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

该项目更广泛的影响/商业潜力在于拟议系统的可及性和准确性。通过消除农民使用无人机系统(UAS)的障碍，农业田间侦察变得更加容易，并将导致更广泛的采用，这将为无人机在农业领域带来巨大的新市场。这一系统将通过允许有针对性地使用化学品，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，来自拟议系统的高质量数据将导致农业资源的更有效利用，这将为农民节省大量资金，同时有助于减少化学物质和其他污染物向环境的排放。该系统的广泛使用还将允许从更广泛的地理空间、更高的时间频谱获得更大的数据积累，这是农业大数据工作迫切需要的，旨在进一步提高精准农业决策支持系统的水平。这个小型企业创新研究(SBIR)第一阶段项目将研究利用无人机系统(UAS)和平板电脑开发具有近实时农业数据收集/处理能力的系统的可行性。缺乏高分辨率的数据采集系统是当前精准农业技术的一大绊脚石。现有的数据收集方法既耗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的方法)排除了现场图像处理，并延误了对时间敏感的实地情况报告的交付。拟议的系统将测试新的数据收集和数据预处理方法，这些方法消除了计算昂贵的算法，从而允许使用平板电脑进行现场数据处理。这一目标将通过利用十多年积累的作物数据来开发最佳的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，它将提供满足农民需求的高质量农业数据？对空间和时间分辨率的要求。

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.