Identification of leaf disease in cotton crop using image processing.

In the Indian state of Vidarbha, cotton is the main crop grown for the production of cotton fabric. Cotton leaf diseases harm the cotton crop, greatly reducing the crop yield. To increase the output of the cotton crop, early detection of cotton leaf diseases is required. The projections of Alternaria, Bacterial Blight, and Curl leaf diseases, as well as their classification, are presented in this work. The Support Vector Machine (SVM) is used for simple data and Resnet is used for complex data to differentiate between the illnesses of cotton leaves. The thirteen separate features used for categorization include the color moments of each RGB image channel and texture characteristics utilizing the Gray Level Co-occurrence Method (GLCM) algorithm. SVM and Resnet compared here, it shows that Resnet is better performer and adds more generalisation with 97% accuracy. [ABSTRACT FROM AUTHOR]

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