Computer Vision and Machine Learning in Paddy Diseases Identification and Classification: A Review

Abstract

Food security of the Indian population depends upon the agriculture. The annual crop loss due to the pestsand diseases is a severe problem which is addressed by the researcher time and again Paddy is one of the common crops found in most of the agriculture areas in India. Paddy plants exhibit health condition mainly in stem and leaf. Farmers are still using the traditional method and experts suggestions in identifying the diseases. Research in disease identification and classification process is going on for the past three decades but its automation is still an open challenge. Image segmentation based computer vision techniques, supervised and unsupervised classification algorithms of machine learning are widely used in the analysis of vegetation health analysis. Pattern recognition is playing a major role in identifying the features causing the diseases. The focus of the work is on investigating the performance of various segmentation techniques with support vector machine and K-Neural Network classification algorithms using statistical features. The hybrid IP-PR (Image Processing-Pattern Recognition)techniques are used for paddy disease classification..The performance of the classification algorithm is primarily depending upon the intelligence of the segmentation techniques. The properly segmented outputs give a better accuracy.