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
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Implementation of a Fruit Quality Classification Application Using an Artificial Intelligence Algorithm

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

(Received May 16, 2021; Accepted November 24, 2021)

Keywords: real-time object detection, object track, fruit classification, machine learning, neural network model

Fruit quality classification in the consumer market has become a greater burden following the decrease in the young adult population engaged in agriculture in Taiwan owing to its labor-intensiveness. We propose a system to identify the external quality of fruit that utilizes a camera as an image sensor and an artificial intelligence algorithm as a classifier. This application is suitable for real operating environments. Fruit are mainly detected by the “you only look once” (YOLO)-V3 algorithm, with the designated fruit continuously tracked using the characteristics of the image, such as size, height, width, etc., and the quality of fruit is detected during the tracking process. Finally, the switching gap of the application distinguishes fruit of different quality. The proposed application detects round fruit such as apples, oranges, and lemons using our newly developed process. We also provide a graphical user interface to control and collect data, evaluate models, and monitor the entire system operation to improve the efficiency of the proposed application. The experimental results show that the proposed application achieves an accuracy rate of up to 88% after testing on 6,000 fruit images.

Corresponding author: Yin-Ting Cheng




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