Cotton Leaf Disease Detection Using Machine Learning Approach
Keywords:
Cotton leaf disease, Machine learning, Support Vector Machine, GLCM, Image processing, Precision agriculture.Abstract
Cotton is one of the most important commercial crops, and its productivity is significantly affected by leaf diseases such as Bacterial Blight, Alternaria Leaf Spot, Cercospora Leaf Spot, Fusarium Wilt, and Powdery Mildew. Early and accurate detection of these diseases is necessary to minimize crop loss and improve agricultural yield. Conventional disease diagnosis based on manual inspection by agricultural experts is time-consuming, subjective, and often unavailable in rural areas. This paper presents a machine learning-based approach for cotton leaf disease detection using image processing and Support Vector Machine (SVM) classification. Initially, cotton leaf images are preprocessed through resizing, denoising, and normalization. The leaf region is segmented from the background, and texture features are extracted using the Gray Level Co-occurrence Matrix (GLCM). Important GLCM features such as contrast, energy, homogeneity, and correlation are combined with color features to generate the feature vector. The extracted features are then classified using SVM to identify the disease category. The proposed method is evaluated on a cotton leaf disease dataset containing healthy and diseased leaf images. Experimental results demonstrate that the proposed approach achieves high classification accuracy and effectively distinguishes different cotton leaf diseases. Therefore, the developed system can serve as an efficient and low-cost solution for smart farming and precision agriculture.
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