Detection of Potato Disease Using Image Segmentation and Machine Learning

Abstract

Potato is one of the prominent food crops all over the world. In Bangladesh, potato cultivation has been getting remarkable popularity over the last decades. Many diseases affect the proper growth of potato plants. Noticeable diseases are seen in the leaf region of this plant. Two common and popular leaf diseases of the potato plants are Early Blight (EB) and Late Blight (LB). However, if these diseases were identified at an early stage it would be very helpful for better production of this crop. To solve this problem by detecting and analyzing these diseases image processing is the best option. This paper proposes an image processing and machine learning-based automatic system that will identify and classify potato leaf diseases. In this paper, image segmentation is done over 450 images of healthy and diseased potato leaf, which is taken from publicly available plant village database and seven classifier algorithms are used for recognition and classification of diseased and healthy leaves. Among them, The Random Forest classifier gives an accuracy of 97%. In this manner, our proposed approach leads to a path of automatic plant leaf disease detection.