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

这一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努力和研究中的使用。由于规模、物流和农村宽带互联网接入的缺乏，农业和农学研究方面的数据令人惊讶地缺乏。与此同时，农民和农学家正在以这样或那样的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学研究中的数据驱动决策，包括植物育种和可持续作物管理。来自我们技术的数据可以用于基于无人机的产量预测，用于经济和价值链应用，提供的信息比当前方法提供的更快。这个i-Corps项目将减少成本和时间，以便在季节期间主动分析农作物田地的无人机系统图像，以量化描述作物健康、生长和发育的指标。这项技术是一种软件和工作流程，它使用遥感、摄影测量和计算机视觉技术，从农作物田地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，而不是依赖昂贵的海量图像正射镶嵌和高级GPS测量来提取和分析地块。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现场。这项创新还提供了指标的定制分区和生成数据的质量控制。这项创新是植物科学中一种廉价的高通量田间表型鉴定方法。这项创新可以为一系列决策和预测应用程序的早季产量预测做出重大贡献。在种业中定期考虑使用廉价的航空图像进行生长分析，将改善遗传收益和精准农业管理区的产品放置。精确农业管理将通过实时、反应迅速、高分辨率的作物健康评估来促进。改进的作物模型和产量预测对许多部门具有经济价值。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.