外文摘要：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.
外文关键词：deep learning;Apple disease;severity estimation;frog eye leaf spot;two-stage method
作者：Zhang, Yuting;Cheng, Hong;Liu, Bo;Fan, Hongyu;Cai, Jinjin
作者单位：Hebei Agr Univ;Hebei Key Lab Agr Big Data
期刊名称：FRONTIERS IN PLANT SCIENCE
期刊影响因子：0.0
出版年份：2024
出版刊次：14
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台