A Hybrid Ai-Based Target-Node Algorithm for Detecting and Classifying Plant Disease Regions
Abstract
Agriculturalists often struggle to identify diseases on leaves accurately. They traditionally rely on visual inspection, but this method is not entirely reliable. To address this issue, a user-friendly and efficient plant disease detection system that can accurately identify leaf diseases is needed. By increasing the number of datasets used to train and test the models, their classification and identification accuracy can improve, resulting in higher percentages of accuracy. In this study, Convolutional Neural Networks (CNNs) were explored as a popular option for image identification and classification using machine learning (ML) approaches. This is due to CNNs' innate ability to automatically extract essential image features and understand spatial hierarchies. The experiments with different categories of disease findings demonstrate that the proposed EAICS detects the target node with a region classification accuracy of 98.34%, which is better than 6.19% of the existing traditional CNN accuracy.
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References
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