I-Corps: Market research for unmanned aircraft system imaging of agricultural fields

I-Corps项目的更广泛影响是促进无人机在农业和自然资源商业活动和研究中的使用。由于规模、物流和农村宽带互联网接入的缺乏，农业和农艺学研究的数据令人惊讶地缺乏。与此同时，农民和农学家正在以某种方式适应精准农业作物管理，需要景观尺度的高分辨率数据。该项目将大大减少从无人机图像生成准确的作物生长指标所需的时间，然后根据信息采取行动。该项目的开发将改善农业和农艺研究中的数据驱动决策，包括植物育种和可持续作物管理。我们的技术数据可用于经济和价值链应用中基于无人机的产量预测，提供的信息比现有方法更快。I-Corps项目将减少分析农田无人机系统图像的成本和时间，以在季节期间主动量化描述作物健康，生长和发育的指标。该技术是软件和工作流程，使用遥感，摄影测量和计算机视觉技术从农田的原始无人机图像中自动提取研究地块和管理区的复制图像，而不是依赖于昂贵的大规模图像正射镶嵌和高等级GPS测量进行地块提取和分析。这项创新可以通过消除对互联网接入或高性能计算机的需求而应用于该领域。这项创新还提供了自定义的指标分区，以及对生成的数据进行质量控制。该创新是植物科学中高通量田间表型分析的廉价方法。这项创新可以为一系列决策和预测应用程序的早期产量预测做出重大贡献。在种子行业使用廉价的航空图像进行生长分析的定期审议将改善精准农业管理区的遗传增益和产品布局。精准农业管理将通过对作物健康状况的实时、快速、高分辨率评估来促进。改进的作物建模和产量预测对许多部门都具有经济价值。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this I-Corps project is to promote the use of drones in agriculture and natural resource commercial endeavors and research. Due to scale, logistics, and lack of rural broadband internet access, there is a surprising lack of data in agriculture and agronomic research. At the same time, farmers and agronomists are in one way or another adapting to precision agriculture crop management and need high-resolution data at the landscape scale. This project will drastically reduce the time needed to generate accurate crop growth metrics from drone imagery, and then act on the information. Development of this project will improve data-driven decision-making in agriculture and agronomic research, including plant breeding and sustainable crop management. Data from our technology can be used for drone-based yield prediction for economic and value-chain applications, with information provided sooner than is available from current methods.This I-Corps project will reduce the cost and time to analyze unmanned aircraft system imagery of crop fields for quantification of metrics describing crop health, growth, and development, proactively during the season. The technology is software and workflows that use techniques from remote sensing, photogrammetry, and computer vision to automatically extract replicate images of research plots and management zones from raw drone imagery of crop fields, instead of relying on expensive and massive image ortho-mosaics and high-grade GPS measurements for plot extraction and analysis. The innovation can be applied in the field by eliminating the need for internet access or high-performance computers. The innovation also provides custom zoning of metrics, and quality control of the data generated. The innovation is an inexpensive method of high-throughput field phenotyping in the plant sciences. The innovation can contribute significantly to early-season yield prediction for a range of decision-making and forecasting applications. Implementation of regular consideration of growth analysis using inexpensive aerial imagery in the seed industry will improve genetic gain and product placement in precision agriculture management zones. Precision agriculture management will be facilitated by real-time, responsive, high-resolution assessments of crop health. Improved crop modeling and yield predictions are economically valuable to many sectors.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.