SBIR Phase I: Development of AI and Software Platform for Cost-Effective Soil Carbon Measurement and Assessment

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是实现准确的土壤健康测量。该项目将开发新的机器学习（ML）模型，用于准确的土壤碳预测。该系统将生成一个具有统计学和农艺学意义的土壤碳采样设计，可根据土地所有者的偏好和地块的特征进行定制。 设想的系统将集成手持式测量探头和基于云的数据分析和管理平台，以实现自动化，可扩展，具有成本效益和权威性的土壤碳采样。拟议项目将开发一种新的机器学习（ML）模型，用于土壤碳的原位预测和分析以及自动采样计划的开发。该系统集成了土壤分层和采样计划设计算法;与手持式探头集成的软件;以及集成的基于云的分析和数据管理平台。该研究将解决技术挑战，包括：（1）检测复杂的协变量;（2）将分层与土壤碳验证协议相结合;（3）开发新的数字土壤学技术;（4）开发能够近实时验证测量计划的机器学习工具。该系统的输出将支持并行区域模型和自适应采样计划。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/ commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable accurate soil health measurements. The proposed project will develop new machine learning (ML) models for accurate soil carbon predictions. The system will generate a statistically- and agronomically-significant soil carbon sampling design customizable for landowner preferences and the parcel’s characteristics. The envisioned system will integrate a handheld measurement probe and a cloud-based data analysis and management platform for automated, scalable, cost-effective, and authoritative soil carbon sampling. The proposed project will develop a novel machine learning (ML) model for the in-situ prediction and analysis of soil carbon and development of automated sampling plans. The proposed system integrates a soil stratification and sampling plan design algorithm; software integrated with a handheld probe; and an integrated cloud-based analysis and data management platform. The research will address technical challenges including: (1) detecting complex covariates; (2) aligning stratification with soil carbon verification protocols; (3) developing novel digital pedology techniques; and (4) developing machine learning tools able to iterate measurement plans in near real-time. The system's outputs will enable parallel regional models and adaptive sampling plans.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.