A Survey on Paddy Crop Disease Detection Using Machine Learning and Artificial Intelligence Models

It is a challenge to identify paddy diseases and insects because the structure of paddy diseases and pests is intricate, and the aspects of various species of paddy diseases and insects are quite similar. In order to stop the further spread of diseases and insects, it is essential that these pests, which include a wide variety of insects and diseases, are detected and categorized as quickly as possible. Deep complex neural networks, also known as CNNs, are widely acknowledged to be the most cutting-edge approach to image identification at the moment. This paper will discuss several deep neural network and machine learning techniques that are utilized for the identification of paddy diseases on the basis of images of paddy leaf that are diseased with certain diseases. This paper carried out an in-depth investigation into the amount of published articles that discussed a variety of diseases that can affect paddy as well as other types of plants and fruits, and evaluated these publications using significant criteria. These requirements are the image data collection size, the number of diseases, the pretreatment, the segmentation approach, the classification type, the classification accuracy, and other similar factors.