEa are made. Our key recommendations for future work to improve models for sugarcane breeding applications are to explore G-specific thermal time base temperatures for germination and canopy development processes, and to improve linkages between carbon availability and canopy development.
Why it matches plant phenotyping methodsサトウキビの生育・遺伝子型×環境効果を予測する3つの作物モデルを較正・評価し、観測形質との性能検証と改善提案を行っており、計算的な表現型推定が研究の中心である。
abstractOur objectives were to (1) calibrate, (2) assess, and (3) identify weaknesses and recommend improvements to, three sugarcane models, DSSAT-Canegro, Mosicas and APSIM-Sugar
An accurate model for predicting sugarcane yield will benefit many aspects of managing growth and harvest of sugarcane crops. In this study, Sentinel-1 and Sentinel-2 satellite imagery were used in combination with climate, soil and elevation data to predict field-level sugarcane yield across the multiple sugar mill areas in the Wet Tropics of Australia at different time steps over four consecutive growing seasons (2016–2019). A total of ≈1400 field-level measurements were used to train predictive machine learning models of cane yield (t/ha), commercial cane sugar (CCS, %), sugar yield (t/ha), crop varieties and ratoon numbers. We compared the predictive performance of models based on both satellite imagery only and a fusion of satellite imagery with climate, soil and topographical information. Randomized search on hyperparameters was the method used to optimize and identify the most accurate decision tree-based machine model. Overall, gradient boosting was the most accurate method for predicting sugarcane attributes. The analysis resulted in cane yield, CCS and sugar yield predicted at the field level with R² of up to 0.51 (RMSE = 16 t/ha), 0.63 (RMSE = 1 %) and 0.62 (RMSE = 2 t/ha) as soon as four months before the harvest season. It was also found that sugarcane varieties could be mapped with an accuracy of up to 73.4 %, while the differentiation of planted and ratoon crops exhibited the lowest accuracy of 45.4 %. Using a novel SHapley Additive exPlanations (SHAP) approach to explain the output of our machine learning models we found that Sentinel-2 derived spectral indices were the most important in predicting cane yield as well as differentiating sugarcane varieties and ratoon numbers. In contrast, climate and elevation derived predictors were the most important in predicting CCS and sugar yield. At the whole sugar mill area level, spatially averaged field-level results predicted mill area cane yield, CCS and sugar yield with R² of 0.75 (RMSE = 4.6 t/ha), 0.80 (RMSE = 0.6 %) and 0.77 (RMSE = 1 t/ha). Early season prediction of sugarcane yields at both field- and mill-area level could be valuable for informing fertilizer application, harvest scheduling and marketing decisions.
Why it matches plant phenotyping methods衛星画像と機械学習を統合し、圃場レベルのサトウキビ収量・糖度などの作物形質を予測し、モデル性能を比較・検証している。単なる生物学的実験のルーチン測定ではなく、形質取得・推定ワークフローが中心である。
abstractSentinel-1 and Sentinel-2 satellite imagery were used in combination with climate, soil and elevation data to predict field-level sugarcane yield
Total chlorophyll content of sugarcane is an important indicator of plant health, directly correlated to the photosynthetic potential of the crop. With recent technological advancements, portable chlorophyll meters have largely replaced biochemical chlorophyll estimation, requiring laborious extraction procedure with solvents like acetone and dimethyl sulphoxide. Chlorophyll meters determine only ‘greenness’ index, which has to be converted into scientifically standard units in order to make the data comprehensive. Prediction models for inter-conversion of chlorophyll units are available for crops like rice, wheat, sorghum, barley, maize, etc., but not for sugarcane till date. In the present study, total chlorophyll content was recorded in diverse sugarcane germplasm and commercial hybrids using both non-destructive and destructive sampling methods. A strong positive correlation was observed between meter readings (SPAD and CCI) with total chlorophyll content estimated using 80% acetone (r = 0.800 and 0.793) and dimethyl sulphoxide (r = 0.915 and 0.868). Regression models for the best fit curve between meter reading and extracted chlorophyll values of the tested sugarcane germplasm and hybrids were non-linear, polynomial equations of the second order. The model developed was validated in an independent experiment wherein sugarcane variety Co 86032 was subjected to increasing nitrogen levels. Highly significant linear regression was found between observed and predicted values of all estimates of total chlorophyll content with almost negligible prediction error. Thus, the model calibrated and validated for sugarcane germplasm and commercial hybrids would be a small yet significant step towards aiding high-throughput phenotyping in sugarcane thereby accelerating crop improvement programmes.
Why it matches plant phenotyping methods非破壊クロロフィル測定値から総クロロフィル量を推定する回帰モデルを開発し、独立実験で検証しており、植物表現型取得法が中心である。
abstractPrediction models for inter-conversion of chlorophyll units are available for crops like rice, wheat, sorghum, barley, maize, etc., but not for sugarcane till date.
One of the major limitations for sugarcane genetic improvement is the low heritability of yield in the early stages of breeding, mainly due to confounding inter-plot competition effects. In this study, we investigate an indirect selection index (Si), developed based on traits correlated to yield (indirect traits) that were measured using an unmanned aerial vehicle (UAV), to improve clonal assessment in early stages of sugarcane breeding. A single-row early-stage clonal assessment trial, involving 2134 progenies derived from 245 crosses, and a multi-row experiment representative of pure-stand conditions, with an unrelated population of 40 genotypes, were used in this study. Both experiments were screened at several stages using visual, multispectral, and thermal sensors mounted on a UAV for indirect traits, including canopy cover, canopy height, canopy temperature, and normalised difference vegetation index (NDVI). To construct the indirect selection index, phenotypic and genotypic variance-covariances were estimated in the single-row and multi-row experiment, respectively. Clonal selection from the indirect selection index was compared to single-row yield-based selection. Ground observations of stalk number and plant height at six months after planting made from a subset of 75 clones within the single-row experiment were highly correlated to canopy cover (rg = 0.72) and canopy height (rg = 0.69), respectively. The indirect traits had high heritability and strong genetic correlation with cane yield in both the single-row and multi-row experiments. Only 45% of the clones were common between the indirect selection index and single-row yield based selection, and the expected efficiency of correlated response to selection for pure-stand yield based on indirect traits (44%–73%) was higher than that based on single-row yield (45%). These results highlight the potential of high-throughput phenotyping of indirect traits combined in an indirect selection index for improving early-stage clonal selections in sugarcane breeding.
Why it matches plant phenotyping methodsUAV搭載の可視・マルチスペクトル・熱センサーでサトウキビの形態・生理形質を高スループット取得し、育種選抜への統合方法を評価しており、表現型取得が研究の中心である。
titleHigh-Throughput Phenotyping of Indirect Traits for Early-Stage Selection in Sugarcane Breeding
Sugarcane is a giant tropical grass in the botanical genus, Saccharum, the stalks of which are the world’s primary source of sugar (sucrose). After wheat, sugarcane is the second largest export crop in Australia with a total annual revenue of about $2.5 billion AUD. \n \nThere is a need for accurate and efficient yield estimation models for sugarcane crops, primarily because most of the cane is forward sold in the months leading up to harvest, and for logistical reasons including equipment allocation and harvest scheduling. Existing methods rely on hyperspectral satellite imagery and grower’s estimates, both of which have some limitations. \n \nUnmanned aerial vehicles (UAVs), mounted with a visual spectrum (red-green-blue, i.e. RGB) camera may present an efficient and cost-effective method for capturing spatial and spectral data about sugarcane crops and, if processed and analysed properly, this data could be used to estimate the quantity of usable cane stalks in a canefield. Such a technique would be valuable for the sugarcane industry. \n \nIn this research project, the existing literature relating to crop height determination by UAV photogrammetry survey, visible-band spectral analysis of vegetation, and sugarcane yield estimation has been reviewed. A methodology was developed and a field study carried out to survey sugarcane crops using a consumer-grade UAV at approximately monthly intervals for three months leading up to harvest, to process the data into 3D digital models using photogrammetry software, to analyse the spatial and spectral properties of the data to find correlations with empirical yield data as recorded during a monitoring survey of the harvest, and to develop yield prediction models using linear regression and multiple linear regression techniques. \n \nThe results demonstrate that UAV-based photogrammetry is a suitable method to create digital models of the crop’s surface, and that the height of this surface model correlates strongly with empirical yield at all survey epochs. Such a technique is useful for assessing crop variability within fields. Unfortunately, however, mature cane is vulnerable to damage by wind and rain, which can affect its height and subsequently thwart observations about growth rate and yield predictions that are based on height. Visible-band vegetation indices exhibited low or erratic correlations with yield and were subject to influence from many factors including changing ambient light conditions and yellowing of the cane due to frost, thus rendering them an unreliable predictor of yield. \n \nThe conclusions of this project indicate promising potential for UAV photogrammetry survey in the sugarcane industry, with recommendations for future research to improve the yield prediction models by input of additional independent variables to overcome the obstacles discovered in this project.
Why it matches plant phenotyping methodsUAVフォトグラメトリでサトウキビの作物表面高を抽出し、実測収量との相関を検証して収量推定モデルを開発しており、表現型取得・推定手法が研究の中心である。
abstractto process the data into 3D digital models using photogrammetry software, to analyse the spatial and spectral properties of the data to find correlations with empirical yield data
Abstract. The objective of this research work aims at crop acreage estimation at mill catchment level, derivation of sugarcane phenology and yield estimation at field level. The study was carried out in Kisan Sahkari Chini Mill catchment, Nanauta, Saharanpur, Uttar Pradesh. Extensive and systematic field sampling was carried out for ground-truth observations, biophysical measurements (LAI and above/below canopy PAR) and mill-able cane yield through crop cutting experiments. Major emphasis were laid on sugarcane crop discrimination, biophysical parameter estimation, generation of phenological metrics and yield model development for sugarcane crop at mill catchment level. Sugarcane crop discrimination and its acreage estimation was done using multi-sensor satellite data. The sugarcane classification accuracies were > 92% for LISS-IV, > 86% for Landsat-8 and > 83% for LISS-III classified image. The sugarcane phenological matrices at field level derived using time-series of NDVI for a period of 2015–2016 through TIMESAT software. To retrieve the biophysical parameters particularly leaf area index, best predictive function developed with vegetation indices (EVI, NDVI, SAVI) through correlation and regression analysis along this cane yield estimation attempted with multi-date (eight-day) NDVI from Landsat OLI. Yield models developed for ratoon cane and planted cane explained variance in yield significantly with coefficient of determination (R2) values equal to 0.83 and 0.69, respectively. Similar predictive functions were also established with monthly composite dataset for village-level yield estimates with step wise regression (R2 = 0.83) (P = 0.00001), Multi linear regression (MLR) (R2 = 0.792) (P = 0.00081) and Random forest regression (R2 = 0.466) (P = 0.038).
Why it matches plant phenotyping methods衛星時系列データと回帰モデルを用いて、サトウキビのLAI、フェノロジー、圃場レベル収量を推定する手法を開発・評価しており、単なる生物学的実験の routine 測定ではない。ただし作付面積推定も含むため、植物形質推定に該当する部分を中心に採録する。
abstractMajor emphasis were laid on sugarcane crop discrimination, biophysical parameter estimation, generation of phenological metrics and yield model development for sugarcane crop at mill catchment level.
The capabilities and utility of UAV LiDAR and surface from motion photogrammetry have been of wide discussion in the remote sensing community and assumptions made, often speculative, about the potential strengths and limitations of these systems. Here, we employ a side-by-side test of the CSIRO Hovermap LiDAR and Micasense RedEdge multispectral camera simultaneously mounted to a single UAV platform to acquire a time-series data set from both sensors over the growth cycle of a sugarcane crop in northeast Queensland, Australia. The primary aim was to compare the ability of each system to accurately measure crop height, over a single growing cycle. A secondary aim examined the correlation between these measures and the sugarcane biophysical parameters of stalk population (stalks·m −2 ), total fresh biomass (TFB, t·ha -1 ) and cane yield ( Yield , t cane·ha -1 ). The experimental design included a randomised complete block design of four nitrogen fertiliser treatments (0, 70, 110, 150 and 190 Nkg·ha -1 ) with four replications to assess if either optical measure could detect significant effects of nitrogen application on crop growth. Both systems demonstrated similar capabilities for accurately measuring crop heights throughout the growth period with statistically significant coefficients of determination observed when comparing the maximum ( Adj R 2 = .885, F(1,118) = 910.806, p ≤ .001) and mean ( Adj R 2 = .929, F(1,118) = 1548.404, p ≤ .001) crop height estimations of both instruments. In addition, both systems responded similarly in the detection of differences in crop structural properties response to nitrogen treatments. Only Hovermap, however, demonstrated the capacity to obtain sufficient ground returns over the course of the growth period to enable comparisons to the biophysical samples using a ground-to-non-ground return ratio ( Stalk Population: Adj R 2 = .788, F(1,18) = 70.688, p ≤ .001, TFB: Adj R 2 = .713, F(1,18) = 48.198, p ≤ .001, Yield: Adj R 2 = .707, F(1,18) = 46.921, p ≤ .001) with the best results of RedEdge obtained when comparing mean height measures ( Stalk Population Adj R 2 = .502, F(1,18) = 20.172, p ≤ .001, TFB: Adj R 2 = .309, F(1,18) = 9.481, p = 0.006, Yield: Adj R 2 = .322, F(1,18) = 10.028, p ≤ .005). The results suggest that although both systems are comparable for accurate crop height measurements, and as such, provide early detection of potential problems, UAV LiDAR provided more consistent and significant correlations with optical remotely sensed data to the biophysical parameters of sugarcane.
Why it matches plant phenotyping methodsUAV LiDARとフォトグラメトリを用いたサトウキビ草高測定の比較・精度評価が中心であり、バイオマスや収量との関連も検証しているため、植物表現型計測手法研究に該当する。
abstractThe primary aim was to compare the ability of each system to accurately measure crop height, over a single growing cycle.
The aim of this work was to use spectroscopic methods and partial least squares discriminant analysis (PLS-DA) for the early prediction of genotype resistance or susceptibility to sugarcane borer. The sugarcane leaf +1 was directly analyzed with no sample preparation by ultraviolet-visible-near-infrared (UV-VIS-NIR), middle-infrared (MID), and near-infrared (NIR) spectroscopies. Also, laser-induced breakdown spectroscopy (LIBS) was used to analyze pellets of dried and ground leaves and stalks of sugarcane. Classification models were built using PLS-DA. The models built using UV-VIS-NIR, MID or NIR spectra exhibited ideal sensitivity, specificity, and classification errors, i.e., 1 for both sensitivity and specificity and 0 for classification errors. Regarding the models built using LIBS spectra, those using spectra of pellets made from dried and ground leaves also presented ideal sensitivity, specificity, and classification errors; on the other hand, models built using the spectra of pellets made of dried and ground stalks did not present ideal values for these parameters. Thus, the models built, except for the one using LIBS of pellets made of stalks, showed excellent predictive capacity, making them suitable for predicting the resistance or susceptibility of sugarcane genotypes in the early stages of a plant's life.
Why it matches plant phenotyping methods植物葉・茎の分光データから害虫抵抗性／感受性を早期推定する測定・分類ワークフローが研究の中心で、感度・特異度・分類誤差による技術評価も行っているため。
abstractThe aim of this work was to use spectroscopic methods and partial least squares discriminant analysis (PLS-DA) for the early prediction of genotype resistance or susceptibility to sugarcane borer.
In order to realize automatic production of sugarcane seed, a sugarcane node recognition analysis based on multi-threshold and multi-scale wavelet transform is proposed. The laser displacement sensor is used to obtain the surface contour signal of sugarcane. After analyzing the signal characteristics, the 5th order of Daubechies wavelet base db5, is chosen as the wavelet mother function, and the signal is decomposed to eight layers by discrete wavelet. The fifth, sixth, and seventh layer coefficients are extracted to perform threshold processing, and the reconstructed signals processed by each threshold value are superimposed to characterize the characteristics of the sugarcane nodes. A multisensory redundancy algorithm based on Gauss membership function is proposed to improve the accuracy of recognition. Experiments show that the recognition rate of the algorithm is 100%, the maximum positioning error is less than 2.5 mm, and the maximum delay is 0.25 s. Compared with other four algorithms based on image processing, the proposed algorithm has higher effectiveness and recognition rate.
Why it matches plant phenotyping methodsレーザー変位センサとウェーブレット解析によりサトウキビ節の特徴を抽出・認識する手法が研究の中心であり、認識精度や位置誤差も検証しているため、植物器官の測定・抽出手法として適格です。
abstracta sugarcane node recognition analysis based on multi-threshold and multi-scale wavelet transform is proposed
SugarcaneField / plotRootPhysiological trait estimationRoot system architecture
Plant root systems play many key roles including nutrient and water uptake, interface with soil microorganisms and resistance to lodging. As for other crops, large and systematic studies of sugarcane root systems have always been hampered by the opaque and solid nature of the soil. In recent years, methods for efficient extraction of DNA from soil and for species-specific DNA amplification have been developed. Such tools could have potential to greatly improve root phenotyping and health diagnostic capability in sugarcane. In this paper, we present a fast, specific and efficient method for the quantification of sugarcane live root cells in soil samples. Previous studies were typically based on mass and length, so we established a calibration to convert root DNA quantity to live root mass. This diagnostic was validated on field samples and used to investigate the fate of the root system after harvest prior to regrowth of the ratoon crop. Two weeks after harvest, the sugarcane roots from the previous crop were still viable. This raises the question of the role that the root system of the harvested crop plays in the performance of the next crop and demonstrates how this test can be used to answer research questions.
Why it matches plant phenotyping methods糖蔗の生根量をDNA量から推定する方法を開発し、根質量への較正と圃場サンプルでの検証を行っており、根系フェノタイピングが研究の中心である。
abstractwe present a fast, specific and efficient method for the quantification of sugarcane live root cells in soil samples.
Sugarcane breeding programmes rank the resistance of genotypes to Puccinia kuehnii, causal agent of orange rust, according to levels of disease severity. However, during the screening stages, this method of assessment can lead to precipitous elimination of genotypes with promising agronomic traits but showing mild symptoms of rust such as flecks or lesions that do not produce spores. This study aimed to propose a new method to classify the resistance of sugarcane genotypes to orange rust by counting sporulating lesions. Five sugarcane varieties with different levels of resistance to P. kuehnii were inoculated with two pathogen populations under controlled conditions. The disease severity (SEV), total number of lesions (TNL), and total number of sporulating lesions (TNSL) were evaluated in a 20 cm leaf fragment from the most diseased leaf. The TNL and TNSL evaluations were performed at 11, 16 and 21 days after inoculation (DAI) and SEV at 21 DAI. The thresholds of 80% and 8% of sporulating lesions (SL) separated susceptible from the intermediate varieties and intermediate from the resistant ones, respectively. It is proposed that the method of counting sporulating lesions be used in screening genotypes for resistance to P. kuehnii in sugarcane breeding programmes.
Why it matches plant phenotyping methodsサトウキビの病害抵抗性を評価するため、罹病病変と胞子形成病変の計数法を新たに提案しており、植物表現型の取得方法が研究の中心である。
abstractThis study aimed to propose a new method to classify the resistance of sugarcane genotypes to orange rust by counting sporulating lesions.
In order to make effective decisions on sustainable development, it is essential for sugarcane-producing countries to take into account sugarcane acreage and sugarcane production dynamics. The availability of sugarcane biophysical data along the growth season is key to an effective mapping of such dynamics, especially to tune agronomic models and to cross-validate indirect satellite measurements. Here, we introduce a dataset comprising 3,500 sugarcane observations collected from October 2014 until October 2015 at four fields in the São Paulo state (Brazil). The campaign included both non-destructive measurements of plant biometrics and destructive biomass weighing procedures. The acquisition plan was designed to maximize cost-effectiveness and minimize field-invasiveness, hence the non-destructive measurements outnumber the destructive ones. To compensate for such imbalance, a method to convert the measured biometrics into biomass estimates, based on the empirical adjustment of allometric models, is proposed. In addition, the paper addresses the precisions associated to the ground measurements and derived metrics. The presented growth dynamics and associated precisions can be adopted when designing new sugarcane measurement campaigns.
Why it matches plant phenotyping methodsサトウキビのバイオメトリクスからバイオマスを推定する手法を提案し、地上測定データセットと測定精度も提示しており、植物形質の取得・推定が中心である。
abstractHere, we introduce a dataset comprising 3,500 sugarcane observations collected from October 2014 until October 2015 at four fields in the São Paulo state (Brazil).
Reproduction assets foundThe paper's ground reference sugarcane dataset (3,500 observations of biometrics, LAI, and biomass from four fields in São Paulo state) is published openly on the 4TU Centre for Research Data repository. The authors' MATLAB analysis scripts are only available upon request, so they do not qualify as public assets.Dataset · publicThe ground measurements, published for public use, can be found on the 4TU Centre for Research Data
repository, see (Data Citation 1).Open asset ↗4TU Centre for Research Datapdf-page:11 lines:1-40Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The use of unmanned aerial vehicles (UAVs) as remote sensing platforms has tremendous potential for describing detailed site-specific features of crops, especially in early post-emergence, which was not possible previously with satellite images. This article describes an object-based image analysis (OBIA) procedure for UAV images, designed to map and extract information about skips in sugarcane planting rows. The procedure consists of three consecutive phases: (1) identification of sugarcane planting rows, (2) identification of the existent sugarcane within the crop rows, and (3) skip extraction and creation of field-extent crop maps. Results based on experimental fields achieved skip rates of between 2.29% and 10.66%, indicating a planting operation with excellent and good quality, respectively. The relationship of estimated versus observed skip length had a coefficient of determination of 0.97, which was confirmed by the value of the enhanced Wilmott concordance coefficient of 0.92, indicating good agreement. The OBIA procedure allowed a high level of automation and adaptability, and it provided useful information for decision making, agricultural monitoring, and the reduction of operational costs.
Why it matches plant phenotyping methodsUAV画像に対するOBIA手法を開発し、サトウキビの植栽列・存在株・欠株長を自動抽出して観測値と検証しているため、植物状態の取得が研究の中心である。
abstractThis article describes an object-based image analysis (OBIA) procedure for UAV images, designed to map and extract information about skips in sugarcane planting rows.
The building of multivariate calibration models using near-infrared spectroscopy (NIR) and partial least squares (PLS) to estimate the lignin content in different parts of sugarcane genotypes is presented. Laboratory analyses were performed to determine the lignin content using the Klason method. The independent variables were obtained from different materials: dry bagasse, bagasse-with-juice, leaf, and stalk. The NIR spectra in the range of 10 000-4000 cm -1 were obtained directly for each material. The models were built using PLS regression, and different algorithms for variable selection were tested and compared: iPLS, biPLS, genetic algorithm (GA), and the ordered predictors selection method (OPS). The best models were obtained by feature selection with the OPS algorithm. The values of the root mean square error prediction (RMSEP), correlation of prediction ( R P ), and ratio of performance to deviation (RPD) were, respectively, for dry bagasse equal to 0.85, 0.97, and 2.87; for bagasse-with-juice equal to 0.65, 0.94, and 2.77; for leaf equal to 0.58, 0.96, and 2.56; for the middle stalk equal to 0.61, 0.95, and 3.24; and for the top stalk equal to 0.58, 0.96, and 2.34. The OPS algorithm selected fewer variables, with greater predictive capacity. All the models are reliable, with high accuracy for predicting lignin in sugarcane, and significantly reduce the time to perform the analysis, the cost and the chemical reagent consumption, thus optimizing the entire process. In general, the future application of these models will have a positive impact on the biofuels industry, where there is a need for rapid decision-making regarding clone production and genetic breeding program.
Why it matches plant phenotyping methodsNIRスペクトルとPLS/変数選択によるサトウキビ各部位のリグニン含量推定モデルを構築・比較し、予測性能を評価している。植物形質の取得・推定手法が研究の中心である。
abstractThe building of multivariate calibration models using near-infrared spectroscopy (NIR) and partial least squares (PLS) to estimate the lignin content in different parts of sugarcane genotypes is presented.
The use of unmanned aerial systems (UAS) as remote-sensing platforms has tremendous potential for obtaining detailed, site-specific descriptions of crop features, which would be very useful for precision agriculture. In sugarcane plantations, for example, cane height can be an indicator of yield and other parameters because it is highly influenced by the soil, total sugar content, leaf nitrogen content, temperature and light intensity. This article describes the generation of crop surface models (CSMs) from high-resolution images that were obtained using a UAS to estimate sugarcane height. Using a UAS with an on-board RGB camera, we created densified three-dimensional point clouds of the study area in two different flight line directions (North/South and East/West) using structure from motion (SfM) with multi-view stereo (MVS). Then, the digital surface model (DSM) and digital terrain model (DTM) were extracted and used to create CSMs. Maps of sugarcane height were created based on this information. We investigated the influences of different flight line directions (N/S and E/W) on sugarcane height estimations and their accuracy by comparing our maps with ground references. From the validation conducted using both flight lines, the average heights were closer to the field-verified data. The resulting maps showed differences in sugarcane height that were confirmed by field measurements. This method has potential for future use by sugarcane-related industries, researchers and farmers to estimate average crop height.
Why it matches plant phenotyping methodsUAS画像とSfM/MVS点群からサトウキビ高さを推定する取得・解析手法を開発・検証しており、植物形質測定が中心である。
abstractThis article describes the generation of crop surface models (CSMs) from high-resolution images that were obtained using a UAS to estimate sugarcane height.
Sugarcane is an important economic resource for many tropical countries and optimizing plantations is a serious concern with economic and environmental benefits. One of the best ways to optimize the use of resources in those plantations is to minimize the occurrence of gaps. Typically, gaps open in the crop canopy because of damaged rhizomes, unsuccessful sprouting or death young stalks. In order to avoid severe yield decrease, farmers need to fill the gaps with new plants. Mapping gap density is therefore critical to evaluate crop planting quality and guide replanting. Current field practices of linear gap evaluation are very labor intensive and cannot be performed with sufficient intensity as to provide detailed spatial information for mapping, which makes replanting difficult to perform. Others have used sensors carried by land vehicles to detect gaps, but these are complex and require circulating over the entire area. We present a method based on processing digital mosaics of conventional images acquired from a small Unmanned Aerial Vehicle (UAV) that produced a map of gaps at 23.5 cm resolution in a study area of 8.7 ha with 92.9% overall accuracy. Linear Gap percentage estimated from this map for a grid with cells of 10 m × 10 m linearly correlates with photo-interpreted linear gap percentage with a coefficient of determination (R2)= 0.9; a root mean square error (RMSE) = 5.04; and probability (p)
Why it matches plant phenotyping methodsUAV画像とデジタルモザイク処理によりサトウキビの植栽ギャップという植物群落状態を抽出・地図化し、精度と既存評価法との相関を検証しているため、手法が中心です。
abstractWe present a method based on processing digital mosaics of conventional images acquired from a small Unmanned Aerial Vehicle (UAV) that produced a map of gaps at 23.5 cm resolution
Ground cover is an important physiological trait affecting crop radiation capture, water-use efficiency and grain yield. It is challenging to efficiently measure ground cover with reasonable precision for large numbers of plots, especially in tall crop species. Here we combined two image-based methods to estimate plot-level ground cover for three species, from either an ortho-mosaic or undistorted (i.e. corrected for lens and camera effects) images captured by cameras using a low-altitude unmanned aerial vehicle (UAV). Reconstructed point clouds and ortho-mosaics for the whole field were created and a customised image processing workflow was developed to (1) segment the 'whole-field' datasets into individual plots, and (2) 'reverse-calculate' each plot from each undistorted image. Ground cover for individual plots was calculated by an efficient vegetation segmentation algorithm. For 79% of plots, estimated ground cover was greater from the ortho-mosaic than from images, particularly when plants were small, or when older/taller in large plots. While there was a good agreement between the ground cover estimates from ortho-mosaic and images when the target plot was positioned at a near-nadir view near the centre of image (cotton: R2=0.97, sorghum: R2=0.98, sugarcane: R2=0.84), ortho-mosaic estimates were 5% greater than estimates from these near-nadir images. Because each plot appeared in multiple images, there were multiple estimates of the ground cover, some of which should be excluded, e.g. when the plot is near edge within an image. Considering only the images with near-nadir view, the reverse calculation provides a more precise estimate of ground cover compared with the ortho-mosaic. The methodology is suitable for high throughput phenotyping for applications in agronomy, physiology and breeding for different crop species and can be extended to provide pixel-level data from other types of cameras including thermal and multi-spectral models.
Why it matches plant phenotyping methodsUAV画像・オルソモザイクから作物プロットの地表被覆率を抽出する画像処理ワークフローを開発・比較検証しており、植物形質取得法が中心である。
abstracta customised image processing workflow was developed to (1) segment the 'whole-field' datasets into individual plots, and (2) 'reverse-calculate' each plot from each undistorted image.