引用本文:于业达, 顾偌铖, 唐运林, 韦俊宏, 潘国庆, 陈通.基于深度学习的草地贪夜蛾自动识别[J].西南大学学报(自然科学版),2019,41(9):24~31
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基于深度学习的草地贪夜蛾自动识别
于业达, 顾偌铖, 唐运林, 韦俊宏, 潘国庆, 陈通1,2,3
1. 西南大学 非线性电路与智能信息处理重庆市重点实验室, 重庆 400715;2.
2. 西南大学 家蚕基因组生物学国家重点实验室, 重庆 400715;3.
3. 西南大学 微孢子虫感染与防控重庆市重点实验室, 重庆 400715
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.
关键词:  草地贪夜蛾  识别  深度学习  深度卷积神经网络  特征融合
DOI:10.13718/j.cnki.xdzk.2019.09.004
分类号:Q969.436.5;TP391.412
基金项目:中央高校基本业务费团队项目(XDJK2018AA001);家蚕基因组生物学国家重点实验室自设课题(2019-03);中央高校基本业务费面上项目(XDJK2019C010).
A CNN-Based Automatic Identification System for Spodoptera frugiperda
YU Ye-da, GU Ruo-cheng, TANG Yun-lin, WEI Jun-hong, PAN guo-qing, CHEN Tong1,2,3
1. Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing, Southwest University, Chongqing 400715, China;2.
2. State Key Laboratory of Silkworm Genome Biology, Southwest University, Chongqing 400715, China;3.
3. Chongqing Key Laboratory of Microsporidia Infection and Control, Chongqing 400715, China
Abstract:
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.
Key words:  Spodoptera frugiperda  recognition  deep learning  convolutional neural networks (CNN)  feature fusion
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