Supervision System of the Fattening Process of Cattle in Rotational Grazing using Fuzzy Classification

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. Cattle diagnostics 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 system to diagnose weight loss or gain in cattle under a rotational grazing scheme, considering the health status of the animal and the pasture. The diagnostic system is based on a fuzzy classifier that uses fuzzy logic to define the rules that characterize the diagnostic process, and fuzzy reasoning to determine the current situation given an input. In addition, the fuzzy classifier optimizes the rules using genetic algorithms, which modify the membership functions, providing a more accurate system for diagnosis. We tested our proposal with experimental cases, with promising results. The accuracy metrics have high values, indicating a low error rate in terms of false positives. In general, the values of the quality metrics are very good, with an accuracy close to 100% and an Area Under the Curve close to 1.