TY - GEN
T1 - Prediction of soil nitrogen content using E-nose and radial basis function
AU - Norbu, Jigme
AU - Pobkrut, Theerapat
AU - Thepudom, Treenet
AU - Namgyel, Thinley
AU - Chaiyasit, Teerayut
AU - Thazin, Yu
AU - Kerdcharoen, Teerakiat
N1 - Publisher Copyright:
© 2018 IEEE
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Existing soil nutrient determining methods are still a concern as most of them entail arduous field sampling followed by rigorous testing procedures, both of which are time consuming and expensive. Conversely, farmers require cheap and instant information about the soil nutrient management for quick decision making or they will have to risk their crop. Electronic nose (E-nose) is an emerging technology that has potential application in monitoring of soil nutrient abundance. In this work, e-nose coupled with Radial Basis Function (RBF) is employed to determine the amount of nitrogen (N) which is one of the main nutrients in the soil. The results demonstrate that not only does the e-nose clearly discriminate the odors of soil with different N concentration, but also can evidently predict the total N content with accuracy of 96.2% using RBF. Hence, e-nose and RBF network could be a promising alternative to conventional soil testing methods.
AB - Existing soil nutrient determining methods are still a concern as most of them entail arduous field sampling followed by rigorous testing procedures, both of which are time consuming and expensive. Conversely, farmers require cheap and instant information about the soil nutrient management for quick decision making or they will have to risk their crop. Electronic nose (E-nose) is an emerging technology that has potential application in monitoring of soil nutrient abundance. In this work, e-nose coupled with Radial Basis Function (RBF) is employed to determine the amount of nitrogen (N) which is one of the main nutrients in the soil. The results demonstrate that not only does the e-nose clearly discriminate the odors of soil with different N concentration, but also can evidently predict the total N content with accuracy of 96.2% using RBF. Hence, e-nose and RBF network could be a promising alternative to conventional soil testing methods.
KW - Electronic nose
KW - Nitrogen
KW - Radial basis function
KW - Volatile organic compounds
UR - https://www.scopus.com/pages/publications/85062224098
U2 - 10.1109/ECTICon.2018.8619904
DO - 10.1109/ECTICon.2018.8619904
M3 - Conference contribution
AN - SCOPUS:85062224098
T3 - ECTI-CON 2018 - 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology
SP - 309
EP - 312
BT - ECTI-CON 2018 - 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2018
Y2 - 18 July 2018 through 21 July 2018
ER -