Abstract

Average daily gain is an indicator of the growth rate, feed efficiency, and current health status of livestock species including pigs. Continuous monitoring of daily gain in pigs aids producers to optimize their growth performance while ensuring animal welfare and sustainability, such as reducing stress reactions and feed waste. Computer vision has been used to predict live body weight from video images without direct handling of the pig. In most studies, videos were taken while pigs were immobilized at a weighing station or feeding area to facilitate data collection. An alternative approach is to capture videos while pigs are allowed to move freely within their own housing environment, which can be easily applied to the production system as no special imaging station needs to be established. The objective of this study was to establish a computer vision system by collecting RGB-D videos to capture top-view red, green, and blue (RGB) and depth images of nonrestrained, growing pigs to predict their body weight over time. Over a period of 38 d, eight growers were video recorded for approximately 3 min/d, at the rate of six frames per second, and manually weighed using an electronic scale. An image-processing pipeline in Python using OpenCV was developed to process the images. Specifically, each pig within the RGB frame was segmented by a thresholding algorithm, and the contour of the pig was identified to extract its length and width. The height of a pig was estimated from the depth images captured by the infrared depth sensor. Quality control included removing pigs that were touching the fence and sitting, as well as those showing extremely distorted shape or motion blur owing to their frequent movement. Fitting all of the morphological image descriptors simultaneously in linear mixed models yielded prediction coefficients of determination of 0.72–0.98, 0.65–0.95, 0.51–0.94, and 0.49–0.93 for 1-, 2-, 3-, and 4-d ahead forecasting, respectively, of body weight in time series cross-validation. Based on the results, we conclude that our RGB-D sensor-based imaging system coupled with the Python image-processing pipeline could potentially provide an effective approach to predict the live body weight of nonrestrained pigs from images.

Highlights

  • The average daily gain is important to enhance swine production as it can be used to determine animal growth rates and possible health challenges

  • The image descriptor volume had the highest correlation with body weight (0.90), followed by length (0.89), width (0.83), and height (0.70)

  • Time series cross-validation based on a window size of 14 d was used to derive the prediction R2

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Summary

Introduction

The average daily gain is important to enhance swine production as it can be used to determine animal growth rates and possible health challenges. Identifying changes in average daily gain is critical for efficient management of pig nutrition, feed efficiency, and detection of disease outbreaks. The labor-based measurement of live body weight using electronic scales is intensive, time-consuming, and may induce stress to pigs or cause injury to producers. Automatic scales typically integrated into feeding systems are still cost-prohibitive, and more than one animal may show up on the scale during weighing, which compromise the accuracy of the data. Reducing labor costs and enhancing welfare are expected to increase swine production and secure the sustainability of meat production. Developing new technologies that require less human involvement and are pig-friendly are essential

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