AUTOMATED CORN LEAF DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORKS

Abstract

The detection of plant diseases is essential for ensuring sustainable crop production and preventing yield losses. In recent years, gadgets gaining knowledge of strategies have shown amazing promise for automatic and accurate disorder detection. These studies give a technique for corn plant disease detection using Convolutional Neural Networks (CNNs). The proposed method harnesses the strength of deep mastering to routinely learn discriminative functions from corn plant snapshots, enabling accurate sickness categories. A complete dataset of categorized corn plant photos, protecting diverse diseases and wholesome conditions, is used for schooling the CNN version. Sizeable experiments reveal the effectiveness of the CNN-primarily based technique, accomplishing high accuracy costs in identifying corn plant illnesses. The proposed technique holds extensive capability for supporting farmers. Within the early detection and analysis of corn plant diseases, aiding in powerful disease control and advanced crop yield.