Intelligent Automatic Feeder for Pregnant Sows
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i2.1260Keywords:
Pregnant Sows, Decision Tree Algorithm, Non-parametric supervised learning, Internet of ThingsAbstract
Pork consumption has increased considerably in recent years, making pig reproduction of vital importance. Feeding sows during the gestational period involves several factors that impact their health, well-being, and postpartum outcomes. Automated feeding systems for sows, unlike feed delivery by human feeders, can measure and adjust the quantity and quality of feed provided at scheduled times with diets established by human experts, allowing for the establishment of a nutritional program for each stage of pregnancy. They also allow for the calculation, using probabilistic and statistical tools, of future diets for the gestational period, which promotes the health and well-being of the sow. This research paper describes, and presents the results obtained from experiments carried out with intelligent automatic feeders for pregnant sows, implemented with Internet of Things (IoT) devices and operated with a predictive computing algorithm, which uses decision trees to predict feeding for the gestation stages of sows, using the weight of the sows, as well as the amount and type of feed provided to the sow. The experiments were carried out with a typical feeding process performed by a human operator, against a feeding process provided with automatic feeders to two different sets of pregnant sows. The results obtained with the automatic feeders show weights closer to the standard weights established for the gestational stages of the sows.
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