ABSTRACT
Plants and crops suffering from pest infestations significantly affect agricultural productivity. Traditionally, farmers and agricultural experts rely on manual monitoring to detect and diagnose plant diseases. However, this process can be slow, costly, and prone to inaccuracies. One visible indicator of plant disease is the presence of spots or abnormalities on the leaves. This paper presents a model for automatic plant disease detection using leaf image classification techniques. The proposed system leverages image processing techniques and Convolutional Neural Networks (CNN) to effectively identify plant diseases CNN, a specialized type of neural network designed to process pixel data, is highly effective in image classification tasks. By employing CNN, our model can accurately detect diseases in plants based on leaf images. The system streamlines the detection process, reducing the need for manual labour and improving accuracy. Ultimately, this approach aims to enhance agricultural practices by providing a faster, more reliable, and cost-efficient method for disease identification.
