Background and Objectives Precision feeding aims to define the right feeding strategy according to individual’s nutrient requirements, in order to improve health and reduce feed cost. Usually, the nutrient requirements of gestating sows are provided by a mechanistic nutritional model requiring input data such as age and body status. This paper propose to predict the daily nutritional requirements, with only the data measured by sensors. According to various digital farm configurations, we explore and evaluate Machine Learning (ML) methods to predict nutrient requirements of gestating sows. Material and Methods Behavioural data of gestating sows are extracted from sensors data collected on 73 sows from parities 1 to 9. Their nutrient requirements concerned metabolisable energy (ME, in MJ/d) and standard ileal digestible lysine (SID Lys, in g/ d). Various digital farm configurations are proposed, from low-cost to more expensive equipments (electronic feeder and drinker, connected weight scale, accelerometers and video analysis software), producing various data at different levels of detail on sow behavior. Nine ML algorithms were trained on these 23 scenarios to predict daily energy and lysine for each sow. Results proposed by the ML algorithms are compared with outputs given by the nutritional model InraPorc. Results Using a Random Forest algorithm, the RMSE were lower with data feeder alone (1.22 MJ/d for ME and 0.53 g/d for SID Lys, 2.4 and 4.02% of mean absolute error respectively) compared those obtained with combined data from feeders and accelerometers (1.01 MJ/d and 0.29 g/d, 1.9 and 2.1%). The inclusion of the sows’ characteristics reduced the RMSE, on average, by 20% for ME and by 35% for Lys. Discussion and Conclusion This study highlights that daily requirements of gestating sows can be predicted accurately thanks to behavioural data provided by sensors. It paves the way to propose simpler solutions for the application of precision feeding on farms.