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:54-60Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published23 Jul 2026Journal of Intelligent Decision Making and Information ScienceCited by 0 · OpenAlex ↗
- 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:1-61Code / dataset availability confirmedCrossref · checked 15 Sept 2026
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:1-48Code / dataset availability confirmedCrossref · checked 5 Sept 2026
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:228-248Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
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:757-778Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
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:112-131Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
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:278-299Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
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:1-59Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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:350-378Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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:65-90Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
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:709-821Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
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:109-124Code / dataset availability confirmedCrossref · checked 14 Sept 2026
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:1-57Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
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:1-92Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
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:147-178Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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
•
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:1-56Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published24 May 2023International Conference on Contemporary Academic ResearchCited by 1 · OpenAlex ↗
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:1-46Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
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:155-167Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
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:1-40