用于监测和优化耕作实践的残留物密度分割
Residue Density Segmentation for Monitoring and Optimizing Tillage Practices
摘要：
“免耕”和覆盖种植往往被认为是农业固碳的主要简单、最佳管理做法。然而，问题的根源更为复杂，这些方法的潜在好处取决于许多因素，包括田地的土壤类型、地形和管理历史。我们不是使用计算机视觉方法来简单地将田地分类为静止与免耕，而是通过概率深度学习分割方法来识别田地上残留物的覆盖程度，以实现对碳保持潜力和实现的更准确分析。这一方法不仅将为目前实施的做法提供更准确的见解，而且还能够更准确地确定最有可能采用新做法以显著影响农业碳固存的领域。
英文摘要：
"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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
