Comparison of computer vision system and colour spectrophotometer for colour measurement of mozzarella cheese

Minz, P. S.; Saini, Charanjiv Singh

doi:10.1016/j.afres.2021.100020

Cited by 15 publications

(9 citation statements)

References 27 publications

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“…Moreover, XYZ values are used to estimate the CIE L * a * b * color values [50]. 9 and 10 explain these CIE L * a * b * color values that are normalized to the reference white [51].…”

Section: Color Spaces Transformations and Yellowness Index Computationmentioning

confidence: 99%

Introduction of LSSVR for the Prediction of the Yellowness Index

Yeo,

Saptoro

2024

IJCDS

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Data-driven models including principal component regression (PCR), partial least square regression (PLSR), and least square support vector regression (LSSVR) have been widely applied as predictive models in various applications. However, studies employing regression models to estimate the yellowness index (YI) are scarce in the literature. This study, therefore, focuses on developing non-destructive YI measurements using regression models. The collected RGB calculated XYZ and obtained CIE LAB values were set as the input variables. Meanwhile, the YI value was denoted as the output variable. Results indicated that the LSSVR model outperforms PCR and PLSR models in predicting YI in which the root means square errors of LSSVR for the training and testing datasets were found to be 261,406% to 294,218% and 725% to 772% lower than PLSR and PCR, respectively. LSSVR is also attributed to higher coefficients of determination (R 2 ) that are superior to PLSR and PCR, whose R 2 values are very close to 1. Nonetheless, the computational times of training and testing datasets for LSSVR are much longer than those of PLSR and PCR. Consequently, a highly accurate LSSVR model-based YI sensor shows promising applications particularly if the computational load can be further minimized.

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“…Moreover, XYZ values are used to estimate the CIE L * a * b * color values [50]. 9 and 10 explain these CIE L * a * b * color values that are normalized to the reference white [51].…”

Section: Color Spaces Transformations and Yellowness Index Computationmentioning

confidence: 99%

Introduction of LSSVR for the Prediction of the Yellowness Index

Yeo,

Saptoro

2024

IJCDS

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“…For example, work [1] reports the results of research on the information technology of computer vision for remote measurement of symptoms of schizophrenia using smartphones. Study [2] shows the use of computer vision technologies to measure the color of mozzarella cheese. Research using computer vision technologies in [3] makes it possible to obtain reliable data for the analysis of physiological and environmental indicators of the soybean crown.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Research using computer vision technologies in [3] makes it possible to obtain reliable data for the analysis of physiological and environmental indicators of the soybean crown. The variety of application of information technologies of computer vision is proved by a large number of scientific works, such as [1][2][3].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Recognition of aerial photography objects based on data sets with different aggregation of classes

Prystavka

Dukhnovska

Kovtun

et al. 2023

EEJET

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The object of this work is the recognition algorithms of aerial photography objects, namely, the analysis of recognition accuracy based on data sets with different aggregation classes. To solve this problem, an information system for object recognition based on aerial photography data has been developed. An architecture based on neural network architectures of the ConvNets group with structural modifications was chosen and used to create the information system. The use of a convolutional neural network of the ConvNets group in the architecture of the information system for the recognition of objects of aerial photography gives high accuracy rates when training the information system and validating its results. But the authors did not find any studies on the learning of the neural network of the ConvNets group. Therefore, it was decided to conduct an analysis in which case the ConvNets network will provide validation results with higher accuracy when the training takes place on datasets with or without class aggregation. The authors performed an analysis of the accuracy of recognition of aerial photography objects based on data sets with different aggregation classes. The dataset used for neural network training consisted of 3-channel labeled images of 64x64 pixels size. Based on the analysis, the optimal number of epochs for training is selected, which makes it possible to recognize aerial photography objects with greater accuracy and speed. It was concluded that greater accuracy in image classification is achieved for sampling without crossing data from different classes (without aggregation of classes). The result of the work is recommended for use in the automation of dataset filling and information filtering of visual images

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“…The computer vision system has been increasingly used due to its advantages over other technologies. It is a nondestructive technique, which is easily used, economical and does not require sampling (Minz and Saini, 2021). Furthermore, it allows the monitoring of visual aspects, in real time; the analysis of the surface texture, the measurement of geometric characteristics, among others, with the advantage of allowing measurements and analysis simultaneously (Minz and Saini, 2021).…”

Section: Technologies Of Industry 40mentioning

confidence: 99%

“…It is a nondestructive technique, which is easily used, economical and does not require sampling (Minz and Saini, 2021). Furthermore, it allows the monitoring of visual aspects, in real time; the analysis of the surface texture, the measurement of geometric characteristics, among others, with the advantage of allowing measurements and analysis simultaneously (Minz and Saini, 2021). For these reasons, it has been used in different situations, such as in the detection of manufacturing defects, food industry, agriculture, medicine, automotive industry, among others (Heng et al, 2021;Minz and Saini, 2021).…”

Section: Technologies Of Industry 40mentioning

confidence: 99%

Industry 4.0 Solutions Network for Wire Harness Test Equipment Industry

Proceedings of the International Conference on Industrial Engineering and Operations Management

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In recent years there has been an increasing competitiveness in the automotive sector with companies having to respond to an increasingly diverse choice of customers. Additionally, the incorporation of several technological features in cars and the consequent dependence on several electrical systems has increased the complexity and demands of the wire harness manufacturing and testing process. The high variability that these products present has required the production of test equipment highly diverse and, therefore, with a high customisation factor. The adoption of Industry 4.0 technologies has proven to confer flexibility to production processes, increasing their efficiency, without compromising quality, production costs and product manufacturing cycle times. Thus, the application of this type of technologies in the industrial context of the production of harness test equipment could prove beneficial. This paper aims to propose a solution network composed by Industry 4.0 technologies and based on the Internet of Things concept, which, in the context of manufacturing methodologies, could make the productive system more efficient and less prone to failures. As a case study, a wire harness test equipment company was analysed.

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Comparison of computer vision system and colour spectrophotometer for colour measurement of mozzarella cheese

Cited by 15 publications

References 27 publications

Introduction of LSSVR for the Prediction of the Yellowness Index

Introduction of LSSVR for the Prediction of the Yellowness Index

Recognition of aerial photography objects based on data sets with different aggregation of classes

Industry 4.0 Solutions Network for Wire Harness Test Equipment Industry

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