An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands
Abstract
:1. Introduction
2. Major Constraints of Agricultural Productivity in Drylands
2.1. Land Degradation
2.2. Water Scarcity Issues and Sustainable Development Goals
2.3. Climate Variability
2.4. Overexploitation of Groundwater
2.5. Socioeconomic Drivers
2.6. Droughts
2.7. Conventional Technology
3. Traditional Approaches Used for Irrigation Scheduling
3.1. Weather-Based Irrigation Scheduling
3.2. Plant-Based Irrigation Scheduling
Stem Diameter Fluctuations
3.3. Irrigation Scheduling Based on Soil Moisture
4. Innovative Smart Irrigation Approaches
4.1. State-of-the-Art Smart Irrigation Technologies
4.1.1. Artificial Intelligence (AI) and Deep Learning
4.1.2. Model Predictive Irrigation Systems
4.1.3. Variable-Rate Irrigation (VRI)
4.1.4. Unmanned Aerial Vehicles (UAVs) for Irrigation Management
4.2. Forecasting Smart Irrigation Technology with DSSIS
5. Future Prospects
- I.
- Variability in soil texture is a vital source of uncertainty because it influences the current and potential soil water storage estimates both vertically and latterly in a field. Therefore, site-specific soil analysis is one way to rectify this problem and obtain the exact soil parameter information needed for accurate irrigation scheduling. Site-specific soil test-based information integrated with smart irrigation systems can help to improve WUE in arid and semiarid regions. This method is called soil test-based irrigation prescription (STIP). The proper execution of STIP needs specific field soil sampling, analysis of soil properties and development of a soil database. This soil information with crop and weather data can be integrated with a model or decision support system to forecast an irrigation event.
- II.
- Most of the experiments related to smart irrigation systems were conducted on a small scale in research fields or under controlled environmental conditions, which cannot represent commercial farming practices. Therefore, more on-farm studies in large fields are needed for a clear understanding about the implementation of smart irrigation technology.
- III.
- Most of the commercial smart irrigation systems offered by different irrigation companies help to improve water use efficiency, but the high cost of these state-of-the-art devices is a serious challenge for farmers. Moreover, these commercial smart irrigation systems are custom-built, meaning difficulty in control and adaptability. Therefore, affordable and user-friendly equipment should be manufactured at a local level.
- IV.
- Most of the farmers in dryland regions are not well educated and should be trained through practical demonstration of smart irrigation systems by expert extension workers. Furthermore, governments should provide subsidies to farmers for dissemination of such technologies on a large scale.
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Strategy | Outcomes | References |
---|---|---|
Fuzzy logic | Optimization | [169] |
ANN | Decrease in evaporation due to schedule and savings observed in water and electrical energy | [170] |
Fuzzy logic | The fuzzy controller system can be effectively applied to PA applications such as water-saving agriculture areas, for example, the croplands, the nursery gardens and the greenhouses. | [171] |
Fuzzy logic controller | Drip irrigation prevents wastage of water and evaporation | [172] |
Fuzzy decision support system | The system provided improved irrigation suggestions in terms of timing and water saving. | [173] |
ANN feedforward | Optimization of water resources in a smart farm | [174] |
Machine learning algorithm | Prediction and tackles drought conditions | [175] |
Fuzzy logic | Obtained a higher level of accuracy to expertly use water for irrigation | [176] |
ANN | Neural network models with one hidden layer with four neurons for sugar beet and five neurons for wine grape provided excellent predictions of well-watered canopy temperature | [177] |
ANN | The proposed model was able to predict the timing and quantity of irrigation water | [178] |
LoRa-based machine learning | This system led to a 46% reduction in water usage, and the plants looked better than they would have with conventional watering | [179] |
Type of UAVs Used | Purpose | References |
---|---|---|
Unmanned helicopter | Mapping of crop water stress, index for irrigation scheduling | [221] |
Unmanned helicopter | Assess water stress variability in a commercial vineyard | [222] |
Unmanned helicopter | The fuzzy controller system can be effectively applied to water stress detection in an almond orchard | [223] |
Multi-copter engines | Water stress detection in grapevine | [224] |
Fixed wing | Detection of water stress in citrus cultivars | [224] |
Fixed wing | Water stress detection in fruit tress | [225] |
Fixed wing | Soil moisture estimation at different soil levels | [226] |
Quadcopter | Estimation of canopy cover maps for irrigation management of peanut and cotton | [227] |
Quadcopter | Identification of nonuniformly irrigated areas in olive groves and vineyard crops | [228] |
Hexacopter | Soil moisture content prediction under different irrigation treatments in maize crop | [229] |
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Ahmed, Z.; Gui, D.; Murtaza, G.; Yunfei, L.; Ali, S. An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands. Agronomy 2023, 13, 2113. https://doi.org/10.3390/agronomy13082113
Ahmed Z, Gui D, Murtaza G, Yunfei L, Ali S. An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands. Agronomy. 2023; 13(8):2113. https://doi.org/10.3390/agronomy13082113
Chicago/Turabian StyleAhmed, Zeeshan, Dongwei Gui, Ghulam Murtaza, Liu Yunfei, and Sikandar Ali. 2023. "An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands" Agronomy 13, no. 8: 2113. https://doi.org/10.3390/agronomy13082113
APA StyleAhmed, Z., Gui, D., Murtaza, G., Yunfei, L., & Ali, S. (2023). An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands. Agronomy, 13(8), 2113. https://doi.org/10.3390/agronomy13082113