249 Predicting body weight of finishing pigs using machine and deep learning algorithms. (30th November 2020)

• Record Type: Journal Article
• Authors: He, Yuqing; Tiezzi, Francesco; Maltecca, Christian
• Is Part Of: Journal of animal science. Volume 98(2020)Supplement 4
• Page Start: 176
• Page End: 176
• Publication Date: 2020-11-30
• Subjects: Pigs -- prediction -- feeding behavior; Livestock -- Periodicals; Livestock; Electronic journals; Periodicals; 636.005
• DOI: 10.1093/jas/skaa278.324
• Languages: English
• ISSNs: 0021-8812

Abstract

Abstract: Understanding and exploiting feeding patterns in swine could allow a reduced feed waste and minimized sorting losses. The objectives of this study were to evaluate the ability to predict whether a pig reached a target weight at finishing by using several algorithms and to compare the prediction using varying amounts of data during the growing period. Data were collected on 655 pigs from 75 to 166 days of age. Pigs were housed with 8 to 15 pigs and a Feed Intake Recording Equipment in each pen. Feed consumption, occupation time, and body weight per visit were recorded when a pig visited the feeder. Lasso Regression (LS), a machine learning algorithm: Random Forest (RF), and a deep learning algorithm: Long-short Term Memory (LSTM) network, were used to forecast whether pigs can reach 129 kg at the finishing stage (159–166 d). Times of visits, a sum of feed consumption, a sum of occupation time in the feeder every day, and age were used as predictors. Data were split into 6 slices by 14 days and used to calibrate the models and their predictive ability was tested with data corresponding to the last 8 days of the study period. The greatest correlation coefficients were 0.799, 0.828, and 0.868 using slice 6 (145–158 d) to train the LS, RF, and LSTM, respectively. The LS and LSTM algorithms had a smaller root mean squared error, 0.863 and 0.895 compared to the RF with 1.375 in the prediction. Overall, LS and LSTM performed best. Predictions using data closest to the finishing stage proved better. This study connects the dynamics of feeding behavior and feed intake data to growth using prediction methods that will hopefully accelerate the mainstream application of electronic feeders in pig production systems.