Publication: Soil sensing survey robots based on electronic nose
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Issued Date
2014-01-01
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ISSN
15987833
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2-s2.0-84920173607
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Mahidol University
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SCOPUS
Bibliographic Citation
International Conference on Control, Automation and Systems. (2014), 1604-1609
Suggested Citation
Theerapat Pobkrut, Teerakiat Kerdcharoen Soil sensing survey robots based on electronic nose. International Conference on Control, Automation and Systems. (2014), 1604-1609. doi:10.1109/ICCAS.2014.6987829 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/33712
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Title
Soil sensing survey robots based on electronic nose
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Abstract
© 2014 Institute of Control, Robotics and Systems (ICROS). Nowadays autonomous robots are used in many filed of agriculture for increasing efficiency, and especially reducing the cost of the scarce human labor. Mobile ground robot and flying drone can be used to survey the farmland collecting necessary information such as ambient and crop conditions, soil fertility, pest and disease, etc. In this work, we have implemented an electronics nose (e-nose) based on metal oxide gas sensors installed on a mobile robot for detection of organic volatile compounds contained in the soil surface to examine the fertility of soil. Farmers can use such information to manage crops fertilizing or watering. To demonstrate this idea, we have mapped the soil volatiles at different places and having different types of soil, using laboratory-made portable e-nose and mobile e-nose robot. Therefore, the experiment was separated into 2 parts. The first part was to collect soils from different places and to measure the soil volatiles using portable e-nose that has eight metal-oxide gas sensors, in order to classify and confirm the difference between each sample. Secondly, the array of six gas sensors was then installed on a semi-autonomous six-wheeled robot chassis in order to measure real situations of soil surface located at four different places. It was found that both portable and robotized e-noses can identify and confirm the difference of soil volatiles when using principal component analysis (PCA) as pattern recognition.