Measuring the Cap Diameter of White Button Mushrooms ( Agaricus bisporus ) by Using Depth Image Processing

Abstract

Highlights A new background segmentation algorithm for depth image was developed. Cap diameter of white button mushroom was measured automatically. The average of diameter measurement error was 4.94%. This work can provide online decision support for selectively harvesting of Agaricus bisporus . Abstract. With the increase in the production and yield of white button mushrooms (Agaricus bisporus), efficient harvesting has become a challenge. Automatic selective harvesting has gradually become a solution. The diameter of the mushroom cap is an essential indicator of the harvesting standard. To provide guidance for selective harvesting, this article presents a method for target detection and measuring the diameter of mushroom caps by using depth image processing. According to the three-dimensional structure characteristics of the mushroom, a novel method is proposed to segment it from the compost it grows on. In this method, compost is regarded as the floor of the sea and mushrooms as standing islands. With the rise of sea level, the compost is gradually submerged, and the target of Agaricus bisporus is stable. These features were used to realize the background segmentation. After background segmentation, the pixel coordinates of the contour points of the mushroom caps are transformed into world coordinates, and the cap diameter is measured by Hough transform. In total, 380 mushrooms depicted in 25 depth images were used to test the developed algorithms. The results showed that 92.37% of the mushrooms were correctly detected. The missed detection rate was less than 8%, and the false detection rate was 1.96%. The average diameter measurement error was 4.94%, and the average process time to measure a single mushroom was approximately 0.50 s. The method proposed in this article can provide online decision support for automatic selective harvesting of Agaricus bisporus, which can improve the quality and efficiency of its production. Keywords: Background segmentation, Computer vision, Diameter measurement, Edible fungus, Hough transform.