A Novel Chicken Meat Quality Evaluation Method Based on Color Card Localization and Color Correction

Abstract

Among all the chicken meat quality evaluation metrics, color is one of the most significant factors directly related to the freshness of meat inducing the purchase desire. Biochemical tests for evaluating meat quality may contaminate or damage the test samples. Visual rating method is subjectively inefficient and difficult to realize online detection. Colorimeter has the disadvantages of complicated timeconsuming operations, high technical requirements and expensive instruments. This paper proposes a low-cost, contactless chicken meat quality evaluation method by examining the color image of chicken meat. Specifically, the meat image is acquired by the camera of a smartphone. To eliminate the chromatic aberration, a pre-defined color card is put beside meat and automatically localized to extract the captured color information for color correction. Finally, the corrected colors of all the experimental meat samples are analyzed by hierarchical clustering to achieve 3 different quality levels.

Study on the nitrogen content estimation model of cotton leaves based on “image-spectrum-fluorescence” data fusion

“…Digital image technology can identify the subtle differences between the surface layers of samples within the range of visible light. When collecting digital images of cotton leaves, it has been found that the influence of uncertain factors, such as the external environment and light intensity, is likely to lead to inaccurate and inconsistent image colors (You et al, 2020). In this study, the effect of these random errors on image color features was minimized by taking pictures in sufficient sunlight and correcting the standard color card.…”

“…Fluorescence signals can predict the growth status of plants and explain the changes in plant physiological and biochemical processes (Zivcak et al, 2015;Jia, 2016). Nitrogen application rate can notably affect the characteristic changes of chlorophyll fluorescence parameters when used to monitor crop nitrogen content (He et al, 2020), which indicates that fluorescence parameters are ideal indicators of crops' nutritional status and can directly monitor nitrogen content (Feng et al, 2015;Li et al, 2020a). Compared with hyperspectral and chlorophyll fluorescence, digital image technology is simple to operate, visualize, and apply.…”

Engineering Strategies for Advancing Optical Signal Outputs in Smartphone‐Enabled Point‐of‐Care Diagnostics

“…[57] To reduce background signal and increase detection accuracy, a light filter can be embedded to block excitation light and only pass emission light to the CMOS sensor. [58] The excitation light sources are usually LEDs with certain wavelength or lasers, on the grounds that [142] Copyright 2020, IEEE. b) Circle shape test region localization.…”

“…Therefore, a color correction for doctors’ displays was also performed using the color chart and a colorimeter to ensure the display color reproducibility. You et al [ 142 ] proposed a low‐cost, contactless chicken meat quality evaluation method by examining the color image of chicken meat. The image color was corrected using a multivariate linear regression model with reference to a color chart placed beside the meat.…”

Identification of the differentially expressed genes in the leg muscles of Zhedong white geese (Anser cygnoides) reared under different photoperiods

“…Meat color is one of the key indicators for consumers to evaluate the quality of meat and also is one of the most important quality traits influencing consumer purchasing decisions ( You et al, 2020 ). In poultry production, light is a key factor affecting the poultry meat color.…”