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

doi:10.1016/j.biosystemseng.2021.11.020

	A multi-branch convolutional neural network with density map for aphid counting
	Li, Rui 1,3,6; Wang, Rujing 1,3,4,6; Xie, Chengjun 1,3,6; Chen, Hongbo 1,3,4,6; Long, Qi 5; Liu, Liu 2; Zhang, Jie 1,3,6; Chen, Tianjiao 1,3,6; Hu, Haiying 1,3,6; Jiao, Lin 1,3,4,6
刊名	BIOSYSTEMS ENGINEERING
	2022
卷号	213
关键词	Density map Aphid counting Convolutional neural network Deep learning
ISSN号	1537-5110
DOI	10.1016/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
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
WOS记录号	WOS:000793358200007
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
内容类型	期刊论文
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.Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai, 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.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, 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).