Plants Disease Classification and Recommendation System using Deep Learning

Abstract

The agriculture sector is the backbone of the global economy, and the health of plants and crops is of paramount importance to ensure food security and sustainable agriculture. Plant diseases pose a significant threat to crop yield and quality, making early detection and effective management crucial. This project presents a comprehensive solution for plant/crop disease identification and management, leveraging the power of deep learning. The primary objective of this project is to develop a robust system capable of accurately classifying plant and crop diseases from images, followed by recommending suitable treatments or interventions. The project begins by creating a diverse and extensive dateset of plant images affected by various diseases, covering a wide range of plant species. The deep learning model employed in this project is a Convolutional Neural Network (CNN), which has proven to be highly effective in image classification tasks. The model undergoes rigorous training on the dataset, learning to recognize unique disease patterns and symptoms in plants. In addition to disease classification, the system is designed to recommend appropriate interventions, such as treatment options and preventive measures, based on the identified disease