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 CNNbased 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.