外文摘要：Classification of rice disease is one significant research topics in rice phenotyping. Recognition of rice diseases such as Bacterialblight, Blast, Brownspot, Leaf smut, and Tungro are a critical research field in rice phenotyping. However, accurately identifying these diseases is a challenging issue due to their high phenotypic similarity. To address this challenge, we propose a rice disease phenotype identification framework which utilizing the transfer learning and SENet with attention mechanism on the cloud platform. The pre-trained parameters are transferred to the SENet network for parameters optimization. To capture distinctive features of rice diseases, the attention mechanism is applied for feature extracting. Experiment test and comparative analysis are conducted on the real rice disease datasets. The experimental results show that the accuracy of our method reaches 0.9573. Furthermore, we implemented a rice disease phenotype recognition platform based microservices architecture and deployed it on the cloud, which can provide rice disease phenotype recognition task as a service for easy usage.
外文关键词：Transfer learning;SENet;rice disease recognition;machine learning as service;microservices framework
作者：Tian, Yongchao;Xu, Huanliang;Yuan, Peisen;Xia, Ye
作者单位：Nanjing Agr Univ
期刊名称：FRONTIERS IN PLANT SCIENCE
期刊影响因子：0.0
出版年份：2024
出版刊次：14
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