Mango Leaf Disease Detection Using Deep Learning

Abstract

Abstract: Agriculture is one of the essential sectors for the survival of humankind. At the same time, digitalization touching across all the fields that became easier to handle various difficult tasks. Adapting technology as well as digitalization is very crucial for the field of agriculture to benefit the farmer as well as the consumer. Due to adopting technology and regular monitoring, one can able to identify the diseases at the very initial stages and those can be eradicated to obtain a better yield of the crop. Crops growth and yield are essential aspects that influence the field of agriculture as well as farmer economically, socially, and in every possible way. So, it is necessary to have close monitoring at various stages of crop growth to identify the diseases at right time. But humans naked may not be sufficient and sometimes it would be misleading scenarios arise. In this aspect, automatic recognition and classification of various diseases of a specific crop are necessary for accurate identification. This thought gave inspiration for the present proposed framework. The proposed framework is main concentrated on deep learning techniques using convolutional neural networks such as image classification and image recognition system. By using these system we can detect leaf diseases in various plants there are three different leaf diseases such as Anthracnose, Powdery Mildew, Red Rust of Mango has been identified in a dataset consisting of 1200 images of diseased and healthy mango leaves. The proposed CNN model achieves an accuracy of 93.67% for identifying the leaf diseases in mango plant thereby showing the feasibility of its usage in real time applications.