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A multi-branch convolutional neural network with density map for aphid counting

文献类型：期刊论文

作者Li, Rui1,2,3; Wang, Rujing1,2,3,4; Xie, Chengjun1,2,3; Chen, Hongbo1,2,3,4; Long, Qi5; Liu, Liu6; Zhang, Jie1,2,3; Chen, Tianjiao1,2,3; Hu, Haiying1,2,3; Jiao, Lin1,2,3,4
刊名BIOSYSTEMS ENGINEERING
出版日期2022
卷号213
关键词Density map Aphid counting Convolutional neural network Deep learning
ISSN号1537-5110
DOI10.1016/j.biosystemseng.2021.11.020
通讯作者Xie, Chengjun(cjxie@iim.ac.cn) ; Chen, Hongbo(hbchen@iim.ac.cn)
英文摘要In agriculture, aphids always cause major damage in wheat, corn and rape, which significantly affect the crop yield. Manual aphid counting approaches are often labour consuming and time-costing for Integrated Pest Management (IPM). In addition, the results of existing aphid counting methods based on computer vision are not satisfactory due to the complex background and the dense distribution. In order to address these problems, a novel multi-branch convolutional neural network (Mb-CNN) with density map for aphid counting is developed in this paper. In this approach, the aphid images are firstly fed into multi-branch convolutional neural networks, which have three branches for extracting the feature maps of different scales. Then, an aphid density map is generated via Mb-CNN, which contains the distribution information of aphids. Finally, the counting of aphids is estimated by using the density map. Experiment results on our dataset demonstrate that our Mb-CNN achieves the performance of 10.22 Mean Absolute Error (MAE) and 12.24 Mean Squared Error (MSE) in the aphid counting, which outweighs the state-of-the-art approaches. (c) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.
资助项目National Key Technology R&D Program of China[ACAIM190101] ; National Natural Science Foundation of China[32171888] ; National Natural Science Foundation of China[61773360] ; Dean's Fund of Hefei Institute of Physical Science, Chinese Academy of Sciences[YZJJ2020QN21]
WOS研究方向Agriculture
语种英语
WOS记录号WOS:000793358200007
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
资助机构National Key Technology R&D Program of China ; National Natural Science Foundation of China ; Dean's Fund of Hefei Institute of Physical Science, Chinese Academy of Sciences
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/130839]  
专题中国科学院合肥物质科学研究院
通讯作者Xie, Chengjun; Chen, Hongbo
作者单位1.Intelligent Agr Engn Lab Anhui Prov, Hefei, Peoples R China
2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
3.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
4.Univ Sci & Technol China, Hefei 230026, Peoples R China
5.South China Agr Univ, Coll Engn, Guangzhou, Peoples R China
6.Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai, Peoples R China
推荐引用方式
GB/T 7714		 Li, Rui,Wang, Rujing,Xie, Chengjun,et al. A multi-branch convolutional neural network with density map for aphid counting[J]. BIOSYSTEMS ENGINEERING,2022,213.
APA Li, Rui.,Wang, Rujing.,Xie, Chengjun.,Chen, Hongbo.,Long, Qi.,...&Liu, Haiyun.(2022).A multi-branch convolutional neural network with density map for aphid counting.BIOSYSTEMS ENGINEERING,213.
MLA Li, Rui,et al."A multi-branch convolutional neural network with density map for aphid counting".BIOSYSTEMS ENGINEERING 213(2022).

入库方式： OAI收割

来源：合肥物质科学研究院

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