  "No-till" and cover cropping are often identified as the leading simple, best
management practices for carbon sequestration in agriculture. However, the root
of the problem is more complex, with the potential benefits of these approaches
depending on numerous factors including a field's soil type(s), topography, and
management history. Instead of using computer vision approaches to simply
classify a field a still vs. no-till, we instead seek to identify the degree of
residue coverage across afield through a probabilistic deep learning
segmentation approach to enable more accurate analysis of carbon holding
potential and realization. This approach will not only provide more precise
insights into currently implemented practices, but also enable a more accurate
identification process of fields with the greatest potential for adopting new
practices to significantly impact carbon sequestration in agriculture.
