Subbarayudu Chatla, Kubendiran Mohan
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632 014, India.
Sci Rep. 2025 Jul 24;15(1):26873. doi: 10.1038/s41598-025-08071-6.
In India, agriculture remains the primary source of livelihood for many people. Pathogen attacks in crops and plants significantly diminish both the yield and quality of production, leading to financial losses. As a result, identifying diseases in crops is highly important. As the population grows, the demand for rice also rises. Therefore, disease management is vital in rice cultivation, and rapid identification of rice diseases is critical for timely pesticide application and effective control. Consequently, there is a need to boost agricultural productivity by adopting new technologies. Deep learning is a popular area of research in various fields. This research aims to design and propose a new automated model using a deep learning model for the disease identification and categorization of paddy leaves. The system follows a structured workflow comprising several stages: image acquisition, pre-processing, feature extraction, feature selection, and classification. Images of paddy leaves were obtained from the paddy doctor dataset hosted on Kaggle. The data is pre-processed by choosing the RoIs, labelling, enhancement, and segmentation using adaptive thresholding and grouped using K-means clustering. The MobileNetV3 model, a pre-trained transfer learning approach, extracted colour, shape, and texture features. The vital features are selected using the hybrid Genghis Khan Shark Optimization (GKSO) with Simulated Annealing (SA) algorithm. The chosen features are subsequently fed into the CatBoost for disease classification. The deep learning techniques introduced for disease identification and classification have been compared with various conventional classifiers, and the system's performance has been validated using metrics such as accuracy, sensitivity, and F1-score. Performance investigations prove that the technique efficiently yields a higher accuracy of 98.52%, outperforming state-of-the-art techniques.
在印度，农业仍然是许多人的主要生计来源。农作物和植物遭受病原体攻击会显著降低产量和生产质量，导致经济损失。因此，识别作物病害非常重要。随着人口增长，对水稻的需求也在增加。所以，水稻种植中的病害管理至关重要，快速识别水稻病害对于及时施用农药和有效防治至关重要。因此，有必要采用新技术来提高农业生产率。深度学习是各个领域中一个热门的研究领域。本研究旨在设计并提出一种新的自动化模型，使用深度学习模型对水稻叶片的病害进行识别和分类。该系统遵循一个结构化的工作流程，包括几个阶段：图像采集、预处理、特征提取、特征选择和分类。水稻叶片图像取自Kaggle上托管的水稻医生数据集。通过选择感兴趣区域、标注、增强以及使用自适应阈值进行分割并采用K均值聚类进行分组来对数据进行预处理。MobileNetV3模型，一种预训练的迁移学习方法，提取颜色、形状和纹理特征。使用混合的成吉思汗鲨鱼优化（GKSO）与模拟退火（SA）算法选择重要特征。随后将所选特征输入到CatBoost中进行病害分类。将为病害识别和分类引入的深度学习技术与各种传统分类器进行了比较，并使用准确率、灵敏度和F1分数等指标对系统性能进行了验证。性能研究证明，该技术有效地产生了98.52%的更高准确率，优于现有技术。
