Robust Guidance and Selective Spraying Based on Deep Learning for an Advanced Four-Wheeled Farming Robot

Abstract

Complex farmland backgrounds and varying light intensities make the detection of guidance paths more difficult, even with computer vision technology. In this study, a robust line extraction approach for use in vision-guided farming robot navigation is proposed. The crops, drip irrigation belts, and ridges are extracted through a deep learning method to form multiple navigation feature points, which are then fitted into a regression line using the least squares method. Furthermore, deep learning-driven methods are used to detect weeds and unhealthy crops. Programmed proportional–integral–derivative (PID) speed control and fuzzy logic-based steering control are embedded in a low-cost hardware system and assist a highly maneuverable farming robot in maintaining forward movement at a constant speed and performing selective spraying operations efficiently. The experimental results show that under different weather conditions, the farming robot can maintain a deviation angle of 1 degree at a speed of 12.5 cm/s and perform selective spraying operations efficiently. The effective weed coverage (EWC) and ineffective weed coverage (IWC) reached 83% and 8%, respectively, and the pesticide reduction reached 53%. Detailed analysis and evaluation of the proposed scheme are also illustrated in this paper.