A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies
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 det...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/11/5/460 |
| _version_ | 1797533527477059584 |
|---|---|
| author | Renjie Huang Tingshan Yao Cheng Zhan Geng Zhang Yongqiang Zheng |
| author_facet | Renjie Huang Tingshan Yao Cheng Zhan Geng Zhang Yongqiang Zheng |
| author_sort | Renjie Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:16:48Z |
| format | Article |
| id | doaj.art-1f30eb05ec8b4740b7a916a857da629a |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-10T11:16:48Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-1f30eb05ec8b4740b7a916a857da629a2023-11-21T20:20:45ZengMDPI AGAgriculture2077-04722021-05-0111546010.3390/agriculture11050460A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus FliesRenjie Huang0Tingshan Yao1Cheng Zhan2Geng Zhang3Yongqiang Zheng4School of Computer and Information Science, Southwest University, Chongqing 400715, ChinaNational Engineering Research Center for Citrus Technology, Citrus Research Institute, Southwest University, Chongqing 400712, ChinaSchool of Computer and Information Science, Southwest University, Chongqing 400715, ChinaSchool of Computer and Information Science, Southwest University, Chongqing 400715, ChinaNational Engineering Research Center for Citrus Technology, Citrus Research Institute, Southwest University, Chongqing 400712, ChinaCitrus 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.https://www.mdpi.com/2077-0472/11/5/460pest managementautomatic motor-driven e-trapcomputer visioncitrus fly detection and recognitionconvolutional neural networks |
| spellingShingle | Renjie Huang Tingshan Yao Cheng Zhan Geng Zhang Yongqiang Zheng A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies Agriculture pest management automatic motor-driven e-trap computer vision citrus fly detection and recognition convolutional neural networks |
| title | A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies |
| title_full | A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies |
| title_fullStr | A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies |
| title_full_unstemmed | A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies |
| title_short | A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies |
| title_sort | motor driven and computer vision based intelligent e trap for monitoring citrus flies |
| topic | pest management automatic motor-driven e-trap computer vision citrus fly detection and recognition convolutional neural networks |
| url | https://www.mdpi.com/2077-0472/11/5/460 |
| work_keys_str_mv | AT renjiehuang amotordrivenandcomputervisionbasedintelligentetrapformonitoringcitrusflies AT tingshanyao amotordrivenandcomputervisionbasedintelligentetrapformonitoringcitrusflies AT chengzhan amotordrivenandcomputervisionbasedintelligentetrapformonitoringcitrusflies AT gengzhang amotordrivenandcomputervisionbasedintelligentetrapformonitoringcitrusflies AT yongqiangzheng amotordrivenandcomputervisionbasedintelligentetrapformonitoringcitrusflies AT renjiehuang motordrivenandcomputervisionbasedintelligentetrapformonitoringcitrusflies AT tingshanyao motordrivenandcomputervisionbasedintelligentetrapformonitoringcitrusflies AT chengzhan motordrivenandcomputervisionbasedintelligentetrapformonitoringcitrusflies AT gengzhang motordrivenandcomputervisionbasedintelligentetrapformonitoringcitrusflies AT yongqiangzheng motordrivenandcomputervisionbasedintelligentetrapformonitoringcitrusflies |