基于深度学习的草地贪夜蛾自动识别
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摘要
草地贪夜蛾是一种严重破坏农作物的重大洲际害虫,对我国农业生产造成了极大的威胁.尽管一系列防治措施已经展开,但如何有效辨别草地贪夜蛾仍然是防控工作中的一大难题.为了建立一个有效的识别算法,课题组开展了一系列研究工作,主要贡献在于:①采集了不同地域、不同生长区间的草地贪夜蛾及相似物种图片,建立了一个草地贪夜蛾识别数据库;②利用基于特征融合的深度学习算法,建立了一个三通道T型深度卷积神经网络(T-CNN),在现有数据集上平均识别率达到97%,为草地贪夜蛾的智能识别与防控工作提供了技术支撑.
Spodoptera frugiperda is a serious crop-destroying pest, which poses a great threat to agricultural production in China. Although a series of preventive measures have been adopted, how to identify the pest effectively is still a major problem in the field. In a study reported in this paper, a series of work was done to establish an effective recognition algorithm. Our main contributions were as follows. First, pictures of S. frugiperda and similar species were collected from different regions, and a recognition database of S. frugiperda was established. Secondly, using a deep-learning algorithm based on feature fusion, we constructed a three-channel T-type deep convolution neural network(T-CNN), whose average recognition rate was over 97% on the existing data sets, thus providing technical support for the smart identification and control of S. frugiperda.
Spodoptera frugiperda is a serious crop-destroying pest, which poses a great threat to agricultural production in China. Although a series of preventive measures have been adopted, how to identify the pest effectively is still a major problem in the field. In a study reported in this paper, a series of work was done to establish an effective recognition algorithm. Our main contributions were as follows. First, pictures of S. frugiperda and similar species were collected from different regions, and a recognition database of S. frugiperda was established. Secondly, using a deep-learning algorithm based on feature fusion, we constructed a three-channel T-type deep convolution neural network(T-CNN), whose average recognition rate was over 97% on the existing data sets, thus providing technical support for the smart identification and control of S. frugiperda.
引文
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[3] NAGOSHI R N,MEAGHER R L.Behavior and Distribution of the Two Fall Armyworm Host Strains in Florida [J].Florida Entomologist,2004,87(4):440-449.
[4] 张磊,靳明辉,张丹丹,等.入侵云南草地贪夜蛾的分子鉴定 [J].植物保护,2019,45(2):19-24,56.
[5] 郭井菲,赵建周,何康来,等.警惕危险性害虫草地贪夜蛾入侵中国 [J].植物保护,2018,44(6):1-10.
[6] LECUN Y,BENGIO Y,HINTON G.Deep Learning [J].Nature,2015,521(7553):436.
[7] SCHMIDHUBER J.Deep Learning in Neural Networks:An Overview [J].Neural Networks,2015,61:85-117.
[8] NGIAM J.Multimodal Deep Learning [C] //Proceedings of the 28th International Conference on Machine Learning (ICML-11) [EB/OL].(2011-06-28) [2019-08-03] http://ai.stanford.edu/～ang/papers/icml11-Multimodal Deep Learning.pdf.
[9] DENG L.Deep Learning:Methods and Applications [J].Foundations and Trends in Signal Processing,2014,7(3/4):197-387.
[10] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet Classification with Deep Convolutional Neural Networks [C] //NIPS [EB/OL].(2012-01-01) [2019-08-03] http://xueshu.baidu.com/usercenter/paper/show?paperid=a5a134e176e1689276201a17412a145a&site=xueshu_se.
[11] LU Y Z.Food Image Recognition by Using Convolutional Neural Networks (CNNs) [EB/OL].(2016-12-03) [2019-08-03] http://xueshu.baidu.com/usercenter/paper/show?paperid=a5a134e176e1689276201a17412a145a&site=xueshu_se.
[12] SUZUKI H,MATSUMOTO T,CHUA L O.A CNN Handwritten Character Recognizer [J].International Journal of Circuit Theory and Applications,1992,20(5):601-612.
[13] 郭井菲,静大鹏,太红坤,等.草地贪夜蛾形态特征及与3种玉米田为害特征和形态相近鳞翅目昆虫的比较 [J].植物保护,2019,45(2):7-12.
[14] AHMED O.Food Image Recognition by Using Bag-of-SURF Features and HOG Features [C] //International Conference on Human-Agent Interaction [EB/OL].(2015-10-21) [2019-08-03] http://xueshu.baidu.com/usercenter/paper/show?paperid=a5a134e176e1689276201a17412a145a&site=xueshu_se.
[15] DIAZESCOBER J,KOBER V.A Robust HOG-Based Descriptor for Pattern Recognition [C] //Applications of Digital Image Processing XXXIX,SPIE [EB/OL].(2016-08-26) [2019-08-03] http://xueshu.baidu.com/usercenter/paper/show?paperid=3c8fe9b309a20796eac24747e88963bc&site=xueshu_se.
[16] 罗元,崔叶,王艳,等.基于融合DCT和LBP特征提取的面部表情识别 [J].半导体光电,2014,35(2):330-333,349.
[17] LI X D,YANG W D,DEZERT J.An Airplane Image Target 0 s Multi-Feature Fusion Recognition Method [J].自动化学报,2012,38(8):1298-1307.
[18] SUN Q S,ZENG S G,LIU Y,et al.A New Method of Feature Fusion and Its Application in Image Recognition [J].Pattern Recognition,2005,38(12):2437-2448.
[19] POISOTAS H.Integrated Management of the Fall Army Worm on Maize:A Guide for Farmer Field Schools in Africa [C].Rome:Food and Agriculture Organization,2018.
[20] 杜燕飞,初梓瑞.人工智能助力草地贪夜蛾防控打赢“虫口夺粮”攻坚战 [EB/OL].(2019-7-18) [2019-8-11].http://country.people.com.cn/n1/2019/0718/c419842-31243174.html.
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