Intelligent plant disease detection using twin attention optimal convolutional neural network

Abstract

Farming is one of the most important ways for people in India to make a living. Rice is a staple food, and when farmers successfully harvest rice crops, pests often attack them, which costs agriculture a lot of money. There are now a lot of new AI-based ways to help with this problem in rice plants. But those ways don’t work very well because they take a long time and make mistakes when sorting things. This article talks about a new hybrid deep learning (DL) method for finding leaf diseases in rice plants. This process has four main steps: pre-processing, segmentation, feature extraction, and classification. A hybrid DL-based twin attention convolutional neural network (CNN) model classifies segmented images into healthy and unhealthy leaves. But this method has the problem of overfitting. An optimization method based on chaotic slime mould (CSM) solves this problem. The proposed method is compared with bidirectional long short-term memory (Bi-LSTM), recurrent neural network (RNN), deep neural network (DNN), and deep belief network (DBN). The suggested method has an overall accuracy of 99.56%, an F-measure of 99.21%, a sensitivity of 99.16%, a specificity of 98.56%, a precision of 99.26%, a mean absolute error (MAE) of 0.004, a mean squared error (MSE) of 0.004, and a root mean square error (RMSE) of 0.06.