http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Algorithms, Hardware Systems, and Virtual Environments for Perception-Guided Robotic Weed Management
Weed competition is one of the most limiting factors affecting yield and profitability in crop production. In addition to the shortage and cost of labor for weed management, the widespread occurrence of herbicide-resistant weeds due to the heavy reliance on herbicides is threatening agricultural sustainability and global food security. The introduction of robot-based automation for weed management has great potential to effectively address weed issues and reduce the drudgery of manual labor. The progress with robotic weed control requires improvements in the robots' perception to understand complex agricultural environments and weed control actuators. Vision-based robot perception, a process of identifying and interpreting visual information in order to represent and understand the environment, is one of the most promising routes toward robotic weed management. This dissertation aims to develop algorithms and systems for perception-guided robotic weed management, where effective weed detection is essential. Despite the promise of robotic weed management, several technological advances must be made in the areas of plant detection and weeding actuation before robots are fully capable of autonomous weeding. In this regard, this dissertation has four specific objectives towards achieving precision weed management. The first objective is to study the influence of image quality and light consistency on the performance of convolutional neural networks (CNNs) for weed detection. State-of-the-art CNNs rely on a vast number of training images, which are time-consuming and expensive to collect and annotate. The second objective is to develop an image synthesis and semi-supervised learning pipeline to reduce the need for annotated training images for weed detection. Virtual environments are powerful tools and have been widely used for developing robotic systems. However, virtual environments for agriculture are lacking. The third objective is to develop a photometric-based framework to facilitate the synthesis of 3D agricultural vegetation scenes that are both geometrically and optically detailed. In the fourth objective, algorithms and hardware systems have been developed for vision-guided automatic micro-volume herbicide spray using linearly actuated nozzles on a mobile robot platform. The system precisely applies a micro-volume of herbicide liquid on every single weed plant. Compared to the traditional application approach where herbicides are broadcasted, the system greatly reduces herbicide usage. The electronic version of this dissertation is accessible from https://hdl.handle.net/1969.1/198652
"Major Subject: Biological and Agricultural Engineering"
Doctor of Philosophy Texas A&M University 2022 https://hdl.handle.net/1969.1/198652
Major Biological and Agricultural Engineering