Deep Learning Models to Predict Finishing Pig Weight Using Point Clouds

The selection of animals to be marketed is largely completed by their visual assessment, solely relying on the skill level of the animal caretaker. Real-time monitoring of the weight of farm animals would provide important information for not only marketing, but also for the assessment of health and well-being issues. The objective of this study was to develop and evaluate a method based on 3D Convolutional Neural Network to predict weight from point clouds. Intel Real Sense D435 stereo depth camera placed at 2.7 m height was used to capture the 3D videos of a single finishing pig freely walking in a holding pen ranging in weight between 20–120 kg. The animal weight and 3D videos were collected from 249 Landrace × Large White pigs in farm facilities of the FZEA-USP (Faculty of Animal Science and Food Engineering, University of Sao Paulo) between 5 August and 9 November 2021. Point clouds were manually extracted from the recorded 3D video and applied for modeling. A total of 1186 point clouds were used for model training and validating using PointNet framework in Python with a 9:1 split and 112 randomly selected point clouds were reserved for testing. The volume between the body surface points and a constant plane resembling the ground was calculated and correlated with weight to make a comparison with results from the PointNet method. The coefficient of determination (R2 = 0.94) was achieved with PointNet regression model on test point clouds compared to the coefficient of determination (R2 = 0.76) achieved from the volume of the same animal. The validation RMSE of the model was 6.79 kg with a test RMSE of 6.88 kg. Further, to analyze model performance based on weight range the pigs were divided into three different weight ranges: below 55 kg, between 55 and 90 kg, and above 90 kg. For different weight groups, pigs weighing below 55 kg were best predicted with the model. The results clearly showed that 3D deep learning on point sets has a good potential for accurate weight prediction even with a limited training dataset. Therefore, this study confirms the usability of 3D deep learning on point sets for farm animals’ weight prediction, while a larger data set needs to be used to ensure the most accurate predictions

Deep Learning Models to Predict Finishing Pig Weight Using Point Clouds

The selection of animals to be marketed is largely completed by their visual assessment, solely relying on the skill level of the animal caretaker. Real-time monitoring of the weight of farm animals would provide important information for not only marketing, but also for the assessment of health and well-being issues. The objective of this study was to develop and evaluate a method based on 3D Convolutional Neural Network to predict weight from point clouds. Intel Real Sense D435 stereo depth camera placed at 2.7 m height was used to capture the 3D videos of a single finishing pig freely walking in a holding pen ranging in weight between 20–120 kg. The animal weight and 3D videos were collected from 249 Landrace × Large White pigs in farm facilities of the FZEA-USP (Faculty of Animal Science and Food Engineering, University of Sao Paulo) between 5 August and 9 November 2021. Point clouds were manually extracted from the recorded 3D video and applied for modeling. A total of 1186 point clouds were used for model training and validating using PointNet framework in Python with a 9:1 split and 112 randomly selected point clouds were reserved for testing. The volume between the body surface points and a constant plane resembling the ground was calculated and correlated with weight to make a comparison with results from the PointNet method. The coefficient of determination (R2 = 0.94) was achieved with PointNet regression model on test point clouds compared to the coefficient of determination (R2 = 0.76) achieved from the volume of the same animal. The validation RMSE of the model was 6.79 kg with a test RMSE of 6.88 kg. Further, to analyze model performance based on weight range the pigs were divided into three different weight ranges: below 55 kg, between 55 and 90 kg, and above 90 kg. For different weight groups, pigs weighing below 55 kg were best predicted with the model. The results clearly showed that 3D deep learning on point sets has a good potential for accurate weight prediction even with a limited training dataset. Therefore, this study confirms the usability of 3D deep learning on point sets for farm animals’ weight prediction, while a larger data set needs to be used to ensure the most accurate predictions

Determining the Presence and Size of Shoulder Lesions in Sows Using Computer Vision

Shoulder sores predominantly arise in breeding sows and often result in untimely culling. Reported prevalence rates vary significantly, spanning between 5% and 50% depending upon the type of crate flooring inside a farm, the animal’s body condition, or an existing injury that causes lameness. These lesions represent not only a welfare concern but also have an economic impact due to the labor needed for treatment and medication. The objective of this study was to evaluate the use of computer vision techniques in detecting and determining the size of shoulder lesions. A Microsoft Kinect V2 camera captured the top-down depth and RGB images of sows in farrowing crates. The RGB images were collected at a resolution of 1920 × 1080. To ensure the best view of the lesions, images were selected with sows lying on their right and left sides with all legs extended. A total of 824 RGB images from 70 sows with lesions at various stages of development were identified and annotated. Three deep learning-based object detection models, YOLOv5, YOLOv8, and Faster-RCNN, pre-trained with the COCO and ImageNet datasets, were implemented to localize the lesion area. YOLOv5 was the best predictor as it was able to detect lesions with an [email protected] of 0.92. To estimate the lesion area, lesion pixel segmentation was carried out on the localized region using traditional image processing techniques like Otsu’s binarization and adaptive thresholding alongside DL-based segmentation models based on U-Net architecture. In conclusion, this study demonstrates the potential of computer vision techniques in effectively detecting and assessing the size of shoulder lesions in breeding sows, providing a promising avenue for improving sow welfare and reducing economic losses

Effects of Farrowing Stall Layout and Number of Heat Lamps on Sow and Piglet Production Performance

Most farrowing facilities in the United States use stalls and heat lamps to improve sow and piglet productivity. This study investigated these factors by comparing production outcomes for three different farrowing stall layouts (traditional, expanded creep area, expanded sow area) and use of one or two heat lamps. Data were collected on 427 sows and their litters over one year. Results showed no statistical differences due to experimental treatment for any of the production metrics recorded, excluding percent stillborn. Parity one sows had fewer piglets born alive (p \u3c 0.001), lower percent mortality (p = 0.001) and over-lay (p = 0.003), and a greater number of piglets weaned (p \u3c 0.001) with lower average daily weight gain (ADG) (p \u3c 0.001) and more uniform litters (p = 0.001) as compared to higher parity sows. Farrowing turn, associated with group/seasonal changes, had a significant impact on most of the production metrics measured. Number of piglets born influenced the percent stillborn (p \u3c 0.001). Adjusted litter size had a significant impact on percent mortality (p \u3c 0.001), percent over-lay (p \u3c 0.001), and number of piglets weaned (p \u3c 0.001). As the number of piglets weaned per litter increased, both piglet ADG and litter uniformity decreased (p \u3c 0.001). This information can be used to guide producers in farrowing facility design

Evaluation of low-cost depth cameras for agricultural applications

Low-cost depth-cameras have been used in many agricultural applications with reported advantages of low cost, reliability and speed of measurement. However, some problems were also reported and seem to be technology related, so understanding the limitations of each type of depth camera technology could provide a basis for technology selection and the development of research involving its use. The cameras use one or a combination of two of the three available technologies: structured light, time-of-flight (ToF), and stereoscopy. The objectives were to evaluate these different technologies for depth sensing, including measuring accuracy and repeatability of distance data and measurements at different positions within the image, and cameras usefulness in indoor and outdoor settings. Then, cameras were tested in a swine facility and in a corn field. Five different cameras were used: (1) Microsoft Kinect v.1, (2) Microsoft Kinect v.2, (3) Intel® RealSense™ Depth Camera D435, (4) ZED Stereo Camera (StereoLabs), and (5) CamBoard Pico Flexx (PMD Technologies). Results indicate that there were significant camera to camera differences for ZED Stereo Camera and Kinect v.1 camera (p \u3c 0.05). All cameras showed an increase in the standard deviation as the distance between camera and object increased; however, the Intel RealSense camera had a larger increase. Time-of-flight cameras had the smallest error between different sizes of objects. Time-of-flight cameras had non-readable zones on the corners of the images. The results indicate that the ToF technolog

Effects of Farrowing Stall Layout and Number of Heat Lamps on Sow and Piglet Production Performance

Most farrowing facilities in the United States use stalls and heat lamps to improve sow and piglet productivity. This study investigated these factors by comparing production outcomes for three different farrowing stall layouts (traditional, expanded creep area, expanded sow area) and use of one or two heat lamps. Data were collected on 427 sows and their litters over one year. Results showed no statistical differences due to experimental treatment for any of the production metrics recorded, excluding percent stillborn. Parity one sows had fewer piglets born alive (p \u3c 0.001), lower percent mortality (p = 0.001) and over-lay (p = 0.003), and a greater number of piglets weaned (p \u3c 0.001) with lower average daily weight gain (ADG) (p \u3c 0.001) and more uniform litters (p = 0.001) as compared to higher parity sows. Farrowing turn, associated with group/seasonal changes, had a significant impact on most of the production metrics measured. Number of piglets born influenced the percent stillborn (p \u3c 0.001). Adjusted litter size had a significant impact on percent mortality (p \u3c 0.001), percent over-lay (p \u3c 0.001), and number of piglets weaned (p \u3c 0.001). As the number of piglets weaned per litter increased, both piglet ADG and litter uniformity decreased (p \u3c 0.001). This information can be used to guide producers in farrowing facility design

Statistical and machine learning approaches to describe factors affecting preweaning mortality of piglets

High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the U.S. Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, litter, environment, and piglet parameters on PWM. Then, different models (beta-regression and machine learning model: a random forest [RF]) were evaluated. Finally, the RF model was used to predict PWM and overlays for all listed contributing factors. On average, the mean birth weight was 1.44 kg, and the mean mortality was 16.1% where 5.55% was for stillbirths and 6.20% was contributed by overlays. No significant effect was found for seasonal and location variations on PWM. Significant differences were observed in the effects of litter lines on PWM (P \u3c 0.05). Landrace-sired litters had a PWM of 16.26% (±0.13), whereas Yorkshire-sired litters had 15.91% (±0.13). PWM increased with higher parity orders (P \u3c 0.05) due to larger litter sizes. The RF model provided the best fit for PWM prediction with a root mean squared errors of 2.28 and a correlation coefficient (r) of 0.89 between observed and predicted values. Features’ importance from the RF model indicated that, PWM increased with the increase of litter size (mean decrease accuracy (MDA) = 93.17), decrease in mean birth weight (MDA = 22.72), increase in health diagnosis (MDA = 15.34), longer gestation length (MDA = 11.77), and at older parity (MDA = 10.86). However, in this study, the location of the farrowing crate, seasonal differences, and litter line turned out to be the least important predictors for PWM. For overlays, parity order was the highest importance predictor (MDA = 7.68) followed by litter size and mean birth weight. Considering the challenges to reducing the PWM in the larger litters produced in modern swine industry and the limited studies exploring multiple major contributing factors, this study provides valuable insights for breeding and production management, as well as further investigations on postural transitions and behavior analysis of sows during the lactation period

Effects of Farrowing Stall Layout and Number of Heat Lamps on Sow and Piglet Production Performance

Most farrowing facilities in the United States use stalls and heat lamps to improve sow and piglet productivity. This study investigated these factors by comparing production outcomes for three different farrowing stall layouts (traditional, expanded creep area, expanded sow area) and use of one or two heat lamps. Data were collected on 427 sows and their litters over one year. Results showed no statistical differences due to experimental treatment for any of the production metrics recorded, excluding percent stillborn. Parity one sows had fewer piglets born alive (p \u3c 0.001), lower percent mortality (p = 0.001) and over-lay (p = 0.003), and a greater number of piglets weaned (p \u3c 0.001) with lower average daily weight gain (ADG) (p \u3c 0.001) and more uniform litters (p = 0.001) as compared to higher parity sows. Farrowing turn, associated with group/seasonal changes, had a significant impact on most of the production metrics measured. Number of piglets born influenced the percent stillborn (p \u3c 0.001). Adjusted litter size had a significant impact on percent mortality (p \u3c 0.001), percent over-lay (p \u3c 0.001), and number of piglets weaned (p \u3c 0.001). As the number of piglets weaned per litter increased, both piglet ADG and litter uniformity decreased (p \u3c 0.001). This information can be used to guide producers in farrowing facility design

Evaluation of low-cost depth cameras for agricultural applications

Low-cost depth-cameras have been used in many agricultural applications with reported advantages of low cost, reliability and speed of measurement. However, some problems were also reported and seem to be technology related, so understanding the limitations of each type of depth camera technology could provide a basis for technology selection and the development of research involving its use. The cameras use one or a combination of two of the three available technologies: structured light, time-of-flight (ToF), and stereoscopy. The objectives were to evaluate these different technologies for depth sensing, including measuring accuracy and repeatability of distance data and measurements at different positions within the image, and cameras usefulness in indoor and outdoor settings. Then, cameras were tested in a swine facility and in a corn field. Five different cameras were used: (1) Microsoft Kinect v.1, (2) Microsoft Kinect v.2, (3) Intel® RealSense™ Depth Camera D435, (4) ZED Stereo Camera (StereoLabs), and (5) CamBoard Pico Flexx (PMD Technologies). Results indicate that there were significant camera to camera differences for ZED Stereo Camera and Kinect v.1 camera (p \u3c 0.05). All cameras showed an increase in the standard deviation as the distance between camera and object increased; however, the Intel RealSense camera had a larger increase. Time-of-flight cameras had the smallest error between different sizes of objects. Time-of-flight cameras had non-readable zones on the corners of the images. The results indicate that the ToF technolog

Effects of farrowing stall layout and number of heat lamps on sow and piglet behavior

Farrowing stalls are used in the United States swine industry to reduce pre-weaning piglet mortality, enable efficient individual animal management, and decrease facility construction and operating costs. The quantity and quality of space provided for sows and piglets in farrowing stalls are important economic and welfare considerations. To further explore the impacts of farrowing stall space allocation, a large-scale field study was conducted to compare sow and piglet behavior when housed in three farrowing stall layouts (TSL – traditional stall layout, ECSL – expanded creep area stall layout, ESCSL – expanded sow and creep area stall layout) with either one or two heat lamps (1HL and 2HL, respectively). A computer vision system classified posture budgets and behaviors of 322 sows and piglet location for 324 litters. Linear mixed models were developed to compare behavior and piglet pre-weaning mortality metrics between experimental treatments. Results show sows in ESCSL spent more time lying compared to sows in ECSL (p = 0.028) and less time sitting compared to sows in TSL and ECSL (p \u3c 0.01). Sows with the 2HL treatment had an increase in percentage lying (p = 0.017) and a decrease in percentage standing (p = 0.045) compared to sows with the 1HL treatment. Number of piglets, parity, and batch also influenced sow postural behavior (p \u3c 0.05). Sow lying orientation was not impacted by HL treatment. Sow postures and behaviors were influenced by day of lactation (p \u3c 0.001). Piglets with 2HL treatment spent more time in the heated region and less time in the creep and sow regions for all stall layouts on all days of lactation observed (p \u3c 0.001). In the ESCSL, piglets had a greater percentage of time in the sow region compared to ECSL piglets (p \u3c 0.004). Piglets did not spend equal percentages of time between the two creep or two HL regions (p \u3c 0.001), and piglet location was correlated with sow lying orientation for most of the creep regions analyzed (p \u3c 0.01). Increases in piglet pre-weaning mortality were correlated with increases in sow lying (p = 0.027) and decreases in standing (p = 0.025) and feeding (p \u3c 0.001). However, correlations with sow posture were likely due to the impacts of day of lactation (p \u3c 0.001). No correlations were found between piglet location and pre-weaning mortality (p\u3e 0.05). Results can guide producers to consider wider sow areas in farrowing stalls to better meet sow behavioral needs and to include larger heated areas to meet piglet behavioral needs during lactation