A Review on Potato Disease Detection and Classification by exploiting Deep Learning Techniques

Abstract

The edible potato comes in fifth for human consumption and fourth among main food crops. Since it is a crop that is vegetative grown, many pests and disease can be passed along from one generation to the next. Crop diseases, which have a negative impact on food security as well as economic losses, have a significant impact on the production and quality of yields from potato crops. Thus, the application of unique and precise deep learning-based algorithms for disease detection and classification is highly required. Identifying weaknesses in agricultural products, particularly potatoes requires the use of machine vision and image processing techniques. Deep learning and image processing have been used in agriculture to classify and number of disease and pests affecting potatoes has grown, and study in this area is still continuing. The use of artificial intelligence and image processing in agriculture for the classification and identification of potato pests and disease has grown, and work in this area is still ongoing. Different deep learning techniques, such as VGG19, VGG16, Google Net, Alex Net, and convolution neural network methods, can be used to address the disease problem in potatoes. These methods also examined multiple classes of potato diseases as: Healthy, Black Leg, Black Scurf, Pink Rot, Common Scab, etc. Food safety could be seriously threatened by the spread of potato disease. In this article, deep learning techniques for early detection of potato disease are discussed.