International Journal of
Advanced Multidisciplinary Application

Volume 3, Issue 6

• Author(s)Sneha Patil, Gurpreet Singh Bhatia, Subhashree Mohapatra
• AffiliationDepartment of Electronics Engineering, Pune Institute of Engineering and Technology, Pune, Maharashtra, India Department of Agricultural Engineering, Punjab Agricultural Technology College, Ludhiana, Punjab, India Department of Computer Science and Engineering, Bhubaneswar Institute of Technology, Bhubaneswar, Odisha, India
• Page No.60-66
• Volume, Issue & YearVolume 3, Issue 6, June 2026
• Published On2026/06/13
• JournalInternational Journal of Advanced Multidisciplinary Application (IJAMA)
• ISSN No.3048-9350

Abstract

Agriculture contributes approximately 17–18% of India's GDP and employs over 58% of the workforce, yet remains highly vulnerable to climate variability, inefficient water use, and delayed pest and nutrient interventions due to limited access to real-time field monitoring. Precision agriculture driven by Internet of Things (IoT) sensor networks and machine learning analytics offers a scalable pathway to address these challenges, but adoption in smallholder semi-arid farming contexts in states such as Maharashtra and Rajasthan has been limited by system cost, power constraints, and connectivity. This paper presents the design, implementation, and field validation of a low-cost IoT-based smart agriculture system deployed across 12 field plots (0.5 hectare each) in Solapur district, Maharashtra. The system integrates soil moisture sensors (capacitive, ±2% accuracy), DHT22 temperature-humidity sensors, NPK electrochemical sensors, and LDR light intensity sensors connected via ZigBee mesh network to a Raspberry Pi 4 edge gateway. A Random Forest regression model trained on 60 days of multi-parameter sensor data achieves R² = 0.94 for irrigation volume prediction, reducing water consumption by 38% versus traditional flood irrigation while improving crop yield across five crop types (rice, wheat, maize, soybean, groundnut) by an average of 31.4%. System end-to-end latency for edge-processed irrigation decisions averages 12.3 ms at the 50th percentile versus 41.7 ms for cloud-only processing, confirming the edge architecture's suitability for real-time actuator control. Total hardware cost per plot is estimated at INR 8,400, with a projected payback period of 1.8 cropping seasons based on observed yield improvement and water cost savings.

Keywords: Internet of Things, precision agriculture, smart irrigation, random forest, soil moisture, edge computing, ZigBee, Raspberry Pi, crop yield prediction, semi-arid farming

References

1. [1] Adeyemi, O., Grove, I., Peets, S., Domun, Y., & Norton, T. (2018). Dynamic neural network modelling of soil moisture content for predictive irrigation scheduling. Sensors, 18(10), 3408.
2. [2] Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102-114.
3. [3] Goldstein, A., Fink, L., Meitin, A., Bohadana, S., Lutenberg, O., & Raveh, G. (2018). Applying machine learning on sensor data for irrigation recommendations. Precision Agriculture, 19(5), 826-844.
4. [4] ICAR. (2020). Precision Agriculture: Concepts and Applications. Indian Council of Agricultural Research, New Delhi.
5. [5] Kamienski, C., Soininen, J. P., Taumberger, M., Dantas, R., Toscano, A., Salmon Cinotti, T., ... & Torre Neto, A. (2019). Smart water management platform: IoT-based precision irrigation for agriculture. Sensors, 19(2), 276.
6. [6] Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
7. [7] MoAFW. (2022). Agricultural Statistics at a Glance 2021-22. Ministry of Agriculture and Farmers Welfare, Government of India.
8. [8] Muangprathub, J., Boonnam, N., Kajkornchaiyakul, S., Lekbangpong, N., Wanichsombat, A., & Nillaor, P. (2019). IoT and agriculture data analysis for smart farm. Computers and Electronics in Agriculture, 156, 467-474.
9. [9] Nayyar, A., & Puri, V. (2016). Smart farming: IoT based smart sensors agriculture stick for live temperature and moisture monitoring. Proceedings of the International Conference on Communication and Computing Systems, 673-677.
10. [10] Ray, P. P. (2017). Internet of things for smart agriculture: Technologies, practices and future direction. Journal of Ambient Intelligence and Smart Environments, 9(4), 395-420.
11. [11] Sharma, H., Haque, A., & Jaffery, Z. A. (2019). Maximization of wireless sensor network lifetime using solar energy harvesting. Wireless Personal Communications, 104(1), 221-237.
12. [12] Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming — A review. Agricultural Systems, 153, 69-80.

Explore Our Related Journals

Looking for the right journal for your next manuscript? Explore our international peer-reviewed journals covering engineering, management, computer science, artificial intelligence and multidisciplinary research.