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.

这项小型企业创新研究（SBIR）项目的更广泛的影响/商业潜力是为农民提供按需使用空中遥感通过无人驾驶汽车（UAV）来检测和识别现场杂草的能力。 目前，杂草越来越多地变得耐除草剂。 农民必须花更多的钱，并将更多的化学品应用于其领域，从而减少利润和环境退化。 同时，无人机的成本大大降低，而FAA越来越放松法规。 无人机带来强大的好处：它们可以按需操作（而不是通过载人飞行进行成像的长时间交付时间）；与卫星替代品相比，它们还提供了更高的分辨率。 但是，农民需要的不仅仅是图像。他们需要可行的情报，以便可以做出决定。 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映射。 多种技术必须以整合的方式聚集在一起，以便将其作为完整的解决方案。 一个能够遵循具有非凡精度的飞行路径的稳定平台，无论风和其他干扰都是必要的，以避免数据收集不准确的“垃圾中 /垃圾”现象，这对于机器视觉算法作为清晰的图像尤其重要。 关于图像处理和机器视觉，一个关键方面是设计这些，以便鉴于板上可用的资源并在可用的时间内迅速执行，它们可以有效地执行。 目的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结果并采取行动。