A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare

Abstract

Several precision livestock farming (PLF) technologies, conceived for optimizing farming processes, are developed to detect the physical and behavioral changes of animals continuously and in real-time. The aim of this review was to explore the capacity of existing PLF technologies to contribute to the assessment of pig welfare. In a web search for commercially available PLF for pigs, 83 technologies were identified. A literature search was conducted, following systematic review guidelines (PRISMA), to identify studies on the validation of sensor technologies for assessing animal-based welfare indicators. Two validation levels were defined: internal (evaluation during system building within the same population that were used for system building) and external (evaluation on a different population than during system building). From 2,463 articles found, 111 were selected, which validated some PLF that could be applied to the assessment of animal-based welfare indicators of pigs (7% classified as external, and 93% as internal validation). From our list of commercially available PLF technologies, only 5% had been externally validated. The more often validated technologies were vision-based solutions (n = 45), followed by load-cells (n = 28; feeders and drinkers, force plates and scales), accelerometers (n = 14) and microphones (n = 14), thermal cameras (n = 10), photoelectric sensors (n = 5), radio-frequency identification (RFID) for tracking (n = 2), infrared thermometers (n = 1), and pyrometer (n = 1). Externally validated technologies were photoelectric sensors (n = 2), thermal cameras (n = 2), microphone (n = 1), load-cells (n = 1), RFID (n = 1), and pyrometer (n = 1). Measured traits included activity and posture-related behavior, feeding and drinking, other behavior, physical condition, and health. In conclusion, existing PLF technologies are potential tools for on-farm animal welfare assessment in pig production. However, validation studies are lacking for an important percentage of market available tools, and in particular research and development need to focus on identifying the feature candidates of the measures (e.g., deviations from diurnal pattern, threshold levels) that are valid signals of either negative or positive animal welfare. An important gap identified are the lack of technologies to assess affective states (both positive and negative states).

Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

“…Such system should then ideally be externally validated, and its performance assessed, in order to establish its potential for a wide and useful implementation. Considering the high accuracy (≥ 81.5%) reached by the neural network in our study, we believe that the performance of this system could be similar, or higher, than the performance of existing microphone-based systems, which are aimed at classify stress vocalizations and coughing (> 73% 25 ).…”

“…These results suggest that an automated recognition system can be developed for this highly commercial species to allow real-time discrimination of emotional states by valence or context of production. To our knowledge, none of the currently existing monitoring technology (Precision Livestock Farming) developed for pigs can assess the valence of the animals’ emotions 25 . Such a system would thus be highly useful to enable famers to keep track of this important component of animal welfare.…”

Evaluation of an Active LF Tracking System and Data Processing Methods for Livestock Precision Farming in the Poultry Sector

“…Relevant dataprocessing studies are lacking, although they could help to standardize this process to generate comparable datasets across different studies. The benefit of this work is most essential in light of rapid development in technology in order to manage and improve the welfare of animals within commercial livestock systems [1,2,[30][31][32][33][34][35]. We showed that the data processed by a simple filtering of registrations associated with short durations (<1 min) of stays was suitable for monitoring the number of transitions per individual per zone, accounting for 91% of the actual variance (as observed by video).…”

Interactive Rooting Towers and Behavioural Observations as Strategies to Reduce Tail Biting on Conventional Pig Fattening Farms

“…Due to the fact that the entire fattening period was not recorded in our study, but only on pre-defined days, it was not possible to evaluate the days prior to a tail biting outbreak retrospectively. Automatic monitoring for reliable behavioural indicators for a forthcoming tail biting outbreak using advanced technology (Precision Livestock Farming) would allow early, preventive interventions [ 63 , 64 ]. The automatic detection of low tail posture as an early warning sign was successfully implemented on a commercial farm using 3D cameras [ 62 ].…”