SBIR Phase I: Precision Farming Operating System for Personal Unmanned Aerial Vehicles with Intelligent Field Adaptive Data Collection Protocol

SBIR第一阶段：具有智能现场自适应数据收集协议的个人无人机精准农业操作系统

• 批准号: 1549330
• 负责人: Lei Tian
• 金额: $ 15万
• 依托单位: Ag-Sensus, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1549330&HistoricalAwards=false
• 关键词: SBIR; Phase; Precision; Farming; Operating

The broader impact/commercial potential of this project lies in the accessibility and accuracy of the proposed system. By eliminating the hurdle of using Unmanned Aerial System (UAS) for farmers, agricultural field scouting becomes more accessible and will lead to wider adoption, which will lead to significant new market for UAS in agriculture. This system will provide significant savings to crop-growers by allowing targeted application of chemicals. More than $8 billion dollars are spent on herbicide and pesticides in US annually, and the high quality data from the proposed system will lead to a more efficient use of resources in agriculture, which will provide significant savings to farmers, while at the same time, help reduce the release of chemicals and other pollutants into the environment. Wide spread usage of this system will also allow greater data accumulation from a wider geospatial, higher temporal spectrum that is desperately needed for the agricultural Big Data efforts aimed at further advancements in the precision farming decision support systems.This Small Business Innovation Research (SBIR) Phase I project will examine the feasibility of developing a system that will have near real time agricultural data collection/processing capabilities using Unmanned Aerial Systems (UAS) and tablet computers. The lack of a high-resolution data collection system is a great stumbling block to current precision agriculture technology. Existing methods of data collection are time consuming and yield low-quality data. Current aerial data collection methods (include some that utilize UAS) preclude on-site image processing, and delays the delivery of time-sensitive field situation reports. The proposed system will test novel data collection and data pre-processing methods that eliminate computationally expensive algorithms thereby allowing on-site data processing using tablet pcs. This goal will be reached by utilizing over a decade worth of accumulated crop data to develop the optimum data collection processes and data processing algorithms. The end result will be a prototype system that will provide high-quality agricultural data that meet the farmers? requirement in spatial and temporal resolutions.

该项目更广泛的影响/商业潜力在于拟议系统的可访问性和准确性。通过消除农民使用无人机系统（UAS）的障碍，农业领域的侦察变得更加容易，并将导致更广泛的采用，这将为农业中的UAS带来重要的新市场。该系统将通过允许有针对性地施用化学品为作物种植者节省大量资金。美国每年在除草剂和杀虫剂上花费超过80亿美元，拟议系统的高质量数据将导致更有效地利用农业资源，这将为农民节省大量资金，同时有助于减少化学品和其他污染物向环境中的释放。该系统的广泛使用还将允许从更广泛的地理空间积累更多的数据，更高的时间谱，这是迫切需要的农业大数据的努力，旨在进一步推进精准农业决策支持系统。这个小企业创新研究（SBIR）第一阶段项目将研究开发一个系统的可行性，将有近真实的时间农业数据收集/使用无人机系统（UAS）和平板电脑的处理能力。缺乏高分辨率的数据收集系统是当前精准农业技术的一大障碍。现有的数据收集方法既费时又产生低质量的数据。目前的航空数据收集方法（包括一些利用无人机系统的方法）排除了现场图像处理，并延迟了对时间敏感的现场情况报告的交付。该系统将测试新的数据收集和数据预处理方法，消除计算昂贵的算法，从而允许现场数据处理使用平板电脑。这一目标将通过利用十多年积累的作物数据来开发最佳数据收集过程和数据处理算法来实现。最终结果将是一个原型系统，该系统将提供满足农民需求的高质量农业数据？空间和时间分辨率的要求。