Sep 2026· Water Practice & Technology· 0 citations· 26 references
TL;DR
This study investigates the application of circular economy solutions, such as water recycling and reuse, combined with desalination processes to meet increased water demands and develop sustainable and efficient strategies to secure adequate water resources for irrigation.
Abstract
A conceptual view of the design of the IoT and desalination system.
Water scarcity represents one of the greatest challenges for sustainable agriculture in island environments, where climate change exacerbates drought conditions. This study focuses on optimizing water management for agricultural crops on islands using advanced Internet of Things (IoT) and Machine Learning (ML) technologies. By deploying IoT sensors, real-time data is collected to monitor parameters such as soil moisture, temperature, and rainfall, which are used to estimate irrigation requirements. These data are analyzed using machine learning algorithms to predict the necessary water quantities, achieving maximum crop yield with minimal waste. Furthermore, this study investigates the application of circular economy solutions, such as water recycling and reuse, combined with desalination processes to meet increased water demands. The aim is to develop sustainable and efficient strategies to secure adequate water resources for irrigation while promoting sustainability and reducing the environmental footprint of agriculture. The expected outcomes will provide new directions for adopting ‘smart’ irrigation methods, contributing to water management under conditions of climate uncertainty and limited resources, while enhancing the resilience of island communities to the impacts of climate change.
The findings corroborate the utility of water efficient irrigation system and its prospect in augmenting productivity of agriculture and can offer understanding for sustainable farming practices, that are essential when it comes to water scarcity and food security in the global context.
M. Mustafa, Hakan Kutucu· Frontiers in Sustainable Foo...· 0 citations
It is concluded that combining ML and IoT is fundamental to achieving sustainable agricultural development and the United Nations Sustainable Development Goals, particularly Zero Hunger, Clean Water, Responsible Consumption and Production, Responsible Consumption and Production, and Climate Action.
Mustapha Malami Idina, Mubarak Jibril Yeldu, A. Gulumbe· International Journal of Mul...· 0 citations
Climate change has increasingly disrupted agricultural productivity in Benin City, Edo State,
Nigeria, manifesting through erratic rainfall patterns, prolonged dry seasons, flooding events,
and rising temperatures. These challenges have significantly affected smallholder farmers who
rely on traditional irrigation me...
I. Okafor· WORLD JOURNAL OF INNOVATION...· 0 citations
A robust IoT-enabled smart irrigation framework that leverages the ESP32 microcontroller and a suite of environmental sensors integrated with machine learning for dynamic decision-making integrated with machine learning for dynamic decision-making is presented.
Suraksha Kardile, S. Nalbalwar, Tejas U. Mahagaonkar· International Journal of Lat...· 0 citations
By integrating solar-powered IoT infrastructure with ML-based analytics, class-imbalance handling, and computational-efficiency evaluation, the proposed framework offers a practical and scalable solution for energy-aware suitability assessment in resource-constrained farming environments.
Yusra Mansoor, Huma Jamshed, M. Khouj et al.· Computers, Materials & C...· 0 citations
The IoT-Driven Precision Agriculture (IoT-PA) system optimizes agricultural resource use using distributed sensor networks, real-time soil moisture and weather monitoring, and intelligent irrigation control to maximize crop growth and avoid water waste.
T. G. Sakthivel, R. Ashok, A. M et al.· Journal of Visualized Experi...· 0 citations
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