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

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是实现准确的土壤健康测量。拟议的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意义和农学意义的土壤碳采样设计，该设计可根据土地所有者的偏好和地块的特征进行定制。设想的系统将集成一个手持测量探头和一个基于云的数据分析和管理平台，以实现自动化、可扩展、经济高效和权威的土壤碳采样。拟议的项目将开发一种新的机器学习(ML)模型，用于现场预测和分析土壤碳，并制定自动采样计划。该系统集成了土壤分层和采样方案设计算法；集成了手持探头的软件；以及基于云的集成分析和数据管理平台。这项研究将解决技术挑战，包括：(1)检测复杂的协变量；(2)使分层与土壤碳验证协议保持一致；(3)开发新的数字土壤学技术；(4)开发能够近乎实时地重复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抽样计划。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.