Abstract
Rice is one of the staple crops in the world, as it is a rich source of protein,
minerals, fibre, and vitamins. It is cultivated almost in every part of the world, but its
productivity decreases due to several diseases. If these diseases are identified at the
initial stages, then preventive measures can be taken, but their symptoms are quite
similar for human eyes to recognize them correctly. Therefore, there is an immense
need to apply automated techniques for recognizing rice diseases. Various Artificial
Intelligence (AI) based prototypes have been surveyed in this chapter. These
techniques were proposed by researchers for diagnosing rice disease. Here, our main
goal is to present ideas on how Pretrained Neural Networks can be used in the
recognition of rice diseases. Therefore, a brief description of AI techniques and their
comparison is also outlined.
Keywords: Convolution Neural Networks, Deep Learning, Disease classification, Image Classification, Machine Learning, Pretrained models, Rice diseases, Transfer Learning.
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