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Estimation of Physical Characteristics of Peach Leaves Using K-means Clustering in the L*a*b* Color Space

Published: 28 February 2024 Publication History

Abstract

Evaluating the health of a peach tree based on the characteristics of its leaves is a common practice in botany and agriculture. The length, width, area, and perimeter of peach leaves represent their basic physical characteristics, which can be used further to determine the healthy status of a peach tree from the affected status based on leaf symptoms of diseases or nutrient deficiencies. In this study, we compare the segmentation outcomes across twenty-five different color spaces, and L*a*b* color space yields the best result using the proposed metric function scores. Furthermore, seven segmentation algorithms in the L*a*b* color space are compared using the same metric function and K-means algorithm is identified as the most effective one among assessed algorithms. Based on these results, we employ the proposed procedure using K-means clustering in the L*a*b* color space to estimate physical characteristics of peach leaves.

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    ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
    October 2023
    589 pages
    ISBN:9798400707988
    DOI:10.1145/3633637
    Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    Published: 28 February 2024

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    Author Tags

    1. agriculture
    2. clustering algorithms
    3. image segmentation

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