Citrus recognition in orchard scene based on modified HSV-morphology method

Jinyuan Yan; Kaiqi Liang; Chenxing Liu; Ming Gao

doi:10.54254/2755-2721/88/20241637

Jinyuan Yan^* , Kaiqi Liang , Chenxing Liu , Ming Gao

College of Computer Science and Technology (College of Date Science), Taiyuan University of Technology, Shanxi, China

^* Corresponding author: yanjinyuan6@gmail.com

Published: 27 August 2024

DOI: https://doi.org/10.54254/2755-2721/88/20241637

Abstract

The orchard has always been an important scene for citrus pickers, and the existence of factors such as leaf occlusion and color similarity always lead to the difficulty of robot recognition. Based on a citrus image dataset collected from actual orchard scenes, we extract image features and build a mathematical model to identify and count the number of oranges in each image, and show the distribution of apples in the entire dataset. Firstly, this paper establishes a model based on HSV method, focusing on recognizing yellow citrus. Secondly, with the further analysis of the data set, it is found that the cyan citrus exists. Therefore, this paper defines the empirical HSV range of two kinds of citrus at the same time and introduces contour processing to optimize the model. In order to accurately distinguish the immature citrus from the leaves with similar colors and the citrus occluded by leaves, this paper uses more detailed color threshold setting and morphological operation, and makes the eccentricity of the contour less than 0.85. At the same time, considering the size and overlap of citrus, morphological operations such as dilation and erosion and contour detection techniques are applied to further improve the recognition ability of the model. Finally, the experimental results show that the average accuracy of the established model reaches 92.1%, which has a good recognition effect on citrus.

Keywords: Citrus recognition , Orchard scene , HSV method , Morphology , Contour feature

ACE Vol.88

ISSN (Print): 2755-2721

ISSN (Online): 2755-273X

ISBN (Print): 978-1-83558-603-7

ISBN (Online): 978-1-83558-604-4

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Article Information

Received: 21 August 2024

Published: 27 August 2024

DOI: https://doi.org/10.54254/2755-2721/88/20241637

Article Type: Research Article

Cite this article

Yan,J.;Liang,K.;Liu,C.;Gao,M. (2024). Citrus recognition in orchard scene based on modified HSV-morphology method. Applied and Computational Engineering,88,64-71.

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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About this Volume

Volume Title: ACE Vol.88

Part of Series: Applied and Computational Engineering

ISSN: 2755-2721 (Print) / 2755-273X (Online)

Article Information

Cite this article

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