A deep learning approach for the classification of diseased plant leaf images

Abstract

Plant growth monitoring and plant protection are the key elements in the plant production industry. These factors influence the quality and productivity of the plant and its yields. Diseases are the major factor that vitiates the plant health. More often they harm plant parts like fruit, flower, leaf or stem, but quite often the severity of diseases may even result in plant death. In recent years, computer vision techniques, machine learning algorithms, and deep learning models have gained importance due to their capability of dealing with complex data with precision. These techniques are well known for pattern recognition and classification problems. Therefore in this work, a multilayer convolutional neural network is proposed for the classification of diseased plant leaf images. The real-time images of four different plants in healthy and diseased condition are collected for validating the performance of the proposed model. Results, when compared with other methods, shows the higher classification accuracy of the proposed model.