https://doi.org/10.4081/jae.2024.1568

Zanthoxylum infructescence detection based on adaptive density clustering

Vol. 55 No. 2 (2024)
Published: 26 March 2024
Adaptive density clustering, infructescence detection, yield estimation, Zanthoxylum
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Authors

Diwei Wu
College of Computer & Information Science, Chongqing Normal University, Chongqing; Chongqing Center of Engineering Technology Research on Digital Agricultural & Service, Chongqing, China.
Shaohua Zeng
zsh_cqu@126.com
College of Computer & Information Science, Chongqing Normal University, Chongqing; Chongqing Center of Engineering Technology Research on Digital Agricultural & Service, Chongqing, China.
Shuai Wang
Chongqing Master Station of Agricultural Technology Promotion, Chongqing, China.
Yanan Chen
Chongqing Wanzhou District Station of Soil Fertilizer and Agricultural Ecological Protection, Chongqing, China.
Yidan Xu
Chongqing Beibei District Agriculture and Rural Committee, Chongqing, China.

To determine the Zanthoxylum yield, infructescence detection during the early fruiting stage is a prerequisite. The purpose of this research is to determine and quantify the infructescences on photos of Zanthoxylum’s early fruit-bearing branches that are gathered in their natural habitat. Consequently, a two-phase machine vision-based algorithm for identifying Zanthoxylum infructescences is proposed. First, the fruits of Zanthoxylum infructescences are extracted by extracting the histogram of oriented gradient (HOG) feature map and excess green minus excess red (ExGR) index from the branch image of the plant. The second involves roughly and adaptively classifying fruits based on the density of their position distribution. Rough clusters are then combined using an optimization model to produce the best possible clustering outcome. Experiments with normal samples demonstrate that the proposed approach receives a precision of 96.67%, a Recall of 91.07%, and an F1-score of 0.93. Compared to ADPCkNN, DBSCAN, and OPTICS algorithms, the suggested algorithm performs better in robustness and attains a higher F1-score and recall. In the meantime, its competitiveness is demonstrated in the deep learning-based method experiments. The tests demonstrate its efficacy in adaptively detecting the infructescences of branch images of Zanthoxylum.

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How to Cite

Wu, D. (2024) “<i>Zanthoxylum</i> infructescence detection based on adaptive density clustering”, Journal of Agricultural Engineering, 55(2). doi: 10.4081/jae.2024.1568.
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