Zanthoxylum bungeanum Fruit Detection by Adaptive Thresholds in HSV Space for an Automatic Picking System

Zanthoxylum bungeanum is widely cultivated, and some automatic picking systems or robots are researched to pick the pepper fruit instead of human hands. The detection algorithm is very important to an automatic picking system. Hue, saturation, and value (HSV) color space is widely used in color image segmentation. There is a fixed threshold based on HSV color space, or there is a dynamic threshold based on the Otsu method after the color images are converted into the gray images, because the Otsu method is very popular for the gray pictures. After the evaluation of the fixed threshold and the Otsu method for the pepper fruit detection, we propose an adaptive threshold method directly based on the HSV color space, called AHT and BBSV, which means adaptive hue threshold and balance between saturation and value. There is an adaptive threshold of separate hue, saturation, and luminance component. The hue threshold is obtained according to the conditions of whether there is soil, rock, and the pepper fruit in the image. The saturation and luminance thresholds are obtained by keeping the sum of saturation and luminance unchangeable. There are many hundreds of pictures for the test dataset. Our proposed method works very well, and the recall rate, accuracy, and false alarm can separately achieve 100%, 100%, and 0%, and to evaluate the location precision, we address two metrics: the ratio of the overlap area between the detection region and the ideal region and the location error. The ratio of the overlap area of our method is above 64%, and the location error is about 8%. All performance has been greatly improved compared with the fixed threshold. [GRAPHICS] .