Table 2.

Overview of PLF technology and applications.

Reference	Application	Involved Methods/Technologies	Main Objective/Function
[89]	Animal behavior	GPS sensors	Tracking location
[64]	Animal behavior	A neck collar with series of sensors	Detection of estrus events through analysis of rumination rate, and the feeding and resting behavior
[67]	Animal behavior	Accelerometers in combination with GPS-based data	Discrimination between several kinds of feeding related behaviors for grazing animals Classification of multiple cattle behaviors
[90]	Animal behavior	A machine learning method	Pig cough detection-processing all incoming sounds and automatically identifying the number of coughs
[69]	Animal behavior	Cameras and microphones Sound tool based on an algorithm	Find a correlation between vocalization and behavior
[71]	Animal behavior	A non-invasive imaging system such as VGG-face model, Fisherfaces, and convolutional neural networks	Pig-face recognition
[74]	Animal health and welfare	Microphones for cough sounds	Detect bovine respiratory disease
[70]	Animal health and welfare	Air sensors	Prediction the onset of Coccidiosis by monitoring the concentration of volatile organic compounds in the air
[76]	Animal health and welfare	Algorithm developed through image analysis	Automatic detection of lameness in dairy cows individually
[80]	Feed management	An automated feeding system	Control the amount of feed provided, and the ambient temperature to optimize animal growth and reduce ammonia emission
[58]	Feed management	A feed sensor	Measure and control the amount of feed delivered to individual feeders
[78]	Feed management	A next-generation feeding system	Provide feed with a variety of nutrient specifications to tailor both the amount and composition of the feed
[79]	Feed management	A computer vision based system CNN models using a low-cost RGB-D camera	Measures cow individual feed intake
[81]	Feed management	NIRS technology	Evaluation of physio-chemical composition of TMR and manure in dairy farms
[82]	Weight management	Weighing system based on image analysis	Determine the weight of individual or group of animals (specifically pigs)
[85,86,87,88]	Automatic milking systems	Time of flight (ToF) depth sensing cameras Algorithmic solutions from depth images and point-cloud data Machine learning based vision for smart MAS Combination of thermal imaging and stereovision techniques	Teat Detection Teat detection and tracking Capability for faster and accurate teat detection Teat sensing