Application of machine learning to increase the precision of na automated swine nutrition system
DOI:
https://doi.org/10.14488/1676-1901.v22i1.4586Keywords:
Machine Learning, Artificial Intelligence, Artificial Neural Network, Linear Regression, Swine NutritionAbstract
With the constant computational evolution, the feasibility of implementing machine learning models has been developing in several areas of activity. One of the areas of great relevance for national economic development is agribusiness, and among its sub-areas, pig farming has a representative share of the market. Among the swine production cost items, the one with the greatest representation and the need for precise control is feed consumption. Aiming at increasing the accuracy of an automated swine nutrition system, this article aims to demonstrate a composition of supervised artificial intelligence model to predict the most relevant variables to achieve the smallest possible error between the programed dosage and the dosage performed by the equipment. The considered machine learning model is linear regression. To verify the accuracy and performance of the models, the main error metrics of quantitative analysis are used. The results presented in the paper indicate the linear regression machine learning model achieves a better generalization of
predicted variable and can reduce the absolute error of the dosage of each animal treatment, in average, by 7,4 times.
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