农机化研究2025，Vol.47Issue(7)：9-17,9.DOI:10.13427/j.issn.1003-188X.2025.07.002
基于改进YOLOv7模型的水田复杂环境稻株识别
Rice Plant Recognition in Complex Paddy Field Environments Based on Improved YOLOv7 Model
摘要
Abstract
A rice plant recognition method based on the improved YOLOv7 model was proposed to address the accurate recognition of rice plants in the complex environment of paddy fields.Using offline and online data enhancement methods to improve model training effect,enhance generalization,and mitigate overfitting.The backbone feature extraction net-work was replaced by GhostNet network in YOLOv7 model to enhance the adaptive feature extraction capability of the model and simplify the computation of model parameters.Introduced lightweight attention mechanism in YOLOv7 back-bone feature extraction network to enhance the feature extraction capability of the backbone feature extraction network.The CIoU loss function in the YOLOv7 model was replaced with the EIoU loss function to improve the regression effect of the model prediction frame.The ablation tests showed that the overall mean accuracy of the constructed GhostNet-YOLOv7 model was 89.3%,which was 4.1,7.6,6.5 and 0.7 percentage points improved over the original YOLOv7,YOLOv5s,YOLOXs,and MobilenetV3-YOLOv7 models.The mean accuracy means were 91.2%,89.1%,87.5%,and 88.4%in sunny,cloudy,algae and weed backgrounds,respectively,and the accurate recognition of rice was achieved under different lighting conditions and complex backgrounds.The results of the study can provide a practical method for accurate recognition of rice plants in the complex environment of paddy fields.关键词
机器视觉/识别/深度学习/YOLOv7/GhostNet/水稻Key words
machine vision/identification/deep learning/YOLOv7/GhostNet/rice分类
水产学引用本文复制引用
陈学深,梁俊,汤存耀,张恩造,陈彦学,党佩娜,齐龙..基于改进YOLOv7模型的水田复杂环境稻株识别[J].农机化研究,2025,47(7):9-17,9.基金项目
广东省自然基金项目(2021A1515010831) （2021A1515010831）
广州市科技计划项目(202206010125) （202206010125）
广东省杰出青年基金项目(2019B151502056) （2019B151502056）
国家自然科学基金项目(51575195) （51575195）
