A Review of Sensors and Machine Learning in Animal Farming
Ahmed Yaseer, Heping Chen
Abstract
Livestock is the primary source of meat, dairy, eggs, leather, wool, etc. Because of increased demand, increasing herd size maintained by fewer manpower is becoming important. Precision Livestock Farming (PLF) utilizing digital technologies such as smart sensors, advanced controls, intelligent robotics, Internet of Things (IoT), big data and machine learning has been emerged to make the animal farming process automated and economically and environmentally sustainable. Animal Farming now requires intelligent techniques to monitor animal welfare and predict animal health 24/7 in real-time. Big data gathered by the IoT devices and sensors are processed with machine learning for prediction and control. This paper reviews the latest sensor technologies and machine learning techniques that can be used as a decision support tool for making the animal farming process more profitable and insightful.