Abstract
With the prosperity of artificial intelligence, more and more jobs will be replaced by robots. The future of precision agriculture (PA) will rely on autonomous robots to perform various agricultural operations. Real time kinematic (RTK) assisted global positioning systems (GPS) are able to provide very accurate localization information with a detection error less than \pm 2 cm under ideal conditions. Autonomously driving a robotic vehicle within a furrow requires relative localization of the vehicle with respect to the furrow centerline. This relative location acquisition requires both the coordinates of the vehicle as well as all the stalks of the crop rows on both sides of the furrow. This extensive number of coordinate acquisitions of all the crop stalks demand onerous geographical survey of entire fields in advance. Additionally, real-time RTK-GPS localization of moving vehicles may suffer from satellite occlusion. Hence, the above-mentioned \pm 2 cm accuracy is often significantly compromised in practice. Against this background, we propose sets of computer vision algorithms to coordinate with a low-cost camera (50 US dollars), and a LiDAR sensor (1500 US dollars) to detect the relative location of the vehicle in the furrow during early, and late growth season respectively. Our solution package is superior than most current computer vision algorithms used for PA, thanks to its improved features, such as a machine-learning enabled dynamic crop recognition threshold, which adaptively adjusts its value according to the environmental changes like ambient light, and crop size. Our in-field tests prove that our proposed algorithms approach the accuracy of an ideal RTK-GPS on cross-track detection, and exceed the ideal RTK-GPS on heading detection. Moreover, our solution package neither relies on satellite communication nor advance geographical surveys. Therefore, our low-complexity, and low-cost solution package is a promising localization strategy as it is able to provide the same level of accuracy as an ideal RTK-GPS, yet more consistently, and more reliably, as it requires no external conditions or hassle of the work demanded by RTK-GPS.