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Using Geospatial AI to Monitor Rice Productivity in Cambodia

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Thinking Machines supported the Asian Development Bank’s assessment of agricultural productivity gains from improved irrigation programs. We generated plot-level rice yield estimates for over 67,000 plots of land in Cambodia by training an AI model to predict yield using open-source geospatial data: Extended rice yield data to over 50x more plots using machine learning, with predictions ranging from 3.8 to 5.5 tons per ha and an accuracy of +/- 0.56 tons per ha. Along with the Asian Development Bank, this project was done with the support of the government of Cambodia. Since 2013, the Asian Development Bank (ADB) has devoted substantial resources to help increase agricultural productivity, reduce rural poverty, and improve the overall climate and disaster resilience of irrigation systems in select provinces in Cambodia. Traditionally, they have measured the impact of their programs on agricultural productivity by conducting on-the-ground surveys and meticulously recording each househol

SUMMARY Thinking Machines supported the Asian Development Bank’s assessment of agricultural productivity gains from improved irrigation programs. We generated plot-level rice yield estimates for over 67,000 plots of land in Cambodia by training an AI model to predict yield using open-source geospatial data: An AI model was trained on data gathered through household surveys in the areas that were recipients of ADB’s irrigation projects. The model used open geospatial data to indicate the rice crops’ growing condition and health throughout the different farming seasons. The rice yield…

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