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

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，通过无人驾驶飞行器（UAV）使用航空遥感来检测和识别田间杂草。 目前，杂草越来越具有除草剂抗性。 农民需要花更多的钱，在田地里使用更多的化学品，导致利润减少和环境退化。 与此同时，无人机的成本大大降低，而联邦航空局越来越放松飞行规定。 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需要很长的准备时间）;与卫星替代品相比，它们还提供高得多的分辨率。 然而，农民需要的不仅仅是图像;他们需要可操作的情报，以便做出决策。 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施用并提高产量。SBIR第一阶段项目提出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，有效的杂草识别和绘图。 多种技术必须以集成的方式结合在一起，才能作为一个完整的解决方案提供。 一个稳定的平台能够以非常高的精度跟踪飞行路径，而不受风和其他干扰的影响，这是避免数据收集不准确时可能发生的“垃圾输入/垃圾输出”现象所必需的-这对于机器视觉算法尤其重要，因为清晰的图像可以大大提高精度。 关于图像处理和机器视觉，一个关键的方面是设计这些，使得它们可以在给定的机载资源下有效地执行，并在可用的时间内快速执行。 我们的目标是以用户友好的方式向农民展示这些算法的结果，以便他们可以快速确认或拒绝（视情况而定）结果并采取行动。

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.