Hybrid CNN-SVM Framework for Rice Leaf Disease Detection to Promote Sustainable Agriculture

Rice is one of the most significant crops and a key factor in ensuring the safety of food around the world. On the other hand, rice supply may be threatened by a number of diseases that harm the plant's various sections, particularly the leaves. Rice Neck Blast, Rice Leaf Blast, Rice Brown Spot, Rice Blast, Brown Spot, Sheath Blight, Bacterial Stripe, and False Smut in rice leaves are common illnesses that affect rice leaves. Depending on the disease, damaged plant tissues each class's accuracy, which ranges from 0.97 to 0.98, is good. Safe nutrition & sustained rice production can employ a variety of strategies, including natural control methods, insecticides, tolerant crops, and agricultural practices. Early diagnosis of rice leaf diseases is a major worry for the farming community, and the Nikon D5300 and Realme XT, which are outfitted with deep computing techniques like convolutional neural networks (CNN) and Support Vector Machines (SVM), can detect them with high accuracy