Image processing and machine learning techniques to detect and classify paddy leaf diseases: A review

Abstract

In Asian countries, paddy is one of the major staple foods, and the agricultural sector is the largest employer in India’s economy. Hence, an effective mechanism should be adopted for food security and its production. Due to infections caused by pests like virus, fungus and bacteria, there is a huge loss in quality and quantity of the rice. This results in a huge loss to the farmers. This survey presents the different image processing techniques for paddy disease identification and further classification. The challenges involved in each step of diseases detection and classification are analyzed and discussed. Image preprocessing, segmentation, feature extraction and classification are the main stages involved in paddy disease identification and classification. All these four stages are well addressed here. This survey gives a complete overview along with the challenges of each stage and also focuses the research challenge and research gap. This survey concludes that automatic paddy leaf disease detection, and classification requires much more advancement. There is a need for more advanced techniques to automate the system effectively and efficiently.