MKU-1: Modular Real-Time Sensor Networks and Edge Computing for Precision Crop Management in the Eastern Economic Corridor

Main Article Content

Montri Phothisonothai
Taddaow Khumpook
Natthapon Pannurat
Warayost Lamaisri

Abstract

This paper presents an Internet of Things (IoT)-based smart farming framework designed for the precision cultivation of medicinal and economic crops in Thailand’s Eastern Economic Corridor (EEC), with experimental validation on Capsicum annuum (chili pepper) and Ocimum tenuiflorum (holy basil). The proposed system integrates a multi-sensor network for continuous monitoring of key environmental and soil parameters, including air temperature, relative humidity, light intensity, soil moisture, soil temperature, electrical conductivity, soil pH, and nutrient levels. Sensor data are processed in real time using the MKU-1 modular control unit, which operates as an edge-computing platform to support closed-loop control of irrigation, fertigation, and lighting systems. Field experiments comparing system-assisted cultivation with conventional non-system cultivation demonstrated clear improvements in plant growth and environmental stability. System-assisted plots consistently exhibited greater plant height, higher leaf density, and more stable soil moisture and pH levels across seasonal variations. Feature-importance analysis revealed that air temperature (0.41), light intensity (0.17), and relative humidity (0.13) were the most influential factors affecting cultivation performance, with an overall analytical accuracy of 96.55%. These results highlight the effectiveness of data-driven environmental control in optimizing crop growth. In addition, an economic assessment for a representative 10-rai cultivation area indicated that the proposed system is financially viable, achieving an estimated return on investment of approximately 25% per year and a positive net present value over a five-year period. The results confirm that the proposed framework enhances cultivation efficiency, reduces resource variability, and supports sustainable smart agriculture practices aligned with Thailand’s BCG and EEC development strategies.

Article Details

How to Cite
Phothisonothai, M., Khumpook, T., Pannurat, N., & Lamaisri, W. (2026). MKU-1: Modular Real-Time Sensor Networks and Edge Computing for Precision Crop Management in the Eastern Economic Corridor. Journal of Engineering Technology Access (JETA) (Online), 6(1). retrieved from https://ph02.tci-thaijo.org/index.php/JETA/article/view/263081
Section
Research Articles

References

Office of Agricultural Economics (OAE). (2023). Thailand agricultural statistics. Ministry of Agriculture and Cooperatives, Bangkok, Thailand.

Eastern Economic Corridor of Innovation (EECi). (2025). Innovation platforms in EEC: BIOPOLIS and advanced agriculture. Rayong, Thailand.

Heble, S., Kumar, S., & Sastry, R. V. (2018). A low power IoT network for smart agriculture [Paper presentation]. IEEE International Conference on Smart Agriculture.

Kamilaris, A., & Prenafeta-Boldú, F. X. (2016). Agri-IoT: A semantic framework for Internet of Things-based smart farming. Computers and Electronics in Agriculture, 125, 1–12.

Wongpatikaseree, K., et al. (2018). IoT-based traceability system for smart agriculture [Paper presentation]. International Conference on Smart Agriculture.

Bandara, H., et al. (2020). Interoperable traceability in agrifood supply chains. Sensors, 20(12).

Maha, A., et al. (2019). Wireless sensor networks for precision agriculture: A review. Agriculture, 13(1).

Jindarat, S., & Wuttidittachotti, P. (2015). Smart farm monitoring using Raspberry Pi and Arduino [Paper presentation]. International Conference on Internet of Things and Cloud Technologies (I4CT), Kuching, Malaysia.

Zamora-Izquierdo, M. A., et al. (2019). Smart farming IoT platform based on edge and cloud computing. Biosystems Engineering, 177, 4–17.

Jukan, K., et al. (2019). A survey on the interplay between edge and cloud computing. IEEE Communications Surveys & Tutorials, 21(4), 3593–3621.

Citoni, P., et al. (2019). Long range IoT communication for agriculture [Paper presentation]. IEEE Sensors Conference.

Ryu, D.-H., et al. (2015). Developing ubiquitous sensor network platform using Internet of Things. Journal of Sensors.

Sensirion AG. (2022). SHT31 humidity and temperature sensor datasheet. Sensirion AG.

ROHM Semiconductor. (2009). Ambient light sensor (ALS) applications in portable electronics. ROHM Semiconductor.

Changsha Zoko Link Technology Co., Ltd. (2024). 7-in-1 integrated soil sensor datasheet. Changsha Zoko Link Technology Co., Ltd.

Phowong, S., Srichanpiyom, K., Puangploy, P., Euafua, C., & Phothisonothai, M. (2023). A low-cost edge and cloud computing-based smart agriculture platform for lifecycle Andrographis paniculata planting [Paper presentation]. ECTI Conference (ECTI-CON 2023).

Botchkarev, A. (2015). Estimating the Accuracy of the Return on Investment (ROI) Performance Evaluations. Interdisciplinary Journal of Information, 10, 217–233.

Brealey, R., Myers, S., & Allen, F. (2020). Principles of Corporate Finance. McGraw-Hill Education.