Green Grape Detection and Picking-Point Calculation in a Night-Time Natural Environment Using a Charge-Coupled Device (CCD) Vision Sensor with Artificial Illumination

Juntao Xiong; Zhen Liu; Rui Lin; Rongbin Bu; Zhiliang He; Zhengang Yang; Cuixiao Liang

doi:10.3390/s18040969

Sensors (Mar 2018)

Green Grape Detection and Picking-Point Calculation in a Night-Time Natural Environment Using a Charge-Coupled Device (CCD) Vision Sensor with Artificial Illumination

Juntao Xiong,
Zhen Liu,
Rui Lin,
Rongbin Bu,
Zhiliang He,
Zhengang Yang,
Cuixiao Liang

Affiliations

Juntao Xiong: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Zhen Liu: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Rui Lin: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Rongbin Bu: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Zhiliang He: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Zhengang Yang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Cuixiao Liang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China

DOI: https://doi.org/10.3390/s18040969
Journal volume & issue: Vol. 18, no. 4
p. 969

Abstract

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Night-time fruit-picking technology is important to picking robots. This paper proposes a method of night-time detection and picking-point positioning for green grape-picking robots to solve the difficult problem of green grape detection and picking in night-time conditions with artificial lighting systems. Taking a representative green grape named Centennial Seedless as the research object, daytime and night-time grape images were captured by a custom-designed visual system. Detection was conducted employing the following steps: (1) The RGB (red, green and blue). Color model was determined for night-time green grape detection through analysis of color features of grape images under daytime natural light and night-time artificial lighting. The R component of the RGB color model was rotated and the image resolution was compressed; (2) The improved Chan–Vese (C–V) level set model and morphological processing method were used to remove the background of the image, leaving out the grape fruit; (3) Based on the character of grape vertical suspension, combining the principle of the minimum circumscribed rectangle of fruit and the Hough straight line detection method, straight-line fitting for the fruit stem was conducted and the picking point was calculated using the stem with an angle of fitting line and vertical line less than 15°. The visual detection experiment results showed that the accuracy of grape fruit detection was 91.67% and the average running time of the proposed algorithm was 0.46 s. The picking-point calculation experiment results showed that the highest accuracy for the picking-point calculation was 92.5%, while the lowest was 80%. The results demonstrate that the proposed method of night-time green grape detection and picking-point calculation can provide technical support to the grape-picking robots.

Published in Sensors

ISSN: 1424-8220 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Technology: Chemical technology
Website: http://www.mdpi.com/journal/sensors

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