Data mining for evaluating animal performance from weighing platform big data

Precision livestock farming technologies, such as IoT-enabled weighing platforms, generate big data that require rigorous processing to ensure correctness and integrity. However, literature lacks a reliable procedure for automatically filtering misreadings from weighing platform big data, hindering accurate evaluation of performance indicators. This study addresses this gap through developing and validating a data mining procedure consisting in four steps: removes outliers based on 1) shrunk body weight ( SBW ), 2) discrepancies between weighing platform measurements, 3) estimation of initial and final live body weight ( BW ), and 4) deviations between measured and estimated live BW from a Generalized Additive Model (GAM). Data were collected from 12 experiments (1152 steers, 583,321 samples) conducted in Campanelli Innovation Center. The data mining procedure effectively removed outliers from loss of calibration, partial or even multiple animals standing on the platform. When compared with previous methodologies, our procedure was 20x more accurate. Mined data were used to fit GAM to predict BW . The average daily gain calculated from mined data showed an error (–0.02 ± 0.74%) 40x more accurate than using raw data (7.99 ± 29.42%) or previous methodologies and showed no statistical difference with measured data. Considering that our proposed methodology needs SBW and its measurement is stressful and lead to performance loss, an extreme gradient boosting regression model was trained to predict initial and final SBW and were highly accurate (RMSE: 0.0216 kg; R 2 : 0.9954). The proposed data mining procedure improved data correctness and integrity, generating high-quality datasets that can be used for early detection of animal disease, evaluating impacts on performance from new feeding additives, and supporting data-driven management decisions. In addition, the proposed procedure can be easily implemented in similar set ups.