High-Accuracy Adaptive Low-Cost Location Sensing Subsystems for Autonomous Rover in Precision Agriculture

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 system (GPS) is able to provide very accurate localization information with a detection error less than ±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 abovementioned ±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.

Emerging Line of Research Approach in Precision Agriculture: An Insight Study

“…However, the GPS signal's availability could be impacted due to occlusion towards GPSenabled Real-Time Kinematic (RTK) in farming. This problem is addressed in Levoir et al [25] by evolving out with a smart rover that uses sophisticated image processing and statistical analysis to perform localization tasks by the rover. Further studies show that integrating GPS with the sensory application could improve the data acquisition with more accuracy (Rodriguez et al [26]).…”

Simultaneous Localization and Mapping (SLAM) for Synthetic Aperture Radar (SAR) Processing in the Field of Autonomous Driving

“…T-GMs have a lower localization requirement due to their lower maximum map resolution, which results in a position accuracy of 3 cm being sufficient to obtain an artifactfree image of the environment. Nowadays, such position accuracy in the lower centimeter range can be achieved with a differential Global Navigation Satellite Systems (GNSS) [12]. In SAR processing, however, the range-compressed data must be phase-corrected according to the position of the radar sensor.…”

A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops

“…Global path planning [1], mapping [11], localization [12] and decision-making [13] are only some of the required tools that each year undergo heavy research from the scientific community to achieve the necessary requirements for full automation. Among all requirements, a low financial impact constitutes a fundamental goal in order to provide genuinely large-scale competitive solutions [14]. Indeed, expensive sensors and demanding computational algorithms significantly impact the actual usefulness of robotics platforms, essentially preventing their large-scale adoption and introduction into the agricultural world.…”