Edge Architecture for High-Accuracy Disease Identification in Apple Plants Using Transfer Learning Approach

The agriculture sector is increasingly adopting drones for early -stage disease identification, highlighting the need for an improved Artificial Intelligence model in disease detection. While popular pre -trained architectures like DenseNet, EfficientNet, and Inception require cloud computing for implementation, edge architecture offers a cost-effective alternative for early -stage disease identification. Evaluating the effectiveness of edge architecture in disease identification is crucial. This study focuses on developing an edge architecture -based system that continuously detects diseases at the edge node. The proposed approach utilizes a CNN - based architecture, specifically the modified MobileNet_V2, for edge -based disease identification. Experimental evaluation on a benchmark dataset demonstrates the efficacy of the disease detection network, outperforming existing methods in recognizing and detecting infected regions. The proposed mechanism achieves an overall accuracy of 99.93% for scab, black -rot, and Apple Rust, with improved F1 scores compared to existing methods.