Abstract

Coffee is one of the most widely consumed beverages globally and plays a significant role in the economy of many countries, including India. However, coffee plants are susceptible to various diseases, such as Leaf rust, Leaf miner, Cercospora which can cause substantial crop losses. Hence, early detection and diagnosis are essential for effective disease management. In this study, we propose a novel approach using convolutional neural networks (CNN) with a Softmax classifier for the accurate detection of Coffee Leaf Disease. The performance of the trained model was evaluated using a separate test dataset, and achieved an impressive accuracy of 98%. The high accuracy obtained demonstrates the efficiency of proposed approach in automating the detection of Coffee Leaf Disease in coffee plants. The work contributes to the advancement of precision agriculture and offers a valuable tool for coffee farmers to mitigate the impact of coffee leaf disease on their crops.