Vision-Based Row Detection Algorithms Evaluation for Weeding Cultivator Guidance in Lentil

Abstract

It is important to detect crop rows accurately for field navigation. In order to accurate weeding, cultivator guidance system should detect the crop center line precisely. The methods of vision-based row detection for lentil field were studied. Monochrome and color images were used in this research. The color images are transformed into grey scale images in two different formulas to make comparing among them and find an optimal one. In order to detect the center of the crop row rapidly and effectively, Hough transform and gravity center image processing algorithms were applied to acquired images. The field crop images were segmented into two parts by using optimal thresholding (plant and soil as a background), then Hough transform was applied on these binary images. Gray scale images were used in gravity center method. The center line detection algorithms were tested for two weed distribution density, include general and intensive. It was observed that both systems successfully detects and calculates the pose and orientation of the crop row on synthetic images. The mean errors between the calculated and manually estimated lines were obtained. Mean errors for Hough transform and gravity center methods were 8 and 10 mm with standard deviations of 7 and 12 mm in general distribution density and 12 and 16mm with standard deviation of 11 and 15mm in high distribution density, respectively. Computational time for Hough transform and gravity center were 0.7 and 0.4 s for general distribution density and 1.2 and 0.8 s for high distribution density, respectively.