Analysis of beef quality according to color changes using computer vision and white-box machine learning techniques

Sánchez, Claudia N.; Orvañanos-Guerrero, María T.; Domínguez-Soberanes, Julieta; Álvarez-Cisneros, Yenizey M.

doi:10.1016/j.heliyon.2023.e17976

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…The results showed that the highest detection accuracy for PLSR and SVM models was 90.9% and 93.3%, respectively. Sánchez et al [26] proposed an approach for multivariate analysis of beef color changes using white-box machine learning techniques. Firstly, they used a computer vision system (CVS) to capture the color of the beef pieces.…”

Section: Application Of Machine Vision Technology On Quality Detectio...mentioning

confidence: 99%

Research Progress on Quality Detection of Livestock and Poultry Meat Based on Machine Vision, Hyperspectral and Multi-Source Information Fusion Technologies

Xu,

Han,

Zhao

et al. 2024

Foods

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Presently, the traditional methods employed for detecting livestock and poultry meat predominantly involve sensory evaluation conducted by humans, chemical index detection, and microbial detection. While these methods demonstrate commendable accuracy in detection, their application becomes more challenging when applied to large-scale production by enterprises. Compared with traditional detection methods, machine vision and hyperspectral technology can realize real-time online detection of large throughput because of their advantages of high efficiency, accuracy, and non-contact measurement, so they have been widely concerned by researchers. Based on this, in order to further enhance the accuracy of online quality detection for livestock and poultry meat, this article presents a comprehensive overview of methods based on machine vision, hyperspectral, and multi-sensor information fusion technologies. This review encompasses an examination of the current research status and the latest advancements in these methodologies while also deliberating on potential future development trends. The ultimate objective is to provide pertinent information and serve as a valuable research resource for the non-destructive online quality detection of livestock and poultry meat.

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Section: Application Of Machine Vision Technology On Quality Detectio...mentioning

confidence: 99%

Research Progress on Quality Detection of Livestock and Poultry Meat Based on Machine Vision, Hyperspectral and Multi-Source Information Fusion Technologies

Xu,

Han,

Zhao

et al. 2024

Foods

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show abstract

“…The white balance algorithm with contrast limited adaptive histogram equalization (CLAHE) enhances the input images. Before applying CLAHE, the input image color space is transferred to the LAB color space [36]. The LAB color space consists of three components: L-channel (luminance), which represents the luminance information; A-channel, which represents the color information along the greenmagenta axis; and B-channel, which represents the color information along the blue-yellow axis.…”

Section: Image Enhancement Based On Balanced Colors and Brightnessmentioning

confidence: 99%

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Alsaeedi,

Al-juboori,

Al-Mahmood

et al. 2023

Sustainability

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Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems. Color improvement should achieve a balance where no new colors appear, as this could interfere with accurate identification and diagnosis of the disease. This is considered one of the challenges in this field. This work proposes an effective model for olive disease diagnosis, consisting of five modules: image enhancement, feature extraction, clustering, and deep neural network. In image enhancement, noise reduction, balanced colors, and CLAHE are applied to LAB color space channels to improve image quality and visual stimulus. In feature extraction, raw images of olive leaves are processed through triple convolutional layers, max pooling operations, and flattening in the CNN convolutional phase. The classification process starts by dividing the data into clusters based on density, followed by the use of a deep neural network. The proposed model was tested on over 3200 olive leaf images and compared with two deep learning algorithms (VGG16 and Alexnet). The results of accuracy and loss rate show that the proposed model achieves (98%, 0.193), while VGG16 and Alexnet reach (96%, 0.432) and (95%, 1.74), respectively. The proposed model demonstrates a robust and effective approach for olive disease diagnosis that combines image enhancement techniques and deep learning-based classification to achieve accurate and reliable results.

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“…In this article, these methods will only be highlighted briefly with a few examples provided. The reader is encouraged to look for additional information as scientists are currently busy developing inexpensive rapid methods to predict different characteristics of meat ( Sánchez et al, 2023 ). Figure 3 shows, side by side, comparison of the drip loss test, centrifugation test, cooking loss test, low field nuclear magnetic resonance ( NMR ), and confocal laser scanning microscopy results obtained from the same meat samples.…”

Section: Introductionmentioning

confidence: 99%

Measuring water holding capacity in poultry meat

Barbut

2024

Poultry Science

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Analysis of beef quality according to color changes using computer vision and white-box machine learning techniques

Cited by 10 publications

References 30 publications

Research Progress on Quality Detection of Livestock and Poultry Meat Based on Machine Vision, Hyperspectral and Multi-Source Information Fusion Technologies

Research Progress on Quality Detection of Livestock and Poultry Meat Based on Machine Vision, Hyperspectral and Multi-Source Information Fusion Technologies

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Measuring water holding capacity in poultry meat

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