PLFYNet-based edge-deployable detection system for Ginkgo biloba leaf diseases.

ABSTRACT:

Introduction

Target detection is a pivotal technology for precise monitoring of leaf-used Ginkgo biloba diseases in precision agriculture. However, complex plantation environments impose significant constraints on existing detection systems, manifesting as degraded detection accuracy, suboptimal efficiency, and prohibitive computational overhead for edge deployment. This study aims to develop a lightweight deep learning model tailored for real-time disease detection on resource-constrained embedded devices.

Methods

First, a comprehensive multi-class dataset was constructed, containing 7,158 augmented images covering three disease categories: chlorosis, insect pest, and physical damage. Five lightweight architectures were systematically evaluated, and an optimized reconstruct

SUBMITTER: Wang J 

PROVIDER: S-EPMC12696162 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature