Management System for the Fattening Process of Bovines in Rotational Grazing using Diagnosis and Recommendation Systems

Abstract

Cattle breeding has been one of the most important industrial sectors in the world since it is related to food security and the survival of the human race. Management of the cattle fattening process is a fundamental procedure for cattle breeders because it allows them to make strategic decisions, such as timely treatment in case of any abnormality (e.g., weight gain in herds, in their paddocks). This article aims to present a management system for the cattle fattening process under a rotational grazing scheme, considering the health status of the animal and the pasture, which should diagnose weight loss or gain in bovines and recommend actions when is required. The diagnostic process is based on a fuzzy system that defines rules that characterize the diagnostic process to determine the current situation given an input. Furthermore, the fuzzy classifier optimizes its rules by means of genetic algorithms by modifying its membership functions, providing a more accurate system for diagnosis. On the other hand, the recommendation system is based on a classification model of pasture crops, in which the best pasture is recommended given the soil variables. We tested our proposal with experimental cases, with promising results. For the fuzzy classifier, the accuracy metrics are very good, with values of accuracy close to 100% and of Area Under the Curve close to 1. For the classification model were used several machine learning techniques, resulting in the best classifier the random forest technique, with an accuracy of 98.61%.