Optimized routing algorithm with AlexNet-ShuffleNet for plant leaf disease and infectious classification in IoT
Year: 2025
Type: article
Abstract: In agriculture, utilizing images to detect plant leaf diseases is a vital area in precision farming. Typically, trained professionals physically inspect plant tissues to identify disease range. Nowadays, AI has made foremost paces in detecting and classifying plant diseases. Moreover, Internet of Things (IoT) has several applications, containing Agricultural-IoT (AIoT), which is considered to elevate agricultural yields. This paper intends to develop an approach in IoT for plant disease classification. Initially, simulation of IoT is done and the IoT nodes route sensed plant leaf images by proposed Serial Exponential Golf Optimization Algorithm (SEGOA), which is established by modifying Golf Optimization Algorithm (GOA) using Exponential Weighted Moving Average (EWMA) to the destination, where plant leaf disease detection is executed. To extract the RoI, CNN is used to discover diseased part in plant leaf. Then, plant leaves is classified as healthy and diseased subclasses by employing AlexNet-ShuffleNet. Moreover, the disease types is classified more into fungal/bacterial/viral infection using the AlexNet-ShuffleNet. Performance of adopted work is assessed by utilizing the metrics, such as energy, accuracy, sensitivity, and specificity. Overall outcome of AlexNet-ShuffleNet give a promising result, such as accuracy of 94.6%, sensitivity of 98.7% and specificity of 94%.
Source: Journal of Neonatal Surgery
Authors: Nasim Shah, Ashutosh Gupta, Arun Kumar, D S Chouhan
Institution: Sir Padampat Singhania University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7085217175