4.3 Article

MEASURING THE CAP DIAMETER OF WHITE BUTTON MUSHROOMS (AGARICUS BISPORUS) BY USING DEPTH IMAGE PROCESSING

Journal

APPLIED ENGINEERING IN AGRICULTURE
Volume 37, Issue 4, Pages 623-633

Publisher

AMER SOC AGRICULTURAL & BIOLOGICAL ENGINEERS
DOI: 10.13031/aea.14356

Keywords

Background segmentation; Computer vision; Diameter measurement; Edible fungus; Hough transform

Funding

  1. National Key Research and Development Project [2019YFE0125100]
  2. Basic Research Project of the Key Scientific Research Project Plan of Henan University [19zx015]
  3. National Natural Science Foundation of China [61805073]

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This article introduces a method using depth image processing to detect and measure the diameter of white button mushroom caps, allowing for selective harvesting. It proposes a novel approach to segment mushrooms from compost, utilizing a metaphor of sea level and islands for background segmentation to accurately measure cap diameter.
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.

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