Evaluating The Efficiency of RNN Model for Real Time Fruit Disease Detection

Abstract

  Introduction: Fruit disease detection has been considered essential in maintaining nutrition security and productivity in agriculture in that respect the paper has presented a method that involves using RNNs in the automatic fruit disease classification they had gathered a phenomenal dataset of images of fruits and used it for training their regular RNN model these findings further turn out to be highly useful in characterizing the appearance of diseases in fruits and vegetables including that which causes contagious bacterial and viral infections RNN performs an incredibly good job of modeling sequential flavor image information which may be prime imperative for early detection these content generated within the appearance consider as opposed to other deep learning approaches it seems that RNN is an essential constraint in this task the discovery brought about by this research forms a rational approach toward transformation in agriculture and creating a difference in nutritional output