外文摘要：The analysis of plant phenotype parameters is closely related to breeding, so plant phenotype research has strong practical significance. This paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle). First, the multi-output model identifies Arabidopsis accession lines and regression to predict Arabidopsis's 22-day growth status. The experimental results showed that the model had excellent performance in identifying Arabidopsis lines, and the model's classification accuracy was 99.92%. The model also had good performance in predicting plant growth status, and the regression prediction of the model root mean square error (RMSE) was 1.536. Next, a new dataset was obtained by increasing the time interval of Arabidopsis images, and the model's performance was verified at different time intervals. Finally, the model was applied to classify Arabidopsis organelles to verify the model's generalizability. Research suggested that deep learning will broaden plant phenotype detection methods. Furthermore, this method will facilitate the design and development of a high-throughput information collection platform for plant phenotypes.
外文关键词：deep learning;breeding;Arabidopsis thaliana;organelle;plant phenotypes
作者：Han, Zhongzhi;Zhao, Longgang;Liu, Fei;Deng, Limiao;Zhu, Hongfei;Liu, Hang;Wu, Guangxia
作者单位：Tiangong Univ;Qingdao Agr Univ
期刊名称：PLANTS-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：13(9)
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