nt, scalable, and user-friendly agricultural disease management solutions. Data Availability The leaf images utilized in our research were gathered from various reputable sources, including the UCI and Kaggle datasets, along with specific images obtained from the IEEE dataset repository. The links to the dataset are: 1. kaggle. https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases . 2. UCI Machine Learning Repository. https://doi.org/10.24432/C5R013 . 3. IEEE Dataport. https://ieee-dataport.org/documents/indian-rice-disease-dataset-irdd (doi: 10.21227/4rmf-gd63 ). Funding Statement The author(s) received no specific funding for this work. References 1. Gulati A, Kapur D, Bouton MM. R
Open resource ↗Kaggle · vbookshelf/rice-leaf-diseases · lines:292-309Unverified paper record
Multi-model machine learning for automated identification of rice diseases using leaf image data.
PloS one · 16 Sept 2025 · 10.1371/journal.pone.0307461
Abstract
Rice, a staple meal for about half of the world's population, is critical to global food security, especially in Asia. However, diseases have a severe impact on rice production, resulting in significant yield losses or outright crop failure. Traditional techniques of identifying rice diseases are time-consuming, labor-intensive, and rely heavily on specialist knowledge. As a result, a rapid, cost-effective, and automated method for detecting rice illnesses is critical for modernizing agricultural techniques and ensuring sustainable food production. This paper presents a novel hybrid deep-learning and machine-learning framework for automatically identifying rice plant diseases from leaf photos. We extracted deep features from rice leaf images using pre-trained CNN models-MobileNetV2, DarkNet19, and ResNet18. These features are then classified using machine learning classifiers with various kernel functions, which apply a strong 10-fold cross-validation technique to assure model reliability. Using a medium Gaussian kernel of the SVM classifier, the proposed system achieved a classification accuracy of 98.61%, specificity of 98.85%, and sensitivity of 97.25%. The framework is computationally efficient and scalable, allowing for greater dataset testing. The proposed technique provides a dependable and efficient solution for accurate identification of rice leaf diseases, reducing farmers' reliance on manual inspection and supporting timely intervention.
Plant phenotyping relevance
イネ葉画像から病害状態を自動推定する深層学習・機械学習手法が研究の中心であり、交差検証による性能評価も行っているため、植物フェノタイピング手法として含める。
abstractThis paper presents a novel hybrid deep-learning and machine-learning framework for automatically identifying rice plant diseases from leaf photos.
abstractThese features are then classified using machine learning classifiers with various kernel functions, which apply a strong 10-fold cross-validation technique to assure model reliability.
Code and data availability
The paper's Data Availability statement lists three public leaf-image datasets used as phenotyping inputs: a Kaggle rice leaf diseases dataset, the UCI Machine Learning Repository rice leaf dataset, and the IEEE Dataport Indian Rice Disease Dataset (IRDD). No author analysis code, models, or checkpoints are shared.
f images utilized in our research were gathered from various reputable sources, including the UCI and Kaggle datasets, along with specific images obtained from the IEEE dataset repository. The links to the dataset are: 1. kaggle. https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases . 2. UCI Machine Learning Repository. https://doi.org/10.24432/C5R013 . 3. IEEE Dataport. https://ieee-dataport.org/documents/indian-rice-disease-dataset-irdd (doi: 10.21227/4rmf-gd63 ). Funding Statement The author(s) received no specific funding for this work. References 1. Gulati A, Kapur D, Bouton MM. Reforming Indian agriculture. Economic & Political Weekly. 2020;55(11):35–42. 2. Haggblade S
Open resource ↗UCI Machine Learning Repository · 10.24432/C5R013 · lines:292-309various reputable sources, including the UCI and Kaggle datasets, along with specific images obtained from the IEEE dataset repository. The links to the dataset are: 1. kaggle. https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases . 2. UCI Machine Learning Repository. https://doi.org/10.24432/C5R013 . 3. IEEE Dataport. https://ieee-dataport.org/documents/indian-rice-disease-dataset-irdd (doi: 10.21227/4rmf-gd63 ). Funding Statement The author(s) received no specific funding for this work. References 1. Gulati A, Kapur D, Bouton MM. Reforming Indian agriculture. Economic & Political Weekly. 2020;55(11):35–42. 2. Haggblade S, Hazell P, Reardon T. The rural non-farm economy: prospects f
Open resource ↗IEEE Dataport · 10.21227/4rmf-gd63 · lines:292-309This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.