PORTABLE SOIL SENSORS FOR AGRICULTURAL DIGITALIZATION AND CROP OPTIMIZATION FOR INDONESIAN FARMERS IN THAILAND
DOI:
https://doi.org/10.55681/devote.v5i3.7047Keywords:
portable soil sensor, precision agriculture, soil monitoring, farmer empowerment, technology transferAbstract
Indonesian farmers working in the mountainous agricultural area of Chiang Mai Province, Thailand, still depend largely on visual observation, hand-feel assessment, manual thermometers, and accumulated experience when determining soil condition, fertilizer dosage, irrigation timing, and crop suitability. This situation limits access to comparable field data and may lead to inefficient input allocation and avoidable operational expenditure. This community service activity aimed to implement a portable precision soil sensor as an appropriate technology for agricultural digitalization, while strengthening farmers’ capacity to convert soil information into practical cultivation decisions. The activity used a Participatory Action Research approach through needs assessment, joint problem mapping, technology preparation, field deployment, demonstration, assisted practice, observation, and reflection with members of the Indonesian Farmers Union in Thailand. The sensor was used to provide real-time readings of soil moisture, pH, nutrient status, and water requirements. The readings were then interpreted for irrigation prioritization, fertilizer allocation, crop selection, and repeated monitoring of different land plots. The implementation produced an initial transition from experience-only assessment toward decisions supported by measurable field evidence. Farmers became able to operate the device, compare readings among sampling points, discuss the meaning of each parameter, and relate the information to routine land management. Program observations indicated potential fertilizer-use efficiency of 15–25% and potential overall production and operational-cost efficiency of 20–30%; however, these values remain preliminary projections because controlled multi-season yield and financial data were not available. The activity demonstrates that a portable soil sensor can function simultaneously as a measurement instrument, a farmer-learning medium, and a basis for more efficient and sustainable agricultural management. The program also supports SDGs 2, 9, and 17 through food-production optimization, practical innovation, and cross-country institutional partnership.
Downloads
References
Astakhova, T., Kolbanev, M., Romanova, A., & Shamin, A. (2019). Model of digital agriculture. International Journal of Open Information Technologies, 7(12), 63–69.
Ayers, R. S., & Westcot, D. W. (1985). Water quality for agriculture (Vol. 29). Food and agriculture organization of the United Nations Rome.
Basso, B., & Antle, J. (2020). Digital agriculture to design sustainable agricultural systems. Nature Sustainability, 3(4), 254–256.
Bhakta, I., Phadikar, S., & Majumder, K. (2019). State‐of‐the‐art Technologies in Precision Agriculture: A Systematic Review. Journal of the Science of Food and Agriculture, 99(11), 4878–4888. https://doi.org/10.1002/jsfa.9693
Bhat, S. A., & Huang, N.-F. (2021). Big Data and AI Revolution in Precision Agriculture: Survey and Challenges. Ieee Access, 9, 110209–110222. https://doi.org/10.1109/access.2021.3102227
Boutraa, T. (2010). Improvement of water use efficiency in irrigated agriculture: a review. Journal of Agronomy, 9(1), 1–8.
Cavazza, F. (2020). The digital irrigated agriculture: advances on decision modelling to accompany the sector in exploiting new opportunities.
Chandio, A. A., Ozdemir, D., Gokmenoglu, K. K., Usman, M., & Jiang, Y. (2024). Digital agriculture for sustainable development in China: The promise of computerization. Technology in Society, 76, 102479.
Chandra, R., & Collis, S. (2021). Digital agriculture for small-scale producers: challenges and opportunities. Communications of the ACM, 64(12), 75–84.
Dara, R., Hazrati Fard, S. M., & Kaur, J. (2022). Recommendations for ethical and responsible use of artificial intelligence in digital agriculture. Frontiers in Artificial Intelligence, 5, 884192.
Dhal, S., Wyatt, B. M., Mahanta, S., Bhattarai, N., Sharma, S., Rout, T., Saud, P., & Acharya, B. S. (2024). Internet of Things (IoT) in digital agriculture: An overview. Agronomy Journal, 116(3), 1144–1163.
Floch, P., & Molle, F. (2013). Irrigated agriculture and rural change in Northeast Thailand: Reflections on present developments. Governing the Mekong: Engaging in the Politics of Knowledge, 185–212.
Fountas, S., Espejo-García, B., Kasimati, A., Mylonas, N., & Darra, N. (2020). The future of digital agriculture: technologies and opportunities. IT Professional, 22(1), 24–28.
Hussein, A. H. A., Jabbar, K. A., Mohammed, A., & Jasim, L. (2024). Harvesting the Future: AI and IoT in Agriculture. E3s Web of Conferences, 477, 90. https://doi.org/10.1051/e3sconf/202447700090
Kavitha, R., & Gitanjali, J. (2023). Role of 5G Technology in Enhancing Agricultural Mechanization. Iop Conference Series Earth and Environmental Science, 1258(1), 12010. https://doi.org/10.1088/1755-1315/1258/1/012010
Kendall, H., Clark, B., Li, W., Jin, S., Jones, G., Chen, J., Taylor, J. A., Li, Z., & Frewer, L. J. (2021). Precision Agriculture Technology Adoption: A Qualitative Study of Small-Scale Commercial “Family Farms” Located in the North China Plain. Precision Agriculture, 23(1), 319–351. https://doi.org/10.1007/s11119-021-09839-2
Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS: Wageningen Journal of Life Sciences, 90(1), 1–16.
Kolady, D., Sluis, E. V. d., Uddin, M. M., & Deutz, A. P. (2020). Determinants of Adoption and Adoption Intensity of Precision Agriculture Technologies: Evidence From South Dakota. Precision Agriculture, 22(3), 689–710. https://doi.org/10.1007/s11119-020-09750-2
Kumnerdpet, W., & Sinclair, A. J. (2011). Implementing participatory irrigation management in Thailand. Water Policy, 13(2), 265–286.
Lajoie-O’Malley, A., Bronson, K., Burg, S., & Klerkx, L. (2020). The future (s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents. Ecosystem Services, 45, 101183.
Mainuddin, M., Gupta, A., & Onta, P. R. (1997). Optimal crop planning model for an existing groundwater irrigation project in Thailand. Agricultural Water Management, 33(1), 43–62.
Mohammed, N., Tahir, H. T., Othman, S. S., Hawar, D. A., Abdulkadir, M. A., & Korkmaz, C. (2025). Smart Irrigation Systems: A Comprehensive Review of IoT, AI, and Sustainable Agriculture Technologies. A Review Article. Kirkuk University Journal for Agricultural Sciences (KUJAS), 16(4), 266–281.
Monchusi, B. B., Kgopa, A. T., & Mokwana, T. I. (2024). Integrating IoT and AI for Precision Agriculture: Enhancing Water Management and Crop Monitoring in Small-Scale Farms. Iconic, 2024, 151–158. https://doi.org/10.59200/iconic.2024.017
Muthumanickam, D., Poongodi, C., Kumaraperumal, R., Pazhanivelan, S., & Ragunath, K. P. (2022). Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture. Agriculture, 12(10), 1745. https://doi.org/10.3390/agriculture12101745
Napathorn, C., & Kuruvilla, S. (2017). Human resource management in Indonesia, Malaysia, and Thailand. In Routledge handbook of human resource management in Asia (pp. 333–354). Routledge.
Nochaiwong, S., Ruengorn, C., Awiphan, R., Phosuya, C., Ruanta, Y., Kanjanarat, P., Wongpakaran, N., Wongpakaran, T., & Thavorn, K. (2022). Transcultural Adaptation and Psychometric Validation of the Thai-Brief Resilient Coping Scale: A Cross-Sectional Study During the Coronavirus Disease 2019 Pandemic in Thailand. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-26063-8
Nurmalasari, R., & Puspitasari, P. (n.d.). Variety of Agricultural Machine Innovations by Utilizing Renewable Energy. In Advanced Materials towards Energy Sustainability (pp. 37–61). CRC Press.
Nurmalasari, R., Puspitasari, P., Marsono, & Suyetno, A. (2023). Development of a Smart Pest Repellent Machine Using Solar Power and Ultrasonic Sensors for Agricultural Productivity. AIP Conference Proceedings, 2687. https://doi.org/10.1063/5.0121141
Ozdogan, B., Gacar, A., & Aktas, H. (2017). Digital agriculture practices in the context of agriculture 4.0. Journal of Economics Finance and Accounting, 4(2), 186–193.
Pasupuleti, M. K. (2024). Smart Agriculture: Harnessing Digital Technologies for Precision Farming and Sustainable Practices. 174–203. https://doi.org/10.62311/nesx/905766
Prabhahar, P., Shriram, S., Shankar, K., Mohanraj, V., & Thilagarajan, R. (2025). Harnessing Automated Irrigation Technologies to Enhance Sustainability of Agriculture: A Pathway to Food Security. International Journal of Environment and Climate Change, 15(12), 240–252.
Pretty, J. (2007). Agricultural Sustainability: Concepts, Principles and Evidence. Philosophical Transactions of the Royal Society B Biological Sciences, 363(1491), 447–465. https://doi.org/10.1098/rstb.2007.2163
Queiroz, D. M., Coelho, A. L. de F., Valente, D. S. M., & Schueller, J. K. (2020). Sensors applied to Digital Agriculture: A review. Revista Ciência Agronômica, 51(spe), e20207751.
Quý, V. K., Nguyen, V.-H., Anh, D., Quý, N. M., Ban, N. T., Lanza, S., Randazzo, G., & Muzirafuti, A. (2022). IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. Applied Sciences, 12(7), 3396. https://doi.org/10.3390/app12073396
Ray, S., & Majumder, S. (2024). Water management in agriculture: Innovations for efficient irrigation. Modern Agronomy, 169–185.
Rieser, A., Bonn, U., & Höynck, S. (n.d.). Irrigation Performance Assessment in Thailand.
Roychowdhury, T., Tokunaga, H., Uchino, T., & Ando, M. (2005). Effect of arsenic-contaminated irrigation water on agricultural land soil and plants in West Bengal, India. Chemosphere, 58(6), 799–810.
Shen, S., Basist, A., & Howard, A. (2010). Structure of a digital agriculture system and agricultural risks due to climate changes. Agriculture and Agricultural Science Procedia, 1, 42–51.
Shrestha, S., Chapagain, R., & Babel, M. S. (2017). Quantifying the impact of climate change on crop yield and water footprint of rice in the Nam Oon Irrigation Project, Thailand. Science of the Total Environment, 599, 689–699.
Sinitsa, Y., Borodina, O., Gvozdeva, O., & Kolbneva, E. (2021). Trends in the development of digital agriculture: a review of international practices. BIO Web of Conferences, 37, 172.
Soma, T., & Nuckchady, B. (2021). Communicating the benefits and risks of digital agriculture technologies: Perspectives on the future of digital agricultural education and training. Frontiers in Communication, 6, 762201.
Soussi, A., Zero, E., Sacile, R., Trinchero, D., & Fossa, M. (2024). Smart Sensors and Smart Data for Precision Agriculture: A Review. Sensors, 24(8), 2647. https://doi.org/10.3390/s24082647
Tang, S., Zhu, Q., Zhou, X., Liu, S., & Wu, M. (2002). A conception of digital agriculture. IEEE International Geoscience and Remote Sensing Symposium, 5, 3026–3028.
Weltzien, C. (2016). Digital agriculture or why agriculture 4.0 still offers only modest returns. Landtechnik, 71(2), 66–68.
Xie, C. (2025). The Role of Modern Agricultural Technologies in Improving Agricultural Productivity and Land Use Efficiency. Frontiers in Plant Science, 16. https://doi.org/10.3389/fpls.2025.1675657
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Viola Malta Ramadhani, Riana Nurmalasari, Sh Mohd Firdaus Sh Abdul Nasir

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.









