Disease Identification and Classification From Pearl Millet Leaf Images Using Machine Learning Techniques

Abstract

Plant disease plays a crucial role in the reduction as well as degradation of production and yield in the area of precision agriculture and is a major concern for farmers and agriculturists. Hence, the detection and identification of diseases among the crops is essential. In this chapter, the CNN model for the identification and classification of different plant diseases through its leaf images is used. Four diseases such as ergot, downy mildew, blast, and rust in the pearl millet crops are considered in this work. The images of the pearl millet crop are considered for the five classes: healthy, ergot, downy mildew, rust, and blast. The dataset consists of 2074 images. The dataset is trained for the 30 epochs. The proposed approach is compared with the various existing methodologies such as naïve Bayesian, decision tree, support vector machine, and random forest. The simulation result shows that the proposed approach using the CNN outperforms the existing approaches in terms of accuracy and loss.