Internet of Things‐based root disease classification in alfalfa plants using hybrid optimization‐enabled deep convolutional neural network

Abstract

SummaryIn this article, water cycle spider monkey optimization‐based deep convolutional neural network (WCSMO‐based deep CNN) is designed to classify root diseases in alfalfa plants. In the proposed method, the alfalfa plant root images are accessed remotely using a camera and transferred to the sink node to classify the disease. The proposed water cycle spider monkey optimization (WCSMO) algorithm performs the routing. Once the root images are received at base station or sink node, the images undergo preprocessing, segmentation, and classification. In the preprocessing module, a median filter is utilized to reduce the image noise. The preprocessed output is subjected to the segmentation phase, which is carried out using enhanced fuzzy C‐means clustering. The segmented results are passed to the classification step, where the deep convolutional neural network (deep CNN) is employed to categorize the root illness after being trained by the proposed WCSMO algorithm, which combines the water cycle algorithm and spider monkey optimization. The developed method achieved effective performance in terms of the metrics like sensitivity, accuracy, and specificity with the value of 0.9169, 0.9198, and 0.92. Moreover, the proposed WCSMO algorithm obtained the average energy and average throughput of 0.26641 J, and 91.483%, respectively.