A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies

Renjie Huang, Tingshan Yao, Cheng Zhan, Geng Zhang and Yongqiang Zheng
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Renjie Huang: School of Computer and Information Science, Southwest University, Chongqing 400715, China
Tingshan Yao: National Engineering Research Center for Citrus Technology, Citrus Research Institute, Southwest University, Chongqing 400712, China
Cheng Zhan: School of Computer and Information Science, Southwest University, Chongqing 400715, China
Geng Zhang: School of Computer and Information Science, Southwest University, Chongqing 400715, China
Yongqiang Zheng: National Engineering Research Center for Citrus Technology, Citrus Research Institute, Southwest University, Chongqing 400712, China

Agriculture, 2021, vol. 11, issue 5, 1-27

Abstract: Citrus flies are important quarantine pests in citrus plantations. Electronic traps (e-traps) based on computer vision are the most popular types of equipment for monitoring them. However, most current e-traps are inefficient and unreliable due to requiring manual operations and lack of reliable detection and identification algorithms of citrus fly images. To address these problems, this paper presents a monitoring scheme based on automatic e-traps and novel recognition algorithms. In this scheme, the prototype of an automatic motor-driven e-trap is firstly designed based on a yellow sticky trap. A motor autocontrol algorithm based on Local Binary Pattern (LBP) image analysis is proposed to automatically replace attractants in the e-trap for long-acting work. Furthermore, for efficient and reliable statistics of captured citrus flies, based on the differences between two successive sampling images of the e-trap, a simple and effective detection algorithm is presented to continuously detect the newly captured citrus flies from the collected images of the e-trap. Moreover, a Multi-Attention and Multi-Part convolutional neural Network (MAMPNet) is proposed to exploit discriminative local features of citrus fly images to recognize the citrus flies in the images. Finally, extensive simulation experiments validate the feasibility and efficiency of the designed e-trap prototype and its autocontrol algorithm, as well as the reliability and effectiveness of the proposed detection and recognition algorithms for citrus flies.

Keywords: pest management; automatic motor-driven e-trap; computer vision; citrus fly detection and recognition; convolutional neural networks (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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