您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Intelligent Grading of Tobacco Leaves Using an Improved Bilinear Convolutional Neural Network

文献类型： 外文期刊

作者： Lu, Mengyao 1 ; Wang, Cong 1 ; Wu, Wenbiao 1 ; Zhu, Dinglian 2 ; Zhou, Qiang 3 ; Wang, Zhiyong 3 ; Chen, Tian'en 1 ; Jiang, Shuwen 1 ; Chen, Dong 1 ;

作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Nongxin Nanjing Smart Agr Res Inst, Nanjing 211808, Peoples R China

3.Anhui Wannan Tobacco Co Ltd, Xuancheng 242000, Peoples R China

关键词： Bilinear convolutional neural network; deep learning; multi-level features; tobacco image classification; tobacco grading

期刊名称：IEEE ACCESS （ 影响因子：3.9； 五年影响因子：4.1 ）

ISSN： 2169-3536

年卷期： 2023 年 11 卷

页码：

收录情况： SCI

摘要： At present, tobacco leaf grading relies mainly on manual classification, which is highly intensive with respect to labor, materials, and cost; in addition, the performance of manual grading is poor. The realization of automatic grading is an urgent requirement in the tobacco industry. To address this need, we developed assembly line equipment and an RGB image classification method for tobacco grading. There is little difference in appearance between different grades of tobacco leaves, but there are some differences in high-level semantic features; therefore, tobacco grading is fundamentally a fine-grained visual categorization task. It is difficult to classify tobacco using hand-crafted image features. Therefore, in our method, we use a pyramid structure, attention mechanism, and angle decision loss function to improve the bilinear convolutional neural network (a fine-grained visual categorization framework) for tobacco grading. The feature extraction and classification model was trained using 66,966 images, to effectively extract high-level semantic features from a global tobacco image and multi-scale features from a local tobacco image. In an online test on assembly line equipment, the proposed model was able to classify six main grades of tobacco with an accuracy of 80.65%, which is higher than that of the state-of-the-art model. Additionally, the time required to classify each tobacco leaf was 42.1 ms. This work is of great significance for the industrial application of tobacco grading models and grading equipment, and it provides a theoretical reference for the quality grading of other agricultural products.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台