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.

小型企业创新研究（SBIR）I阶段项目的更广泛的影响/委托是开发一种讲述技术，使美国的角落能够基于现场特征的情况来降低氮的氮肥应用。美国消耗的10％的足迹用于玉米的氮肥生产。离子会造成人类健康的硝基污染，从而降低了水生生态系统，并散发出温室的污染，以使气候变化成为一种关键的社交元素美国玉米农民面临遵循最佳管理实践所面临的挑战。这项小型企业创新创新研究（SBIR）I阶段项目旨在证明使用低 - 最低的玉米es可见的Yellowness的技术性成本摄像机在阶段I项目中使用的机器人不足。当玉米高约1-2英尺时，通过操纵量的传感器是通过观察到的，是在田野上的氮气差异。模型调整。该奖项反映了NSF'SF'Stutory任务，并使用基金会的知识分子优点和更广泛的影响审查标准进行了评估。