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RISS 인기검색어
- 검색키워드
- 저자 : Chien Erh Lin (검색결과 2 건)
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Dan Jeric Arcega Rustia,Chien Erh Lin,Jui-Yung Chung,Yi-Ji Zhuang,Ju-Chun Hsu,Ta-Te Lin 한국응용곤충학회 2020 Journal of Asia-Pacific Entomology Vol.23 No.1
This work presents an automated insect pest counting and environmental condition monitoring system using integrated camera modules and an embedded system as the sensor node in a wireless sensor network. The sensor node can be used to simultaneously acquire images of sticky paper traps and measure temperature, humidity, and light intensity levels in a greenhouse. An image processing algorithm was applied to automatically detect and count insect pests on an insect sticky trap with 93% average temporal detection accuracy compared with manual counting. The integrated monitoring system was implemented with multiple sensor nodes in a greenhouse and experiments were performed to test the system’s performance. Experimental results show that the automatic counting of the monitoring system is comparable with manual counting, and the insect pest count information can be continuously and effectively recorded. Information on insect pest concentrations were further analyzed temporally and spatially with environmental factors. Analyses of experimental data reveal that the normalized hourly increase in the insect pest count appears to be associated with the change in light intensity, temperature, and relative humidity. With the proposed system, laborious manual counting can be circumvented and timely assessment of insect pest and environmental information can be achieved. The system also offers an efficient tool for long-term insect pest behavior observations, as well as for practical applications in integrated pest management (IPM).
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( Dan Jeric Arcega Rustia ),( Chien Erh Lin ),( Jui-yung Chung ),( Ta-te Lin ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
Insect pest identification is very important for greenhouse management. Having the knowledge of what insects exist in their greenhouse, farmers will be able to determine which pesticide will be more effective to prevent insect pest outbreaks and protect their crops. The most common technique to monitor insect pests is the use of strips of yellow sticky papers. Insects trapped on these yellow sticky papers are usually counted by human inspection without the assistance of any machine or device. To replace this inefficient method, this work presents a multi-class insect identification method for yellow sticky paper, obtained from wireless cameras using cascaded convolutional neural networks (CNN). The designed algorithm makes use of a marker-based image segmentation technique for object detection. The objects are sorted using an insect vs. non-insect filter CNN model to remove non-insect objects such as glare, dirt, and water droplets with 88-95% counting accuracy, while the multi-class insect classifier has an accuracy of 86-92%. The CNN models are optimized based on accuracy and computation time for real-time insect pest monitoring application. The combined algorithm can process each yellow sticky paper image with an average processing time of 13-15 seconds and 2-3 seconds using a quad-core Cortex A53 1.2GHz CPU and GTX1080 2.2GHz GPU, respectively. This work can be applied for real-time and remote insect pest monitoring using wireless camera networks and for observing insect population dynamics of different species.
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