A Survey on Diseases Detection and Classification of Agriculture Products using Image Processing and Machine Learning

Quality agriculture production is the essential trait for any nation’s economic growth. So, recognition of the deleterious regions of plants can be considered as the solution for saving the reduction of crops and productivity. The past traditional approach for disease detection and classification requires enormous amount of time, extreme amount of work and continues farm monitoring. In the last few years, advancement in the technology and researchers’ focus in this area makes it possible to obtain optimized solution for it. To identify and detect the disease on agriculture product various popular methods of the fields like machine learning, image processing and classification approaches have been utilized. This paper presents various existing techniques used to detect the disease of agriculture product. Also, paper surveys the mythologies utilized for disease detection, segmentation of the affected part and classification of the diseases. It also includes the summary of various feature extraction techniques, various segmentation techniques and various classifiers along with benefits and drawbacks.