Researchers from China evaluated a lightweight deep learning-based approach for supervision of sow behaviour preceding and during farrowing.
Sow farrowing requires supervision to accurately detect issues such as dystocia, piglet suffocation, and excessively low temperatures. Early detection of farrowing problems and proper interventions increase the average number of live born piglets per sow per year. They also the improve piglets’ health and performance. Manual inspection is time-consuming, labour-intensive, and highly subjective. Therefore, there is an increasing need for automatic supervision. Computer-vision technology based on lightweight deep learning is a persistent, non-invasive method that allows rapid processing of sow farrowing video data.
Data collection
The team selected 35 sows in the perinatal period and their piglets for this trial. They installed cameras in the farrowing rooms above the farrowing crates and recorded the pigs for 24 hours. The researchers used the YOLOv5s-6.0 network structure to build a model to detect 4 sow postures including lateral lying, sternal lying, standing, and sitting and the newborn piglets.
The algorithm was deployed on the embedded artificial-intelligence computing platform of the Jetson Nano series. The team used indices such as the precision, recall rate, and detection speed to assess the performance of different algorithms. In addition, they assessed the generalisation ability and the anti-interference ability of the model in 4scenarios: complex light, the time of the first piglet’s birth, different colours of heat lamps, and turning on heat lamp at night.
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