SBIR Phase I: Aerial Weed Scout with Robust Adaptive Control for Site-Specific Weed Control

SBIR 第一阶段：具有稳健自适应控制的空中杂草侦察，用于特定地点的杂草控制

• 批准号: 1520588
• 负责人: Gregory Rose
• 金额: $ 15万
• 依托单位: Intelinair, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1520588&HistoricalAwards=false
• 关键词: SBIR; Phase; Aerial; Weed; Scout

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to provide the farmer with the ability, on demand, to use aerial remote sensing via an unmanned aerial vehicle (UAV) to detect and identify weeds in the field. Presently, weeds are increasingly becoming herbicide-resistant. Farmers are required to spend more money and apply greater amounts of chemicals to their field leading to less profit and environmental degradation. In the meantime, UAVs have greatly decreased in cost while the FAA is increasingly loosening regulations to fly. UAVs bring powerful benefits: They can be operated on-demand (as opposed to long lead times for imaging via manned flight); and they also provide much higher resolution as compared with satellite alternatives. However, farmers needs more than images; they need actionable intelligence so decisions can be made. With the high resolution images provided by the UAV, and the advances in analytics, machine learning, and signature matching, these technologies will be used to help farmers efficiently manage their herbicide applications and improve their yield.This SBIR Phase I project proposes to demonstrate the feasibility of an aerial weed scout by developing and testing the onboard image processing algorithm and L1 robust adaptive control for accurate, efficient weed identification and mapping. Multiple technologies must come together in an integrative fashion in order to deliver this as a complete solution. A stable platform capable of following flight paths with extraordinary precision regardless of wind and other disturbances is necessary to avoid the "garbage-in /garbage-out" phenomenon that can occur with inaccurate data collection - this is especially critical for machine vision algorithms as crisp images greatly improve accuracy. Regarding image processing and machine vision, one critical aspect is designing these such that they can be efficiently executed given the resources available on-board, and speedily executed in the time available. The goal is to present the results of these algorithms to the farmer in a user friendly way so that they can quickly confirm or reject (as appropriate) the results and take action.

这个小企业创新研究（SBIR）项目的更广泛的影响/商业潜力是为农民提供能力，根据需要，通过无人机（UAV）使用空中遥感来检测和识别田地里的杂草。目前，杂草越来越具有抗除草剂性。农民被要求花更多的钱，在他们的田地里使用更多的化学品，导致利润减少和环境恶化。与此同时，无人机的成本大大降低，而美国联邦航空局越来越放松对飞行的管制。无人机带来了强大的优势：它们可以按需操作（而不是通过载人飞行进行成像的长交付时间）；与卫星相比，它们还提供了更高的分辨率。然而，农民需要的不仅仅是图像；他们需要可操作的情报，这样才能做出决定。借助无人机提供的高分辨率图像，以及分析、机器学习和特征匹配方面的进步，这些技术将用于帮助农民有效地管理除草剂的使用并提高产量。该SBIR一期项目旨在通过开发和测试机载图像处理算法和L1鲁棒自适应控制来证明空中杂草侦察的可行性，以实现准确、高效的杂草识别和映射。为了提供完整的解决方案，多种技术必须以集成的方式结合在一起。一个稳定的平台能够以非凡的精度跟踪飞行路径，而不受风和其他干扰是必要的，以避免“垃圾进/垃圾出”现象，可能发生的不准确的数据收集-这对机器视觉算法尤其重要，因为清晰的图像大大提高了精度。关于图像处理和机器视觉，一个关键的方面是设计它们，使它们能够在给定的可用资源下有效地执行，并在可用的时间内快速执行。目标是以用户友好的方式将这些算法的结果呈现给农民，以便他们可以快速确认或拒绝（适当的）结果并采取行动。