A Data-Driven Intelligent Decision-Making Model for Irrigation Scheduling
Agriculture relies heavily on irrigation, but this essential practice consumes a significant portion of our increasingly scarce freshwater resources. Climate change and urban expansion exacerbate this challenge, making efficient irrigation management a critical factor in global sustainability. Additionally, excessive irrigation harms the environment by polluting waterways, depleting water sources, and salinizing soil.
This webinar presents a groundbreaking data-driven intelligent irrigation scheduling model that optimizes water use efficiency and promotes agricultural sustainability. By leveraging readily available soil moisture and evapotranspiration data from the High-Resolution Land Data Assimilation System (HRLDAS), the model provides precise irrigation recommendations tailored to specific field conditions.
Key benefits of the decision model include:
- 20-40% water savings: Achieve significant water conservation while ensuring optimal crop growth.
- Increased crop yield: Maximize production by providing the right amount of water at the right time.
- Reduced environmental impact: Minimize water pollution and soil salinization for a healthier environment.
- Cost-effective and easy to implement: No expensive sensors or data subscriptions required, making it accessible to all farmers.
During this webinar, you will learn the decision support system in depth:
- How the data-driven model works and its key features
- The benefits of ET-Water Balance and soil-moisture based irrigation scheduling methods.
- How deep reinforcement learning further optimizes irrigation decisions.
- Real-world examples and case studies showcasing the model's effectiveness.
- How to access and utilize the model through the free WaterSmart Data Information Portal.
Date and Time
Location
Hosts
Registration
- Date: 18 Dec 2023
- Time: 12:00 PM to 01:00 PM
- All times are (UTC-05:00) Eastern Time (US & Canada)
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- Starts 07 December 2023 01:38 PM
- Ends 18 December 2023 01:38 PM
- All times are (UTC-05:00) Eastern Time (US & Canada)
- No Admission Charge
Speakers
Haoteng Zhao of USDA
A Data-Driven Intelligent Decision-Making Model for Irrigation Scheduling
Agriculture relies heavily on irrigation, but this essential practice consumes a significant portion of our increasingly scarce freshwater resources. Climate change and urban expansion exacerbate this challenge, making efficient irrigation management a critical factor in global sustainability. Additionally, excessive irrigation harms the environment by polluting waterways, depleting water sources, and salinizing soil.
This webinar presents a groundbreaking data-driven intelligent irrigation scheduling model that optimizes water use efficiency and promotes agricultural sustainability. By leveraging readily available soil moisture and evapotranspiration data from the High-Resolution Land Data Assimilation System (HRLDAS), the model provides precise irrigation recommendations tailored to specific field conditions.
Key benefits of the decision model include:
- 20-40% water savings: Achieve significant water conservation while ensuring optimal crop growth.
- Increased crop yield: Maximize production by providing the right amount of water at the right time.
- Reduced environmental impact: Minimize water pollution and soil salinization for a healthier environment.
- Cost-effective and easy to implement: No expensive sensors or data subscriptions required, making it accessible to all farmers.
During this webinar, you will learn the decision support system in depth:
- How the data-driven model works and its key features
- The benefits of ET-Water Balance and soil-moisture based irrigation scheduling methods.
- How deep reinforcement learning further optimizes irrigation decisions.
- Real-world examples and case studies showcasing the model's effectiveness.
- How to access and utilize the model through the free WaterSmart Data Information Portal.
Biography:
Dr. Haoteng Zhao is a scientist with expertise in hydrology, remote sensing, and data-driven modeling. Currently, he works as a Postdoctoral Research Associate at the USDA ARS Hydrology and Remote Sensing Laboratory, where he leads research projects on developing intelligent irrigation scheduling models and improving water use efficiency in agriculture.
Dr. Zhao's research focuses on leveraging advanced technologies like High-Resolution Land Data Assimilation System (HRLDAS) and deep reinforcement learning to optimize irrigation management for various crops and field conditions. He is deeply committed to using his scientific knowledge to address water scarcity challenges and promote sustainable agricultural practices.
Prior to joining the USDA ARS, Dr. Zhao earned his Ph.D. in Earth Systems and Geoinformation Sciences from George Mason University. His doctoral research focused on developing an integrated remote sensing and hydrological modeling framework for precision irrigation scheduling. Dr. Zhao is actively involved in the scientific community, regularly presenting his research at international conferences and publishing his findings in peer-reviewed journals. Dr. Zhao’s current research interests include crop monitoring, smart agriculture, remote sensing, image processing, web development, and machine learning.
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