SBIR Phase II: Creating high-quality, lower-cost soil maps using machine learning algorithms

这个小企业创新研究（SBIR）第二阶段项目的更广泛/商业影响将是为农学家和农民大规模制作高质量（准确/高分辨率）的土壤地图。准确的土壤信息是更好、更有效的作物/土壤管理的基本驱动力。这一新的技术分支将在美国大陆存在的各种种植系统中提供开发的地图产品，交叉经济和环境可持续性。该项目的目标是为不同的土地管理者提供特定地点的土壤肥力测绘信息。预期成果包括更环保的农场管理、粪肥和养分管理规划、精准农业、土地利用规划、种植决策、植物压力源评估、田间调节、作物轮作以及产量预测/解释。其他好处包括提高农场盈利能力和土壤健康。这项技术将提高作物产量，同时降低投入成本，从而提高长期遭受低利润率的行业的盈利能力。预期的项目成果通过推进科学，改善美国公民的生活和健康，并有可能通过增加农业成功来增加税收和就业机会，从而达到美国国家科学基金会的目标。这项创新技术有三个组成部分，使其与目前用于绘制基本土壤养分地图的最佳技术不同。第一个是应用广义景观量化来驱动适应景观变异性的最佳土壤样本采集，从而消除了收集不必要的土壤样本的需要。第二个组件利用先进的机器学习算法，能够使用少量独特收集的土壤样本来产生准确的预测。最后，该技术是一种可转让的模型，不需要额外的硬件来实现其结果。正如设想的那样，该技术可以选择适当的协变量马赛克来捕捉相关的土壤变异，而不考虑种植制度和管理实践。该项目的范围将有利于整个美国的行种植系统，特别是针对玉米-大豆，土豆，小麦和棉花生产。与目前可用的方法不同的是，对于具有挑战性（成本过高）的测绘目标，这种新技术将使这些目标易于获得，成本效益高，精度可靠。该奖项反映了美国国家科学基金会的法定使命，并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/commercial impact of this Small Business Innovation Research (SBIR) Phase II project will be to produce high-quality (accurate/high-resolution) soil maps for agronomists and farmers at scale. Accurate soil information is a fundamental driver of better, more-efficient crop/soil management. This new branch of technology will deliver developed map products across various cropping systems that exist in the continental U.S., intersecting economic and environmental sustainability. Making site-specific soil fertility mapping information accessible to a diversity of land stewards is the goal of this project. Expected outcomes include more environmentally responsible farm management and manure and nutrient-management planning, precision farming, land use planning, planting decisions, evaluating stressors on plants, field conditioning, crop rotation, and prediction/interpretation of yields. Other benefits are increased farm profitability and increased soil health. This technology will result in increased crop yield while allowing for decreased input costs, leading to higher profitability in an industry that chronically suffers from low profit margins. The anticipated project outcomes meet NSF goals by advancing science, improving the lives and health of U.S. citizens, and potentially generating increased tax revenues and jobs via increased farm success.This innovative technology has three components that differentiate it from the best current technologies used to produce maps of essential soil nutrients. The first is applying generalized landscape quantification to drive optimal soil sample collection accommodating landscape variability, thereby eliminating the need to collect unnecessary soil samples. The second component leverages advanced machine-learning algorithms that are able to use the small number of uniquely collected soil samples to produce accurate predictions. Finally, the technology is a transferable model that does not necessitate additional hardware to achieve its results. As envisioned, this technology can select appropriate covariate mosaics to capture relevant soil variability irrespective of cropping system and management practices. The scope of this project will be beneficial to row cropping system across the U.S., specifically targeting corn-soy, potatoes, wheat, and cotton production. Unlike currently available methods that produce inadequate data for challenging (cost-prohibitive) mapping targets, this new technology will render those targets accessible and cost-effective with reliable accuracies.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.