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In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.
I’m Cody Coleman and I’m really excited to share my research on how careful data selection can make ML development faster, cheaper, and better by focusing on quality rather than quantity. So for example, in 2015, fidget spinners were all the rage. I’m super excited to chat with you all today. AB : Got it. Thank you.
I’m Cody Coleman and I’m really excited to share my research on how careful data selection can make ML development faster, cheaper, and better by focusing on quality rather than quantity. So for example, in 2015, fidget spinners were all the rage. I’m super excited to chat with you all today. AB : Got it. Thank you.
I’m Cody Coleman and I’m really excited to share my research on how careful data selection can make ML development faster, cheaper, and better by focusing on quality rather than quantity. So for example, in 2015, fidget spinners were all the rage. I’m super excited to chat with you all today. AB : Got it. Thank you.
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