Odor Classification in Cattle Ranch based on Electronic Nose

Main Article Content

Humaira
Rahmat Hidayat
Zhi-Hao Wang
Hendrick

Abstract

Unpleasant smell and pollution are the side effects during the cattle ranch activities. That is the reason why the cattle ranch is placed far from the housing residents. The cattle ranch areas are usually not covered by the internet network, but it is also important to monitoring the pollutant in the cattle ranch. The pollutant gases are also produced during the cattle ranch activities such as hydrogen, oxygen, methane, and carbon dioxide. To classify the odor or unpleasant smell in the air, the electronic Nose (e-Nose) become an effective system to monitor and classify the odor in real time. This research, we proposed an e-Nose system that able to classify the odor in cattle ranch. The Backpropagation method is selected to create the e-Nose model. This e-Nose system is able to transmit data to server without the internet network. The Lora Network has been applied by using point to point method. The web application is also made to display the real time data monitoring and prediction of the odor. Based on our test, the e-Nose accuracy is 99% in real time prediction.

Article Details

How to Cite
[1]
Humaira, R. Hidayat, Z.-H. Wang, and Hendrick, “Odor Classification in Cattle Ranch based on Electronic Nose”, Int. J. Data. Science., vol. 2, no. 2, pp. 104-111, Dec. 2021.
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Articles

References

A. D. Wilson, “Application of electronic-nose technologies and VOC-biomarkers for the noninvasive early diagnosis of gastrointestinal diseases,” Sensors (Switzerland), vol. 18, no. 8, 2018, doi: 10.3390/s18082613.

G. J. Jong, Hendrick, Z. H. Wang, K. S. Hsieh, and G. J. Horng, “A Novel Feature Extraction Method an Electronic Nose for Aroma Classification,” IEEE Sens. J., vol. 19, no. 22, pp. 10796–10803, 2019, doi: 10.1109/JSEN.2019.2929239.

H. Hendrick, R. Hidayat, G. J. Horng, and Z. H. Wang, “Non-Invasive Method for Tuberculosis Exhaled Breath Classification Using Electronic Nose,” IEEE Sens. J., no. c, 2021, doi: 10.1109/JSEN.2021.3061616.

X. zhe Zheng, Y. bin Lan, J. min Zhu, J. Westbrook, W. C. Hoffmann, and R. E. Lacey, “Rapid Identification of Rice Samples Using an Electronic Nose,” J. Bionic Eng., vol. 6, no. 3, pp. 290–297, 2009, doi: 10.1016/S1672-6529(08)60122-5.

C. Bambang Dwi Kuncoro, A. Armansyah, N. H. Saad, A. Jaffar, C. Y. Low, and S. Kasolang, “Wireless e-Nose sensor node: State of the art,” Procedia Eng., vol. 41, no. December, pp. 1405–1411, 2012, doi: 10.1016/j.proeng.2012.07.328.

A. Lavric and V. Popa, “Internet of things and LoRaTM low-power wide- area networks challenges,” Proc. 9th Int. Conf. Electron. Comput. Artif. Intell. ECAI 2017, vol. 2017-Janua, pp. 1–4, 2017, doi: 10.1109/ECAI.2017.8166405.

H. Lin, C. Jung, and H. Hendrick, “NB-IoT Application on Decision Support System of Building Information Management,” doi: 10.1007/s11277-020-07389-w.

E. Priya and S. Srinivasan, “Automated object and image level classification of TB images using support vector neural network classifier,” Biocybern. Biomed. Eng., vol. 36, no. 4, pp. 670–678, 2016, doi: 10.1016/j.bbe.2016.06.008.

Y. Tian, G. Yang, Z. Wang, H. Wang, E. Li, and Z. Liang, “Apple detection during different growth stages in orchards using the improved YOLO-V3 model,” Comput. Electron. Agric., vol. 157, no. October 2018, pp. 417–426, 2019, doi: 10.1016/j.compag.2019.01.012.

A. D. Wilson, “Applications of Electronic-Nose Technologies for Noninvasive Early Detection of Plant, Animal and Human Diseases,” Chemosensors, vol. 6, no. 4, p. 45, 2018, doi: 10.3390/chemosensors6040045.

F. Wu, T. Wu, and M. R. Yuce, “An internet-of-things (IoT) network system for connected safety and health monitoring applications,” Sensors (Switzerland), vol. 19, no. 1, 2019, doi: 10.3390/s19010021.