A Vision-Based Counting and Recognition System for Flying Insects in Intelligent Agriculture

Rapid and accurate counting and recognition of flying insects are of great importance, especially for pest control. Traditional manual identification and counting of flying insects is labor intensive and inefficient. In this study, a vision-based counting and classification system for flying insects...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 18; H. 5; S. 1489
Hauptverfasser: Zhong, Yuanhong, Gao, Junyuan, Lei, Qilun, Zhou, Yao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 09.05.2018
MDPI
Schlagworte:
Animal behavior
Bats
counting and recognition system
flying insect
Insects
Raspberry PI
SVM
YOLO
YOLO
Raspberry PI
SVM
counting and recognition system
flying insect
ISSN:1424-8220, 1424-8220
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Zusammenfassung:Rapid and accurate counting and recognition of flying insects are of great importance, especially for pest control. Traditional manual identification and counting of flying insects is labor intensive and inefficient. In this study, a vision-based counting and classification system for flying insects is designed and implemented. The system is constructed as follows: firstly, a yellow sticky trap is installed in the surveillance area to trap flying insects and a camera is set up to collect real-time images. Then the detection and coarse counting method based on You Only Look Once (YOLO) object detection, the classification method and fine counting based on Support Vector Machines (SVM) using global features are designed. Finally, the insect counting and recognition system is implemented on Raspberry PI. Six species of flying insects including bee, fly, mosquito, moth, chafer and fruit fly are selected to assess the effectiveness of the system. Compared with the conventional methods, the test results show promising performance. The average counting accuracy is 92.50% and average classifying accuracy is 90.18% on Raspberry PI. The proposed system is easy-to-use and provides efficient and accurate recognition data, therefore, it can be used for intelligent agriculture applications.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s18051489