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  • Presentation | GC21F: Advancing Sustainable and Resilient Agriculture and Irrigation Through AI and Remote Sensing IV Poster
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  • GC21F-0714: Integrating Ground Surveys and Remote Sensing to Monitor Adoption and Abandonment Dynamics of Climate-Resilient, Water-Efficient Direct Seeded Rice for Sustainable Irrigation Management in India
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  • Board 0714‚ Hall EFG (Poster Hall)
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Author(s):
Parmita Ghosh, Corteva Agriscience (First Author)
Sonal Bakiwala, Corteva Agriscience (Presenting Author)
Annie Rana, Associate Data Scientist
Anu Swatantran, Corteva Agriscience
Raman Babu, Team Leader


Rice is a staple food in India, but traditional ways of growing rice use large amounts of water and produce significant methane emissions, contributing to climate change. As India faces increasing water scarcity and changing rainfall patterns, it is important to find rice-growing methods that use less water and are better for the environment. Our experiments found that Dry Direct Seeded Rice (DSR) can increase yields by 34%, save over half the water, and nearly eliminate methane emissions compared to the traditional puddled transplanted rice (TPR) method. However, it is not well understood how widely DSR is used across India. To answer these questions, we used satellite images and machine learning to track where and when DSR is being used in different parts of India. We found that many farmers started with DSR but switched back to TPR mid-season, leading to abandonment of DSR. We improved our model to recognize not just DSR and TPR, but also fields where DSR was abandoned mid-season, mapping these patterns with 78.7% accuracy. These findings can help advisors provide better support and training, empowering farmers to achieve more profitable and sustainable DSR cultivation, and making rice farming in India more resilient to climate change.



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