S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials, Volume 33, Number 9(1) (2021)
Copyright(C) MYU K.K.
pp. 3027-3036
S&M2671 Technical Paper of Special Issue
https://doi.org/10.18494/SAM.2021.3240
Published: September 10, 2021

Portable Device for Ornamental Shrimp Counting Using Unsupervised Machine Learning [PDF]

Chi-Tsai Yeh and Ming-Sheng Ling

(Received December 29, 2020; Accepted May 26, 2021)

Keywords: image segmentation, unsupervised learning, overlapping, counting, portable device

With the rapid development of emerging technologies, intelligent agriculture is incorporating techniques such as the Internet of Things, big data, cloud computing, artificial intelligence, blockchains, and fifth-generation mobile communication to improve work efficiency, prevent various disasters, and change the sales mode of agricultural products. Ornamental fishery is a part of agriculture and accounts for a significant proportion of commercial trade. This paper introduces image processing technology to help ornamental fisheries calculate the number of shrimps quickly. To solve the problem of overlapping live shrimps when counting, K-means unsupervised machine learning is adopted to determine the area of one shrimp. In addition, the proposed method using unsupervised machine learning is able to count different types of shrimp with high accuracy, such as crystal red shrimps, fire red shrimps, and Takashi Amano shrimps. We also analyze two background subtraction techniques, hue/saturation/value (HSV) histogram-based detection and Sobel edge detection, to compare the accuracy and calculation time of this application.

Corresponding author: Chi-Tsai Yeh


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Cite this article
Chi-Tsai Yeh and Ming-Sheng Ling, Portable Device for Ornamental Shrimp Counting Using Unsupervised Machine Learning, Sens. Mater., Vol. 33, No. 9, 2021, p. 3027-3036.



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