Classification of rice leaf diseases based on the deep convolutional neural network architectures: Review.

Timely analysis and recognition of leaf diseases in plants ensures good quality and productivity of crops in the field of agriculture. Recognition of rice leaf diseases is one of the biggest challenges for the farmers due to incomplete awareness regarding latest sophisticated techniques in the field of leaf disease identification. Much of the growth of rice is disturbed due to the prevalence of rice leaf diseases. Earlier detection of rice leaf diseases done manually by farmers which is very laborious and time consuming. However, requisite of automatic identification of rice leaf diseases helps farmers to save their agricultural crops more efficiently. Innovations in the field of computer vision and deep learning meets the expectations and proves to be the best solution for classification of rice leaf diseases using convolutional neural networks using samples of rice leaf diseases. In this review paper, the major focus is on performance analysis of detection of rice leaf diseases based on the architectures employed. [ABSTRACT FROM AUTHOR]