Deep Leaf Detect Implementation: Utilizing CNN for Accurate Leaf Disease Detection in Agricultural Systems

Abstract

Using artificial intelligence and state-of-the-art technology, the "Deep Leaf Detect Implementation: Utilizing CNN for Accurate Leaf Disease Detection in Agricultural Systems" is a cutting-edge agricultural initiative that focuses specifically on detecting the various leaf diseases. The cutting-edge method utilized in this presented research to provide automatic, real-time diagnosis of numerous leaf diseases is the Convolutional Neural Network (CNN). The system makes it possible for farmers to get timely with an efficient information about the condition of their crops by integrating CNN into the system. This ability is essential for making decisions quickly and enables farmers to carry out focused interventions, like applying the right amount of pesticides. Furthermore, the project's part on detecting the leaf diseases, aligns with the main objective of enhancing farming methods. The dataset used here contains 6000 images of tomato leaves and contains the five numbers of different diseases that occur. It acknowledges the importance of data-driven insights in well-informed decision-making, which is essential for farming that is both productive and sustainable.