Abstract:
The identification of plant diseases is highly significant to avoid losses in quantity and productivity of agricultural production. Problems in the agriculture industry includes are minimized by using more deep learning and image processing techniques. This review mostly emphasizes on rice diseases detection of input pictures of sick rice plants captured by DL and other imaging methods. In addition, remarkable DL and image processing concepts in plant detection and classification mentioned disease. Different classification methods, such as traditional neural networks (CNNs) are applied in a variety of agricultural research applications. Various input data produces results of different quality and, therefore, the selection of a classification method is an important task. Conventional Neural Networks (CNN) different classification techniques used in various agricultural research applications. The choice of a classification technique is a crucial undertaking since various input data yield results of varying quality.
