A Systematic Study: Implication of Deep Learning in Plant Disease Detection

Abstract

Identification of plant disease using conventional methods is crucial in the world of agriculture. An important challenge for sustainable farming has recently attracted the attention of researcher for automatic disease detection utilizing hyper spectral images. The recommended and previously used technologies are imperfect in scope and utterly dependent on deep learning models. From reduced data, convolutional neural networks are seeing the greatest outcomes for diagnosing and forecasting illnesses. Several of the neural network processing techniques now in use have been discussed in this article, with an emphasis on crop disease detection. A number of the current neural network processing techniques have been discussed in this review, with an emphasis on crop disease detection. Before outlining prospective applications for hyper spectral data processing, the study underlined the findings from the evaluation of several different deep learning models that are now in use. By enhancing system performance and accuracy, this survey's development will aid future research in understanding deep learning's capabilities while identifying plant ailments.