Intelligent Diagnosis of Potato Leaf Diseases using Deep Learning

Potato cultivation faces significant threats from plant diseases, such as early blight and late blight, which detrimentally impact yield and quality. Addressing this challenge, this study presents a novel Convolutional Neural Network (CNN) model designed for automated disease detection in potato plants. Leveraging advanced CNN architectures, particularly MobileNet, this study achieves a remarkable accuracy of 96.6% in disease classification tasks. This innovative approach eliminates the need for manual inspection processes, offering a scalable and efficient solution for early disease identification and mitigation. Finally, this study highlights the transformative potential of deep learning methodologies in enhancing agricultural productivity and disease management strategies.