Version 1
: Received: 23 October 2023 / Approved: 23 October 2023 / Online: 23 October 2023 (10:07:09 CEST)
How to cite:
Chang, C.; Chen, H. Robust Guidance and Precise Spraying of a Four-wheeled Agricultural Robot based on Deep Learning Approach. Preprints2023, 2023101427. https://doi.org/10.20944/preprints202310.1427.v1
Chang, C.; Chen, H. Robust Guidance and Precise Spraying of a Four-wheeled Agricultural Robot based on Deep Learning Approach. Preprints 2023, 2023101427. https://doi.org/10.20944/preprints202310.1427.v1
Chang, C.; Chen, H. Robust Guidance and Precise Spraying of a Four-wheeled Agricultural Robot based on Deep Learning Approach. Preprints2023, 2023101427. https://doi.org/10.20944/preprints202310.1427.v1
APA Style
Chang, C., & Chen, H. (2023). Robust Guidance and Precise Spraying of a Four-wheeled Agricultural Robot based on Deep Learning Approach. Preprints. https://doi.org/10.20944/preprints202310.1427.v1
Chicago/Turabian Style
Chang, C. and Hung-Wen Chen. 2023 "Robust Guidance and Precise Spraying of a Four-wheeled Agricultural Robot based on Deep Learning Approach" Preprints. https://doi.org/10.20944/preprints202310.1427.v1
Abstract
This paper presents a deep learning-based multi-guidance line detection approach, enabling an autonomous four-wheeled agricultural robot to navigate strip farmland. The integration of Proportional-Integral-Derivative (PID) and Fuzzy Logic Control (FLC) systems optimizes the velocity and heading angle of robot, facilitating smooth navigation along identified guidance line. Enhance cornering maneuverability with real-time kinematics (RTK)-assisted Global Navigation Satellite System (GNSS) (RTK-GNSS) positioning. Additionally, the spraying system combined with deep learning can effectively identify weeds and crop nutrient deficiencies to achieve precise spraying. The guidance system prioritizes irrigation lines for navigation, with additional guidance from crop and furrow lines. Trigonometric analysis determines the angular deviation between the identified guidance line and the vertical line of the top view image. Experiments under diverse weather conditions demonstrate the stable navigation of robot at 12.5 cm/s, achieving up to 90% accuracy in weed and nutrient deficiency identification. The spraying accuracy for weeds and deficiencies averages 87% and 73%, respectively, underscoring the system's contribution to sustainable and precision horticulture practices.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
