Publication

Abstract : Plant diseases mostly harm the leaves, resulting in a loss in agricultural output’s quality and quantity. Plant disease is the most common cause of large-scale crop mortality. India is a country where people’s livelihoods are heavily reliant on agriculture. The disease has caused chaos in the agricultural industry. The human eye’s perception is not quite as sharp as it needs to be to notice minute variations in the sick leaf region. It needs a complex process that requires both plant expertise and a large amount of processing time. As a result, plant diseases can be detected using machine learning. The disease detection method includes image acquisition, image pre-processing, image segmentation, feature extraction, and classification. To prevent crops at the initial stage from diseases, it is essential to develop an automatic system to diagnose plant diseases and identify its category. The goal of the proposed research is to examine several machine algorithms for plant disease prediction. The paper proposed a framework for disease and healthiness detection in plants and the classification of diseases based on symptoms appearing on a leaf. The diseases are grouped into three categories in the paper: bacterial, viral, and fungal. To conclude, the research paper investigates all of these factors and uses several machine learning(DL) techniques and deep learning(DL) techniques. The machine learning(ML) techniques used in the research work are SVM, KNN, RF(Random Forest), LR (Logistic Regression), and the deep learning(DL) technique used is-Convolutional Neural Network(CNN) for disease prediction in the plants. Following that, a comparison of machine learning and deep learning methodologies was conducted. The RF(Random forest) has the highest accuracy of 97.12 % among machine learning classifiers, however, in comparison to the deep learning model mentioned in the study, the CNN classifier has the highest accuracy of 98.43 %