Advancements in automated drip irrigation systems: A comprehensive review of sensor-based and volume-controlled approaches

Introduction

Water is a critical input in agriculture, especially in a country like India where nearly 60% of farming is dependent on monsoon rains. Over the decades, India has seen a steady decline in per capita water availability, from over 6000m³ in 1947 to an expected 760 m³ by 2025. At the same time, agriculture continues to consume more than 80% of the total freshwater used in the country. Traditional irrigation practices like flood irrigation result in huge water losses-both through evaporation and seepage. With erratic monsoons and growing competition from industrial and urban sectors, optimizing agricultural water use has become a national priority [17] . Micro-irrigation methods, especially drip irrigation, offer a promising solution. Drip irrigation minimizes water losses by delivering water directly to the root zone. However, even the most efficient drip systems can become suboptimal if not operated based on real-time crop needs. Automated drip irrigation systems use sensors, controllers, and digital interfaces to manage irrigation with precision. By collecting and analyzing data such as soil moisture, temperature, humidity, and evapotranspiration (ET), these systems ensure crops receive the exact amount of water they need, exactly when they need it. Automation also reduces human error, labor costs, and allows for remote monitoring and control [1] . This review evaluates advancements made in the domain of automated drip irrigation systems based on recent academic research conducted across multiple Indian universities. The focus is on understanding design principles, field performance, technological components, cost-benefit analysis, and environmental impact. By critically examining these systems, this paper aims to provide a clear roadmap for future development and adoption of smart irrigation in India.

Need for Automation in Irrigation

The traditional approach to irrigation in India, particularly flood irrigation, results in significant inefficiencies-only about 35% to 40% of applied water is actually used by the crops, with the rest lost through evaporation, deep percolation, and runoff. The mismatch between crop water requirements and irrigation supply leads not only to water wastage but also to crop stress, yield reduction, and soil degradation. Furthermore, reliance on manual labor makes irrigation inconsistent, especially during peak agricultural seasons or in labor-scarce rural areas [17] . In contrast, automated irrigation systems eliminate guesswork by delivering precise volumes of water based on real-time or pre-programmed inputs. These systems consider various parameters like soil moisture, crop growth stage, weather forecasts, and evapotranspiration (ET). The use of such technology helps farmers maintain optimal soil moisture conditions, thus enhancing plant health and yield [2] . Studies reviewed in this paper unanimously agree on the critical role of automation in modern irrigation. For instance www.extensionjournal.com [19] emphasized that irrigation automation in India is essential due to unreliable monsoons and fragmented landholdings. Similarly, [22] demonstrated that using a mobile-controlled timer-based drip system eliminated human dependency and improved crop water use efficiency. The transition to automated systems not only improves productivity but also enables remote operation and monitoring, offering convenience and reliability to farmers.

Technological Components Used in Smart Irrigation

Systems: Smart irrigation systems rely on a combination of electronic, mechanical, and digital technologies to automate and optimize water delivery. Key components include sensors, microcontrollers, solenoid valves, flow meters, and mobile/web-based software. These technologies work together to monitor field conditions, control water flow, and schedule irrigation precisely. Their integration enhances water-use efficiency, crop health, and labour savings.

Microcontrollers and IoT Platforms:

Microcontrollers act as the brain of smart irrigation systems, processing sensor data and controlling devices like valves and pumps. Commonly used units include Arduino, ESP32, and Raspberry Pi, known for their low cost and versatility. IoT platforms such as Blynk, Firebase, and ThingSpeak enable remote monitoring, data storage, and real-time communication between the system and the user. Together, they enable efficient, automated, and data-driven irrigation management.

Arduino Mega 2560:

The Arduino Mega 2560 is an opensource microcontroller built around the ATmega2560 chip, designed to handle multiple input/output operations simultaneously. With 54 digital I/O pins and 16 analog inputs, it's perfect for managing a variety of components in irrigation systems, like sensors, solenoid valves, and relays. The board is easily programmable using the Arduino IDE, making it beginner-friendly and supported by a large online community. Its combination of flexibility, reliability, and scalability makes it an excellent choice for small to medium-sized automated irrigation setups [5] .

ESP8266 NodeMCU:

The ESP8266 NodeMCU is a costeffective, low-power Wi-Fi microcontroller, commonly used in IoT-based irrigation systems. It comes with built-in Wi-Fi, allowing it to wirelessly communicate with sensors, apps, and cloud services, making remote monitoring and control a breeze. This enables real-time updates to the irrigation system, helping to adjust schedules and water usage more efficiently. The NodeMCU is also compatible with the Arduino IDE, making it simple to program, whether you're a beginner or more experienced with coding [22] .

Raspberry Pi:

The Raspberry Pi is a powerful, single-board computer that offers much more processing capability than typical microcontrollers, making it ideal for larger, more complex irrigation systems. It can run full operating systems like Raspberry Pi OS and supports a variety of programming languages, enabling advanced automation and integration with cloud services. The Raspberry Pi also allows for realtime data analysis, including weather predictions and predictive irrigation models, which can optimize water usage. Its versatility and computing power make it a great choice for smart farming projects that need both flexibility and robust performance [22] .

Arduino Mega 2560 ESP8266 NodeMCU Raspberry Pi

Sensors:

Sensors are essential components in automated irrigation systems, used to monitor soil, environmental, and system conditions. Common types include soil moisture sensors, temperature and humidity sensors, and digital flow meters. These sensors collect real-time data that helps optimize irrigation scheduling, conserve water, and improve crop productivity. Their integration enables precise, datadriven irrigation management.

Soil Moisture Sensors: Soil moisture sensors are used to measure the water content in the soil in real time. Capacitive sensors detect changes in dielectric constant, while resistive sensors measure electrical resistance. When soil moisture falls below a set level, these sensors can trigger irrigation automatically. This helps maintain optimal moisture levels for plant growth and saves water [25] .

Temperature and Humidity Sensors: These sensors are used to monitor the surrounding environmental conditions, such as air temperature and relative humidity. The data helps in calculating evapotranspiration rates, which are important for deciding how much water the crop needs. By adjusting irrigation based on weather, these sensors improve water-use efficiency. They support climate-based irrigation scheduling.

Digital Flow Meters: Digital flow meters measure the exact volume of water passing through irrigation pipes. They help detect issues like leaks, blockages, or excessive flow. These devices ensure precise and controlled water delivery, improving overall system performance. Their use enhances water-use efficiency in automated irrigation systems. www.extensionjournal.com precise and timely water delivery to specific crop zones [14] .

Relays act as switching devices that enable the controller to operate multiple solenoid valves or pumps without overloading the system. By integrating relays, the controller can manage different irrigation zones sequentially or simultaneously, depending on the scheduling logic [6] . All reviewed studies effectively used solenoid valves in combination with relays to automate water distribution across various plots, ensuring efficient zone-wise irrigation, reduced manual intervention, and improved water-use efficiency.

LCDs, Power Units, and Cooling

Fans: LCD displays are essential components in automated irrigation systems as they provide real-time feedback and status updates, such as soil moisture levels, temperature, valve status, and system alerts. These displays help users monitor system performance on-site without needing a mobile device or computer. Power units, including converters and transformers, are used to regulate and stabilize voltage supply to the various electronic components of the system, ensuring their safe and reliable operation. Overvoltage or voltage fluctuation can damage sensitive sensors and controllers, so regulated power supply is crucial. Cooling fans are incorporated, especially in enclosed control units, to prevent overheating of microcontrollers and other hardware during continuous operation. Cooling fans ensured thermal stability, improving system durability and uninterrupted performance in high-temperature field conditions [23] . and analyzing automated irrigation systems. Tools like webbased mobile apps, the Oro Smart Drip App, and cloud platforms such as Firebase, Blynk, and ThingSpeak enable real-time scheduling, data logging, remote access, and visualization of system performance-enhancing efficiency and ease of use for farmers.

Web-Based Mobile Applications: Modern smart irrigation systems are integrated with web-based mobile applications that allow users to remotely monitor and control irrigation schedules. These apps can calculate the required irrigation duration based on real-time environmental data such as evapotranspiration (ET), crop coefficients (Kc), and soil moisture levels. Once calculated, the application can trigger valve operations via cloud-based commands, offering convenience, automation, and precision in water management [13] .

Oro Smart Drip App: The Oro Smart Drip app offers an advanced set of features for managing the automated irrigation system. It enables zone-wise scheduling, real-time sensor data monitoring, and system status updates. The app also provides alerts for flow errors, low moisture levels, and system malfunctions. Additionally, users can perform manual overrides directly from the app, ensuring flexibility and control in case of emergency or unexpected conditions.

Firebase and Blynk: These cloud-based platforms are widely used in IoT-based irrigation systems for data logging, remote communication, and interface development.

Firebase allows real-time data storage and synchronization across connected devices. Blynk provides a customizable mobile dashboard for controlling devices and visualizing sensor data [3] .

ThingSpeak: ThingSpeak is primarily used for data analytics and visualization, where environmental parameters like temperature, humidity, and soil moisture can be plotted, analyzed, and stored. These platforms enhance the accessibility and functionality of irrigation systems, making them smart, scalable, and user-friendly [18] . Time-Based Irrigation Systems: Time-based irrigation systems are the simplest form of automation where water is applied at fixed time intervals, regardless of real-time crop or soil conditions. These systems are programmed to irrigate for specific durations-such as 30 minutes once or twice a day-using a timer or microcontroller [5] . They are best suited for areas with consistent climate and uniform crop water demand. This method requires minimal technical infrastructure and is ideal for smallholder farmers seeking low-cost automation. However, a key limitation is the lack of adaptability: the system cannot adjust to sudden weather changes, evapotranspiration variations, or soil moisture levels, potentially leading to under-or over-irrigation, which can stress the crop or waste water.

Design Approaches in

Sensor-Based Irrigation Systems: Sensor-based irrigation systems rely on continuous monitoring of environmental parameters such as soil moisture, temperature, and humidity. These systems use either capacitive or resistive soil moisture sensors to detect the water content in the root zone. When the soil moisture drops below a predefined threshold, the controller activates irrigation automatically [10] . This method is very useful for calibrating both resistive and capacitive sensors in different soil types, achieving accurate, responsive irrigation control. More recent implementations have included microcontrollers like Raspberry Pi or ESP32, which process sensor data and communicate with actuators or cloud platforms. Sensorbased systems are highly adaptive and suited for precision agriculture. They can be fine-tuned for different soil types, crop stages, and weather conditions. Their primary advantage lies in real-time feedback and dynamic response to changing field conditions. However, challenges include sensor maintenance, calibration issues, and reduced accuracy in extreme field conditions like heavy salinity, flooding, or sensor burial due to farm operations.

Volume-Controlled Systems:

In volume-controlled systems, irrigation is scheduled based on the exact quantity of water applied rather than the duration or soil condition alone. The system measures and delivers a pre-set volume of water (e.g., 50 liters per zone), which is especially useful for precise water budgeting, fertigation, and record-keeping. This method enhances accuracy in irrigation and helps avoid both excess and deficit watering [21] . It also enables early detection of pipeline leaks or flow inconsistencies, as any deviation from the expected volume can signal a system fault. Such systems are ideal for crops requiring consistent water dosages or where water is a limited, high-value resource. The main limitation is the need for calibrated flow meters and a reliable power supply, which may increase the initial investment.

ET and Crop

Coefficient-Based Designs: Evapotranspiration (ET) and crop coefficient (Kc)-based systems follow a scientific approach to irrigation scheduling. They use daily reference evapotranspiration (ET₀), derived from weather data such as temperature, solar radiation, humidity, and wind speed, and multiply it by the crop's growth stage-specific Kc value to determine daily water needs. This method mimics irrigation advisory protocols used by agronomists and is especially suitable for high-value or water-sensitive crops like vegetables, flowers, or fruit trees. This approach is accurate and aligns irrigation closely with actual crop requirements, thereby optimizing water use efficiency and improving yields [15] . However, its implementation depends on consistent internet access, availability of weather stations or meteorological APIs, and user understanding of agronomic principles. It is best suited www.extensionjournal.com for large farms, research institutions, or progressive farmers with technical training.

Hybrid Approaches: Hybrid irrigation systems combine two or more control strategies to achieve higher reliability, precision, and efficiency. For instance, volume-based irrigation using flow meters with real-time soil moisture sensors and mobile-based scheduling. Similarly, some systems first estimate crop water demand using ET and Kc, and then validate the need for irrigation through in-field moisture sensors before activating valves. These systems represent the next generation of smart irrigation, offering adaptive control based on both environmental feedback and user-defined thresholds. Hybrid approaches ensure that water is applied only when and where it is needed, reducing waste and improving crop health. With the integration of mobile apps, cloud computing, and wireless sensor networks, these systems also provide remote access, data logging, and analytics for better decision-making. While the initial setup may be complex and costlier, the long-term benefits in terms of water savings, crop performance, and sustainability make them a preferred solution for modern agriculture.

Mobile and Web-Based Automation

One of the most significant advancements in smart irrigation systems is the integration of mobile and web-based platforms. These digital tools enable farmers to control, monitor, and evaluate irrigation processes remotely using smartphones or computers. Such platforms help improve water use efficiency, reduce manual labor, and ensure timely water delivery based on real-time data. The integration of cloud-based services and Internet of Things (IoT) technologies has transformed traditional irrigation into a data-driven and accessible system suitable for both smallholders and large farms.

Role of Mobile Applications

Mobile applications serve as user-friendly control centers for modern irrigation systems. These apps provide graphical interfaces that make system management simple and intuitive, even for non-technical users. With just a few taps, farmers can:  Schedule irrigation based on time or a specific volume of water  Monitor key parameters like soil moisture, temperature, and flow rate  Receive instant notifications regarding irrigation completion, sensor faults, or water supply issues  Manually override automatic settings when needed, providing flexibility during unexpected field conditions This advanced mobile application allowed farmers to select irrigation zones, define water volumes per zone, and remotely monitor digital flow meter readings. The app supported multi-zone control, real-time data tracking, and fault notifications via push alerts. It greatly enhanced the precision and user accessibility of the automated irrigation system and was especially beneficial for managing irrigation remotely in large or fragmented fields.

Web Platforms and IoT Integration:

Web-based dashboards are often used alongside mobile applications to provide enhanced visualization and system-level control. These platforms sync with field sensors, actuators, and mobile apps through cloud services to store, analyze, and present data in real-time. Popular cloud services used in smart irrigation include:  ThingSpeak: A powerful data analytics platform that visualizes sensor data in graphs and charts, helping in historical trend analysis.  Blynk: A versatile IoT platform that enables real-time device control and interactive dashboards for mobile and desktop.  Firebase: Google's real-time database that supports instant synchronization of data across multiple devices.  Google Cloud IoT: Offers secure device connectivity, big data analysis, and scalable infrastructure for large farming operations.

Benefits of Digital Automation Platforms

The integration of mobile and web-based systems brings several tangible benefits to irrigation management:  Remote Access: Operators can schedule, stop, or adjust irrigation from any location using smartphones or computers.  Precision Control: Irrigation schedules can be tailored in real-time based on actual soil moisture, weather, and crop needs.  Scalability: Multiple fields or irrigation zones can be managed through a single unified platform, making the system scalable across farm sizes.  Data-Driven Decision Making: Stored data can be analyzed to determine water productivity, efficiency trends, and inform future irrigation strategies.  Early Fault Detection: Real-time alerts for leaks, flow variations, or sensor errors allow for quick troubleshooting, preventing crop stress or water waste.

Challenges and Limitations

While digital irrigation systems provide numerous benefits, they also face several practical challenges in rural and remote agricultural regions. Poor internet connectivity often disrupts real-time communication between mobile applications and the irrigation system, limiting responsiveness and remote control. Additionally, frequent power supply interruptions can affect the consistent functioning of controllers and sensors unless supported by reliable solar or battery backup units. Another significant barrier is the lack of digital literacy among many smallholder farmers, who may struggle to operate smartphones or navigate app interfaces without proper guidance. Furthermore, integrating various sensors and microcontrollers can be technically complex, requiring custom coding and configuration to ensure smooth operation. To overcome these limitations, it is essential to promote solar-powered automation systems, develop mobile applications with offline functionality, and conduct regular farmer training programs through agricultural extension services or NGOs. Incorporating local language support within apps can further enhance usability and accessibility, making smart irrigation systems more inclusive and effective across diverse farming communities. www.extensionjournal.com

Field Performance Evaluation of Automated Irrigation Systems

Evaluating the effectiveness of a smart irrigation system involves a comprehensive assessment of how well the system distributes water, meets crop requirements, conserves resources, and improves productivity. This is achieved through both hydraulic indicators (measuring the uniformity and consistency of water delivery) and agronomic indicators (assessing yield, water use efficiency, and soil moisture maintenance).

Hydraulic Performance Indicators

Hydraulic performance determines whether the system delivers water uniformly across all emitters and zones. Key indicators include:

Emitter Flow Rate Uniformity: Emitter flow rate uniformity is typically assessed using the coefficient of variation (CV), which measures the consistency of discharge among emitters across the irrigation system. Lower CV values indicate high manufacturing precision and uniform water delivery. A CV below 0.07 is generally considered excellent, reflecting minimal variation in emitter output and ensuring consistent irrigation across the entire field [19] .

Emission Uniformity (EU):

Emission uniformity is a critical parameter that reflects the overall efficiency of a drip irrigation system in delivering water uniformly. It is calculated using the Merriam and Keller method, which considers both the average discharge and variability among emitters. Values ranging from 86% to 91% are considered highly satisfactory, with systems equipped with pressurecompensating emitters often achieving EU values above 90%, indicating reliable and even water application [19] .

Distribution Uniformity (DU): Distribution uniformity indicates how evenly water is applied over the crop root zone and is particularly important for vegetable crops, which are sensitive to water stress. DU values above 85% are typically classified as good, ensuring that all plants within a field receive similar moisture levels. High DU minimizes dry or overly wet zones, leading to uniform crop growth and improved yield performance [19] . These results confirm that well-designed automated drip systems are capable of highly uniform water delivery, which is essential for maximizing crop health and productivity.

Soil Moisture Distribution

Efficient irrigation not only involves water delivery but also how well the moisture penetrates and spreads within the soil profile. In sandy loam and laterite soils, soil moisture increased both laterally and vertically within hours after irrigation [19] . His study captured moisture levels at 2-and 6hours post-irrigation using capacitive and resistive sensors. Plastic and biodegradable mulches were particularly effective in maintaining soil moisture close to field capacity, especially under 100% ET-based irrigation. This moisture stability contributed to healthier crop growth during critical stages such as curd formation in cauliflower. Real-time sensor data allowed the system to optimize irrigation timing and duration, reducing water stress and improving plant Vigor.

Water Use Efficiency and Yield Response

Water Use Efficiency (WUE) is a crucial metric that reflects the effectiveness of water in producing biomass or yield. Smart irrigation systems consistently showed higher WUE compared to conventional methods:  ET₃-based scheduling with plastic mulch achieved the highest WUE, followed by biodegradable mulch. Yield improvements ranged between 25% to 40%, depending on the treatment.  Sensor-based automation contributed to timely water application, which improved nutrient uptake, reduced stress, and supported optimal yield.  This study [16] found that ET and Kc-based irrigation in groundnut crops resulted in better yields and water savings compared to traditional time-based methods, particularly during critical growth stages like flowering and pod formation.

These results confirm that integrating sensor feedback and scientifically scheduled irrigation can enhance both resource efficiency and agricultural output.

Energy and Labor Savings

Another important benefit of automated irrigation systems is the reduction in labor and energy inputs. Other reviewed works demonstrated:  Reduced Manual Effort: Automated scheduling and remote control eliminated the need for daily manual intervention. Mobile alerts reduced the decision-making and monitoring time by more than 80%.  Lower Energy Consumption: Efficient water delivery using pressure-compensating emitters and volume control helped reduce pumping time and energy usage.  Improved Labor Allocation: With reduced irrigation workload, labour could be redirected to other farm operations, improving overall farm productivity.

Such savings are particularly significant in regions facing labour shortages and rising energy costs. Automated systems thus contribute not only to agronomic efficiency but also to economic and operational sustainability.

Economic Analysis of Smart Irrigation Systems

The economic viability of smart irrigation systems depends on the balance between their initial installation cost and long-term benefits in terms of yield improvement, labor savings, water conservation, and operational efficiency. While the upfront investment in automation technology can be relatively high, studies reviewed in this paper consistently show that these systems offer substantial returns over time, particularly for high-value crops such as cauliflower, tomatoes, and groundnut.

Initial Investment Costs

The initial cost of establishing a smart irrigation system involves several essential components. Soil moisture sensors, both capacitive and resistive, typically range from ₹500 to ₹2,500 per unit, while temperature and humidity sensors cost between ₹300 and ₹1,000. Microcontroller units such as Arduino, Raspberry Pi, or ESP32 range from www.extensionjournal.com ₹500 to ₹3,000, depending on features and connectivity. Digital flow meters, crucial for volume-based irrigation systems, are priced at ₹2,000 to ₹5,000. Solenoid valves and relay controllers add another ₹700 to ₹2,000 per unit. Mobile or web-based application platforms (like Oro Smart Drip, Blynk, or Firebase) may incur minimal to moderate costs for integration and cloud storage. Additional expenses include power supply units, voltage regulators, and cooling fans, which total around ₹1,000 to ₹2,000. The drip irrigation infrastructure itself, excluding automation, generally costs ₹35,000 to ₹50,000 per hectare. Finally, installation and technical support-covering configuration, calibration, and user training-add approximately ₹5,000 to ₹10,000. Altogether, the total setup cost for a 1-hectare smart irrigation system ranges from ₹50,000 to ₹80,000, depending on design complexity and component quality.

Operational Costs and Savings

Although smart irrigation systems involve minor recurring costs such as mobile data usage, periodic maintenance, and occasional sensor replacement, they result in substantial operational savings over time. Labor costs are significantly reduced, with automation saving 1-2 labor hours dailyamounting to ₹2,000-₹3,000 per month or ₹20,000-₹30,000 per cropping season for a 1-hectare farm. Water savings are also notable, as automated and ET/Kc-based scheduling systems reduce water usage by 25%-45%, conserving approximately 300,000-400,000 liters per season, depending on the crop and climatic conditions. This not only preserves water resources but also decreases pump operation time, contributing to 15%-25% energy savings. Moreover, smart irrigation enhances crop performance, with studies reporting yield increases of 25%-40%-particularly under plastic mulch or ET-based irrigation methods. In cauliflower cultivation, for example, yields can improve from 200 quintals to 260-280 quintals per hectare, demonstrating the strong economic and agronomic benefits of automated systems.

Benefit-Cost (B:C) Ratio and Payback Period:

The Benefit-Cost (B:C) ratio measures the economic return from an investment relative to its cost, helping assess the profitability of smart irrigation systems. A B:C ratio greater than 1 indicates that the benefits outweigh the costs. The payback period refers to the time required to recover the initial investment through savings and increased crop yield. Smart irrigation systems typically offer a short payback period of 1.5 to 2 seasons, making them economically viable for farmers.

Recommendations for

 Inclusion of Fertigation and Pest Management

Modules: Integrated systems that combine irrigation, fertigation, and even pesticide delivery through smart scheduling can maximize efficiency and minimize labour.

Conclusion

The transition from conventional to automated drip irrigation marks a significant leap toward modern, efficient, and sustainable agriculture. The reviewed studies clearly demonstrate that smart irrigation systems-whether timebased, sensor-driven, volume-controlled, or ET/Kc-basedconsistently improve water use efficiency, enhance crop yields, reduce labor dependency, and optimize energy use. By integrating low-cost microcontrollers, mobile applications, real-time sensors, and cloud-based platforms, these systems are increasingly within reach for Indian farmers. Although initial investment and technical challenges exist, the long-term economic benefits-such as a payback period of less than two cropping seasons-far outweigh the drawbacks. Yield improvements ranging from 25% to 40% and significant resource savings across crops like cauliflower, groundnut, cotton, and eggplant reinforce the system's value. With continued policy support, government subsidies, and farmer-centric innovations, smart irrigation systems have the potential to transform Indian agriculture. They offer a practical pathway toward water security, climate resilience, and enhanced food productivity-making them not just a technological advancement, but a necessity for the future of sustainable farming in India.

10. References 1. Adegboye MA, Ajao LA, Folorunso TA, Mamudu AY, Isah AS. Automatic fertilized-irrigation control and management system. FUW Trends Sci Technol J.

References

1. Alliery M, Gollehon N. Irrigation water management. Agric Resour Environ Indic. 1985:1-23. Available from: http://www.ers.usda.gov/media/873628/irrigationwater. pdf
2. Chamara N, Islam MD, Bai G (Frank), Shi Y, Ge Y. Ag-IoT for crop and environment monitoring: past, present, and future. Agric Syst. 2022;203:103497. Available from: https://doi.org/10.1016/j.agsy.2022.103497
3. Gu Z, Qi Z, Burghate R, Yuan S, Jiao X, Xu J. Irrigation scheduling approaches and applications: a review. J Irrig Drain Eng. 2020;146(6):04020025. Available from: https://doi.org/10.1061/(asce)ir.19434774.0001464
4. Idris MR, Irdayanti MN, Ramlee M. A review of microcontroller design in future industrial revolution. Proc Green Des Manuf. 2024;2750:050031. Available from: https://doi.org/10.1063/5.0149680
5. Kadpan WR, Mustafa FF, Kadhim HT. A review of control automatically water irrigation canal using multi controllers and sensors. J Eur Syst Autom. 2024;57(3):717-727. Available from: https://doi.org/10.18280/jesa.570309
6. Kumar S, Singh P. Evaluation of hydraulic performance of drip irrigation system. Indian Soc Agric Eng. 2007;44(2):104-108.
7. Kumar S, Sharma IP, Raina JN. Effect of levels and application methods of irrigation and mulch materials on strawberry production in north-west Himalayas. J Indian Soc Soil Sci. 2005;53(1):60-65.
8. Mahto MK. Challenges and opportunities for water resources in India: an exploratory story of resilience. 2024:59-80. Available from: https://doi.org/10.1007/978-3-031-62079-9_4
9. Munoth P, Tiwari K, Goyal R, Scholar PD. Sensor based irrigation system: a review. Int J Eng Res Technol (IJERT). 2016;4(23):86-90. Available from: https://www.researchgate.net/publication/303842913
10. Nakayama FS, Bucks DA. Emitter clogging effects on trickle irrigation uniformity. Trans Am Soc Agric Eng. 1981;24(1):77-80.
11. Nandan N, Thippeswamy K. A secure intelligent drip irrigation system using IoT, mobile application. Int Res J Eng Technol. 2018;5(3):3192-3194.
12. Nsoh B, Katimbo A, Guo H, Heeren DM, Nakabuye HN, Qiao X, et al. Internet of Things-based automated solutions utilizing machine learning for smart and realtime irrigation management: a review. Sensors. 2024;24(23):7480. Available from: https://doi.org/10.3390/s24237480
13. Patel J, Patel E, Pati P. Sensor and cloud based smart irrigation system with Arduino: a technical review. Int J Eng Appl Sci Technol. 2019;3:25-29. Available from: http://www.ijeast.com
14. Pereira LS, Paredes P, Jovanovic N. Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach. Agric Water Manag. 2020;241:106357. Available from: https://doi.org/10.1016/j.agwat.2020.106357
15. Pragna G, Kumar MG, Shankar SM. Hydraulic performance evaluation of drip system by developing relationship between discharge and pressure. Int J Pure Appl Biosci. 2017;5(4):758-765.
16. Rao CS, Lal R, Prasad JVNS, Gopinath KA, Singh R, Jakkula VS, et al. Potential and challenges of rainfed farming in India. Adv Agron. 2015;133:113-181. Available from: https://doi.org/10.1016/bs.agron.2015.05.004
17. Selvam AP, Al-Humairi SNS. The impact of IoT and sensor integration on real-time weather monitoring systems: a systematic review. Res Square. 2023;1-71.
18. Sharma P. Hydraulic performance of drip emitters under field condition. IOS J Agric Vet Sci. 2013;2(1):15-20.
19. Sharma KN, Ghosh P, Pradhan PC. Studies of discharge performance of different types of emitters used in drip irrigation. Drain Irrig Water Manag. 2005:186-192.
20. Singh AK, Singh AK, Bhardwaj AK, Verma CL, Mishra VK, Arora S, et al. Automation in scheduling irrigation: a review of concepts and latest recommendations in technology. J Nat Resour Conserv Manag. 2021;2(1):47. Available from: https://doi.org/10.51396/anrcm.2.1.2021.47-56
21. Sizan NS, Dey D, Mia MS. Applications of the Internet of Things (IoT) for developing sustainable agriculture: a review. GUB J Sci Eng. 2022;8(1):7-16. Available from: https://doi.org/10.3329/gubjse.v8i1.62326
22. Venkatapur RB, Nikitha S. Review on closed loop automated irrigation system. Asian Rev Civ Eng. 2017;6(1):9-14. Available from: https://doi.org/10.51983/tarce-2017.6.1.2234
23. Walia S, Sengupta J. IoT based evapotranspiration irrigation system for enhancing water conservation. Futur Trends Comput Technol Data Sci Vol 3 Book 4. 2024:10-29. Available from: https://doi.org/10.58532/v3bict4p1ch2
24. Yu L, Gao W, Shamshiri RR, Tao S, Ren Y, Zhang Y, et al. Review of research progress on soil moisture sensor technology. Int J Agric Biol Eng. 2021;14(4):3242. Available from: https://doi.org/10.25165/j.ijabe.20211404.6404