Abstract:
This project aims to develop a comprehensive system for crop and fertilizer recommendation and plant disease detection using image processing techniques. The goal is to assist farmers in making informed decisions regarding crop selection, appropriate fertilizers, and timely detection of diseases to maximize yield and minimize losses. The system employs machine learning algorithms such as Naive Bayes, Random Forest, and Decision Trees for crop and fertilizer recommendation, while Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) are utilized for plant disease detection. The effectiveness of each algorithm will be evaluated, and the bestperforming models will be integrated into the system. The proposed system holds great potential for enhancing agricultural practices, improving crop yield, and reducing the negative impact of diseases on plants.
