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Deep learning architectures for diagnosing the severity of apple frog-eye leaf spot disease in complex backgrounds
Bo Liu1,2, Hongyu Fan1,2, Yuting Zhang1,2
1College of Information Science and Technology, Hebei Agricultural University, Baoding, China.
Introduction:
In precision agriculture, accurately diagnosing apple frog-eye leaf spot disease is critical for effective disease management. Traditional methods, predominantly relying on labor-intensive and subjective visual evaluations, are often inefficient and unreliable.
Methods:
To tackle these challenges in complex orchard environments, we develop a specialized deep learning architecture. This architecture consists of a two-stage multi-network model. The first stage features an enhanced Pyramid Scene Parsing Network (L-DPNet) with deformable convolutions for improved apple leaf segmentation. The second stage utilizes an improved U-Net (D-UNet), optimized with bilinear upsampling and batch normalization, for precise disease spot segmentation.
Results:
Our model sets new benchmarks in performance, achieving a mean Intersection over Union (mIoU) of 91.27% for segmentation of both apple leaves and disease spots, and a mean Pixel Accuracy (mPA) of 94.32%. It also excels in classifying disease severity across five levels, achieving an overall precision of 94.81%.
Discussion:
This approach represents a significant advancement in automated disease quantification, enhancing disease management in precision agriculture through data-driven decision-making.
