Advances in soil sensors, remote sensing, and forecasting technologies have expanded the data types and integration options for irrigation optimization. This data includes in-situ measurements, satellite-derived products, weather forecasts, hydrological, and machine learning models. However, there is still limited operational guidance on translating soil moisture data into actionable decisions. This paper reviews recent literature and discusses advancements in soil moisture-driven irrigation systems, focusing on how soil moisture information is translated and embedded in decision-support frameworks. Studies show that irrigation efficiency depends on how crop root zones are defined, how moisture is converted into crop-relevant indicators, and how information from multiple depths is synthesized. However, the approaches to this vary widely. Fixed-depth, dynamic, and root-weighted root-zone representations coexist, each balancing accuracy with practical constraints. Similarly, irrigation frameworks range from reactive, sensor-based systems to forecast-informed and hybrid architectures. Each framework reflects different trade-offs between complexity and reliability. The review showed that increasing data integration alone does not necessarily guarantee better information for irrigation decisions. However, improving irrigation support systems requires shifting the emphasis from precision in soil moisture estimation to the transparency and interpretability of irrigation decision logic.
KeywordsIrrigation Decision-Support SystemsIrrigation ManagementPrecision IrrigationAgricultural Water ManagementSoil Moisture
Vuolo, F., Essl, L. and Atzberger, C. (2015) Costs and Benefits of Satellite-Based Tools for Irrigation Management. Frontiers in Environmental Science , 3, Article 52. https://doi.org/10.3389/fenvs.2015.00052
Alvino, A. and Marino, S. (2017) Remote Sensing for Irrigation of Horticultural Crops. Horticulturae , 3, Article 40. https://doi.org/10.3390/horticulturae3020040
Calera, A., Campos, I., Osann, A., D’Urso, G. and Menenti, M. (2017) Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users. Sensors , 17, Article 1104. https://doi.org/10.3390/s17051104
Bousbih, S., Zribi, M., El Hajj, M., Baghdadi, N., Lili-Chabaane, Z., Gao, Q., et al . (2018) Soil Moisture and Irrigation Mapping in a Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data. Remote Sensing , 10, Article 1953. https://doi.org/10.3390/rs10121953
Liu, Y. and Yang, Y. (2022) Advances in the Quality of Global Soil Moisture Products: A Review. Remote Sensing , 14, Article 3741. https://doi.org/10.3390/rs14153741
Peng, J. and Loew, A. (2017) Recent Advances in Soil Moisture Estimation from Remote Sensing. Water , 9, Article 530. https://doi.org/10.3390/w9070530
Loconsole, D., Elia, M., Conversa, G., De Lucia, B., Cristiano, G. and Elia, A. (2025) Soil Moisture Sensing Technologies: Principles, Applications, and Challenges in Agriculture. Agronomy , 15, Article 2788. https://doi.org/10.3390/agronomy15122788
Evett, S.R., Stone, K.C., Schwartz, R.C., O’Shaughnessy, S.A., Colaizzi, P.D., Anderson, S.K., et al . (2019) Resolving Discrepancies between Laboratory-Determined Field Capacity Values and Field Water Content Observations: Implications for Irrigation Management. Irrigation Science , 37, 751-759. https://doi.org/10.1007/s00271-019-00644-4
Ravazzani, G., Corbari, C., Ceppi, A., Feki, M., Mancini, M., Ferrari, F., et al . (2016) From (Cyber)space to Ground: New Technologies for Smart Farming. Hydrology Research , 48, 656-672. https://doi.org/10.2166/nh.2016.112
Nazig, M., Sathiyamoorthy, N.K., Dheebakaran, G., Pazhanivelan, S. and Vadivel, N. (2024) Coupled Weather and Crop Simulation Modeling for Smart Irrigation Planning: A Review. Water Supply , 24, 2844-2865. https://doi.org/10.2166/ws.2024.170
Li, S., Zhu, P., Song, N., Li, C. and Wang, J. (2025) Regional Soil Moisture Estimation Leveraging Multi-Source Data Fusion and Automated Machine Learning. Remote Sensing , 17, Article 837. https://doi.org/10.3390/rs17050837
Zavala Díaz, N.A., Olivares-Rojas, J.C., Zavala Díaz, J., Reyes Archundia, E., Téllez Anguiano, A.D.C., Chávez Campos, G.M., et al . (2024) Study of Machine Learning Techniques for the Estimation of Soil Moisture in Agriculture. International Journal of Combinatorial Optimization Problems and Informatics , 15, 61-71. https://doi.org/10.61467/2007.1558.2024.v15i4.502
Li, W., Awais, M., Ru, W., Shi, W., Ajmal, M., Uddin, S., et al . (2020) Review of Sensor Network-Based Irrigation Systems Using IoT and Remote Sensing. Advances in Meteorology , 2020, 1-14. https://doi.org/10.1155/2020/8396164
Bwambale, E., Abagale, F.K. and Anornu, G.K. (2022) Smart Irrigation Monitoring and Control Strategies for Improving Water Use Efficiency in Precision Agriculture: A Review. Agricultural Water Management , 260, Article 107324. https://doi.org/10.1016/j.agwat.2021.107324
Bwambale, E., Naangmenyele, Z., Iradukunda, P., Agboka, K.M., Houessou-Dossou, E.A.Y., Akansake, D.A., et al . (2022) Towards Precision Irrigation Management: A Review of GIS, Remote Sensing and Emerging Technologies. Cogent Engineering , 9, Article 2100573. https://doi.org/10.1080/23311916.2022.2100573
Martínez-Fernández, J., González-Zamora, A., Sánchez, N., Gumuzzio, A. and Herrero-Jiménez, C.M. (2016) Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of the SMOS Derived Soil Water Deficit Index. Remote Sensing of Environment , 177, 277-286. https://doi.org/10.1016/j.rse.2016.02.064
Bryant, C.J., Spencer, G.D., Gholson, D.M., Plumblee, M.T., Dodds, D.M., Oakley, G.R., et al . (2023) Development of a Soil Moisture Sensor-Based Irrigation Scheduling Program for the Midsouthern United States. Crop , Forage & Turfgrass Management , 9, e20217. https://doi.org/10.1002/cft2.20217
Jabro, J.D., Stevens, W.B., Iversen, W.M., Allen, B.L. and Sainju, U.M. (2020) Irrigation Scheduling Based on Wireless Sensors Output and Soil-Water Characteristic Curve in Two Soils. Sensors , 20, Article 1336. https://doi.org/10.3390/s20051336
Yadav, P.K., Sharma, F.C., Thao, T. and Goorahoo, D. (2020) Soil Moisture Sensor-Based Irrigation Scheduling to Optimize Water Use Efficiency in Vegetables. https://www.irrigation.org/IA/FileUploads/IA/Resources/TechnicalPapers/2018/Soil_Moisture_Sensor-based_Irrigation_YADAV.pdf
El Chami, A., Cortignani, R., Dell’Unto, D., Mariotti, R., Santelli, P., Ruggeri, R., et al . (2023) Optimization of Applied Irrigation Water for High Marketable Yield, Fruit Quality and Economic Benefits of Processing Tomato Using a Low-Cost Wireless Sensor. Horticulturae , 9, Article 390. https://doi.org/10.3390/horticulturae9030390
Ebstu, E.T., Hatiye, S.D., Goshime, D.W., Dingemanse, J.D., Dugassa, D.D., Fitensa, T., et al . (2025) Development and Testing of a Low-Cost Soil Moisture Sensor for Real-Time Irrigation Scheduling. Irrigation and Drainage , 75, 173-187. https://doi.org/10.1002/ird.70026
Sharma, K., Irmak, S. and Kukal, M.S. (2021) Propagation of Soil Moisture Sensing Uncertainty into Estimation of Total Soil Water, Evapotranspiration and Irrigation Decision-Making. Agricultural Water Management , 243, Article 106454. https://doi.org/10.1016/j.agwat.2020.106454
Pramanik, M., Khanna, M., Singh, M., Singh, D.K., Sudhishri, S., Bhatia, A., et al . (2022) Automation of Soil Moisture Sensor-Based Basin Irrigation System. Sm art Agricultural Technology , 2, Article 100032. https://doi.org/10.1016/j.atech.2021.100032
Khan, R., Ali, I., Zakarya, M., Ahmad, M., Imran, M. and Shoaib, M. (2018) Technology-Assisted Decision Support System for Efficient Water Utilization: A Real-Time Testbed for Irrigation Using Wireless Sensor Networks. IEEE Access , 6, 25686-25697. https://doi.org/10.1109/access.2018.2836185
Munyaradzi, M., Hapanyengwi, G., Masocha, M., Mutandwa, E., Raeth, P., Nyambo, B., et al . (2022) Precision Irrigation Scheduling Based on Wireless Soil Moisture Sensors to Improve Water Use Efficiency and Yield for Winter Wheat in Sub-Saharan Africa. Advances in Agriculture , 2022, 1-11. https://doi.org/10.1155/2022/8820764
Navarro-Hellín, H., Martínez-del-Rincon, J., Domingo-Miguel, R., Soto-Valles, F. and Torres-Sánchez, R. (2016) A Decision Support System for Managing Irrigation in Agriculture. Computers and Electronics in Agriculture , 124, 121-131. https://doi.org/10.1016/j.compag.2016.04.003
Brocca, L., Tarpanelli, A., Filippucci, P., Dorigo, W., Zaussinger, F., Gruber, A., et al . (2018) How Much Water Is Used for Irrigation? A New Approach Exploiting Coarse Resolution Satellite Soil Moisture Products. International Journal of Applied Earth Observation and Geoinformation , 73, 752-766. https://doi.org/10.1016/j.jag.2018.08.023
Zappa, L., Schlaffer, S., Brocca, L., Vreugdenhil, M., Nendel, C. and Dorigo, W. (2022) How Accurately Can We Retrieve Irrigation Timing and Water Amounts from (Satellite) Soil Moisture? International Journal of Applied Earth Observation and Geoinformation , 113, Article 102979. https://doi.org/10.1016/j.jag.2022.102979
Zappa, L., Dari, J., Modanesi, S., Quast, R., Brocca, L., De Lannoy, G., et al . (2024) Benefits and Pitfalls of Irrigation Timing and Water Amounts Derived from Satellite Soil Moisture. Agricultural Water Management , 295, Article 108773. https://doi.org/10.1016/j.agwat.2024.108773
Zaussinger, F., Dorigo, W., Gruber, A., Tarpanelli, A., Filippucci, P. and Brocca, L. (2019) Estimating Irrigation Water Use over the Contiguous United States by Combining Satellite and Reanalysis Soil Moisture Data. Hydrology and Earth System Sciences , 23, 897-923. https://doi.org/10.5194/hess-23-897-2019
Torres-Quezada, E., Fuentes-Peñailillo, F., Gutter, K., Rondón, F., Marmolejos, J.M., Maurer, W., et al . (2025) Remote Sensing and Soil Moisture Sensors for Irrigation Management in Avocado Orchards: A Practical Approach for Water Stress Assessment in Remote Agricultural Areas. Remote Sensing , 17, Article 708. https://doi.org/10.3390/rs17040708
Toureiro, C., Serralheiro, R., Shahidian, S. and Sousa, A. (2017) Irrigation Management with Remote Sensing: Evaluating Irrigation Requirement for Maize under Mediterranean Climate Condition. Agricultural Water Management , 184, 211-220. https://doi.org/10.1016/j.agwat.2016.02.010
Vuolo, F., D’Urso, G., De Michele, C., Bianchi, B. and Cutting, M. (2015) Satellite-based Irrigation Advisory Services: A Common Tool for Different Experiences from Europe to Australia. Agricultural Water Management , 147, 82-95. https://doi.org/10.1016/j.agwat.2014.08.004
Corbari, C., Salerno, R., Ceppi, A., Telesca, V. and Mancini, M. (2019) Smart Irrigation Forecast Using Satellite LANDSAT Data and Meteo-Hydrological Modeling. Agricultural Water Management , 212, 283-294. https://doi.org/10.1016/j.agwat.2018.09.005
Gaznayee, H.A.A., Zaki, S.H., Al-Quraishi, A.M.F., Aliehsan, P.H., et al . (2023) Integrating Remote Sensing Techniques and Meteorological Data to Assess the Ideal Irrigation System Performance Scenarios for Improving Crop Productivity. Water , 15, Article 1605. https://doi.org/10.3390/w15081605
Ihuoma, S.O., Madramootoo, C.A. and Kalacska, M. (2021) Integration of Satellite Imagery and in Situ Soil Moisture Data for Estimating Irrigation Water Requirements. International Journal of Applied Earth Observation and Geoinformation , 102, Article 102396. https://doi.org/10.1016/j.jag.2021.102396
Kharrou, M.H., Simonneaux, V., Er-Raki, S., Le Page, M., Khabba, S. and Chehbouni, A. (2021) Assessing Irrigation Water Use with Remote Sensing-Based Soil Water Balance at an Irrigation Scheme Level in a Semi-Arid Region of Morocco. Remote Sensing , 13, Article 1133. https://doi.org/10.3390/rs13061133
Maguire, M.S., Neale, C.M.U., Woldt, W.E. and Heeren, D.M. (2022) Managing Spatial Irrigation Using Remote-Sensing-Based Evapotranspiration and Soil Water Adaptive Control Model. Agricultural Water Management , 272, Article 107838. https://doi.org/10.1016/j.agwat.2022.107838
Feng, X., Bi, S., Li, H., Qi, Y., Chen, S. and Shao, L. (2024) Soil Moisture Forecasting for Precision Irrigation Management Using Real-Time Electricity Consumption Records. Agricultural Water Management , 291, Article 108656. https://doi.org/10.1016/j.agwat.2023.108656
Roy, A., Narvekar, P., Murtugudde, R., Shinde, V. and Ghosh, S. (2021) Short and Medium Range Irrigation Scheduling Using Stochastic Simulation-Optimization Framework with Farm-Scale Ecohydrological Model and Weather Forecasts. Water Resources Researc h , 57, e2020WR029004. https://doi.org/10.1029/2020wr029004
Zhao, H., Di, L., Guo, L., Zhang, C. and Lin, L. (2023) An Automated Data-Driven Irrigation Scheduling Approach Using Model Simulated Soil Moisture and Evapotranspiration. Sustainability , 15, Article 12908. https://doi.org/10.3390/su151712908
Bwambale, E., Abagale, F.K. and Anornu, G.K. (2024) Towards a Modelling, Optimization and Predictive Control Framework for Smart Irrigation. Heliyon , 10, e38095. https://doi.org/10.1016/j.heliyon.2024.e38095
Adeyemi, O., Grove, I., Peets, S., Domun, Y. and Norton, T. (2018) Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling. Sensors , 18, Article 3408. https://doi.org/10.3390/s18103408
Dimitrov, K., Chivarov, N. and Chivarov, S. (2025) Concept of a Modular Wide-Area Predictive Irrigation System. AgriEngineering , 7, Article 430. https://doi.org/10.3390/agriengineering7120430
Chen, X., Qi, Z., Gui, D., Gu, Z., Ma, L., Zeng, F., et al . (2019) A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity. Agronomy , 9, Article 686. https://doi.org/10.3390/agronomy9110686
Zhao, H., Di, L. and Sun, Z. (2022) Watersmart-Gis: A Web Application of a Data Assimilation Model to Support Irrigation Research and Decision Making. ISP RS International Journal of Geo - Information , 11, Article 271. https://doi.org/10.3390/ijgi11050271
Li, H., Li, J., Shen, Y., Zhang, X. and Lei, Y. (2018) Web-Based Irrigation Decision Support System with Limited Inputs for Farmers. Agricultural Water Management , 210, 279-285. https://doi.org/10.1016/j.agwat.2018.08.025
Guo, D., Wang, Q.J., Ryu, D., Yang, Q., Moller, P. and Western, A.W. (2022) An Analysis Framework to Evaluate Irrigation Decisions Using Short-Term Ensemble Weather Forecasts. Irrigation Science , 41, 155-171. https://doi.org/10.1007/s00271-022-00807-w
Chen, X., Feng, S., Qi, Z., Sima, M.W., Zeng, F., Li, L., et al . (2023) Optimizing Irrigation Strategies to Improve Water Use Efficiency of Cotton in Northwest China Using RZWQM2. Agriculture , 12, Article 383. https://doi.org/10.3390/agriculture12030383
Brinkhoff, J., Hornbuckle, J. and Ballester Lurbe, C. (2019) Soil Moisture Forecasting for Irrigation Recommendation. IFAC - PapersOnLine , 52, 385-390. https://doi.org/10.1016/j.ifacol.2019.12.586
Lozoya, C., Mendoza, C., Aguilar, A., Román, A. and Castelló, R. (2016) Sensor-Based Model Driven Control Strategy for Precision Irrigation. Journal of Sensors , 2016, 1-12. https://doi.org/10.1155/2016/9784071
Clutter, M. and DeJonge, K. (2022) Optimizing Soil Moisture Sensor Depth for Irrigation Management Using Universal Multiple Linear Regression. Journal of the ASABE , 65, 739-749. https://doi.org/10.13031/ja.15044
Saseendran, S.A., Trout, T.J., Ahuja, L.R., Ma, L., McMaster, G.S., Nielsen, D.C., et al . (2015) Quantifying Crop Water Stress Factors from Soil Water Measurements in a Limited Irrigation Experiment. Agricultural Systems , 137, 191-205. https://doi.org/10.1016/j.agsy.2014.11.005
Bhatti, S., Heeren, D.M., O’Shaughnessy, S.A., Neale, C.M.U., LaRue, J., Melvin, S., et al . (2023) Toward Automated Irrigation Management with Integrated Crop Water Stress Index and Spatial Soil Water Balance. Precision Agriculture , 24, 2223-2247. https://doi.org/10.1007/s11119-023-10038-4
Wu, X., Zhang, W., Liu, W., Zuo, Q., Shi, J., Yan, X., et al . (2017) Root-Weighted Soil Water Status for Plant Water Deficit Index Based Irrigation Scheduling. Agricultural Water Management , 189, 137-147. https://doi.org/10.1016/j.agwat.2017.04.013
Wu, X., Shi, J., Zhang, T., Zuo, Q., Wang, L., Xue, X., et al . (2022) Crop Yield Estimation and Irrigation Scheduling Optimization Using a Root-Weighted Soil Water Availability Based Water Production Function. Field Crops Research , 284, Article 108579. https://doi.org/10.1016/j.fcr.2022.108579
Shi, J., Wu, X., Wang, X., Zhang, M., Han, L., Zhang, W., et al . (2020) Determining Threshold Values for Root-Soil Water Weighted Plant Water Deficit Index Based Smart Irrigation. Agricultural Water Management , 230, Article 105979. https://doi.org/10.1016/j.agwat.2019.105979
Hodges, B., Tagert, M.L., Paz, J.O. and Meng, Q. (2023) Assessing In-Field Soil Moisture Variability in the Active Root Zone Using Granular Matrix Sensors. Agricultural Water Management , 282, Article 108268. https://doi.org/10.1016/j.agwat.2023.108268
Liang, X., Liakos, V., Wendroth, O. and Vellidis, G. (2016) Scheduling Irrigation Using an Approach Based on the Van Genuchten Model. Agricultural Water Management , 176, 170-179. https://doi.org/10.1016/j.agwat.2016.05.030
Conde, G., Guzmán, S.M. and Athelly, A. (2024) Adaptive and Predictive Decision Support System for Irrigation Scheduling: An Approach Integrating Humans in the Control Loop. Computers and Electronics in Agriculture , 217, Article 108640. https://doi.org/10.1016/j.compag.2024.108640
Corbari, C. and Mancini, M. (2023) Irrigation Efficiency Optimization at Multiple Stakeholders’ Levels Based on Remote Sensing Data and Energy Water Balance Modelling. Irrigation Science , 41, 121-139. https://doi.org/10.1007/s00271-022-00780-4
Martelli, A., Rapinesi, D., Verdi, L., Donati, I.I.M., Dalla Marta, A. and Altobelli, F. (2025) Smart Irrigation for Management of Processing Tomato: A Machine Learning Approach. Irrigation Science , 43, 1407-1424. https://doi.org/10.1007/s00271-024-00993-9
Jamal, A., Cai, X., Qiao, X., Garcia, L., Wang, J., Amori, A., et al . (2023) Real-Time Irrigation Scheduling Based on Weather Forecasts, Field Observations, and Human-Machine Interactions. Water Resources Research , 59, e2023WR035810. https://doi.org/10.1029/2023wr035810
Amori, P.N., Heeren, D.M., Shi, Y., Wilkening, E., Goncalves, I.Z., Balboa, G.R., et al . (2025) Scalable Machine Learning Framework for Adaptive Irrigation Management of Maize and Soybean in the U.S. Midwest. Computers and Electronics in Agriculture , 237, Article 110710. https://doi.org/10.1016/j.compag.2025.110710
Madhukumar, N., Wang, E., Everingham, Y. and Xiang, W. (2024) Hybrid Transformer Network for Soil Moisture Estimation in Precision Irrigation. IEEE Access , 12, 48898-48909. https://doi.org/10.1109/access.2024.3378257
Wei, S. and Xu, T. (2025) An LSTM Approach to Deciphering Irrigation Operations from Remote Sensing and Groundwater Levels Records. Agricultural Water Management , 308, Article 109273. https://doi.org/10.1016/j.agwat.2024.109273
Li, X., Zhang, J., Cai, X., Huo, Z. and Zhang, C. (2023) Simulation-Optimization Based Real-Time Irrigation Scheduling: A Human-Machine Interactive Method Enhanced by Data Assimilation. Agricultural Water Management , 276, Article 108059. https://doi.org/10.1016/j.agwat.2022.108059
Nsoh, B., Katimbo, A., DeJonge, K.C., Liang, W., Guo, H., Ge, Y., et al . (2025) Crop2Cloud Platform: Real-Time Data Integration for Agricultural Water Monitoring. Smart Agricultural Technology , 12, Article 101166. https://doi.org/10.1016/j.atech.2025.101166
Gaitan, N.C., Batinas, B.I., Ursu, C. and Crainiciuc, F.N. (2025) Integrating Artificial Intelligence into an Automated Irrigation System. Sensors , 25, Article 1199. https://doi.org/10.3390/s25041199
Torres-Sanchez, R., Navarro-Hellin, H., Guillamon-Frutos, A., San-Segundo, R., Ruiz-Abellón, M.C. and Domingo-Miguel, R. (2020) A Decision Support System for Irrigation Management: Analysis and Implementation of Different Learning Techniques. Water , 12, Article 548. https://doi.org/10.3390/w12020548
Masseroni, D., Gangi, F., Ghilardelli, F., Gallo, A., Kisekka, I. and Gandolfi, C. (2024) Assessing the Water Conservation Potential of Optimized Surface Irrigation Management in Northern Italy. Irrigation Science , 42, 75-97. https://doi.org/10.1007/s00271-023-00876-5
Mirás-Avalos, J.M., Rubio-Asensio, J.S., Ramírez-Cuesta, J.M., Maestre-Valero, J.F. and Intrigliolo, D.S. (2019) Irrigation-Advisor—A Decision Support System for Irrigation of Vegetable Crops. Water , 11, Article 2245. https://doi.org/10.3390/w11112245
Flores Cayuela, C.M., González Perea, R., Camacho Poyato, E. and Montesinos, P. (2022) An ICT-Based Decision Support System for Precision Irrigation Management in Outdoor Orange and Greenhouse Tomato Crops. Agricultural Water Management , 269, Article 107686. https://doi.org/10.1016/j.agwat.2022.107686
Campos, N.G.S., Rocha, A.R., Gondim, R., et al . (2019) Smart & Green: An Internet-of-Things Framework for Smart Irrigation. Sensors , 20, Article 190. https://doi.org/10.3390/s20010190
King, B.A. and Shellie, K.C. (2023) A Crop Water Stress Index Based Internet of Things Decision Support System for Precision Irrigation of Wine Grape. Smart Agricultural Technology , 4, Article 100202. https://doi.org/10.1016/j.atech.2023.100202
Kang, C., Diverres, G., Karkee, M., Zhang, Q. and Keller, M. (2023) Decision-Support System for Precision Regulated Deficit Irrigation Management for Wine Grapes. Computers and Electronics in Agriculture , 208, Article 107777. https://doi.org/10.1016/j.compag.2023.107777
Ale, S., Su, Q., Singh, J., Himanshu, S., Fan, Y., Stoker, B., et al . (2023) Development and Evaluation of a Decision Support Mobile Application for Cotton Irrigation Management. Smart Agricultural Technology , 5, Article 100270. https://doi.org/10.1016/j.atech.2023.100270
Bonet, L., Thomas, F., Martínez-Gimeno, M.A., Tasa, M., Badal, E., Pérez-Pérez, J.G., et al . (2026) A User-Friendly Decision Support Tool for Irrigation Scheduling in Smallholder Olive Orchards. Agricultural Water Management , 324, Article 110131. https://doi.org/10.1016/j.agwat.2026.110131
Kandamali, D.F., Porter, W.M., Porter, E., McLemore, A. and Rains, G.C. (2025) CottonBot: An AI-Driven Cotton Farming Assistant and Irrigation Advisor Using LLM-RAG and Agentic AI Tools. Smart Agricultural Technology , 12, Article 101640. https://doi.org/10.1016/j.atech.2025.101640
Simionesei, L., Ramos, T.B., Palma, J., Oliveira, A.R. and Neves, R. (2020) IrrigaSys: A Web-Based Irrigation Decision Support System Based on Open Source Data and Technology. Computers and Electronics in Agriculture , 178, Article 105822. https://doi.org/10.1016/j.compag.2020.105822
Giusti, E. and Marsili-Libelli, S. (2015) A Fuzzy Decision Support System for Irrigation and Water Conservation in Agriculture. Environmental Modelling & Software , 63, 73-86. https://doi.org/10.1016/j.envsoft.2014.09.020
Wilkening, E.J., Heeren, D.M., Shi, Y., Katimbo, A., Puntel, L.A., Balboa, G.R., et al . (2025) Development of a Machine Learning Framework for an Irrigation Decision Support System. Journal of Natural Resources and Agricultural Ecosystems , 3, 121-131. https://doi.org/10.13031/jnrae.16162
Bonfante, A., Monaco, E., Manna, P., De Mascellis, R., Basile, A., Buonanno, M., et al . (2019) LCIS DSS—An Irrigation Supporting System for Water Use Efficiency Improvement in Precision Agriculture: A Maize Case Study. Agricultural Systems , 176, Article 102646. https://doi.org/10.1016/j.agsy.2019.102646
Amini, A., Emami, S. and Dehghanisanij, H. (2025) Participatory Evaluation of an Irrigation Decision Support System for Water-Saving and Productivity Gains in Lake Urmia Basin. Scientific Reports , 15, Article No. 42480. https://doi.org/10.1038/s41598-025-26567-z
Cavazza, F., Galioto, F., Raggi, M. and Viaggi, D. (2020) Digital Irrigated Agriculture: Towards a Framework for Comprehensive Analysis of Decision Processes under Uncertainty. Future Internet , 12, Article 181. https://doi.org/10.3390/fi12110181
Zhang, J., Guan, K., Peng, B., Jiang, C., Zhou, W., Yang, Y., et al . (2021) Challenges and Opportunities in Precision Irrigation Decision-Support Systems for Center Pivots. Environmental Research Letter s, 16, Article 053003. https://doi.org/10.1088/1748-9326/abe436