Aug 2026· International Journal of Computational and Experimental Science and Engineering· Vol 12· 0 citations· 90 references
TL;DR
A review of one hundred studies published between 2018 and 2025 that address the application of Internet of Things technologies and computational approaches in irrigation management aimed at improving irrigation practices while supporting crop production is presented.
Abstract
The increasing global demand for water has emphasized the importance of irrigation systems capable of efficiently managing water use in precision agriculture. This paper presents a review of one hundred studies published between 2018 and 2025 that address the application of Internet of Things (IoT) technologies and computational approaches in irrigation management. The reviewed literature describes the use of sensor networks, wireless communication systems data-driven control mechanisms and automated decision-support tools aimed at improving irrigation practices while supporting crop production. The studies are organized according to the technologies and methods employed, including sensor-based data acquisition IoT-enabled monitoring platforms and intelligent irrigation control systems. A hybrid classification is also introduced to describe approaches that combine predictive modelling with adaptive irrigation strategies. Additionally, the reviewed works are discussed with respect to several system-related aspects such as water management objectives system architecture, and application scale. The paper also summarizes commonly used datasets and evaluation metrics reported in the literature and outlines key challenges identified by previous studies including data quality, communication reliability scalability and operational costs. Finally, the review highlights research directions that may contribute to the development of more efficient reliable and sustainable irrigation systems for precision agriculture
Agricultural water scarcity poses an escalating threat to global food production, driven by population growth, climate variability, and the inherent inefficiencies of conventional irrigation systems. This paper analyzes the transformational capacity of smart technologies like the Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML), and Remote Sensing (RS) integrated with Geographic Information Systems (GIS) for sustainable agricultural water management. By synthesizing recent empirical studies, the paper analyzes the integrated effect of sensor-based monitoring, predictive analytics, automated irrigation control, and satellite and drone surveillance on water-use efficiency, irrigation optimization, and climate-resilient agriculture. Key findings indicate that IoT-based smart irrigation systems reduce water consumption by 20–55% and improve crop yields by 10-30% under optimized conditions, particularly when used with AI-driven decision-support platforms. RS and GIS technologies enable scalable, cost-effective estimation of evapotranspiration and crop water stress across field to regional scales. Despite these demonstrated benefits, substantial barriers to adoption persist, including high implementation costs, inadequate rural digital infrastructure, limited technical literacy among smallholder farmers, and insufficient policy frameworks. This review emphasized the need for supportive policies, cost-reduction strategies, and capacity-building initiatives to bridge the gap between technological innovation and field-level implementation. Overall, smart water management technologies offered a promising pathway toward sustainable agriculture, improved food security, and responsible water stewardship in an increasingly water-scarce world.
Agriculture is responsible for about 70% of freshwater withdrawals, and water scarcity and inefficient methods of water use remain threats to food security. The traditional way of irrigation causes more water loss and requires innovative solutions to optimize water distribution. This research provides an overview of an intelligent irrigation system, which combines the Internet of Things (IoT), machine learning (ML), and edge computing to optimize water usage and improve crop production. The system uses IoT sensors and edge devices to collect data on the environment and estimate the need for irrigation in real-time. It combines machine learning with system components like the Soil Monitoring Unit (SMU) and Water Management Unit (WMU) to provide scalable, real-time and efficient irrigation management. It is deployed locally at the edge, without the need for cloud computing, has the potential to integrate multi-sensor fusion for storm detection, and offers multiple ML models in a modular, low cost system that can be adapted to various agricultural settings. To optimize irrigation schedules, six supervised machine-learning models were used, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD) and Multi-Layer Perceptron (MLP). DT and RF had the highest classification accuracy of 93% and
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regression scores of 0.98 respectively, indicating high prediction reliability. However, Support Vector Machine (SVM) did not do that well, with an accuracy percentage of 58% and an
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score of 0.18. Although more complex, RF’s accuracy is a great option for larger applications and DT balances efficiency and scalability. The findings corroborate the utility of water efficient irrigation system and its prospect in augmenting productivity of agriculture. Future work will include expanding the field trials and developing hybrid models to further improve adaptability of the system in different agricultural environments. The findings of this research 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
The field of agriculture is encountering considerable difficulties in fulfilling the rising stresses in order to produce food while maintaining sustainability and efficient source utilization. Numerous technologies currently employed in agricultural practices for monitoring crop growth, soil efficiency, and nutrient levels have faced challenges. Some of these technologies have proven inadequate due to variations in frequency and distance range within smart farming applications. To tackle these issues, this review highlights an integration of biosensors, bioelectronics and Internet of Things (IoT) technology into agricultural performs, which has become a hopeful answer. The paper discusses how these sensing elements are integrated with IoT architectures, microcontroller-based nodes, wireless sensor networks, and cloud-enabled analytics to support precision irrigation, nutrient management, disease indication, and yield-oriented crop supervision. In addition, it compares major IoT communication technologies, reviews current applications and limitations, identifies research gaps in field deployment and system integration, and outlines future directions for robust, scalable, and intelligent agricultural biosensing systems.
The survival of the human community depends on the agricultural industry. Various measures have been implemented to boost agricultural productivity. But frequent pest infestations and harsh environmental conditions are the main causes of agricultural loss. In a scenario like this, combining cutting-edge IoT technologies, such as advanced sensors, could boost agricultural productivity and minimize financial loss. Indian agriculture’s problems with low production, wasteful resource use, and climate variability have given rise to smart farming as a potential remedy. An optimized smart farming framework for India is presented in this research, utilizing a Several Strategies enhanced Zebra Optimization Algorithm (MIZOA) in combination with an Internet of Things (IoT)architecture of the Wireless Sensor Network (WSN). An IoT-based smart agriculture monitoring system that uses wireless sensor networks (WSNs) and predictive analytics to track important environmental elements The system makes proactive decisions for the best fertilization, irrigation, and pest control by using machine learning techniques for predictive analytics and WSNs to collect data from dispersed sensor nodes machine learning techniques for predictive analytics and WSNs *-2`to collect data from dispersed sensor nodes. The Improved Zebra Optimization Algorithm with Multiple Strategies (MIZOA), an enhanced version of the ZOA, is proposed in this study to overcome these issues. Furthermore, the predictive analytics component forecasts environmental conditions with a 94 % accuracy rate, which helps with timely interventions and reduces crop stress. Scalability for a range of agricultural applications, such as precision farming and greenhouse monitoring, is provided by this Internet of Things-based technology, which also promotes sustainable farming methods.
Efficient water management is a cornerstone of sustainable precision agriculture, yet traditional irrigation often suffers from over-irrigation due to static scheduling. This paper presents a robust IoT-enabled smart irrigation framework that leverages the ESP32 microcontroller and a suite of environmental sensors (soil moisture, DHT22, rain, and water flow) integrated with machine learning for dynamic decision-making. Unlike threshold-based systems, our approach utilizes a Random Forest classifier to predict irrigation needs based on multivariate environmental inputs, achieving a prediction accuracy of 94.2%. Real-time data is synchronized with the Thing Speak cloud platform, enabling remote monitoring and data-driven insights. Experimental results demonstrate a 35% reduction in water consumption compared to conventional methods while maintaining optimal soil moisture levels for crop growth.
Suraksha Kardile, S. Nalbalwar, Tejas U. Mahagaonkar· International Journal of Lat...· 0 citations
Environmental conditions have become more unstable; therefore, innovative and eco-friendly methods of food production are urgently required. Most existing hydroponic systems lack the capacity for real-time responses and decision-making based on integrated data, similar to contemporary farms. This document outlines the creation of an advanced hydroponic farming system that utilizes Internet of Things (IoT) sensors and a digital twin (DT) simulator to address these challenges. A completely monitored and continuously assessed hydroponic farming simulator operating on a Raspberry Pi, employing various sensors, data management and processing, and automated environmental regulation. The development of this intelligent hydroponic farming system employs a dual-model machine learning pipeline: one that identifies plant diseases through image analysis, and another that assesses plant nutrient levels based on sensor data. The data from the two models are combined using a cloud-based DT, enabling remote access to the DT and offering closed-loop control for irrigation, nutrient dosing, and management of all environmental factors related to crop growth in a hydroponic setting. This research showcases the capability to develop scalable, data-focused precision agriculture solutions that can adapt to the demands of today’s agricultural environment by combining all elements of IoT sensing, machine learning, and DT simulations into one functional hyperphysical system.
Jehangir Arshad, F. Azeem, Ayesha Butt et al.· Future Internet· 0 citations
