The integration of Precision Livestock Farming (PLF) into scientific communities and the next generation applications
The milking robot is a classical PLF application: the smallest manageable production unit in this case is one single quarter of an udder. This issue includes a review of milking-robot utilization and robot milking–pasture combination. Continuing with dairy in this issue, Kinect (3d Camera)-based system is applied for automatic lameness detection. Another approach for automatic lameness detection, gait behaviour and ground reaction forces, is also proposed . Infrared-thermography-based monitoring of body temperature of calves and gene expression of calves undergoing gradual weaning are presented in this special issue.
In meat production, this special issue presents monitoring of drinking behaviour of individual pigs housed in a group using radio frequency identification. We also see the development of automatic surveillance of animal behaviour and welfare using image analysis and machine-learned segmentation technique. Broiler chickens are usually fast-growing, bred intensively with up to 40 000 con-specifics. The farmers’ challenge is to reach high final body weight in a short time while maintaining an efficient feed-conversion rate. An application, presented in this issue offers vocalisation sound pattern identification in young broiler chickens.
This special issue covers the next generation applications. Its content provides evidence of the initial integration of PLF into the community of animal scientists, with a widening and deepening of research, development and evaluation of underlying concepts of PLF for a vast and diverse world of livestock production. Potential applications include individual animal food intake, automatic early detection of illness or stress, and monitoring of animal welfare. The prospects for further developments are manifest.
This article is freely available for one month:
Editorial: Precision livestock farming: a ‘per animal’ approach using advanced monitoring technologies
Authors: I. Halachmi and M. Guarino
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