Sensor Learning Application for Precision Agriculture


  • Wan Nor Shela Ezwane Wan Jusoh Politeknik Tuanku Sultanah Bahiyah, Kulim Hi-Tech Park, 09000 Kulim Kedah, MALAYSIA
  • Md Razak Daud Politeknik Tuanku Sultanah Bahiyah, Kulim Hi-Tech Park, 09000 Kulim Kedah, MALAYSIA
  • Mohd Iqbal Syazwan Azizan Politeknik Tuanku Sultanah Bahiyah, Kulim Hi-Tech Park, 09000 Kulim Kedah, MALAYSIA
  • Shukri Zakaria Politeknik Tuanku Sultanah Bahiyah, Kulim Hi-Tech Park, 09000 Kulim Kedah, MALAYSIA



Sensor, Agriculture, Teaching Tool


This paper presents a Sensor Learning Application for Precision Agriculture that will assist students in getting live data (from the temperature, soil moisture, and humidity sensors) for efficient environment monitoring, which will enable them to increase their understanding of the purpose of learning. The Sensor Learning Application for Precision Agriculture is proposed, where the three sensor kits have been developed as a teaching tool to help students gain the optimum knowledge for real-world application. The agriculture site was developed to describe the real situation to students with the aim for students to experience the use of sensors for real application and to ensure students do not learn only theoretically; they can be exposed to the real environment to collect the data. Sensor Learning Application is hybridized with different sensors, which are the Sun Heat Sensor Detector, Soil Moisture Sensor Kit, and Sensor Monitoring Devices integrated with a Wi-Fi module using ESP32 that will yield a live data feed using Blynk software. This project supports the Sustainable Development Goals (SDG) that successfully increase the quality of education, provide the sensor trainer kit, and indirectly achieve sustainable energy, economic growth, and social sustainability at the agriculture project site.


Download data is not yet available.


Barrios, E., Gemmill-Herren, B., Bicksler, A., Siliprandi, E., Brathwaite, R., Moller, S., ... & Tittonell, P. (2020). The 10 Elements of Agroecology: enabling transitions towards sustainable agriculture and food systems through visual narratives. Ecosystems and People, 16(1), 230-247.

Bouma, T. J., Nielsen, K. L., Eissenstat, D. M., & Lynch, J. P. (1997). Estimating respiration of roots in soil: interactions with soil CO 2, soil temperature and soil water content. Plant and Soil, 195, 221-232.

Chatterjee, R., Das, O., Kundu, R., & Podder, S. (2023). Machine Learning Inspired Smart Agriculture System with Crop Prediction. International Journal for Research in Applied Science & Engineering Technology, 11(1), 1511-1517.

Chen, Z. (2023). A novel improved teaching and learning-based-optimization algorithm and its application in a large-scale inventory control system. International Journal of Intelligent Computing and Cybernetics.

Cimino, A., Gnoni, M. G., Longo, F., Barone, G., Fedele, M., & Le Piane, D. (2023). Modeling & Simulation as Industry 4.0 enabling technology to support manufacturing process design: A real industrial application. Procedia Computer Science, 217, 1877-1886.

Günter, S., Buticchi, G., De Carne, G., Gu, C., Liserre, M., Zhang, H., & Gerada, C. (2018). Load control for the DC electrical power distribution system of the more electric aircraft. IEEE Transactions on Power Electronics, 34(4), 3937-3947.

Huang, C., Han, Z., Li, M., Wang, X., & Zhao, W. (2021). Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis. Australasian Journal of Educational Technology, 37(2), 81-95.

Karanjkar, A. V., & Kumawat, R. (2022). Design of a Smart Baby Cradle Using Blynk and Local Customer Priorities. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 14(02), 159-165.

Laumann, F., von Kügelgen, J., Uehara, T. H. K., & Barahona, M. (2022). Complex interlinkages, key objectives, and nexuses among the Sustainable Development Goals and climate change: A network analysis. The Lancet Planetary Health, 6(5), e422-e430.

Liyang, Liu, P., Li, B., & Yu, X. (2018). Intelligent control system of cucumber production in the greenhouse based on Internet of Things. In Cloud Computing and Security: 4th International Conference, ICCCS 2018, Haikou, China, June 8-10, 2018, Revised Selected Papers, Part V 4 (pp. 395-406). Springer International Publishing.

Montagnini, F., & Metzel, R. (2017). The contribution of agroforestry to sustainable development goal 2: end hunger, achieve food security and improved nutrition, and promote sustainable agriculture. Integrating landscapes: Agroforestry for biodiversity conservation and food sovereignty, 11-45.

Murdan, A. P., & Ramphul, J. S. (2023). LoRa-based Smart Patient Monitoring System. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 15(1), 15-21. Scribbr.

Nižetić, S., Šolić, P., Gonzalez-De, D. L. D. I., & Patrono, L. (2020). Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future. Journal of cleaner production, 274, 122877.

Pandey, P., & Pandey, M. M. (2021). Research methodology tools and techniques. Bridge Center.

Park, Y. S., Konge, L., & Artino Jr, A. R. (2020). The positivism paradigm of research. Academic medicine, 95(5), 690-694.

Polymeni, S., Athanasakis, E., Spanos, G., Votis, K., & Tzovaras, D. (2022). IoT-based prediction models in the environmental context: A systematic Literature Review. Internet of Things, 100612.

Song, D., Chen, X., Wang, M., & Xiao, X. (2023). Flexible sensors for mechatronic engineering education. Sensors International, 100236.

Trigwell, K., Ashwin, P., & Millan, E. S. (2013). Evoked prior learning experience and approach to learning as predictors of academic achievement. British Journal of Educational Psychology, 83(3), 363-378.

Wezel, A., Herren, B. G., Kerr, R. B., Barrios, E., Gonçalves, A. L. R., & Sinclair, F. (2020). Agroecological principles and elements and their implications for transitioning to sustainable food systems. A review. Agronomy for Sustainable Development, 40, 1-13.

Yurui, L., Xuanchang, Z., Zhi, C., Zhengjia, L., Zhi, L., & Yansui, L. (2021). Towards the progress of ecological restoration and economic development in China's Loess Plateau and strategy for more sustainable development. Science of The Total Environment, 756, 143676.




How to Cite

Wan Jusoh, W. N. S. E., Daud, M. R., Azizan, M. I. S., & Zakaria, S. (2023). Sensor Learning Application for Precision Agriculture. Journal of Technology and Humanities, 4(2), 16-23.