Classification of lamb carcass using machine vision: Comparison of statistical and neural network analyses

A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method

“…The learning procedure for developing a neural network can be either supervised or unsupervised. The supervised learning algorithm used in this research was the back propagation algorithm (Chandraratne et al, 2007). Before updating the weights once at the end of the epoch, this algorithm gets the average gradient of the error surface across all cases and minimizes the mean square error (MSE) between input layer values and output layer values.…”

“…The MLP network is a feed-forward network model which, with its simplicity, has the ability to provide good approximations and has been designed to function well in modelling data that are not linearly separable (Hong, 2012). The complexity of the MLP network depends on the number of layers and neurons in each layer (Chandraratne et al, 2007).…”

Optical Methods and Techniques for Meat Quality Inspection

“…The authors reported that image analysis using geometric and texture features is a useful tool for lamb classification. The same researchers also reported on statistical and neural network analyses of the extracted image features for classification of lamb carcasses (Chandraratne et al, 2007).…”

Application of Artificial Neural Networks in Meat Production and Technology

“…In regard to carcass classification of domestic mammals, the research was mainly focused on either improving or replacing methods that are currently used. Many studies were carried out on classification or carcass quality evaluation in bovine carcasses (Borggaard et al, 1996;Hwang et al, 1997;Díez et al, 2003;Hatem et al, 2003;Lu & Tan, 2004), but also for lamb (Chandraratne et al, 2007) or goat (Peres et al, 2010). In these species the principle of grading is similar and consists of visual notes given by the classifier, which are the indicators of lean meat quantity.…”