Agronomic implications of smart irrigation systems in enhancing water productivity and sustainability

Introduction

The FAO estimates that by 2050, there will be approximately 9.1 billion people on the planet, greatly increasing the already high demand for food. Global food production is predicted to rise by 70% in the upcoming years. Crop irrigation currently uses 70% of the freshwater drawn from aquifers, streams, and lakes [1] and that proportion would have to rise to support the anticipated increase in food production. Freshwater scarcity has emerged as one of the most critical constraints limiting agricultural productivity and sustainability worldwide, particularly under changing climatic conditions and increasing food demand [2] . In this case, increased technological options serve as a catalyst for the shift to more intelligent agricultural systems which when put into practice, help to maximize the use of resources like irrigation that are required for food production while also improving the quality and quantity of food produced [3] . The studies have demonstrated that precision farming is a modern approach to farm and plant management that uses a range of cutting-edge technological tools to efficiently distribute and manage the natural resources that are available. The artificial application of water to the soil through the use of pumps, tubes, and sprays is known as irrigation. The irrigation is required typically in areas with irregular rainfall, during dry spells or in areas with regular dehydration [4] . It is a reasonable estimate that over 40% of the redirected irrigation water is used earlier at the farm level, either through deep percolation or surface runoff [5] . Researchers favor paying more attention to practices that have a high rate of waste because these are used to give water resources a higher value efficiency. Due to the likely effects of global warming, research is being done on water adaptation strategies to ensure that there is water available for food production and consumption. How well the irrigation system works depends on a number of well-known issues, including when to irrigate the soil, how much water is appropriate, and how to increase efficiency. There are numerous factors to consider when choosing an irrigation system. such as the type of crop, the farmer's and farm's location, and the season. In general, all of these factors need to include crop specifications, soil topography and properties, system compatibility with the farm's best services, economic viability, and certain social constraints. In this context, smart irrigation systems have gained significant attention as an innovative approach to optimize water delivery based on real-time crop and soil requirements. The application of smart technologies, including IoT, wireless sensor networks (WSNs), and smart sensors, in agriculture has been the subject of numerous studies [6] . By combining irrigation control techniques with intelligent crop water monitoring systems, this study advances our understanding of how to increase water productivity. It is even more intriguing to investigate how the use of various levels and types of technology for smart irrigation can help to grow better crops in the soil while contributing to rational water use. According to studies, precision irrigation offers a viable way to maximize WUE and increase agricultural output by reducing environmental footprints in a time when water resources are becoming increasingly scarce, and sustainability is imperative. Empirical studies have demonstrated that smart irrigation can significantly reduce water consumption while simultaneously enhancing crop yield and farm profitability, highlighting its agronomic and economic benefits [7] . From a sustainability perspective, smart irrigation technologies contribute to improved water resource management by minimizing wastage, promoting groundwater recharge, and supporting climate-resilient agricultural practices [8] . The adoption of precision irrigation frameworks also aligns with sustainable development goals by reducing energy consumption, lowering greenhouse gas emissions associated with pumping, and enhancing long-term soil and ecosystem health. Therefore, smart irrigation systems represent a transformative component of digital agriculture, offering substantial potential to improve water conservation, agronomic efficiency, and environmental sustainability in modern farming systems [9] .

Review Objectives

Irrigation accounts for nearly 70% of global freshwater withdrawals, and inefficient irrigation practices contribute significantly to water wastage, soil degradation, and declining groundwater resources [10] . Traditional irrigation systems are often unable to adapt to spatial and temporal variability in soil and climatic conditions, leading to suboptimal water use and reduced agronomic efficiency [11] . However, existing literature is fragmented across engineering, agronomy, environmental science, and data science domains, creating a need for an interdisciplinary synthesis of knowledge on smart irrigation technologies and their sustainability outcomes [12] . Moreover, many studies focus on technical system design, while fewer reviews address agronomic performance, environmental impacts, and socio-economic implications of smart irrigation adoption. Therefore, a comprehensive review is required to consolidate current knowledge on smart irrigation systems, evaluate their agronomic applications, and assess their role in sustainable water management and climate-resilient agriculture [13] . Such a review can provide researchers, policymakers, and practitioners with a holistic understanding of technological advancements, implementation challenges, and future research directions in smart irrigation systems.

Smart Irrigation Systems

Smart irrigation systems are advanced technological frameworks designed to regulate irrigation scheduling and water application rates based on real-time environmental, soil, and crop data, with the objective of enhancing water-use efficiency and improving agricultural productivity. These systems represent a shift from traditional rule-based irrigation practices toward data-driven, automated decision-making approaches that optimize water resources under variable climatic and soil conditions [9] . In modern agriculture, smart irrigation is recognized as a critical component of digital and climate-smart farming, particularly in regions experiencing water scarcity and erratic rainfall patterns [14] . Smart irrigation systems overcome the limitations arises due to the usage of conventional irrigation systems by integrating sensors, automation hardware, and computational intelligence to dynamically adjust irrigation operations based on actual crop water demand [15] . Thus, smart irrigation is increasingly considered an essential tool for sustainable intensification of agriculture and efficient water management.

Core Components of Smart Irrigation Systems

Smart irrigation systems consist of several interconnected components that collectively enable automated monitoring, decision-making, and actuation of irrigation processes. The primary components include sensing devices, microcontrollers or embedded systems, communication modules, actuators such as valves and pumps, and software platforms for data processing and user interaction.

• Sensors: Sensors are fundamental elements in smart irrigation systems, providing real-time measurements of soil and environmental conditions that influence crop water requirements [16] . Soil moisture sensors are commonly used to measure volumetric water content in the soil profile, allowing precise determination of irrigation thresholds and scheduling decisions. Additional sensors such as temperature, humidity, rainfall, and solar radiation sensors are integrated to estimate evapotranspiration and crop water use under varying climatic conditions. The integration of multi-sensor data enhances the accuracy of irrigation decision-making and reduces the risk of over-or underirrigation [17] . • Microcontrollers and Embedded Systems: Microcontrollers serve as the central processing unit in smart irrigation systems, where sensor data are processed and control decisions are generated. Embedded platforms such as Arduino, Raspberry Pi, ESP32, and other microprocessor-based systems are widely used due to their low cost, flexibility, and compatibility with agricultural sensors [18] . These controllers execute programmed algorithms or machine-learning models to determine irrigation timing, duration, and quantity based on sensor inputs and predefined thresholds.

• Automated Valves and Pumps: Automated valves and pumps act as actuators in smart irrigation systems, enabling physical control of water flow in irrigation networks [15] . Solenoid valves are commonly used to open or close irrigation lines in response to control signals from microcontrollers, ensuring precise water delivery to specific field zones [9] . Automated pumping systems further regulate pressure and flow rate, improving uniformity and efficiency of irrigation distribution. The integration of smart irrigation systems with the Internet of Things (IoT) has significantly expanded their capabilities by enabling real-time data transmission, remote monitoring, and cloud-based analytics. IoT-enabled irrigation systems use wireless communication technologies such as Wi-Fi, GSM, LoRa, and Zigbee to transmit sensor data to cloud platforms or mobile applications [19] . This connectivity allows farmers, researchers, and agronomists to monitor irrigation operations remotely, analyze historical data, and adjust irrigation strategies from any location [20] . Cloud-based IoT platforms facilitate data storage, visualization, and integration with decision-support tools, improving farm management efficiency and reducing labor requirements. Artificial intelligence (AI) and machinelearning (ML) techniques are increasingly incorporated into smart irrigation systems to enhance predictive capabilities and optimize irrigation strategies. Machine-learning models such as neural networks, decision trees, and support vector machines are used to predict crop water requirements based on historical weather data, soil properties, crop growth stages, and sensor measurements [2] . AI-driven irrigation scheduling enables adaptive and site-specific irrigation management, improving water-use efficiency and crop yield under dynamic environmental conditions [21] . These intelligent systems contribute to climate-resilient agriculture by predicting drought stress, optimizing irrigation under water-limited conditions, and supporting sustainable resource management. Precision irrigation uses sensor networks, geographic information systems (GIS), and variable-rate irrigation technologies to apply water according to site-specific crop and soil conditions [22] . By integrating smart irrigation with precision agriculture tools, farmers can achieve targeted water application, reduced wastage, improved nutrient uptake, and enhanced crop performance [23] . Therefore, smart irrigation systems represent a core technological pillar of digital agriculture and sustainable water management in the twenty-first century.

Technical Foundations 4.1 IoT Architecture and Sensors

Smart irrigation system are built on multi layered IOT framework that integrate heterogeneous sensors to continuously track soil and surrounding environmental conditions. A standard set up generally usually include soil moisture probes along with instrument that measure humidity, temperature variations and weather parameters. It helps to transmit the real time data into central control unit, where it determines the amount of irrigation water are needed. These system collect field variables such as soil water content, air temperature and relative humidity that supports context aware irrigation scheduling based on real field conditions [24,25] . Wireless connectivity is essential in linking distributed sensors across large agricultural areas. Low power wide area technology such as LoRaWAN are well suited for farms that provide long range, low energy data transmission. In contrast, NB-IOT relies on cellular coverage for remote fields where installing dedicated gateways may not be practical [26,27] . Zigbee and similar mesh networking standards are typically preferred in smaller fields where low latency and device density matters [24] . The IoT gateway aggregates sensor data and transmit it either to cloud services or to local edge computers. Cloud integration allows users to enables remote monitoring and supporting deeper data analysis and long-term record keeping. [25,28] .

Control Systems and Algorithms

Smart irrigation consists of the main principle of control algorithms that convert sensor readings into irrigation control instructions. Threat Signal Systems Simple rule-based and threshold logic systems can activate irrigation at which the soil moisture drops to lower preset levels. Further complex programs utilize fuzzy logic and machine learning algorithms to include uncertainties in the soil-water-climate interactions as well as automatize more delicate irrigation schedules [29] . Deep LSTM networks, a form of machine learning, could be used to forecast the dynamics of soil moisture and irrigate the soil at the most appropriate time, maximizing water conservation without affecting crop health. Such real-time types of data processing systems usually have predictive models to combine the forecasted weather data with the history of soil records, and they dynamically update irrigation plans based on seasonal trends and drought conditions.

Energy Source

Remote field IoT nodes need to have energy independence. Whereas grid power is optimal to use in cases where possible, renewable energy, like solar panels, is being considered as a way to power sensor nodes, gateways, and actuators to lower costs of operation and enhance sustainability. [25] . Efficient energy harvesting and low-power design ensure long lifetimes for distributed sensors, especially in off-grid deployments.

System Types and Architectures 5.1 IoT-Enabled Drip and Sprinkler Systems

Smart irrigation systems combine sensors and traditional irrigation systems, in this case, drip lines, sprinklers, and solenoid-controlled valves, to modify water supply on-site depending on current soil water conditions [25] . The soil moisture sensors used in drip irrigation directly open valves to apply water in specific quantities and only where the water is necessary which dramatically cuts down water waste as opposed to flood irrigation or manual irrigation [25] . Soil moisture and weather conditions are also broadcast over the air using wireless systems into centralized schedulers that control drip outputs, which in field experiments have been proven to always produce detectable water savings and yield increases [26] .

Autonomous Smart Systems

Complete autonomy smart systems are characterised by the integration of IoT and robotics as well as embedded intelligence.

In such systems, irrigation decisions are not always under the watch of the human beings and the irrigation events are selfadaptive under the changing conditions of the field. These selfregulating controls tend to be based on edge computing, machine learning-based inference on field nodes, and predictive analytics to predict the water requirements of the plant [30] .

Integration with Other Precision Tools Variable-Rate Irrigation (VRI)

It can be combined with Variable-Rate Irrigation (VRI) to apply different levels of water to the areas of a field depending on the soil type, crop needs, and moisture gradients. VRI systems, which are made possible by high-resolution soil maps and sensor networks, enhance the precision of water distribution, save the limited water resources, as well as achieve sustainable agronomy by applying water on the need basis.

Drones and Remote Sensing for Irrigation Monitoring

Smart irrigation is enhanced by drones (UAVs) with thermal or multispectral sensors, which also surpass ground sensors. Spatial variability of crop stress, soil moisture anomalies, and canopy temperature are detected by means of aerial imaging and refeeding irrigation management platforms with the data, to optimize spatial irrigation prescriptions [31] . Such remote sensing integration improves efficiency in water use and promotes the use of adaptive real-time irrigation strategies.

Agronomic Applications 6.1 Water Conservation

Water conservation is one of the most significant agronomic benefits of precision and smart irrigation systems compared with conventional irrigation practices. Technologies such as soil moisture sensors, automated irrigation controllers, and climatebased scheduling tools allow water to be applied according to real-time crop requirements. Several field-based studies have reported substantial reductions in irrigation water use. For example, research published in Agricultural Power Journal demonstrated that IoT-enabled irrigation systems reduced water consumption by approximately 30-47% compared with traditional irrigation methods, while maintaining crop performance [32] . Similarly, broader reviews in Agriculture and Agronomy indicate that precision irrigation technologies can reduce water use by 20-59%, depending on crop type, soil conditions, and climatic factors. These findings highlight the role of smart irrigation in improving water use efficiency and promoting sustainable water resource management.

Crop Yield and Productivity

In addition to conserving water, smart irrigation systems have demonstrated positive impacts on crop yield and productivity. Maintaining optimal soil moisture conditions reduces plant stress during critical growth stages, thereby enhancing vegetative growth, flowering, and grain filling processes. Studies integrating IoT-based irrigation systems have reported yield increases ranging from 12% to 35% compared with conventional irrigation practices. A comprehensive review published on Preprints also summarized multiple experimental trials where precision irrigation improved plant growth parameters, biomass accumulation, and overall productivity through optimized water delivery and real-time monitoring [33] . These yield gains are primarily attributed to reduced water stress, better root zone aeration, and improved nutrient uptake efficiency. Consequently, IoT-driven irrigation technologies not only enhance crop output but also increase overall resource use efficiency.

Soil and Crop Health

Sustainability Outcomes 7.1. Environmental Sustainability

There have been favourable results in the application of smart irrigation and sensor-driven water management practices in terms of environmental sustainability and associated benefits. Groundwater abstraction is one area where the impact is most prominent. This is because farmers make use of accurate irrigation schedules based on the moisture level in the soil and the demand for crop growth [8] . Further, the implementation of solar-assisted irrigation systems also helps in reducing carbon emissions since there would be less dependence on fossil fuelbased energy resources. Solar-based water pumps and controllers help in utilizing clean energy resources and also ensure irrigation availability, especially for remote farmlands.

Findings from various research have recently shown in the literature that there has been a reduction in carbon emissions and energy requirements utilizing renewable energy resources and smart irrigation technology [34] .

Energy Efficiency

Energy Efficiency is one of the major sustainable results achieved through IoT-enabled irrigational systems. IoT-po wered monitoring and control enable efficient pumping by using power only when irrigational cycles are required. This is achieved through automated control systems compared to manual irrigational systems [35] . In addition, solar-based irrigation systems greatly boost energy sustainability as they utilize the energy of the sun for the distribution of water. Not only will these systems be able to decrease the electric bills, but it will also help ensure the steady availability of energy in areas where the energy crisis is a major concern due to the availability of the grid system [36] .

Climate Resilience

Smart irrigation systems are also very significant in ensuring resilience in the face of increased droughts and climatic variability. Smart irrigation systems help in adjusting the irrigation process by considering factors such as weather conditions and growth stages of the crop in the field. Furthermore, their incorporation into climate-smart agriculture systems helps to bring about sustainable intensification with enhanced water productivity and minimized environmental hazards. This technology helps farmers adapt to climatic stresses and therefore helps to stabilize productivity and develop resilient agricultural systems [37] .

Socio-Economic Consideration 8.1. Adoption and Barriers

Despite these benefits, the use of smart irrigation systems is hampered by several socio-economic factors. The major challenge remains the cost involved in the installation of sensors, communication, and irrigation systems, especially on the part of small and marginal farmers. Also, the lack of adequate internet access and technical knowledge of the technicalities involved are major hindrances. Gaps in knowledge, a lack of digital literacy skills, and the risks associated with new and emerging technologies serve as doubleedged swords that impact the adoption rates of such technologies [38] .

Economic Benefits

From an economic viewpoint, various studies assert positive cost-benefit ratios based on the water and energy conservation made possible through smart irrigation systems. This is because lower production costs, especially water pumping and expenses on electricity, positively impact agricultural profits [35] . Increased irrigation efficiency is further coupled with an increase in crop productivity and stability, resulting in increased farm revenues.

In the long run, the overall economic gains can help recover the cost of implementation of such systems, making them economically viable solutions, especially with subsidization or cooperative models of shared ownership [8] . that water management in an irrigated community occurs in a responsible and sustainable manner [38] .

Challenges and Limitations 9.1. Scalability and Accessibility

Cost barriers greatly impede scalability, especially for smallholder farmers with limited financial resources. In addition, large-scale deployment in rural areas is constrained by a lack of localized manufacturing and inadequate maintenance and service networks [33] . Nevertheless, to make this more accessible, business models have to be scalable, community-based service centers, and public-private partnerships should come forward in support of long-term system operation and maintenance [8] .

Data Integration and Security

With the growing reliance of smart irrigation systems on datadriven decision-making, the challenge of interoperability between heterogeneous sensor networks has been pronounced. This is because there is a possibility of the absence of standard communication protocols. In addition, issues pertaining to the privacy and security of data are also emerging as important ones, and this is more pronounced as far as cloud-based mechanisms and remote data monitoring systems are concerned. Data should be transmitted and secured properly [34] . [39] . The coupling of process-based crop models with machine-learning algorithms represents a promising approach for predicting crop water requirements and optimizing irrigation schedules in diverse agro-ecological conditions. Another important research direction involves the standardization of protocols for smart irrigation technologies and the development of scalable solutions tailored to smallholder farmers [40] . Currently, smart irrigation systems vary widely in hardware architecture, communication protocols, and data formats, which limits interoperability and widespread adoption. Standardized frameworks for sensor calibration, data management, and system evaluation are required to ensure reliability, comparability, and scalability of smart irrigation technologies across regions and farming systems [11] . Moreover, low-cost and user-friendly smart irrigation solutions are essential to promote adoption among small and marginal farmers, particularly in developing countries. Longterm field performance and economic impact studies are also critical to evaluate the sustainability and feasibility of smart irrigation systems under real farming conditions [20] . Most existing studies are short-term or experimental, and there is a need for multi-season and multi-location trials to assess yield stability, water savings, and system durability over time.

Comprehensive economic assessments, including cost-benefit analysis, return on investment, and adoption barriers, are required to support policy decisions and large-scale implementation of smart irrigation technologies [41] .

Conclusion

This chapter reviewed the technological foundations, agronomic applications, and sustainability implications of smart irrigation systems, highlighting their growing importance in modern agricultural water management. The synthesis of literature indicates that smart irrigation technologies, including sensorbased monitoring, IoT connectivity, and artificial intelligencedriven decision-support tools, represent a transformative shift from traditional irrigation practices toward data-driven and adaptive water management frameworks. Across diverse cropping systems and agro-ecological regions, smart irrigation systems have consistently demonstrated the potential to enhance water-use efficiency by delivering precise and timely irrigation based on real-time soil and climatic conditions. Empirical evidence further suggests that optimized irrigation scheduling improves crop growth, yield stability, and resource-use efficiency while reducing water losses, nutrient leaching, and environmental degradation. In addition to agronomic benefits, smart irrigation systems contribute to broader sustainability outcomes, including groundwater conservation, reduced energy consumption, and mitigation of climate-related risks in agricultural production systems. Despite these advancements, the review highlights the need for interdisciplinary research integrating agronomy, hydrology, data science, engineering, and socio-economics to fully realize the potential of smart irrigation technologies.

Farmer-centric solutions that emphasize affordability, ease of use, local adaptation, and capacity building are essential to promote widespread adoption, particularly among small and marginal farmers in developing regions. Therefore, collaborative research, policy support, and stakeholder engagement are critical to scaling smart irrigation systems and achieving sustainable and climate-resilient agricultural water management in the future.

References

1. Food and Agriculture Organization. The future of food and agriculture: Trends and challenges. Rome: FAO; 2017.
2. Vallejo-Gómez D, Osorio M, Hincapié CA. Smart irrigation systems in agriculture: A systematic review. Agronomy. 2023;13(2). doi:10.3390/agronomy13020342
3. Abhinaya S. Smart farming using IoT: A solution for optimally monitoring farming conditions. International Journal of Scientific Research in Engineering and Management. 2025;9(1):1-9. doi:10.55041/ijsrem40496
4. Hoffman GJ, Evans RG, Jensen ME, Martin DL, Elliott RL. Design and operation of farm irrigation systems. St. Joseph: American Society of Agricultural and Biological Engineers; 2007.
5. Walker WR. Guidelines for designing and evaluating surface irrigation systems. FAO Irrigation and Drainage Paper. 1989;137.
6. Yang X, et al. A survey on smart agriculture: Development modes, technologies, and security and privacy challenges. IEEE/CAA Journal of Automatica Sinica. 2021;8(2):273302. doi:10.1109/JAS.2020.1003536
7. Pratama AJ, Mandela R. Evaluating the effectiveness of smart irrigation systems in improving agricultural productivity. Agricultural Power Journal. 2024;1(4):1-9. doi:10.70076/apj.v1i4.43
8. Nureen P. Impact of smart irrigation systems on water resource sustainability in India. Journal of Environment. 2024;4(4):1-11. doi:10.47941/je.2411
9. Kalpana P, Smitha L, Madhavi D, Nabi SA, Kalpana G, Kodati S. A smart irrigation system using IoT and advanced machine learning model: A systematic literature review. International Journal of Computational and Experimental Science and Engineering. 2024;10(4):1158-1168. doi:10.22399/ijcesen.526
10. United Nations. The United Nations world water development report 2020: Water and climate change. 2020. doi:10.54679/ulud7700
11. Pereira LS, Cordery I, Iacovides I. Improved indicators of water use performance and productivity for sustainable water conservation and saving. Agricultural Water Management. 2012;108:39-51. doi:10.1016/j.agwat.2011.08.022
12. Tripodi P, Nicastro N, Pane C. Digital applications and artificial intelligence in agriculture toward next-generation plant phenotyping. Crop and Pasture Science. 2023;74(6):597-614. doi:10.1071/CP21387
13. Fereres E, Soriano MA. Deficit irrigation for reducing agricultural water use. Journal of Experimental Botany. 2007;58(2):147-159. doi:10.1093/jxb/erl165
14. K G, Maria John DP. Smart irrigation system using IoT. International Advanced Research Journal in Science, Engineering and Technology. 2024;11(6). doi:10.17148/iarjset.2024.11617
15. Rachid EDD, Alaoui A, Ettaki B, et al. Optimizing sustainable cultivation through smart irrigation. Journal of Information Systems. 2023.
16. Bhattacharya M, Roy A, Pal J. Smart irrigation system using internet of things. Lecture Notes in Networks and Systems. 2021;137:119-129. doi:10.1007/978-981-15-61986_11
17. García L, Parra L, Jimenez JM, Lloret J. IoT-based smart irrigation systems: An overview on the recent trends and applications. Sensors. 2020;20(11):3192.
18. Kim Y, Evans RG, Iversen WM. Remote sensing and control of an irrigation system using a distributed wireless sensor network. IEEE Transactions on Instrumentation and Measurement. 2008;57(7):1379-1387. doi:10.1109/TIM.2008.917198
19. Soussi A, Zero E, Sacile R, Trinchero D, Fossa M. Smart sensors and smart data for precision agriculture: A review. Sensors. 2024;24(8). doi:10.3390/s24082647
20. Howell TA. Enhancing water use efficiency in irrigated agriculture. Agronomy Journal. 2001;93(2):281-289. doi:10.2134/agronj2001.932281x
21. Kushwaha YK, Joshi A, Panigrahi RK, Pandey A. Development of a smart irrigation monitoring system employing wireless sensor networks for agricultural water management. Journal of Hydroinformatics. 2024;26(12):3224-3243. doi:10.2166/hydro.2024.241
22. Saiz-Rubio V, Rovira-Más F. From smart farming towards agriculture 5.0: A review on crop data management. Agronomy. 2020;10(2). doi:10.3390/agronomy10020207
23. Sinwar D, Dhaka VS, Sharma MK, Rani G. AI-based yield prediction and smart irrigation. In: Internet of Things and Analytics for Agriculture. 2020. p. 155-180. doi:10.1007/978-981-15-0663-5_8
24. Kingslin DS, Vaishnavi MK. A comprehensive survey on IoT-based smart irrigation in agriculture. International Journal of Research and Scientific Innovation. 2025;12(7):708-711. doi:10.51244/ijrsi.2025.120700071
25. García L, Parra L, Jimenez JM, Lloret J, Lorenz P. IoTbased smart irrigation systems: An overview on recent trends in sensors IoT systems for irrigation in precision agriculture. Sensors. 2020;20(4). doi:10.3390/s20041042
26. Kashyap PK, Kumar S, Jaiswal A, Prasad M, Gandomi AH. Towards precision agriculture: IoT-enabled intelligent irrigation systems using deep learning neural networks. IEEE Sensors Journal. 2021;21(16):17479-17491. doi:10.1109/JSEN.2021.3069266
27. Rathore T, Gupta DK, Kumar N. Smart irrigation system using IoT. In: Proceedings of the 3rd International Conference on Security, Cyber Computing and Communications. 2023. p. 605-610. doi:10.1109/ICSCCC58608.2023.10176528
28. Komal K, Joon R, Juneja R, Saluja H, Nisha N, Nikhil N. Smart irrigation system using IoT for precision agriculture and water conservation. International Journal of Scientific Research in Engineering and Management. 2025;9(6):1-9. doi:10.55041/ijsrem50738
29. Liu X, Zhao Z, Rezaeipanah A. Intelligent and automatic irrigation system based on internet of things using fuzzy control technology. Scientific Reports. 2025;15(1). doi:10.1038/s41598-025-98137-2
30. Mohamed ZE, Afify MK, Badr MM, Omar OA. IoT-driven smart irrigation system to improve water use efficiency. Scientific Reports. 2026;16(1). doi:10.1038/s41598-02533826-6
31. Karar ME, Alotaibi F, Al Rasheed A, Reyad O. A pilot study of smart agricultural irrigation using unmanned aerial vehicles and IoT-based cloud system. Information Sciences Letters. 2021;10(1):131-140. doi:10.18576/isl/100115
32. Putra F. Food security transformation: Impact analysis of IoT-based irrigation water consumption patterns in climate change adaptation in modern agricultural systems. Agricultural Power Journal. 2024;1(3):39-51. doi:10.70076/apj.v1i3.81
33. Deka RK, Saikia MJ. IoT and computer vision in smart irrigation: A review of cost-effective solutions and future trends. 2025.
34. Abdelhamid MA, Abdelkader TK, Sayed HAA, Zhang Z, Zhao X, Atia MF. Design and evaluation of a solar powered smart irrigation system for sustainable urban agriculture. Scientific Reports. 2025;15(1). doi:10.1038/s41598-02594251-3
35. Daraz U, Bojnec Š, Khan Y. Energy-efficient smart irrigation technologies: A pathway to water and energy sustainability in agriculture. Agriculture. 2025;15(5). doi:10.3390/agriculture15050554
36. Abdelmoneim AA, Kimaita HN, Al Kalaany CM, Derardja B, Dragonetti G, Khadra R. IoT sensing for advanced irrigation management: A systematic review of trends, challenges, and future prospects. Sensors. 2025;25(7). doi:10.3390/s25072291
37. Meyer MA, Früh-Müller A, Lehmann I, Schwarz N. Linking food and land system research in Europe. Land Use Policy. 2023;131. doi:10.1016/j.landusepol.2023.106692
38. Bwambale E, Abagale FK, Anornu GK. Smart irrigation for climate change adaptation and improved food security. In: Irrigation and Drainage -Recent Advances. 2023. doi:10.5772/intechopen.106628
39. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, et al. The DSSAT cropping system model. European Journal of Agronomy. 2003;18(3-4):235-265.
40. Food and Agriculture Organization. The state of the world's land and water resources for food and agriculture 2025. Rome: FAO; 2025. doi:10.4060/cd7488en
41. Evans RG, Sadler EJ. Methods and technologies to improve efficiency of water use. Water Resources Research. 2008;44(7). doi:10.1029/2007WR006200