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Smart Agriculture in Arid Regions: IoT Sensor Networks and ML for Optimal Water Management

Author(s):Nitin Kamat, Roopa Rai

Affiliation: Department of Agricultural Engineering, Punjab Agricultural Technology College, Ludhiana, Punjab, India

Page No: 67-73

Volume issue & Publishing Year: Volume 3, Issue 6, June 2026

published on: 2026/06/13

Journal: International Journal of Advanced Multidisciplinary Application.(IJAMA)

ISSN NO: 3048-9350

DOI:

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Abstract:
Smallholder farming in semi-arid regions like Maharashtra and Rajasthan is highly vulnerable to climate variability, inefficient water use, and delayed pest and nutrient interventions due to limited access to real-time field monitoring. While precision agriculture driven by Internet of Things (IoT) sensor networks and machine learning (ML) analytics offers a scalable solution, regional adoption remains limited by high system costs, power constraints, and connectivity barriers. 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 hectares each) in the Solapur district of Maharashtra. The architecture integrates capacitive soil moisture sensors, DHT22 temperature-humidity sensors, NPK electrochemical sensors, and LDR light intensity sensors connected via a ZigBee mesh network to a Raspberry Pi 4 edge gateway. A Random Forest regression model trained on 60 days of multi-parameter sensor data achieved an R² of 0.94 for irrigation volume prediction. Field results demonstrate a 38% reduction in water consumption compared to traditional flood irrigation, alongside an average crop yield improvement of 31.4% across five crop types: rice, wheat, maize, soybean, and groundnut. Furthermore, the system minimized end-to-end latency for edge-processed irrigation decisions to 12.3 ms at the 50th percentile, compared to 41.7 ms for cloud-only processing, validating the edge architecture's suitability for real-time actuator control. With a total hardware cost estimated at INR 8,400 per plot, the system offers an economically viable solution for smallholders, featuring a projected payback period of 1.8 cropping seasons based on observed water savings and yield improvements.

Keywords: Precision agriculture, Smart irrigation scheduling, Multi-crop yield prediction, Semi-arid farming, NPK electrochemical sensors, Water resource management, Climate-vulnerable agriculture, Soil volumetric water content

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