SBIR Phase I: Development of a multi-output machine learning modeling framework for a hybridized perennial cover crop for specialty crop systems

SBIR 第一阶段：为特种作物系统的杂交多年生覆盖作物开发多输出机器学习建模框架

• 批准号: 2212482
• 负责人: Sloane Rice
• 金额: $ 24.89万
• 依托单位: VITIDORE, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212482&HistoricalAwards=false
• 关键词: SBIR; Phase; Development; multi; output

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project comes through the development of a prototype software modeling technology and associated monitoring methodology that will be used to transform the value proposition of farm-based practices that improve soil carbon sequestration. The solution enables verification of high-quality carbon offsets, provides feedback regarding important agronomic metrics, and can be applied on a large and geographically expansive scale. Specifically, this prototype is tailored to a novel species of hybridized grass planted as a sustainable, carbon-sequestering cover crop in specialty cropping systems. The solution's agronomic value proposition will enable farming operations to play a part in climate change mitigation while also improving profitability. This innovation will apply to millions of acres of specialty crop farms throughout the country, where cover cropping is currently underutilized despite environmental benefits. The solution will encourage a higher rate of adoption of the practice, lowering strain on farming budgets and natural resources. Additionally, the technology prototype will be applicable to other sustainable, land-based, carbon removal practices, extending its value-added potential to a wide variety of farming practices and circumstances.This SBIR Phase I project seeks to develop a machine-learning based, multi-output modeling software system to quantifying soil carbon sequestration associated with specified farming practices and other important agronomic metrics. The farming practice modeled by this prototype is a hybridized, perennial, cool-season grass planted in specialty cropping systems as a cover crop. Cover cropping is paired with the practice of no-till to enable on-farm soil carbon sequestration, which is quantified by the software modeling prototype for third party verification. Inputs for the models come from a combination of remote and in situ monitored data, such as soil and biomass sample analysis, drone-captured multispectral imagery, and satellite imagery. The quantity and type of model inputs is determined by several factors, including the scalability of monitoring costs, their effect on the accuracy of the models, and the requirements of third-party protocols for verifying soil carbon models. Project tasks include the calibration of the monitoring methodology for new target variables, the addition of plant water status and plant nitrogen status models to a soil carbon model, and the delivery of a functional and scalable prototype capable of generating accurate predictive models of environmental changes attributed to the aforementioned cover crop.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.

这个小型企业创新研究(SBIR)第一阶段项目的更广泛的影响/商业潜力来自于原型软件建模技术和相关监测方法的开发，这些技术和监测方法将用于改变基于农场的做法的价值主张，以改善土壤碳封存。该解决方案能够验证高质量的碳补偿，提供有关重要农艺指标的反馈，并可在大规模和地理扩展范围内应用。具体地说，这个原型是为一种新的杂交草种量身定做的，作为一种可持续的、固碳的覆盖作物种植在特殊种植系统中。该解决方案的农学价值主张将使农业经营在缓解气候变化方面发挥作用，同时也提高盈利能力。这项创新将适用于全国数百万英亩的特种农作物农场，尽管对环境有利，但覆盖作物目前仍未得到充分利用。该解决方案将鼓励更高的采用率，减轻农业预算和自然资源的压力。此外，该技术原型将适用于其他可持续的陆上碳移除实践，将其增值潜力扩展到各种耕作实践和环境。该SBIR第一阶段项目旨在开发一个基于机器学习的多输出建模软件系统，以量化与特定耕作实践和其他重要农学指标相关的土壤固碳。由这个原型模拟的耕作实践是一种杂交的多年生冷季草，种植在特殊种植系统中作为覆盖作物。覆盖种植与免耕实践相结合，实现了农田土壤的碳固存，并由软件建模原型进行量化，以供第三方验证。模型的投入来自远程和现场监测数据的组合，例如土壤和生物量样本分析、无人机捕获的多光谱图像和卫星图像。模型输入的数量和类型由几个因素决定，包括监测成本的可扩展性、它们对模型准确性的影响以及验证土壤碳模型的第三方协议的要求。项目任务包括校准新目标变量的监测方法，将植物水分状况和植物氮状况模型添加到土壤碳模型中，以及交付能够为上述覆盖作物造成的环境变化生成准确预测模型的功能和可扩展的原型。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。