Multi-Input Deep Learning Models for Weight Forecasting of Pigs Using Depth Images

Abstract

Accurate weight forecasting is essential for optimizing swine farming operations and enhancing animal welfare. This paper introduces a novel approach for pig weight forecasting, employing multi-input deep learning models that harness both depth images and statistical descriptors. The study conducts a comprehensive comparison of traditional machine learning (ML) models, deep learning (DL) models, hybrid ML and DL models, and multi-input models integrating both time-series data and image features. A meticulously curated dataset comprising timeseries weight measurements and corresponding depth images of pigs forms the foundation of the study. Image descriptors such as length, width, depth, and volume were extracted from the depth images. The proposed multi-input models, employing architectures based on ResNet, XCeption, LSTM, and GRU layers, are meticulously trained and evaluated using this dataset. The performance evaluation is conducted using mean absolute error (MAE) and mean absolute percentage error (MAPE) metrics. The results underscore the superiority of the multi-input models over traditional ML, DL, and hybrid models. Notably, the best-performing model achieves a test MAE of 1.81 kg and a test MAPE of 5.56%. This exceptional performance highlights the importance of leveraging both time-series data and image features for precise weight forecasting in pigs. These findings can hold significant implications for improving the efficiency and sustainability of swine farming practices, offering a pathway towards improved decision-making and animal management protocols.