65 Machine and Deep Learning Modelling Strategies for Body Weight Prediction of Cattle and Swine

Abstract Machine Learning (ML) and Deep Learning (DL) are two sub-fields of that focus on creating predictive models from data. The algorithms used in ML and DL have been widely used in various livestock science studies, but it is uncommon for both ML and DL to be applied to the same problem. This talk presents examples where both ML and DL algorithms were successfully applied for prediction of body weight of cattle and swine. Often referred to as data-driven modelling, ML uses algorithms such as linear regression, Decision Trees, Artificial Neural Networks (ANNs) and Random Forests to generate predictive models from data. These models are then used to make predictions based on new input data. On the other hand, DL is a more advanced sub-field of AI that uses deep neural networks (a larger version of traditional ANNs) to make predictions. While the domain of application of DL is somewhat restricted to problems where datapoints have stronger relationships among them (e.g., digital images, text) DL algorithms can handle more complex relationships between input data and the target variable, making them ideal for solving more challenging problems. In the case of body weight prediction of cattle and swine, both ML and DL algorithms have been successfully used to make predictions. For example, ML algorithms have been used to predict body weight based on factors such as age, gender, and morphometric measurements. On the other hand, DL algorithms have been successfully for either direct predictions based on the relationship between body weight and other physiological parameters, such as feed intake or growth rate, or as an assistive technology for decluttering and segmentation of animal subjects from digital images with variable backgrounds. In conclusion, both ML and DL are useful tools in the field of livestock science, and both have been successfully used in the prediction of body weight of cattle and swine. However, the choice of which approach to use and its success rate will depend on the specific problem at hand and the complexity of the relationships between the input data and the target variable.

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