外文摘要：Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures.
外文关键词：deep learning;Hyperspectral image;linear discriminant analysis;crop variety;channel attention
作者：Li, Xueyong;Zhai, Mingjia;Zheng, Liyuan;Zhou, Ling;Xie, Xiwang;Zhao, Wenyi;Zhang, Weidong
作者单位：Beijing Univ Posts & Telecommun;Henan Inst Sci & Technol;Dalian Maritime 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：15
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