TinyML-Enabled IoT for Sustainable Precision Irrigation

• URL: http://arxiv.org/abs/2601.13054v1
• Date: Mon, 19 Jan 2026 13:43:28 GMT
• Title: TinyML-Enabled IoT for Sustainable Precision Irrigation
• Authors: Kamogelo Taueatsoala, Caitlyn Daniels, Angelina J. Ramsunar, Petrus Bronkhorst, Absalom E. Ezugwu,
• Abstract summary: Small-scale farming communities are disproportionately affected by water scarcity, erratic climate patterns, and a lack of access to advanced, affordable agricultural technologies.<n>This paper presents a novel, edge-first IoT framework that integrates Tiny Machine Learning (TinyML) for intelligent, offline-capable precision irrigation.
• Score: 0.5079758341055661
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Small-scale farming communities are disproportionately affected by water scarcity, erratic climate patterns, and a lack of access to advanced, affordable agricultural technologies. To address these challenges, this paper presents a novel, edge-first IoT framework that integrates Tiny Machine Learning (TinyML) for intelligent, offline-capable precision irrigation. The proposed four-layer architecture leverages low-cost hardware, an ESP32 microcontroller as an edge inference node, and a Raspberry Pi as a local edge server to enable autonomous decision-making without cloud dependency. The system utilizes capacitive soil moisture, temperature, humidity, pH, and ambient light sensors for environmental monitoring. A rigorous comparative analysis of ensemble models identified gradient boosting as superior, achieving an R^2 score of 0.9973 and a Mean Absolute Percentage Error (MAPE) of 0.99%, outperforming a random forest model (R^2 = 0.9916, MAPE = 1.81%). This optimized model was converted and deployed as a lightweight TinyML inference engine on the ESP32 and predicts irrigation needs with exceptional accuracy (MAPE < 1%). Local communication is facilitated by an MQTT-based LAN protocol, ensuring reliable operation in areas with limited or no internet connectivity. Experimental validation in a controlled environment demonstrated a significant reduction in water usage compared to traditional methods, while the system's low-power design and offline functionality confirm its viability for sustainable, scalable deployment in resource-constrained rural settings. This work provides a practical, cost-effective blueprint for bridging the technological divide in agriculture and enhancing water-use efficiency through on-device artificial intelligence.

Related papers

• U-FedTomAtt: Ultra-lightweight Federated Learning with Attention for Tomato Disease Recognition [2.628739907490327]
  Federated learning has emerged as a privacy-preserving and efficient approach for deploying intelligent agricultural solutions.<n>We propose U-FedTomAtt, an ultra-lightweight federated learning framework with attention for tomato disease recognition in resource-constrained and distributed environments.
  arXiv  Detail & Related papers  (2026-02-18T05:00:38Z)
• HydroSense: A Dual-Microcontroller IoT Framework for Real-Time Multi-Parameter Water Quality Monitoring with Edge Processing and Cloud Analytics [0.0]
  HydroSense is an innovative Internet of Things framework that integrates six critical water quality parameters into a unified monitoring system.<n>With a total implementation cost of 32,983 BDT, HydroSense achieves an 85 percent cost reduction compared to commercial systems.
  arXiv  Detail & Related papers  (2026-01-29T12:04:36Z)
• Machine Learning-Based Basil Yield Prediction in IoT-Enabled Indoor Vertical Hydroponic Farms [0.08388908302793013]
  This work explores the integration of indoor vertical hydroponics with Machine Learning (ML) techniques to optimize basil yield while saving water.<n>This research develops a prediction system that uses different ML models and assesses their performance.
  arXiv  Detail & Related papers  (2025-12-15T11:00:34Z)
• Intelligence per Watt: Measuring Intelligence Efficiency of Local AI [39.049258055931524]
  Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure.<n>Two advances enable us to rethink this paradigm: small LMs now achieve competitive performance to frontier models on many tasks.<n>This raises the question: can local inference via redistribute demand from centralized infrastructure?<n>We propose intelligence per watt (IPW), task accuracy divided by unit of power, as a metric for assessing capability and efficiency of local inference.
  arXiv  Detail & Related papers  (2025-11-11T06:33:30Z)
• A neuromorphic continuous soil monitoring system for precision irrigation [32.97821869608533]
  We present a fully energy-efficient neuromorphic irrigation control system that operates autonomously without any need for data transmission or remote processing.<n>We validate this approach using real-world soil moisture data from apple and kiwi orchards applied to a mixed-signal neuromorphic processor.<n>Our results show that local neuromorphic inference can maintain decision accuracy, paving the way for autonomous, sustainable irrigation solutions at scale.
  arXiv  Detail & Related papers  (2025-09-17T15:15:03Z)
• Targeted Deep Architectures: A TMLE-Based Framework for Robust Causal Inference in Neural Networks [41.07059178065932]
  We propose Targeted Deep Architectures (TDA), a new framework that embeds Targeted Likelihood Estimation (TMLE) directly into the network's parameter space.<n>TDA partitions model parameters and iteratively updates them along a targeting gradient.<n>This procedure yields plug-in estimates that remove first-order bias and produce valid confidence intervals.
  arXiv  Detail & Related papers  (2025-07-16T17:24:06Z)
• VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification [42.14439854721613]
  We propose variational autoencoders (VAEs) for disentanglement to extract essential latent features that enable accurate classification of interferences.<n>Our proposed VAE achieves a data compression rate ranging from 512 to 8,192 and achieves an accuracy up to 99.92%.
  arXiv  Detail & Related papers  (2025-04-14T13:38:00Z)
• Microservice Deployment in Space Computing Power Networks via Robust Reinforcement Learning [43.96374556275842]
  It is important to provide reliable real-time remote sensing inference services to meet the low-latency requirements.<n>This paper presents a remote sensing artificial intelligence applications deployment framework designed for Low Earth Orbit satellite constellations.
  arXiv  Detail & Related papers  (2025-01-08T16:55:04Z)
• Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study [41.831392507864415]
  Anomaly detection plays a crucial role in preventive maintenance and spotting irregularities in industrial components. Traditional Machine Learning faces challenges in deploying anomaly detection models in resource-constrained environments like LoRaWAN. Federated Learning (FL) solves this problem by enabling distributed model training, addressing privacy concerns, and minimizing data transmission.
  arXiv  Detail & Related papers  (2024-10-15T13:48:04Z)
• Federated Learning Approach to Mitigate Water Wastage [0.0]
  Residential outdoor water use in North America accounts for nearly 9 billion gallons daily, with approximately 50% of this wasted due to over-watering. Traditional approaches to reducing water wastage have focused on centralized data collection and processing. We propose a federated learning-based approach to optimize water usage in residential and agricultural settings.
  arXiv  Detail & Related papers  (2024-08-21T16:35:40Z)
• Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents [43.282328554697564]
  We propose a highly efficient FL dataset distillation framework on the server side. Unlike previous strategies, our technique enables the server to leverage prior knowledge from pre-trained deep generative models. Our framework converges faster than the baselines because rather than the server trains on several sets of heterogeneous data distributions, it trains on a multi-modal distribution.
  arXiv  Detail & Related papers  (2023-12-03T23:30:48Z)
• Environment-independent mmWave Fall Detection with Interacting Multiple Model [1.9358739203360094]
  mmWave radar is a promising candidate technology for its privacy-preserving and non-contact manner. FADE is a practical fall detection radar system with enhanced accuracy and robustness in real-world scenarios.
  arXiv  Detail & Related papers  (2023-11-15T07:49:46Z)
• Floor Map Reconstruction Through Radio Sensing and Learning By a Large Intelligent Surface [46.593612138308096]
  This paper presents a novel method to translate radio environmental maps obtained at the Large Intelligent Surface to floor plans of the indoor environment built of scatterers spread along its area. We show that the floor plan can be correctly reconstructed using both local and global measurements.
  arXiv  Detail & Related papers  (2022-06-21T21:56:19Z)
• Water Quality Prediction on a Sigfox-compliant IoT Device: The Road Ahead of WaterS [0.27998963147546135]
  We focus on an Internet of Things water quality prediction system, namely WaterS, that can remotely communicate the gathered measurements. The solution addresses the water pollution problem while taking into account the peculiar Internet of Things constraints such as energy efficiency and autonomy. The source code of WaterS ecosystem has been released as open-source, to encourage and promote research activities from both Industry and Academia.
  arXiv  Detail & Related papers  (2020-07-27T11:21:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.