Application of Neural Networks to Morphological Assessment in Bovine Livestock
In conservation and improvement programs of bovine livestock, an important parameter is morphological assessment, which consist of scoring an animal attending to its morphology, and is always performed by highly-qualified staff.
We present in this paper a system designed to help in morphological assessment, providing a score based on a lateral image of the cow. The system consist of two main parts. First, a feature extractor stage is used to reduce the information of the cow in the image to a set of parameters (describing the shape of the profile of the cow). For this stage, a model of the object is constructed by means of point distribution models (PDM), and later that model is used in the searching process within each image, that is carried out using genetic algorithms (GAs). Second, the parameters obtained are used in the following stage, where a multilayer perceptron is trained in order to provide the desired assessment, using the scores given by experts for selected cows.
The system has been tested with 124 images corresponding to 44 individuals of a special rustic breed, with very promising results, taking into account that the information contained in only one view of the cow is not complete.
KeywordsGenetic Algorithm Mean Square Error Global Score Multilayer Perceptron Lateral Image
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