Article: Deep learning architectures for diagnosing the severity of apple frog-eye leaf spot disease in complex backgrounds.
2024 Volume 14, Page(s) 1289497
Abstract: 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 ... ...
| Abstract | 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. |
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| Language | English |
| Publishing date | 2024-01-08 |
| Publishing country | Switzerland |
| Document type | Journal Article |
| ZDB-ID | 2613694-6 |
| ISSN | 1664-462X |
| ISSN | 1664-462X |
| DOI | 10.3389/fpls.2023.1289497 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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