Recent machine learning based approaches for disease detection and classification of agricultural products

Disease infection to agricultural products like plants, fruits and vegetables, results in degradation of quality and quantity of agriculture products. This directly affects the financial source of agriculturists and the human health. Hence, detection of diseases in plants, fruits and vegetables crops at early stages of development leads to reduce loss of yield and quality. The traditional approaches for disease detection requires continues monitoring and observation of farms either by farmers or by experts. But, it is very costly and time consuming. In last few years, various researchers have focused into this area to provide optimized solution. Popular methods have utilized machine learning, image processing and classification based approaches to identify and detect the diseases on agricultural products. The existing techniques for disease detection have utilized various image processing methods followed by various classification techniques. This paper presents an overview of existing reported techniques useful in detection of diseases of agricultural products. A comparative study of different methods based on the type of agricultural product, methodology and its efficiency together with the advantages and disadvantages is also included in this paper.