Development and Field Evaluation of an Autonomous Four-Wheel-Drive Agricultural Vehicle Tracking Crop Rows Using Computer Vision Technology

This study presents the development and field evaluation of an autonomous four-wheel drive (4WD) agricultural prototype equipped with a cost-effective image-based navigation system. While high-precision positioning typically relies on expensive RTK-GNSS systems, this research explores the operational limits of handcrafted feature extraction methods, specifically Canny Edge Detection and Probabilistic Hough Transform, on a resource-constrained Raspberry Pi 4B platform. The methodology includes structured field trials in a 30-metre corn field, with 10 successful autonomous runs conducted under three different lighting scenarios: sunny, cloudy, and twilight. Navigation accuracy was measured using Mean Cross Tracking Error (MCTE) with measurements recorded at 3-metre intervals. Results show that the system achieved its highest stability under cloudy (diffuse) conditions, with a minimum MCTE of 6.2 cm and 95% accuracy. A performance decrease was observed in twilight conditions (MCTE: 12.5 cm) due to a decrease in the signal-to-noise ratio (SNR) and in sunny conditions (MCTE: 8.0 cm) due to shadow-induced interference. The findings indicate that four-wheel drive platforms combined with optimised vision pipelines offer a viable, low-cost alternative for small-scale agricultural automation, provided that environmental lighting variability is addressed.