Real-time detection system of garlic clove bud for garlic clove orientation metering device based on machine vision

Abstract

In order to combine machine vision technology with single garlic clove extraction and garlic clove orientation adjustment devices in garlic planting mechanization to achieve single garlic clove orientational seeding, a real-time detection system of garlic clove bud for garlic clove orientation metering device was developed. Through the analysis of garlic clove images collected from the garlic clove orientation metering device, the curvature characteristics of the bud and root portions in the garlic cloves outer contour and their relationship were discovered, which could be used to identify the garlic clove bud. By sequentially used the methods of resampling the garlic clove outer contour curve after fitting, calculating the curvature of data points using consecutive data points with intervals, and selecting data points at uniform intervals on the specified garlic clove’s outer contour curve segment, it were eliminated gradually and effectively that interference of abnormal protrusions that may appear on the outer contour of the garlic clove, and highlighted the inherent curvature characteristics of the bud and the root. So the effective peak values of the curvature were extracted. The maximum, second-largest, and third-largest effective peak values were used as curvature characteristic parameters of the bud portion and root portion. And based on their proportional relationships, the position of the garlic clove bud was determined. The hardware system with an industrial control computer as the image processing unit was designed and three key parameters within the detection algorithm were optimized through experiments. They are the number of points to be sampled after curve fitting, the number of points to be interpolated when calculating the curvature of data points, and the number of points to be filtered when selecting data points at uniform intervals on the specified garlic clove outer contour curve segment. The optimal parameter combination obtained was 150, 6, and 4, respectively. Verification tests using the garlic clove orientation metering device showed an average detection accuracy of 96.22 %, and an average detection time of 114.9 ms per garlic clove image, which meets the real-time detection requirements of the garlic clove orientation metering device.