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Article

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

1
School of Computer and Information Science, Southwest University, Chongqing 400715, China
2
National Engineering Research Center for Citrus Technology, Citrus Research Institute, Southwest University, Chongqing 400712, China
*
Author to whom correspondence should be addressed.
Academic Editor: Johannes Sauer
Received: 16 March 2021 / Revised: 7 May 2021 / Accepted: 12 May 2021 / Published: 19 May 2021
(This article belongs to the Section Crop Protection, Diseases, Pest and Weeds)
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. View Full-Text
Keywords: pest management; automatic motor-driven e-trap; computer vision; citrus fly detection and recognition; convolutional neural networks pest management; automatic motor-driven e-trap; computer vision; citrus fly detection and recognition; convolutional neural networks
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MDPI and ACS Style

Huang, R.; Yao, T.; Zhan, C.; Zhang, G.; Zheng, Y. A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies. Agriculture 2021, 11, 460. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11050460

AMA Style

Huang R, Yao T, Zhan C, Zhang G, Zheng Y. A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies. Agriculture. 2021; 11(5):460. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11050460

Chicago/Turabian Style

Huang, Renjie, Tingshan Yao, Cheng Zhan, Geng Zhang, and Yongqiang Zheng. 2021. "A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies" Agriculture 11, no. 5: 460. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11050460

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