Computational Intelligence Based Plant Health Management System

The agricultural sector faces significant challenges in ensuring crop health and productivity. One critical aspect is the timely detection and management of plant diseases, as well as the judicious application of fertilizers to optimize growth. In conventional method, labor is the key role for identification of affected leafs and usage of fertilizer. But the conventional method is human centric and it takes more time and less accurate system. In order to overcome the limitations of conventional method and to enhance the soil productivity this paper contains a novel solution: a Plant Leaf Disease Detection and Fertilizer Suggestor system utilizing machine learning and deep learning techniques. On a comparing several algorithms such as CNN, SVM, KNN, etc., the proposed work contains most efficient algorithm Convolution Neural Network CNN. The system aims to automate the process of identifying plant diseases and recommending appropriate fertilizers, thereby enhancing efficiency and accuracy in agricultural practices.