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

SBIR 第一阶段：使用机器学习算法创建高质量、低成本的土壤图

• 批准号: 2051852
• 负责人: Yones Khaledian
• 金额: $ 25.6万
• 依托单位: SOILSERDEM LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2051852&HistoricalAwards=false
• 关键词: SBIR; Phase; Creating; quality; lower

The broader impact/ commercial potential of this SBIR Phase I project will be to produce high-quality, accurate, high-resolution soil maps for agronomists and farmers. Accurate soil information is a fundamental driver of better, more efficient crop/soil management. The new technology would significantly increase farm profitability, lower food costs, and improve environmental protection and sustainability. Making higher quality soil fertility mapping readily available and usable is the goal of this project. This technology is expected to result in increased crop yield while allowing for decreased input costs, leading to higher profits in an industry that chronically suffers from low profit margins. The expected benefits include more environmentally responsible farm management and better manure-management planning, nutrient-management planning, precision farming, land use planning, planting decisions, evaluating stressors on plants, field conditioning, crop rotation, and prediction/interpretation of yields. These will result in increased farm profitability, more efficient application of nitrogen fertilizers, and increased soil health and fertility for plants. This project advances an innovative technology has three key components to produce maps of essential soil nutrients in training fields and beyond — maps that currently require extensive sampling while producing inadequate data. The first is a digital hill-slope position to select optimal sampling locations to represent the soil variability across the landscape, eliminating the need to take unnecessary soil samples. The second element leverages advanced machine-learning algorithms insensitive to the quantity of sample size. The third element is its ability to select suitable remotely sensed information (terrain derivatives and satellite imagery). The technology will select appropriate analysis scales of terrain derivatives to capture all potential soil variability. It will then select and use proper bands of satellite imagery, based on spatial, temporal, and spectral resolution, to decrease the risks of overfitting and computation time. Unlike currently available methods, this technology can predict the soil nutrients inside the training fields and beyond — i.e., this technology has the potential to predict soil properties in neighboring fields — using the soil information obtained from training fields—without the need for additional samples.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.

该SBIR I期项目的更广泛的影响/商业潜力将是为农艺学家和农民提供高质量，准确，高分辨率的土壤图。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。这项新技术将大大提高农场盈利能力，降低食品成本，并提高环境保护和可持续性。该项目的目标是使更高质量的土壤肥力映射易于可用，并且可用。预计该技术将导致农作物产量提高，同时允许增加投入成本，从而在长期遭受低利润率的行业中获得更高的利润。预期的好处包括对环境负责的农场管理和更好的手动管理计划，营养管理计划，精确农业，土地使用计划，种植决策，评估植物的压力，现场调节，作物轮换以及收益率的预测/解释。这些将导致农场盈利能力提高，氮肥的更有效地应用以及植物的土壤健康和生育能力提高。该项目推进了一项创新技术，具有三个关键组成部分，可以在培训领域及其他地区生产基本土壤养分的地图 - 当前需要大量采样的地图在产生数据不足时需要进行大量抽样。首先是一个数字山坡位置，可以选择最佳采样位置来表示整个景观的土壤变异性，从而消除了需要进行不必要的土壤样品的需求。第二个元素利用了对样本量数量不敏感的高级机器学习算法。第三个要素是它可以选择合适的远程感知信息（地形衍生物和卫星图像）的能力。该技术将选择适当的地形衍生物分析量表，以捕获所有潜在的土壤变异性。然后，它将根据空间，临时和光谱分辨率选择并使用适当的卫星图像带，以减少过度拟合和计算时间的风险。与目前可用的方法不同，该技术可以使用从培训领域获得的土壤信息来预测培训领域内及其他地区内部的土壤养分，即该技术有可能预测邻近领域的土壤特性，而无需其他样品。该奖项反映了NSF的法定任务，并通过使用基础的智力效果评估了NSF的法定任务，并通过评估诚实地表达了支持。