Application of an image and environmental sensor network for automated greenhouse insect pest monitoring |
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| Affiliation: | 1. Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taiwan, ROC;2. Tainan District Agricultural Research and Extension Station, Council of Agriculture, Taiwan, ROC;3. Department of Entomology, National Taiwan University, Taiwan, ROC;1. Department of Applied Biology, School of Biology and Basic Medical Sciences, Medical College, Soochow University, Suzhou 215123, China;2. The Third Affiliated Hospital, Medical College, Soochow University, Changzhou 215123, China;3. Gladstone Institute of Cardiovascular Disease, Medical College, Soochow University, Suzhou 215123, China;1. Wageningen Livestock Research, P.O. Box 338, 6700 AH Wageningen, The Netherlands;2. Department of Entomology and Sustainable Agriculture, Stockbridge Technology Centre, North Yorkshire YO8 3TZ, United Kingdom;3. Laboratory of Entomology, Wageningen University and Research, P.O. Box 16, 6700 AA Wageningen, The Netherlands;4. Farm Technology Group, Wageningen University and Research, P.O. Box 16, 6700 AA Wageningen, The Netherlands;1. Cognitive Science Department, Xiamen University, Xiamen 361005, China;2. School of Computer Science & Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom |
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| 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). |
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| Keywords: | Greenhouse management Integrated pest management Image processing Support vector machines Wireless sensor network |
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