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.

这项小型企业创新研究（SBIR）II期项目的更广泛/商业影响将是为农艺师和农民大规模生产高质量（准确/高分辨率）土壤图。准确的土壤信息是更好，提高作物/土壤管理的基本驱动力。这个新的技术分支将在美国大陆存在的各种农作物系统中提供开发的地图产品，从而与经济和环境可持续性相交。 该项目的目的是使特定地点的土壤生育能映射到各种土地管家的信息。预期的结果包括对环境负责的农场管理和肥料以及养分管理计划，精确耕作，土地使用计划，种植决策，评估植物的压力，野外调理，轮作旋转以及产量的预测/解释。其他好处是提高农场盈利能力并增加土壤健康。这项技术将导致农作物产量提高，同时允许投入成本降低，从而在长期遭受较低利润率的行业中提高盈利能力。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，并通过增加农场成功而增加税收和就业机会来实现NSF的目标。这项创新技术具有三种组成部分，可将其与生产基本土壤养分的最佳当前技术区分开。首先是应用广义景观定量，以推动可容纳景观变异性的最佳土壤样品收集，从而消除了收集不必要的土壤样品的需求。第二个组件利用能够使用少量独特收集的土壤样品来产生准确的预测的高级机器学习算法。最后，该技术是可转让的模型，不需要其他硬件即可实现其结果。正如设想的那样，该技术可以选择适当的协变量镶嵌物来捕获相关的土壤变异性，而与作物系统和管理实践无关。该项目的范围将对美国各地的种植系统有益，特别是针对玉米，土豆，小麦和棉花生产。与当前可用的方法可用的数据不足以进行具有挑战性的（成本率）映射目标，这项新技术将使这些目标变得可访问且具有成本效益，并具有可靠的精确度。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力和更广泛影响的评估来审查CRITERIA的评估。