Edge-Based Machine Learning for Crop Water Stress Prediction in LoRaWAN IoT Networks

Pimpalkar, Gajanan P.

doi:https://doi.org/10.55041/ijcope.v2i9.245

Volume 02, Issue 9

Published on: September 2026

EDGE-BASED MACHINE LEARNING FOR CROP WATER STRESS PREDICTION IN LORAWAN IOT NETWORKS

Gajanan P. Pimpalkar

Department of Computer Science Dr. Khatri Mahavidyalaya, Chandrapur, Maharashtra, India 442401

DOI:https://doi.org/10.55041/ijcope.v2i9.245

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

The rapid integration of the Internet of Things (IoT) in precision agriculture has enabled real-time environmental monitoring. However, traditional cloud-centric IoT architectures exhibit significant limitations in remote agricultural settings, including high power consumption, dependency on continuous internet connectivity, and latency in decision-making. This study proposes an edge-intelligence IoT framework designed to predict crop water stress locally on resource-constrained microcontrollers. Utilizing an ESP32 edge node equipped with soil moisture, ambient temperature, and humidity sensors, the system integrates a lightweight Random Forest (RF) machine learning model directly at the hardware level. Instead of transmitting raw continuous data, the edge node computes the Crop Water Stress Index (CWSI) and transmits only actionable irrigation decisions via the LoRaWAN communication protocol. The model is trained and validated using a public Crop Water Stress Dataset. An expected evaluation framework is designed to benchmark the edge-deployed RF model against standard cloud-based execution, focusing on classification accuracy, F1-score, inference latency, and total energy consumption. By shifting the computational workload to the edge, the proposed framework aims to significantly reduce radio transmission frequency, thereby optimizing network bandwidth and maximizing the operational lifespan of battery-powered agricultural IoT nodes.

How to Cite this Paper

Pimpalkar, G. P. (2026). Edge-Based Machine Learning for Crop Water Stress Prediction in LoRaWAN IoT Networks. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(9), 1-9. https://doi.org/10.55041/ijcope.v2i9.245

Pimpalkar, Gajanan. "Edge-Based Machine Learning for Crop Water Stress Prediction in LoRaWAN IoT Networks." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 9, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i9.245.

Pimpalkar, Gajanan. "Edge-Based Machine Learning for Crop Water Stress Prediction in LoRaWAN IoT Networks." International Journal of Creative and Open Research in Engineering and Management 02, no. 9 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i9.245.

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•Published on: Sep 30 2026

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