Application of an image and environmental sensor network for automated greenhouse insect pest monitoring

By D. Rustia, Chien-Erh Lin, Jui-Yung Chung, Yiyang Zhuang, J. Hsu, Ta-Te Lin. 2020

123 citations recorded by the source corpus.

Abstract 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).

Subfields: Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/c41202e963ea65f2bb46458cc85d6df204f32793

How researchers see this paper

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1. Chunming He
2. Yulun Zhang
3. Kai Li
4. Yachao Zhang
5. Longxiang Tang
6. Zhenhua Guo
7. Xiu Li
8. Kai Zhang

Citations in the corpus

• Camouflaged Object Detection with Feature Decomposition and Edge Reconstruction (2023, background): “Research in COD can simultaneously facilitate the development of visual perception for nuance discrimination and promote various valuable real-life applications, ranging from concealed defect detection [17] in industry to pest monitoring [35] in agriculture.”