中文摘要：The strawberry appearance is an essential standard for judging the quality, so it is crucial to accurately identify the strawberry appearance quality for intelligent picking. This study proposed a new strawberry appearance quality detection based on unsupervised deep learning. Firstly, using deep learning (Resnet18, Resnet50, and Resnet101) to extract the strawberry image feature information. And using the t-SNE (t-distribution stochastic neighbor embedding) to reduce the feature vectors' dimension. Finally, the unsupervised learning method (Gaussian Mixture Model) was used to cluster strawberries' feature points. The results showed that: (1) the clustering performance based on Resnet101 was effective in 2-dimensional space, the cluster accuracy was 94.89%, and the validation accuracy was 91.79%. (2) The clustering method based on Resnet50 had good performance in the 3-dimensional space, the cluster accuracy was 96.10%, and the validation accuracy was 93.08%. (3) The accuracy of deep features plus RF (random forest) was 95.00% under limited data. Thus this method will promote intelligent picking strawberry equipment and it will overcome the supervised learning drawback that divides image datasets according to prior knowledge.
外文关键词：deep learning;Unsupervised learning;quality identifying;Picking machine
作者：Han, Zhongzhi;Zheng, Hao;Li, Xuchen;Zhu, Hongfei;Yang, Lianhe;Liu, Xingyu
作者单位：Tiangong Univ;Qingdao Agr Univ;Shandong Univ
期刊名称：PRECISION AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：25（2）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台