Quality Detection Model of Unmanned Harvesting Operation of Open-Field Cabbage Based on Machine Vision

LI Xiaosuo; GUO Wang; ZHU Huaji; GU Jingqiu; LI Qingxue; WU Huarui

doi:10.15933/j.cnki.1004-3268.2025.10.016

Abstract：Accurate quality recognition of harvested cabbage is the premise for quality detection of unmanned harvesting operation of open-field cabbage.In order to solve the problems of complex harvesting background environment,difficulty in obtaining cabbage features due to the fast operation speed of transportation devices,and insufficient identification accuracy for small targets in the process of quality recognition of harvested cabbage,a lightweight harvesting quality detection method based on YOLOv8s was proposed.Firstly,RepVGG module was used to replace some Conv modules in Backbone layer,which could enhance the feature extraction capability of the original model while reducing the number of model parameters.Secondly,CBAM convolutional attention module was introduced to suppress the non-critical feature information in the complex background,so that the model paid more attention to the quality of harvested mature cabbage.Finally,a small target detection Head P2 with a downsample of 4 was added to the Head layer to heighten the detection ability of the model for multi-scale cabbage.The results showed that compared with the original YOLOv8s model,the Precision,Recall and mAP50:95 of the optimized model were improved by 2.5,0.9 and 1.9 percentage points respectively.Compared with the common target detection model,the detection results on the cabbage harvesting operation dataset also had obvious advantages.The improved model can accurately identify the quality of unmanned harvesting operation of open-field cabbage,provide data support for remote control of machine operation parameters,and provide theoretical reference for the research and application of autonomous unmanned precision operation of open-field vegetables.