Plant Disease Detection Using Convolutional Neural Network

Abstract

Plant disease detection is critical in agriculture, significantly impacting crop yield and food supply. This paper introduces an innovative approach utilizing deep learning and computer vision techniques to detect and classify plant diseases. The proposed method involves image processing to analyze leaf health conditions using a dataset of varied leaf images exhibiting disease manifestations. The model is trained to recognize distinct patterns associated with different diseases, enabling early detection and intervention. This AI-based system aims to enhance agricultural productivity, minimize crop losses, and contribute to food security. Furthermore, it has potential application in mobile platforms, providing farmers with a user-friendly tool for managing plant diseases. Nonetheless, challenges such as image condition variability and the necessity for comprehensive, diverse datasets for model training require further research and development.