In the past few decades, the rapid development of agriculture has put forward high requirements for efficient management of water resources, so as to rationally utilize natural resources and increase their sustainability. It is noted that there is a wide gap between demand and water supply, which leads to water scarcity in agriculture. The key reason lies in the difficulty to predict variations in nature. Meanwhile, extraction of water from underground is also increasing due to the migration of people from the village to town and industrialization. Therefore, there is a need for smart farmland water management. However, recent research has shown that traditional farmland water resource management technologies are difficult to meet the new demands, such as the "last one mile" resources in farmland water conservancy scenarios. How can we achieve intelligent farmland water management and solve specific problems in key scenarios? In short, how can we achieve smart farmland water conservancy or smart water conservancy?
A trustworthy smart farmland water conservancy model should meet the following six goals: spatial globalization, time serialization, process automation, application intelligence, management integration, and scientific decision-making. Based on these six goals, smart water conservancy should satisfy the automation, refinement, real-time, and comprehensiveness of water resources management. This Research Topic focuses on big data mining, data correlation analysis, and digital-reality mutual perception in smart water conservancy or smart farmland water conservancy, while considering data intelligence-based water conservancy management and decision-making. Overall, more research is needed on intelligent models in smart water conservancy or smart farmland water conservancy to realize the beautiful vision of interconnected perception and harmony between human and water. To further explore the discipline, we welcome researchers and practitioners from academia and industry to explore the latest advances in smart water conservancy or smart farmland water conservancy technologies.
The topics of interest for this Research Topic include, but are not limited to:
1. Big data in smart water conservancy/smart farmland water conservancy
2. Automated monitoring technology in smart water conservancy/smart farmland water conservancy
3. Data correlation analysis in smart water conservancy/smart farmland water conservancy
4. Data fusion in smart water conservancy/smart farmland water conservancy
5. Digital-reality mutual perception in smart water conservancy/smart farmland water conservancy
6. Data-based intelligent water conservancy management/farmland water conservancy
7. Water decision support in smart water conservancy management/smart farmland water conservancy
8. Comprehensive governance capability of smart water conservancy management/smart farmland water conservancy
In the past few decades, the rapid development of agriculture has put forward high requirements for efficient management of water resources, so as to rationally utilize natural resources and increase their sustainability. It is noted that there is a wide gap between demand and water supply, which leads to water scarcity in agriculture. The key reason lies in the difficulty to predict variations in nature. Meanwhile, extraction of water from underground is also increasing due to the migration of people from the village to town and industrialization. Therefore, there is a need for smart farmland water management. However, recent research has shown that traditional farmland water resource management technologies are difficult to meet the new demands, such as the "last one mile" resources in farmland water conservancy scenarios. How can we achieve intelligent farmland water management and solve specific problems in key scenarios? In short, how can we achieve smart farmland water conservancy or smart water conservancy?
A trustworthy smart farmland water conservancy model should meet the following six goals: spatial globalization, time serialization, process automation, application intelligence, management integration, and scientific decision-making. Based on these six goals, smart water conservancy should satisfy the automation, refinement, real-time, and comprehensiveness of water resources management. This Research Topic focuses on big data mining, data correlation analysis, and digital-reality mutual perception in smart water conservancy or smart farmland water conservancy, while considering data intelligence-based water conservancy management and decision-making. Overall, more research is needed on intelligent models in smart water conservancy or smart farmland water conservancy to realize the beautiful vision of interconnected perception and harmony between human and water. To further explore the discipline, we welcome researchers and practitioners from academia and industry to explore the latest advances in smart water conservancy or smart farmland water conservancy technologies.
The topics of interest for this Research Topic include, but are not limited to:
1. Big data in smart water conservancy/smart farmland water conservancy
2. Automated monitoring technology in smart water conservancy/smart farmland water conservancy
3. Data correlation analysis in smart water conservancy/smart farmland water conservancy
4. Data fusion in smart water conservancy/smart farmland water conservancy
5. Digital-reality mutual perception in smart water conservancy/smart farmland water conservancy
6. Data-based intelligent water conservancy management/farmland water conservancy
7. Water decision support in smart water conservancy management/smart farmland water conservancy
8. Comprehensive governance capability of smart water conservancy management/smart farmland water conservancy
