PSVII-13 Prediction of feed efficiency in finishing pigs using different machine learning algorithms and growth time intervals.
Notice bibliographique
Résumé
Abstract Feed efficiency is a very important trait in the pig industry, but measuring it can be relatively expensive. Utilizing machine learning (ML) to predict feed efficiency can reduce measurement costs and provide valuable insights into feeding patterns. Comparing different algorithms in the ML process helps to ensure that the chosen algorithm is accurate, efficient, interpretable, and well-suited to the specific characteristics of the data. Additionally, predicting feed efficiency using different time intervals can help to identify the optimal time points for measurement, saving time and resources while ensuring accurate and effective feeding practices. For this purpose, 1,480,103 daily feed intake records from 19,591 Canadian Duroc pigs from weeks 9 to 25 of age at the growing period were used. Due to differences in the starting ages, the animal records with more than 30% missing daily records were removed before analysis. Data preprocessing included imputing missing data with the K-nearest neighbours method, removing outliers, and centering and scaling data using the R Caret Package. After data filtering and editing, the daily feed intake records of 14,325 pigs from five consecutive two-week intervals (weeks 11-12, 13-14, 15-16, 17-18, and 19-20) were used jointly with other factors (sex, dam, sire, herd-year-season, pen, common litter, starting age and weight) to predict residual feed intake (RFI) and feed conversion ratio (FCR). The performance of Decision Tree (DEC), Gradient Boosting Method (GBM), K-Nearest Neighbors (KNN), Support Vector Machines (SVM) with a linear kernel, Lasso Regression using the glmnet method (GLMNET), and Random Forest (RF) were tested to identify the best method for predicting feed efficiency using 5-fold cross-validation. Mean absolute error (MAE), root mean square error (RMSE), R², and Pearson’s correlation between predicted and actual data were used to evaluate the model performance. Overall, RF achieved the lowest MAE (0.034 for RFI, 0.037 for FCR) and the highest correlation (0.985 for RFI, 0.986 for FCR) in the training data set, demonstrating superior predictive ability. However, its testing performance declined (MAE: 0.089 for RFI, 0.095 for FCR; correlation: 0.816 for RFI, 0.852 for FCR), suggesting potential overfitting. Additionally, GBM, SVM, and GLMNET also showed strong predictive accuracy, with testing MAE values around 0.072–0.077 for RFI and 0.052–0.073 for FCR, while maintaining correlations above 0.85. In contrast, DEC and KNN exhibited higher error values for MAE and RMSE and lower correlations, highlighting the effectiveness of ensemble-based methods for this predictive task. For predicting RFI and FCR, among the scenarios with two-week intervals, weeks 17-18 were the closest to the complete data scenario. Overall, these results suggest that ensemble-based methods can be used for predicting feed efficiency in pigs, and collecting data in weeks 17-18 of age might reduce the costs of feed intake measurement.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».