Multi-scenario pear tree inflorescence detection based on improved YOLOv7 object detection algorithm.

ABSTRACT: Efficient and precise thinning during the orchard blossom period is a crucial factor in enhancing both fruit yield and quality. The accurate recognition of inflorescence is the cornerstone of intelligent blossom equipment. To advance the process of intelligent blossom thinning, this paper addresses the issue of suboptimal performance of current inflorescence recognition algorithms in detecting dense inflorescence at a long distance. It introduces an inflorescence recognition algorithm, YOLOv7-E, based on the YOLOv7 neural network model. YOLOv7 incorporates an efficient multi-scale attention mechanism (EMA) to enable cross-channel feature interaction through parallel processing strategies, thereby maximizing the retention of pixel-level features and positional information on the feature map

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC10840500 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature