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

SBIR 第二阶段：使用机器学习算法创建高质量、低成本的土壤图

• 批准号: 2304081
• 负责人: Yones Khaledian
• 金额: $ 100万
• 依托单位: SOILSERDEM LLC
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304081&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Creating; quality

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.

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）土壤图。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。这项新的技术分支将为美国大陆的各种种植系统提供开发的地图产品，交叉的经济和环境可持续性。 该项目的目标是使各种土地管理者都能获得特定地点的土壤肥力绘图信息。预期成果包括对环境更负责任的农场管理和粪肥及养分管理规划、精准耕作、土地使用规划、种植决策、评估植物的压力源、田间调节、作物轮作以及产量预测/解释。其他好处是增加农场的盈利能力和提高土壤健康。这项技术将提高作物产量，同时降低投入成本，从而提高长期利润率低的行业的盈利能力。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，以及通过提高农场成功率来增加税收和就业机会，从而达到NSF的目标。这项创新技术有三个组成部分，使其与当前用于绘制土壤基本养分图的最佳技术不同。第一个是应用广义景观量化驱动最佳土壤样本采集容纳景观变异性，从而消除了不必要的土壤样本的收集。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，该技术是一种可转移的模型，不需要额外的硬件来实现其结果。正如所设想的那样，无论种植制度和管理实践如何，该技术都可以选择适当的协变量马赛克来捕获相关的土壤变异性。该项目的范围将有利于美国各地的中耕制度，专门针对玉米-大豆、马铃薯、小麦和棉花生产。与现有的方法，产生不充分的数据，具有挑战性的（成本高昂的）映射目标，这项新技术将使这些目标的访问和成本效益与可靠的精度。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。