Deep learning architectures for diagnosing the severity of apple frog-eye leaf spot disease in complex backgrounds.

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

SUBMITTER: Liu B 

PROVIDER: S-EPMC10800469 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature