基于计算机视觉技术的草地贪夜蛾生殖发育状态精准识别
与产卵预测

doi:10.1016/j.jia.2022.12.003

Journal of Integrative Agriculture ›› 2023, Vol. 22 ›› Issue (7): 2173-2187.DOI: 10.1016/j.jia.2022.12.003

基于计算机视觉技术的草地贪夜蛾生殖发育状态精准识别与产卵预测

收稿日期:2022-07-29 接受日期:2022-10-28 出版日期:2023-07-20 发布日期:2022-10-28

Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda (Lepidoptera: Noctuidae) based on computer vision

LÜ Chun-yang^{1, 2}, GE Shi-shuai^{2, 3}, HE Wei^{2, 3}, ZHANG Hao-wen^{1, 2}, YANG Xian-ming², CHU Bo^{2, 4}, WU Kong-ming^2#

¹ State Key Laboratory of Agricultural Information Service Technology, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
² State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, P.R.China
³ State Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops, Institute of Applied Ecology, Fujian Agriculture and Forestry University, Fuzhou 350002, P.R.China
⁴ College of Plant Protection, Henan Agricultural University, Zhengzhou 450002, P.R.China

Received:2022-07-29 Accepted:2022-10-28 Online:2023-07-20 Published:2022-10-28
About author:LÜ Chun-yang, Mobile: +86-13718651161, E-mail: lcy8511@163.com; #Correspondence WU Kong-ming, E-mail: wukongming@caas.cn
Supported by:
This research was supported by the National Natural Science Foundation of China (31727901), the National Key R&D Program of China (2021YFD1400702) and the Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences.

摘要/Abstract

摘要：

草地贪夜蛾（鳞翅目：夜蛾科）是世界范围内重要的迁飞性农业害虫，自2016年以来入侵非亚洲多个国家，目前对世界粮食安全构成严重威胁。当前，草地贪夜蛾的监测和预警策略主要集中在成虫种群密度，缺乏准确预测成虫繁殖动态的信息技术平台。于是在本研究中，为了识别成虫的发育状况，我们首先利用雌性卵巢图像提取和筛选出五个特征并结合支持向量机（SVM）分类器，以及利用雄性精巢图像获取精巢的圆形特征，对成虫发育时间进行判定。然后，利用实验室测试建立了产卵动态与成虫生殖器官发育时间之间关系的模型。结果表明，雌性卵巢发育等级判断的准确率达到91%，卵巢发育时间实际值与预测值的均方误差（MSE）为0.2431，日产卵量实际值与预测值的平均误差率为12.38%。精巢直径识别误差率3.25%，雄性精巢发育时间预测值和实际值的均方误差为0.7734。综合上述研究成果，开发了草地贪夜蛾生殖发育状态识别及繁殖预测微信小程序，现已开放给植保人员使用。本研究开发了一种自动化方法，可以精准预测草地贪夜蛾种群繁殖动态，有助于建设种群监测预和警系统，供专业专家和当地群众使用。

Abstract:

Spodoptera frugiperda (Lepidoptera: Noctuidae) is an important migratory agricultural pest worldwide, which has invaded many countries in the Old World since 2016 and now poses a serious threat to world food security. The present monitoring and early warning strategies for the fall army worm (FAW) mainly focus on adult population density, but lack an information technology platform for precisely forecasting the reproductive dynamics of the adults. In this study, to identify the developmental status of the adults, we first utilized female ovarian images to extract and screen five features combined with the support vector machine (SVM) classifier and employed male testes images to obtain the testis circular features. Then, we established models for the relationship between oviposition dynamics and the developmental time of adult reproductive organs using laboratory tests. The results show that the accuracy of female ovary development stage determination reached 91%. The mean standard error (MSE) between the actual and predicted values of the ovarian developmental time was 0.2431, and the mean error rate between the actual and predicted values of the daily oviposition quantity was 12.38%. The error rate for the recognition of testis diameter was 3.25%, and the predicted and actual values of the testis developmental time in males had an MSE of 0.7734. A WeChat applet for identifying the reproductive developmental state and predicting reproduction of S. frugiperda was developed by integrating the above research results, and it is now available for use by anyone involved in plant protection. This study developed an automated method for accurately forecasting the reproductive dynamics of S. frugiperda populations, which can be helpful for the construction of a population monitoring and early warning system for use by both professional experts and local people at the county level.

Key words: Spodoptera frugiperda , computer vision , ovary , testis , WeChat applet

LÜ Chun-yang, GE Shi-shuai, HE Wei, ZHANG Hao-wen, YANG Xian-ming, CHU Bo, WU Kong-ming. 基于计算机视觉技术的草地贪夜蛾生殖发育状态精准识别与产卵预测[J]. Journal of Integrative Agriculture, 2023, 22(7): 2173-2187.

LÜ Chun-yang, GE Shi-shuai, HE Wei, ZHANG Hao-wen, YANG Xian-ming, CHU Bo, WU Kong-ming. Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda (Lepidoptera: Noctuidae) based on computer vision[J]. Journal of Integrative Agriculture, 2023, 22(7): 2173-2187.

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基于计算机视觉技术的草地贪夜蛾生殖发育状态精准识别与产卵预测

Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda (Lepidoptera: Noctuidae) based on computer vision

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基于计算机视觉技术的草地贪夜蛾生殖发育状态精准识别 与产卵预测

Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda (Lepidoptera: Noctuidae) based on computer vision

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基于计算机视觉技术的草地贪夜蛾生殖发育状态精准识别与产卵预测