EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5.

ABSTRACT:

Introduction

Current detection methods for apple leaf diseases still suffer some challenges, such as the high number of parameters, low detection speed and poor detection performance for small dense spots, which limit the practical applications in agriculture. Therefore, an efficient and accurate model for apple leaf disease detection based on YOLOv5 is proposed and named EADD-YOLO.

Methods

In the EADD-YOLO, the lightweight shufflenet inverted residual module is utilized to reconstruct the backbone network, and an efficient feature learning module designed through depthwise convolution is proposed and introduced to the neck network. The aim is to reduce the number of parameters and floating point of operations (FLOPs) during feature extraction and feature fusion, thus increa

SUBMITTER: Zhu S 

PROVIDER: S-EPMC9996066 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature