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AI-Powered Smart Irrigation System for Resource-Constrained Small-Scale Farms Using IoT and Predictive Analytics
Subject area: Science,Engineering and Technology · Area of research: AI-Powered Smart Irrigation System
DOI: https://doi.org/10.64388/IREV9I4-1711243
Abstract
Background: Water scarcity poses a critical challenge to global food security, with agriculture consuming approximately 70% of freshwater resources worldwide. Small-scale farms in developing regions face particular challenges in implementing smart irrigation technologies due to limited internet connectivity, high costs, and complexity of existing cloud-based solutions. This paper presents an AI-powered smart irrigation system designed specifically for resource-constrained environments, featuring offline operation, edge-based intelligence, and low-cost hardware implementation. Materials and Methods: The proposed system employs a three-node architecture based on ESP32 microcontrollers communicating via the ESP-NOW protocol. The Brain Node executes the Hargreaves-Samani evapotranspiration model and implements a hybrid AI decision engine called TinyAdjuster. The TinyML model occupies only 65.3 KB with 18.7 ms inference time. Simulation-based validation was conducted across three crops over 30 days. Results: The AI Adaptive system achieves 24.1% water savings versus traditional irrigation and 8.8% versus threshold IoT systems, with 96.2% efficiency. R² > 0.87 for all crops. Learning converges in 25 cycles, reducing error from 2.8 to 0.45 mm/day (84% improvement). Conclusion: The system features offline AI operation, MAD-based triggering, variable irrigation amounts, and adaptive learning. These innovations make it suitable for remote agricultural areas with limited infrastructure.
Keywords
Smart Irrigation, IoT, Edge Computing, TinyML, Hargreaves-Samani, Offline AI, Water Optimization, ESP-NOW, Sustainable Agriculture.
How to cite this paper
@article{1711243,
author = {Wagner Augusto Dias Moreira, Danilson Soares Da Veiga},
title = {AI-Powered Smart Irrigation System for Resource-Constrained Small-Scale Farms Using IoT and Predictive Analytics},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {4},
pages = {2259-2285},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1711243.pdf},
abstract = {Background: Water scarcity poses a critical challenge to global food security, with agriculture consuming approximately 70% of freshwater resources worldwide. Small-scale farms in developing regions face particular challenges in implementing smart irrigation technologies due to limited internet connectivity, high costs, and complexity of existing cloud-based solutions. This paper presents an AI-powered smart irrigation system designed specifically for resource-constrained environments, featuring offline operation, edge-based intelligence, and low-cost hardware implementation. Materials and Methods: The proposed system employs a three-node architecture based on ESP32 microcontrollers communicating via the ESP-NOW protocol. The Brain Node executes the Hargreaves-Samani evapotranspiration model and implements a hybrid AI decision engine called TinyAdjuster. The TinyML model occupies only 65.3 KB with 18.7 ms inference time. Simulation-based validation was conducted across three crops over 30 days. Results: The AI Adaptive system achieves 24.1% water savings versus traditional irrigation and 8.8% versus threshold IoT systems, with 96.2% efficiency. R² > 0.87 for all crops. Learning converges in 25 cycles, reducing error from 2.8 to 0.45 mm/day (84% improvement). Conclusion: The system features offline AI operation, MAD-based triggering, variable irrigation amounts, and adaptive learning. These innovations make it suitable for remote agricultural areas with limited infrastructure.},
keywords = {Smart Irrigation, IoT, Edge Computing, TinyML, Hargreaves-Samani, Offline AI, Water Optimization, ESP-NOW, Sustainable Agriculture.},
month = {October},
doi = {https://doi.org/10.64388/IREV9I4-1711243}
}