Identification of Mango leaf disease using deep learning

Project Code :TCMAPY1173

Objective

The objective of the "Identification of Mango Leaf Disease Using Deep Learning" project is to develop a deep learning-based system capable of accurately classifying various diseases affecting mango leaves. By training on a dataset comprising images representing five different disease classes, the project aims to create a robust model capable of distinguishing between these diseases with high accuracy. Leveraging state-of-the-art deep learning architectures and techniques, the system will analyze the visual features of mango leaves to identify specific symptoms associated with each disease. Ultimately, the goal is to provide farmers and agricultural professionals with a reliable tool for early detection and management of mango leaf diseases, thereby helping to safeguard crop health and improve yields.

Abstract

Plant disease, especially crop plants, is a major threat to global food security since many diseases directly affect the quality of the fruits, grains, and so on, leading to a decrease in agricultural productivity. Farmers have to observe and determine whether a leaf was infected by naked eyes. This process is unreliable, inconsistent, and error prone. Several works on deep learning techniques for detecting leaf diseases had been proposed. Most of them built their models based on limited resolution images using convolutional neural networks (CNNs). In this research, we aim at detecting early disease on mango plant leaves using Deep learning and machine learning approach. After a pre-processing step we will be using the SVM which is a machine learning and CNN algorithm which is a deep learning algorithm. Once after training with this we will be checking the outputs by giving input image and then comparing the algorithms.

KEYWORDS: CNN, SVM Deep Learning

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.