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Sensors and Materials, Volume 34, Number 1(2) (2022)
Copyright(C) MYU K.K.
pp. 135-149
S&M2799 Research Paper of Special Issue
https://doi.org/10.18494/SAM3552
Published: January 27, 2022

Novel Embedded Smart Gateway Framework for Fruit/Vegetable Quality Classification [PDF]

Ming-Chih Chen, Yin-Ting Cheng, and Chun-Yu Liu

(Received May 12, 2021; Accepted November 24, 2021)

Keywords: quality classification, artificial intelligence, edge computing, microprocessors

Recent advances in technology have increased the use of automation in agriculture. Commercially available equipment associated with fruit and vegetable quality classification or collection has a success rate of classification of around 50%. Even though a system can operate continuously, its low recognition rate causes misjudgments in fruit quality. In this work, we propose a new, embedded gateway structure with simple implementation and enhanced success rates for fruit and vegetable quality classification. We have combined an edge-computing embedded development platform with an artificial intelligence (AI) algorithm structure, where microprocessors are used to control the gateway switch of a conveyor, and this platform is used to build a small fruit/vegetable quality classification system, which has been implemented and tested. The system’s hardware is controlled by different pulse widths. By combining AI algorithms of an image sensor for recognition, we have effectively enhanced the system’s capability of fruit and vegetable quality recognition.

Corresponding author: Yin-Ting Cheng


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Cite this article
Ming-Chih Chen, Yin-Ting Cheng, and Chun-Yu Liu, Novel Embedded Smart Gateway Framework for Fruit/Vegetable Quality Classification, Sens. Mater., Vol. 34, No. 1, 2022, p. 135-149.



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