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

Intelinair, Inc., Indianapolis IN

Investigators

Abstract

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.