Marbling classification of lambs carcasses with the artificial neural image analysis

Przybylak, Andrzej; Śłósarz, P.; Boniecki, P.; Koszela, Krzysztof; Przybył, Krzysztof; Wojcieszak, Dawid; Szulc, R.; Ludwiczak, Agnieszka; Górna, Karolina

doi:10.1117/12.2197027

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…Echo intensity values are heavily influenced by intramuscular fat content, and have been highly correlated with interstitial fibrous tissue content in golden retriever muscular dystrophy dogs . In agricultural sciences, echo intensity is used to study marbling in live cattle …”

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confidence: 99%

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Echo intensity and muscle thickness as predictors Of athleticism and isometric strength in middle‐school boys

et al. 2017

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“…8 In agricultural sciences, echo intensity is used to study marbling in live cattle. 9 A skeletal muscle's architecture is the best predictor of its function. 10 As such, observational investigations in adults have examined relationships between both echo intensity and muscle thickness versus various aspects of physical function.…”

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confidence: 99%

Echo intensity and muscle thickness as predictors Of athleticism and isometric strength in middle‐school boys

et al. 2017

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“…An effective identification of the effects of grain weevil feeding on stored grain entailed the designing, manufacturing, and verification of the new, original classification model [7][8][9][10][11]. The commonly recognized separation properties, such as those represented by artificial neural networks, imply the legitimacy of using them in the process of identifying grain damage caused by grain weevil [12].…”

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confidence: 99%

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

et al. 2020

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A significant part of cereal production is intended for agri-food processing, which implies a necessity to search for and implement modern storage systems for this product. Stored grain is exposed to many unfavorable factors, particularly caryopsis macro-damage caused mainly by grain weevil (Sitophilus granarius L.). This triggers a substantial decrease in the value of the stored material, thus resulting in serious economic losses. Due to this fact, it is necessary to take steps to effectively detect this pest’s presence when grain is delivered to storage facilities. The purpose of this work was to identify the representative physical characteristics of wheat caryopsis affected by grain weevil. An automated visual system was developed to ease the detection of damaged kernels and adult weevils. In order to obtain the empirical data, a decision was made to take advance of SKCS 4100 (the Perten Single Kernel Characterization System). The measurements obtained were used to build the training sets necessary in the process of ANN (artificial neural network) learning with digital neural classifiers. Next, a set of identifying neural models was created and verified, and then the optimal topology was selected. The utilitarian goal of the research was to support the decision-making process taking place during grain storage.

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“…Implementation of NIRS as a process analytical technology (PAT) to the food industry involves a multidisciplinary approach in which computational intelligence (CI), particularly machine learning (ML) [3][4][5][6][7][8][9][10], has been investigated. e main advantage of CI is its capacity of handling multiple parameters, facilitating fast and accurate evaluation of samples in an industrial environment [11].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification

Barbon

Mantovani

et al. 2018

Journal of Spectroscopy

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Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIEL∗a∗b∗, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with 77.2% precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.

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Marbling classification of lambs carcasses with the artificial neural image analysis

Cited by 11 publications

References 5 publications

Echo intensity and muscle thickness as predictors Of athleticism and isometric strength in middle‐school boys

Echo intensity and muscle thickness as predictors Of athleticism and isometric strength in middle‐school boys

Neural Visual Detection of Grain Weevil (Sitophilus granarius L.)

Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification

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