Plant Disease Prediction using Deep Learning

Abstract

Since plant diseases are one of the main factors affecting food production and reducing production losses, they must be swiftly identified and treated. India's agricultural sector employs close to 50% of the workforce, thus not having an appropriate solution would affect the livelihood of many people. Different deep learning algorithms have recently found usage in the diagnosis of plant diseases, offering a potent tool with highly accurate results. The objective of this study is to identify an ensemble-based solution by using several algorithms in the process of classifying and diagnosing plant diseases, describing trends, and emphasizing gaps and also comprises complete examination of the literature. The ensemble based solution is based on the top four performing deep learning algorithms using multi-layered perceptron as meta classifier. In this regard, we reviewed 15 studies from the previous three years that address problems with disease detection, dataset characteristics, researched crops, and pathogens in various ways. The proposed ensemble model achieved a maximum accuracy of 98.13% compared to the conventional architectures. For comparing the results, various performance metrics are used such as accuracy, loss, etc.