Storage time prediction of pork by Computational Intelligence

Barbon, Ana Paula A. C.; Barbon, Sylvio; Mantovani, Rafael Gomes; Fuzyi, Estefânia Mayumi; Peres, Louise Manha; Bridi, Ana Maria

doi:10.1016/j.compag.2016.06.028

Cited by 34 publications

(16 citation statements)

References 33 publications

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“…The application of Machine Learning (ML) techniques for food attributes’ prediction and quality evaluation has been widely investigated [10,22,25,26,27,28,29,30]. ML can be applied to extract non-trivial relationships automatically from a training dataset, producing a generalization of knowledge for further predictions [31]. Hence, machine learning promotes high performance as an alternative for an intensive agricultural operational process of the agri-technologies domain [32].…”

Section: Related Workmentioning

confidence: 99%

Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble

Lopes

Ludwig

Barbin

et al. 2019

Sensors

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Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand, naked varieties present superior quality with better visual appearance and nutritional composition for human consumption. Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods. In this paper, we propose CVS combined with the Spatial Pyramid Partition ensemble (SPPe) technique to distinguish between naked and malting types of twenty-two flour varieties using image features and machine learning. SPPe leverages the analysis of patterns from different spatial regions, providing more reliable classification. Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), J48 decision tree, and Random Forest (RF) were compared for samples’ classification. Machine learning algorithms embedded in the CVS were induced based on 55 image features. The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification.

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Section: Related Workmentioning

confidence: 99%

Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble

Lopes

Ludwig

Barbin

et al. 2019

Sensors

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“…Many of CVS apply machine learning (ML) techniques to simulate human decision taking. In fact, ML methods have been widely used for food classification and evaluation of spoilage, frauds, color, or to determine the most relevant parameters to assess meat quality [7–10].…”

Section: Introductionmentioning

confidence: 99%

White striping degree assessment using computer vision system and consumer acceptance test

Kato¹,

Mastelini²,

Campos³

et al. 2019

Asian-Australas J Anim Sci

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Objective The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

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“…The forecasting errors were smaller than 18.00% and the authors suggested the input independence should be high in order to reduce model outputs uncertainty. Fuzzy rule-based systems were used to predict storage times for pork based on five pork quality parameters with the forecasting accuracy of 93.93%-94.41% [11]. A random forest (RF) model was used to predict sugarcane yield based on simulated biomass indices, observed climate and seasonal climate prediction indices [12].…”

Section: Introductionmentioning

confidence: 99%

Spring onion seed demand forecasting using a hybrid Holt-Winters and support vector machine model

et al. 2019

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Demand for spring onion seeds is variable and maintaining its supply is crucial to the success of seed companies. Spring onion seed demand forecasting, which can help reduce the high operational costs increased by long-period propagation and complex logistics, has not previously been investigated yet. This paper provides a novel perspective on spring onion seed demand forecasting and proposes a hybrid Holt-Winters and support vector machine (SVM) forecasting model. The model uses dynamic factors, including historical seed sales, seed inventory, spring onion crop market price and weather data, as inputs to forecast spring onion seed demand. Forecasting error, i.e. the difference between actual and forecasted demand, is assessed. Two advanced machine learning models are trained on the same dataset as benchmark models. Numerical experiments using actual commercial sales data for three spring onion seed varieties show the proposed hybrid model outperformed the statistical-based models for all three forecasting errors. Seed inventory, spring onion crop market price and historical seed sales are the most important dynamic factors, among which seed inventory has short-term influence while other two have mid-term influence on seed demand forecasting. The absolute minimum temperature is the only factor having long-term influence. This study provides a promising spring onion seed demand forecasting model that helps understand the relationships between seed demand and other dynamic factors and the model could potentially be applied to demand forecasting of other crop seeds to reduce total operational costs.

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Storage time prediction of pork by Computational Intelligence

Cited by 34 publications

References 33 publications

Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble

Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble

White striping degree assessment using computer vision system and consumer acceptance test

Spring onion seed demand forecasting using a hybrid Holt-Winters and support vector machine model

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