Development of the precision feeding system for sows via a rule-based expert system

Abstract

: To precisely meet the nutritional requirements of sows during the stages of pregnancy and lactation, a precision feeding system was developed by using the intelligent sow feeder combined with a rule-based expert system and the Internet of Things (IoTs). The model of uncertain knowledge representation was established for inference by using the certainty factor. The daily feeding amount of each sow was calculated by the expert system. An improved pattern matching algorithm Reused Degree Model-RETE (RDM-RETE) was proposed for the decision of daily feeding amount, which sped up inference by optimizing the RETE network topology. A prediction model of daily feeding amount was established by a rule-based expert system and the precision feeding was achieved by an accurate control technology of variable volume. The experimental results demonstrated that the HASH-RDM-RETE algorithm could effectively reduce the network complexity and improve the inference efficiency. The feeding amount decided by the expert system was a logarithmic model, which was consistent with the feeding law of lactating sows. The inferential feeding amount was adopted as the predicted feed intake and the coefficient of correlation between predicted feed intake and actual feed intake was greater than or equal to 0.99. Each sow was fed at different feeding intervals and different feed amounts for each meal in a day. The feed intake was 26.84% higher than that of artificial feeding during lactation days ( p <0.05). The piglets weaned per sow per year (PSY) can be increased by 1.51 compared with that of relatively high levels in domestic pig farms. This system is stable in feeding and lowers the breeding cost that can be applied in precision feeding in swine production.