Adaptive leaf abnormality segmentation and improved plant disease classification by tuned long short‐term memory with recurrent neural network

Abstract

SummaryThe conventional models suffer in providing significant accuracy and detection speed, especially in detecting apple diseases, to assure the healthy development of the apple industry. The main aspect of the proposed scheme is to present a new plant disease classification using intelligent segmentation and classification models. The adaptive leaf abnormality segmentation is enhanced by the solution index‐based Jaya‐Krill Herd optimization (SI‐JKHO). Further, the feature extraction is accomplished using “gray level co‐occurrence matrix, hybrid local binary pattern with local gradient patterns,” color features, and shape features. These collected features are subjected to feature selection using principle component analysis, in which the optimal features are obtained that are further considered to “tuned long short‐term memory with recurrent neural network (T‐LSRNN).” From the validating results, the performance of the enhanced‐JKHO‐LSRNN method is correspondingly secured at 6.66%, 6.66%, 7.86%, and 4.34% higher enriched performance RNN, long short‐term memory, convolutional neural networks, and LSRNN at 35th learning percentage in dataset 3. The results confirm that the developed model can accurately determine plant diseases compared to conventional models based on diverse performance metrics like “accuracy, precision, specificity, sensitivity, false positive rate, and false negative rate,” and so forth.