SBIR Phase I: Immediate Delivery of Massive Aerial Imagery to Farmers and Crop Consultants

SBIR 第一阶段：立即向农民和作物顾问提供大量航空图像

• 批准号: 1549187
• 负责人: Amy Gooch
• 金额: $ 15万
• 依托单位: ViSUS 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 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1549187&HistoricalAwards=false
• 关键词: SBIR; Phase; Immediate; Delivery; Massive

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to accelerate the adoption of data intensive precision agriculture, increasing yields while decreasing farm inputs such as fertilizers and pesticides. This projects removes the software bottleneck (time and labor) in processing large aerial surveys taken by Unmanned Aerial Systems, enabling a cost-effective and timely process to deliver actionable information to farmers. Using frequent high-quality aerial scans, farmers may optimize the use of fertilizers and more finely control the amount of pesticides and herbicides necessary to increase crop yield. Furthermore, farmers mitigate costs and losses by being able to spot problem areas, minimize the spread of plant diseases, and identify issues such as standing water, irrigation malfunctions, and persistent automated machinery errors in planting or cultivation. This project provides special benefit for those customers in poorly connected areas by eliminating the need to upload massive imagery to the cloud for processing. The technology is part of a broad initiative in agriculture addressing the need for a 70% increase food production by 2050 in response to the projected growth of the world's population.This Small Business Innovation Research (SBIR) Phase I project will produce new algorithms for on-the-fly orthorectification, stitching, and normalization of aerial image mosaics and a software prototype. The technology behind this research project is designed from the ground up to process massive data with less memory and increased speed relative to other approaches. These performance gains are enabled by a proprietary streaming cache-oblivious generic image representation, one that enables multichannel giga- and terapixel images to be treated as ordinary images. The proposed innovation overcomes the obstacles of limited compute resources and limited connectivity to the cloud. New algorithms built on the stream-processing image infrastructure enable automated image stitching on any computer configuration, from commodity hardware such as a farmer's laptop PC, to mobile devices, with no loss in image resolution.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力将是加速数据密集型精准农业的采用，提高产量，同时减少化肥和农药等农业投入。该项目消除了处理无人机系统进行的大型航空调查的软件瓶颈（时间和劳动力），从而实现了具有成本效益且及时的流程，向农民提供可操作的信息。通过频繁的高质量航空扫描，农民可以优化肥料的使用，并更精细地控制提高作物产量所需的农药和除草剂的用量。此外，农民能够通过发现问题区域、最大限度地减少植物病害的传播以及识别诸如积水、灌溉故障和种植或耕作过程中持续存在的自动化机械错误等问题来降低成本和损失。该项目无需将大量图像上传到云端进行处理，为那些交通不便地区的客户提供了特殊的好处。该技术是一项广泛的农业计划的一部分，旨在满足到 2050 年将粮食产量增加 70% 的需求，以应对世界人口的预计增长。这个小型企业创新研究 (SBIR) 第一阶段项目将产生用于航空图像马赛克的动态正射校正、拼接和标准化的新算法以及软件原型。该研究项目背后的技术是从头开始设计的，与其他方法相比，用更少的内存和更高的速度来处理海量数据。这些性能提升是通过专有的流式缓存无关的通用图像表示实现的，该表示使多通道千兆像素和兆像素图像能够被视为普通图像。所提出的创新克服了有限的计算资源和有限的云连接的障碍。基于流处理图像基础设施构建的新算法可以在任何计算机配置上实现自动图像拼接，从农民的笔记本电脑等商用硬件到移动设备，而不会损失图像分辨率。