Next Article in Journal
Multiterminal Medium Voltage DC Distribution Network Hierarchical Control
Previous Article in Journal
Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
Previous Article in Special Issue
Optimization and Implementation of Synthetic Basis Feature Descriptor on FPGA
Open AccessArticle

Smart Camera for Quality Inspection and Grading of Food Products

1
School of Electrical and Computer Engineering, Nanfang College of Sun Yat-sen University, Guangzhou 510970, China
2
Electrical and Computer Engineering, Brigham Young University, Provo, UT 84602, USA
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(3), 505; https://doi.org/10.3390/electronics9030505
Received: 25 January 2020 / Revised: 10 March 2020 / Accepted: 16 March 2020 / Published: 19 March 2020
Due to the increasing consumption of food products and demand for food quality and safety, most food processing facilities in the United States utilize machines to automate their processes, such as cleaning, inspection and grading, packing, storing, and shipping. Machine vision technology has been a proven solution for inspection and grading of food products since the late 1980s. The remaining challenges, especially for small to midsize facilities, include the system and operating costs, demand for high-skilled workers for complicated configuration and operation and, in some cases, unsatisfactory results. This paper focuses on the development of an embedded solution with learning capability to alleviate these challenges. Three simple application cases are included to demonstrate the operation of this unique solution. Two datasets of more challenging cases were created to analyze and demonstrate the performance of our visual inspection algorithm. One dataset includes infrared images of Medjool dates of four levels of skin delamination for surface quality grading. The other one consists of grayscale images of oysters with varying shape for shape quality evaluation. Our algorithm achieved a grading accuracy of 95.0% on the date dataset and 98.6% on the oyster dataset, both easily surpassed manual grading, which constantly faces the challenges of human fatigue or other distractions. Details of the design and functions of our smart camera and our simple visual inspection algorithm are discussed in this paper. View Full-Text
Keywords: smart camera; visual inspection; quality grading; evolutionary learning; date grading; oyster grading smart camera; visual inspection; quality grading; evolutionary learning; date grading; oyster grading
Show Figures

Figure 1

MDPI and ACS Style

Guo, Z.; Zhang, M.; Lee, D.-J.; Simons, T. Smart Camera for Quality Inspection and Grading of Food Products. Electronics 2020, 9, 505.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop