Residue Density Segmentation for Monitoring and Optimizing Tillage Practices
This work addresses the problem of accurately assessing residue coverage for farmers and agricultural researchers to better monitor and optimize tillage practices for carbon sequestration.
This paper proposes a probabilistic deep learning segmentation approach to identify the degree of residue coverage across agricultural fields. This method aims to provide more precise insights into current tillage practices and identify fields with high potential for adopting new carbon sequestration practices.
"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.