Artificial Intelligence Optimization of Ventilation for Tropical Livestock Housing

Abstract

Introduction: Biodiversity in pastures is an important factor affecting nutrition of farm animals, their grazing behavior, and pasture management. Different pasture biodiversity characteristics such as variations in species diversity, vegetation density, quality of forage, biomass, and composition of species can impact the behavior of livestock. However, manual observation of the grazing behavior can be time-consuming and can sometimes yield inconsistent results. In this project, a deep learning-based system is proposed to predict grazing preference using indicators of pasture biodiversity. In this way, pasture and grazing information collected is preprocessed to extract relevant information regarding the vegetation and biodiversity in the pasture. Relevant features are used to train a deep learning system to learn the relationship between pasture characteristics and grazing choices made by livestock. Predictions of preferred and less preferred grazing locations are done according to the learned relationship. Accuracy, precision, recall, F1-score, and prediction error of the developed system are used as evaluation metrics. Proposed system provides an efficient approach to analyze the grazing behavior of livestock and can facilitate precision livestock farming. It can help farmers manage pasture and forage resources, conserve biodiversity and make grazing decisions efficiently