Disease Recognization of Plant Using Different Image Processing Algorithm

Abstract

Cultivation plays a vital role in development of the Indian economy. Almost 50% of the nation’s workforce is employed in this industry. The Indian agricultural component is one of the important aspects of Indian economy. So, disease recognition of plants is very important so as to maintain sustainable agriculture. Different plants tend to show different symptoms when they are affected by variety of factors such as virus, bacteria, and climatic conditions. This paper presents survey on different image processing algorithm used for plant disease detection. The first step in plant disease detection is segmentation for which K-means clustering algorithm is used which retrieves textual features from input images. The edge detection of the leaves can also be done by canny edge detection which pays attention to the edges in a very robust way. Then, the feature extraction process is usually done using grey level co-occurrence matrix (GLCM). Followed by feature extraction, the classification comprises of K-nearest neighbour (KNN) which has accuracy about 90% for different inputs. Another classifier which can be used is support vector machine (SVM). Hence, machine learning plays an important role in the disease detection of the plants.