SBIR Phase I: A highly-scalable, rapid, in-season approach to tune a nitrogen model for accurate prediction of a corn crop’s remaining nitrogen need

SBIR 第一阶段：一种高度可扩展、快速的季节性方法，用于调整氮模型，以准确预测玉米作物的剩余氮需求

• 批准号: 2127096
• 负责人: Kent Cavender-Bares
• 金额: $ 25.6万
• 依托单位: ROWBOT SYSTEMS LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2127096&HistoricalAwards=false
• 关键词: SBIR; Phase; highly; scalable; rapid

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of a novel artificial intelligence technology that enables U.S. corn farmers to optimize nitrogen fertilizer applications based on field characteristics, rainfall and growing conditions. More efficient use of nitrogen fertilizer will reduce the carbon footprint of U.S. agriculture because more than 10% of the energy consumed in the U.S. agricultural sector goes toward the production of nitrogen fertilizer for corn. Because nitrogen fertilizer is a critical driver of both input costs and yield, this technology will improve the profitability of U.S. farmers by reducing input costs and maximizing corn yields. This project may enable corn production that creates less nitrogen fertilizer pollution, which threatens human health, degrades aquatic ecosystems and emits greenhouse gases that contribute to climate change. A key social element of this project is a teaching module for middle-school science students that will blend content on soil science, agronomy, and crop management with the challenges faced by U.S. corn farmers to follow best management practices.This Small Business Innovation Research (SBIR) Phase I project seeks to demonstrate the technical feasibility of using machine learning to evaluate the amount of yellowness on the lowest corn leaves visible in images taken from low-cost cameras mounted on ground robots. Characteristic yellowness on corn leaves is a strong indicator of stress caused by insufficient nitrogen, a key nutrient. A prototype neural network model will be iteratively improved, in part by dramatically increasing the available training imagery over the course of this Phase I project. Imagery will be collected on field trials set up across several Mid-Western states. Preliminary data suggest that the extent of characteristic yellowness is an indicator of accumulated nitrogen stress that is observable only under the canopy and not via airborne sensors. A commercially available nitrogen model will be used to estimate accumulated nitrogen stress across small plots created by manipulating the amount of added nitrogen fertilizer when corn is about 1-2 feet high. Tuning will occur by adjusting key model parameters through simulation until the differences are minimized between observed and modeled accumulated nitrogen stress across a field’s small plots. Parallel software development will improve prototype codes running simulations of a leading nitrogen model, enabling rapid model tuning.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.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力是开发一种新型人工智能技术，使美国玉米种植者能够根据田地特征、降雨量和生长条件优化氮肥施用，更有效地利用。氮肥的使用将减少美国农业的碳足迹，因为美国农业部门消耗的能源中有超过 10% 用于生产玉米氮肥，因为氮肥是投入成本和产量的关键驱动因素。技术将会进步通过降低投入成本和最大限度地提高玉米产量，提高美国农民的盈利能力，该项目可以减少氮肥污染，而氮肥污染威胁人类健康，破坏水生生态系统，并排放温室气体，这是造成气候变化的一个关键社会因素。该项目是一个针对中学生理科学生的教学模块，它将土壤科学、农学和作物管理的内容与美国玉米种植者遵循最佳管理实践所面临的挑战相结合。这个小企业创新研究 (SBIR) 第一阶段项目旨在证明使用机器学习来评估安装在地面机器人上的低成本相机拍摄的图像中可见的最低玉米叶子的黄度程度的技术可行性。玉米叶子的黄度特征是氮不足造成的压力的有力指标。 ，一个关键的营养物，原型神经网络模型将得到迭代改进，部分方法是在第一阶段项目过程中大幅增加可用的训练图像，并将在中西部几个州建立的现场试验中收集图像。数据表明，程度特征黄度是累积氮胁迫的指标，只能在冠层下观察到，而不能通过机载传感器观察到，将使用商用氮模型来估计通过操纵玉米时添加的氮肥量而产生的小块土地上的累积氮胁迫。大约 1-2 英尺高，将通过模拟调整关键模型参数进行调整，直到田间小地块上观测到的累积氮压力与模拟的累积氮压力之间的差异最小化。并行软件开发将改进运行领先模拟的原型代码。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。