Automatic pest identification system in the greenhouse based on deep learning and machine vision
Monitoring and understanding pest population dynamics is essential to greenhouse management for effectively preventing infestations and crop diseases. Image-based pest recognition approaches demonstrate the potential for real-time pest monitoring. However, the pest detection models are challenged by...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1255719/full |
| _version_ | 1797671455105744896 |
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| author | Xiaolei Zhang Junyi Bu Xixiang Zhou Xiaochan Wang |
| author_facet | Xiaolei Zhang Junyi Bu Xixiang Zhou Xiaochan Wang |
| author_sort | Xiaolei Zhang |
| collection | DOAJ |
| description | Monitoring and understanding pest population dynamics is essential to greenhouse management for effectively preventing infestations and crop diseases. Image-based pest recognition approaches demonstrate the potential for real-time pest monitoring. However, the pest detection models are challenged by the tiny pest scale and complex image background. Therefore, high-quality image datasets and reliable pest detection models are required. In this study, we developed a trapping system with yellow sticky paper and LED light for automatic pest image collection, and proposed an improved YOLOv5 model with copy-pasting data augmentation for pest recognition. We evaluated the system in cherry tomato and strawberry greenhouses during 40 days of continuous monitoring. Six diverse pests, including tobacco whiteflies, leaf miners, aphids, fruit flies, thrips, and houseflies, are observed in the experiment. The results indicated that the proposed improved YOLOv5 model obtained an average recognition accuracy of 96% and demonstrated superiority in identification of nearby pests over the original YOLOv5 model. Furthermore, the two greenhouses show different pest numbers and populations dynamics, where the number of pests in the cherry tomato greenhouse was approximately 1.7 times that in the strawberry greenhouse. The developed time-series pest-monitoring system could provide insights for pest control and further applied to other greenhouses. |
| first_indexed | 2024-03-11T21:15:48Z |
| format | Article |
| id | doaj.art-49700f19942b437cb03ae96ba2046296 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-11T21:15:48Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-49700f19942b437cb03ae96ba20462962023-09-29T04:21:09ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-09-011410.3389/fpls.2023.12557191255719Automatic pest identification system in the greenhouse based on deep learning and machine visionXiaolei ZhangJunyi BuXixiang ZhouXiaochan WangMonitoring and understanding pest population dynamics is essential to greenhouse management for effectively preventing infestations and crop diseases. Image-based pest recognition approaches demonstrate the potential for real-time pest monitoring. However, the pest detection models are challenged by the tiny pest scale and complex image background. Therefore, high-quality image datasets and reliable pest detection models are required. In this study, we developed a trapping system with yellow sticky paper and LED light for automatic pest image collection, and proposed an improved YOLOv5 model with copy-pasting data augmentation for pest recognition. We evaluated the system in cherry tomato and strawberry greenhouses during 40 days of continuous monitoring. Six diverse pests, including tobacco whiteflies, leaf miners, aphids, fruit flies, thrips, and houseflies, are observed in the experiment. The results indicated that the proposed improved YOLOv5 model obtained an average recognition accuracy of 96% and demonstrated superiority in identification of nearby pests over the original YOLOv5 model. Furthermore, the two greenhouses show different pest numbers and populations dynamics, where the number of pests in the cherry tomato greenhouse was approximately 1.7 times that in the strawberry greenhouse. The developed time-series pest-monitoring system could provide insights for pest control and further applied to other greenhouses.https://www.frontiersin.org/articles/10.3389/fpls.2023.1255719/fulltiny pest detectionimproved YOLOv5pest population dynamicspest trapping systemgreenhouse |
| spellingShingle | Xiaolei Zhang Junyi Bu Xixiang Zhou Xiaochan Wang Automatic pest identification system in the greenhouse based on deep learning and machine vision Frontiers in Plant Science tiny pest detection improved YOLOv5 pest population dynamics pest trapping system greenhouse |
| title | Automatic pest identification system in the greenhouse based on deep learning and machine vision |
| title_full | Automatic pest identification system in the greenhouse based on deep learning and machine vision |
| title_fullStr | Automatic pest identification system in the greenhouse based on deep learning and machine vision |
| title_full_unstemmed | Automatic pest identification system in the greenhouse based on deep learning and machine vision |
| title_short | Automatic pest identification system in the greenhouse based on deep learning and machine vision |
| title_sort | automatic pest identification system in the greenhouse based on deep learning and machine vision |
| topic | tiny pest detection improved YOLOv5 pest population dynamics pest trapping system greenhouse |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1255719/full |
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