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DTSTAMP:20231221T141331Z
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DTSTART;TZID=America/New_York:20231218T120000
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DESCRIPTION:Agriculture relies heavily on irrigation\, but this essential p
 ractice consumes a significant portion of our increasingly scarce freshwat
 er resources. Climate change and urban expansion exacerbate this challenge
 \, making efficient irrigation management a critical factor in global sust
 ainability. Additionally\, excessive irrigation harms the environment by p
 olluting waterways\, depleting water sources\, and salinizing soil.\n\nThi
 s webinar presents a groundbreaking data-driven intelligent irrigation sch
 eduling model that optimizes water use efficiency and promotes agricultura
 l sustainability. By leveraging readily available soil moisture and evapot
 ranspiration data from the High-Resolution Land Data Assimilation System (
 HRLDAS)\, the model provides precise irrigation recommendations tailored t
 o specific field conditions.\n\nKey benefits of the decision model include
 :\n\n- 20-40% water savings: Achieve significant water conservation while 
 ensuring optimal crop growth.\n- Increased crop yield: Maximize production
  by providing the right amount of water at the right time.\n- Reduced envi
 ronmental impact: Minimize water pollution and soil salinization for a hea
 lthier environment.\n- Cost-effective and easy to implement: No expensive 
 sensors or data subscriptions required\, making it accessible to all farme
 rs.\n\nDuring this webinar\, you will learn the decision support system in
  depth:\n\n- How the data-driven model works and its key features\n- The b
 enefits of ET-Water Balance and soil-moisture based irrigation scheduling 
 methods.\n- How deep reinforcement learning further optimizes irrigation d
 ecisions.\n- Real-world examples and case studies showcasing the model's e
 ffectiveness.\n- How to access and utilize the model through the free Wate
 rSmart Data Information Portal.\n\nSpeaker(s): Haoteng Zhao\, \n\nVirtual:
  https://events.vtools.ieee.org/m/389291
LOCATION:Virtual: https://events.vtools.ieee.org/m/389291
ORGANIZER:zhuosen.wang@nasa.gov
SEQUENCE:23
SUMMARY:A Data-Driven Intelligent Decision-Making Model for Irrigation Sche
 duling
URL;VALUE=URI:https://events.vtools.ieee.org/m/389291
X-ALT-DESC:Description: <br /><p>Agriculture relies heavily on irrigation\,
  but this essential practice consumes a significant portion of our increas
 ingly scarce freshwater resources. Climate change and urban expansion exac
 erbate 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 sa
 linizing soil.&nbsp\;</p>\n<p>This webinar presents a groundbreaking data-
 driven intelligent irrigation scheduling model that optimizes water use ef
 ficiency and promotes agricultural sustainability. By leveraging readily a
 vailable soil moisture and evapotranspiration data from the High-Resolutio
 n Land Data Assimilation System (HRLDAS)\, the model provides precise irri
 gation recommendations tailored to specific field conditions.&nbsp\;</p>\n
 <p>Key benefits of the decision model include:&nbsp\;</p>\n<ol data-listch
 ain="__List_Chain_47">\n<li>20-40% water savings:&nbsp\;Achieve significan
 t water conservation while ensuring optimal crop growth.&nbsp\;</li>\n<li>
 Increased crop yield:&nbsp\;Maximize production by providing the right amo
 unt of water at the right time.&nbsp\;</li>\n<li>Reduced environmental imp
 act:&nbsp\;Minimize water pollution and soil salinization for a healthier 
 environment.&nbsp\;</li>\n<li>Cost-effective and easy to implement:&nbsp\;
 No expensive sensors or data subscriptions required\, making it accessible
  to all farmers.&nbsp\;</li>\n</ol>\n<p>During this webinar\, you will lea
 rn the decision support system in depth:&nbsp\;</p>\n<ol data-listchain="_
 _List_Chain_48">\n<li>How the data-driven model works and its key features
 </li>\n<li>The benefits of ET-Water Balance and soil-moisture based irriga
 tion scheduling methods.</li>\n<li>How deep reinforcement learning further
  optimizes irrigation decisions.&nbsp\;</li>\n<li>Real-world examples and 
 case studies showcasing the model's effectiveness.&nbsp\;</li>\n<li>How to
  access and utilize the model through the free WaterSmart Data Information
  Portal.</li>\n</ol>
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