An application of YOLOv8 integrated with attention mechanisms for detection of grape leaf black rot spots.

ABSTRACT: Aiming at the problems of low recognition rate of small target spots in grape leaf images and low detection accuracy due to low resolution of input images. In this paper, an improved recognition network based on YOLO v8 is constructed. In the constructed network, Spatial Pyramid Dilated Convolution (SPD-Conv) is used to replace each stepwise convolution layer and each pooling layer to better capture the detailed features of small targets. Meanwhile, the Efficient Multi-Scale Attention (EMA) Module is incorporated into the Neck part of YOLO v8 to make full use of the feature information of each detection layer and improve the accuracy of feature representation.The Plant Village dataset and the orchard image set are used to test the network performance of the improved model. The experimental

SUBMITTER: Zhu J 

PROVIDER: S-EPMC11999144 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature