Agriculture is significant factor in meeting up with the needs of the future, as well as the world's population that is likely to increase significantly in the coming years but agriculture is highly dependent on the availability of water. In many places erratic rains and scarce water resources have meant traditional irrigation methods have been inefficient and unsustainable. Farmers often use fixed time or judgment in irrigating the fields and this may lead to over irrigation or under irrigation to crops. Both situations have a negative impact on productivity and are wasting precious resources. Recent advancements in digital technology have provided a chance to transform irrigation into a smart process, as well as one that is effective. The integration of mobile applications, sensors, wireless communication and automated control systems means that irrigation does not have to be controlled on the basis of guesswork butof actual conditions in the field. Smart irrigation systems can continuously monitor the soil moisture and temperature, humidity and weather conditions and then determine the exact amount of water that the crops need. This paper is a detailed review of the types of smart irrigation systems based on apps and how they are related to sustainable agriculture. It deals with modern techniques of irrigation, scheduling irrigation in real-time and the application of artificial intelligence and Internet of Things (IoT) in water management for agriculture. The research is aimed at improving the efficiency of water use, minimizing the human efforts and improving the productivity of crops by the means of intelligent systems. Through analyzing the existing research and implemented systems, this paper identifies the main components, advantages and issues of smart irrigation systems. The results highlight the key role that the use of irrigation solutions through apps can play in the sustainable farming and water saving practice, cutting down on operational costs and encouraging environmentally responsible farming activities.

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1. Food and Agriculture Organization (FAO). The Practice of Irrigation and On-Farm Water Management. FAO, 2020.

2. Walker, W. R., & Skogerboe, G. V. Surface Irrigation: Theory and Practice. Prentice Hall, 1987.

3. Koech, R., & Langat, P. “Improving Irrigation Water Use Efficiency: A Review of Advances, Challenges, and Opportunities.” Water, vol. 10, no. 12, 2018.

4. Hamami, L., & Nassereddine, B. “Application of Wireless Sensor Networks in the Field of Irrigation: A Review.” Computers and Electronics in Agriculture, vol. 179, 2020.

5. Velmurugan, “An IoT-Based Smart Irrigation System Using Soil Moisture and Weather Prediction.” 2020.

6. Yang, X., et al. “Reduced Groundwater Use and Increased Grain Production by Optimized Irrigation Scheduling.” Field Crops Research, vol. 275, 2022.

7. Gu, Z., Zhu, T., Jiao, X., Xu, J., & Qi, Z. “Neural Network Soil Moisture Model for Irrigation Scheduling.” Computers and Electronics in Agriculture, vol. 180, 2021.

8. Farooque, A. A., et al. “Forecasting Daily Evapotranspiration Using Artificial Neural Networks.” Irrigation Science, vol. 40, no. 1, 2022.

9. Barkunan, S., Bhanumathi, V., & Sethuram, J. “Smart Sensor for Automatic Drip Irrigation System for Paddy Cultivation.” Computers & Electrical Engineering, 73, 2019.

10. Wang, J., et al. “Drip Irrigation Mode Affects Tomato Yield by Regulating Root–Soil–Microbe Interactions.” Agricultural Water Management, vol. 260, 2022.

11. Çetin, O., & Kara, A. “Assessment of Water Productivity Using Different Drip Irrigation Systems for Cotton.” Agricultural Water Management, 223, 2019.

12. Abioye, E. A., et al. “A Review on Monitoring and Advanced Control Strategies for Precision Irrigation.” Computers and Electronics in Agriculture, vol. 173,

13. Pandey, A. K., & Mukherjee, A. “Advances in IoT- Based Technologies for Smart Agricultural Systems.” Internet of Things and Analytics for Agriculture, 2022.

14. Sharma, P. K., et al. “Assessment of Different Methods for Soil Moisture Estimation: A Review.” Journal of Remote Sensing & GIS, 2018.

15. S. Alam et al., “Pre-Trained Deep Neural Network- Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification,” Agriculture, vol.13, p. 936, 2023.

16. Bhardwaj and S. P. Gupta, “Empowering Sustainable Agriculture Harnessing AI for Enhanced Yields, Resource Efficiency, and Eco Friendly Farming Practices,” IGI Global Scientific Publishing, ch. 7, 2025.

17. Bhuvaneswari et al., “Implementing AI-Powered Chatbots in Agriculture for Optimization and Efficiency,” in Proc. 2nd Int. Conf. Intell. Data Commun. Technol. Internet Things (IDCIoT), IEEE, Jan. 2024.

18. Gupta et al., “A comprehensive review on detection of plant disease using machine learning and deep learning approaches,” Plant Communications, 2022

19. . A. Hashim, S. Hashmat, and L. Riasat, “Hybrid Agricultural Development Support Communication Model (DSC) by Integrating Traditional Practices with ICT-Based and AI-Driven Innovations,” The Progress: A Journal of Multidisciplinary Studies, 6, no. 2, pp. 41–55, Jun. 2025

20. . R. Jain and A. Sharma, “AI Application for Crop Monitoring and Predict Crop Diseases & Soil Qualities,” Int. J. Tech. Res. Sci. (IJTRS), 2024.

21. I. Khan et al., “Enhancing plant disease detection using machine learning approaches for improved agricultural productivity,” Int. J. Electr. Comput. Eng. (IJECE), vol. 14, no. 1, pp. 1–10, 2024.

22. A. Kumar and S. Gupta, “Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms,” AI, vol. 5, no. 1, p. 2, 2021.

23. P. Nair et al., “An Explainable AI Approach for Potato Plant Disease Detection using Enhanced Feature Engineering,” Procedia Computer Science, 2025.

24. N. Patel and A. Mehta, “Proactive Soybean Disease Detection through YOLO Leaf Extraction and ResNet-

25. K. Patel and N. Verma, “Crop Prediction Analysis and Plant Disease Detection Using Machine Learning,” Int. J. Multidiscip. Res. (IJFMR), vol. 6, no. 3, pp. 1–8, 2024

26. . R. Prakash, M. Pisat, A. Reddy, and D. Patil, “Farmsmart: Expert Recommendations, Disease Prediction, and Farmer Market using Machine Learning and Deep Learning,” Int. J. Innov. Res. Sci. Eng. Technol. (IJIRSET), 14, no. 4, pp. 10061–10069,

27. A. Rupe, “Provisional chapter: Soybean Rust,” ResearchGate, 2019.

Agriculture is significant factor in meeting up with the needs of the future, as well as the world's population that is likely to increase significantly in the coming years but agriculture is highly dependent on the availability of water. In many places erratic rains and scarce water resources have meant traditional irrigation methods have been inefficient and unsustainable. Farmers often use fixed time or judgment in irrigating the fields and this may lead to over irrigation or under irrigation to crops. Both situations have a negative impact on productivity and are wasting precious resources. Recent advancements in digital technology have provided a chance to transform irrigation into a smart process, as well as one that is effective. The integration of mobile applications, sensors, wireless communication and automated control systems means that irrigation does not have to be controlled on the basis of guesswork butof actual conditions in the field. Smart irrigation systems can continuously monitor the soil moisture and temperature, humidity and weather conditions and then determine the exact amount of water that the crops need. This paper is a detailed review of the types of smart irrigation systems based on apps and how they are related to sustainable agriculture. It deals with modern techniques of irrigation, scheduling irrigation in real-time and the application of artificial intelligence and Internet of Things (IoT) in water management for agriculture. The research is aimed at improving the efficiency of water use, minimizing the human efforts and improving the productivity of crops by the means of intelligent systems. Through analyzing the existing research and implemented systems, this paper identifies the main components, advantages and issues of smart irrigation systems. The results highlight the key role that the use of irrigation solutions through apps can play in the sustainable farming and water saving practice, cutting down on operational costs and encouraging environmentally responsible farming activities.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Food and Agriculture Organization (FAO). The Practice of Irrigation and On-Farm Water Management. FAO, 2020.

2. Walker, W. R., & Skogerboe, G. V. Surface Irrigation: Theory and Practice. Prentice Hall, 1987.

3. Koech, R., & Langat, P. “Improving Irrigation Water Use Efficiency: A Review of Advances, Challenges, and Opportunities.” Water, vol. 10, no. 12, 2018.

4. Hamami, L., & Nassereddine, B. “Application of Wireless Sensor Networks in the Field of Irrigation: A Review.” Computers and Electronics in Agriculture, vol. 179, 2020.

5. Velmurugan, “An IoT-Based Smart Irrigation System Using Soil Moisture and Weather Prediction.” 2020.

6. Yang, X., et al. “Reduced Groundwater Use and Increased Grain Production by Optimized Irrigation Scheduling.” Field Crops Research, vol. 275, 2022.

7. Gu, Z., Zhu, T., Jiao, X., Xu, J., & Qi, Z. “Neural Network Soil Moisture Model for Irrigation Scheduling.” Computers and Electronics in Agriculture, vol. 180, 2021.

8. Farooque, A. A., et al. “Forecasting Daily Evapotranspiration Using Artificial Neural Networks.” Irrigation Science, vol. 40, no. 1, 2022.

9. Barkunan, S., Bhanumathi, V., & Sethuram, J. “Smart Sensor for Automatic Drip Irrigation System for Paddy Cultivation.” Computers & Electrical Engineering, 73, 2019.

10. Wang, J., et al. “Drip Irrigation Mode Affects Tomato Yield by Regulating Root–Soil–Microbe Interactions.” Agricultural Water Management, vol. 260, 2022.

11. Çetin, O., & Kara, A. “Assessment of Water Productivity Using Different Drip Irrigation Systems for Cotton.” Agricultural Water Management, 223, 2019.

12. Abioye, E. A., et al. “A Review on Monitoring and Advanced Control Strategies for Precision Irrigation.” Computers and Electronics in Agriculture, vol. 173,

13. Pandey, A. K., & Mukherjee, A. “Advances in IoT- Based Technologies for Smart Agricultural Systems.” Internet of Things and Analytics for Agriculture, 2022.

14. Sharma, P. K., et al. “Assessment of Different Methods for Soil Moisture Estimation: A Review.” Journal of Remote Sensing & GIS, 2018.

15. S. Alam et al., “Pre-Trained Deep Neural Network- Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification,” Agriculture, vol.13, p. 936, 2023.

16. Bhardwaj and S. P. Gupta, “Empowering Sustainable Agriculture Harnessing AI for Enhanced Yields, Resource Efficiency, and Eco Friendly Farming Practices,” IGI Global Scientific Publishing, ch. 7, 2025.

17. Bhuvaneswari et al., “Implementing AI-Powered Chatbots in Agriculture for Optimization and Efficiency,” in Proc. 2nd Int. Conf. Intell. Data Commun. Technol. Internet Things (IDCIoT), IEEE, Jan. 2024.

18. Gupta et al., “A comprehensive review on detection of plant disease using machine learning and deep learning approaches,” Plant Communications, 2022

19. . A. Hashim, S. Hashmat, and L. Riasat, “Hybrid Agricultural Development Support Communication Model (DSC) by Integrating Traditional Practices with ICT-Based and AI-Driven Innovations,” The Progress: A Journal of Multidisciplinary Studies, 6, no. 2, pp. 41–55, Jun. 2025

20. . R. Jain and A. Sharma, “AI Application for Crop Monitoring and Predict Crop Diseases & Soil Qualities,” Int. J. Tech. Res. Sci. (IJTRS), 2024.

21. I. Khan et al., “Enhancing plant disease detection using machine learning approaches for improved agricultural productivity,” Int. J. Electr. Comput. Eng. (IJECE), vol. 14, no. 1, pp. 1–10, 2024.

22. A. Kumar and S. Gupta, “Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms,” AI, vol. 5, no. 1, p. 2, 2021.

23. P. Nair et al., “An Explainable AI Approach for Potato Plant Disease Detection using Enhanced Feature Engineering,” Procedia Computer Science, 2025.

24. N. Patel and A. Mehta, “Proactive Soybean Disease Detection through YOLO Leaf Extraction and ResNet-

25. K. Patel and N. Verma, “Crop Prediction Analysis and Plant Disease Detection Using Machine Learning,” Int. J. Multidiscip. Res. (IJFMR), vol. 6, no. 3, pp. 1–8, 2024

26. . R. Prakash, M. Pisat, A. Reddy, and D. Patil, “Farmsmart: Expert Recommendations, Disease Prediction, and Farmer Market using Machine Learning and Deep Learning,” Int. J. Innov. Res. Sci. Eng. Technol. (IJIRSET), 14, no. 4, pp. 10061–10069,

27. A. Rupe, “Provisional chapter: Soybean Rust,” ResearchGate, 2019.