Computer vision systems have the potential to provide automated, non-invasive monitoring of livestock animals, however, the lack of public datasets with well-defined targets and evaluation metrics presents a significant challenge for researchers. Consequently, existing solutions often focus on achieving task-specific objectives using relatively small, private datasets. This work introduces a new dataset and method for instance-level detection of multiple pigs in group-housed environments. The method uses a single fully-convolutional neural network to detect the location and orientation of each animal, where both body part locations and pairwise associations are represented in the image space. Accompanying this method is a new dataset containing 2000 annotated images with 24,842 individually annotated pigs from 17 different locations. The proposed method achieves over 99% precision and over 96% recall when detecting pigs in environments previously seen by the network during training. To evaluate the robustness of the trained network, it is also tested on environments and lighting conditions unseen in the training set, where it achieves 91% precision and 67% recall. The dataset is publicly available for download.

BackgroundOur recent research showed that antibody response to porcine reproductive and respiratory syndrome (PRRS), measured as sample-to-positive (S/P) ratio, is highly heritable and has a high genetic correlation with reproductive performance during a PRRS outbreak. Two major quantitative trait loci (QTL) on Sus scrofa chromosome 7 (SSC7; QTLMHC and QTL130) accounted for ~40 % of the genetic variance for S/P. Objectives of this study were to estimate genetic parameters for PRRS S/P in gilts during acclimation, identify regions associated with S/P, and evaluate the accuracy of genomic prediction of S/P across populations with different prevalences of PRRS and using different single nucleotide polymorphism (SNP) sets.MethodsPhenotypes and high-density SNP genotypes of female pigs from two datasets were used. The outbreak dataset included 607 animals from one multiplier herd, whereas the gilt acclimation (GA) dataset included data on 2364 replacement gilts from seven breeding companies placed on health-challenged farms. Genomic prediction was evaluated using GA for training and validation, and using GA for training and outbreak for validation. Predictions were based on SNPs across the genome (SNPAll), SNPs in one (SNPMHC and SNP130) or both (SNPSSC7) QTL, or SNPs outside the QTL (SNPRest).ResultsHeritability of S/P in the GA dataset increased with the proportion of PRRS-positive animals in the herd (from 0.28 to 0.47). Genomic prediction accuracies ranged from low to moderate. Average accuracies were highest when using only the 269 SNPs in both QTL regions (SNPSSC7, with accuracies of 0.39 and 0.31 for outbreak and GA validation datasets, respectively. Average accuracies for SNPALL, SNPMHC, SNP130, and SNPRest were, respectively, 0.26, 0.39, 0.21, and 0.05 for the outbreak, and 0.28, 0.25, 0.22, and 0.12, for the GA validation datasets.ConclusionsModerate genomic prediction accuracies can be obtained for PRRS antibody response using SNPs located within two major QTL on SSC7, while the rest of the genome showed limited predictive ability. Results were obtained using data from multiple genetic sources and farms, which further strengthens these findings. Further research is needed to validate the use of S/P ratio as an indicator trait for reproductive performance during PRRS outbreaks.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0230-0) contains supplementary material, which is available to authorized users.

The objective of this study was to estimate genetic associations for gilt growth, compositional, and structural soundness with sow longevity and lifetime reproduction. Performance and pedigree information from 1,447 commercial females from 2 genetic lines were included in the data analyzed. Growth was expressed as days to 113.5 kg BW (DAYS) and compositional traits included loin muscle area (LMA), 10th rib backfat (BF10), and last rib backfat (LRF). Structural soundness traits included body structure traits [length (BL), depth (BD), width (BWD), rib shape (BRS), top line (BTL), and hip structure (BHS)], leg structure traits [front legs: legs turned (FLT), buck knees (FBK), pastern posture (FPP), foot size (FFS), and uneven toes (FUT); rear legs: legs turned (RLT), leg posture (RLP), pastern posture (RPP), foot size (RFS), and uneven toes (RUT)], and overall leg action (OLA). Lifetime (LT) and removal parity (RP) were considered as longevity traits whereas lifetime reproductive traits included lifetime total number born (LNB), lifetime number born alive (LBA), number born alive per lifetime day (LBA/LT), and percentage productive days from total herd days (PD%). Genetic parameters were estimated with linear animal models using the average information REML algorithm. Second, to account for censored longevity and lifetime reproduction records, genetic parameters were estimated using Markov Chain Monte Carlo and Gibbs sampling methods. Similar estimates were obtained across the analysis methods. Heritability estimates for growth and compositional traits ranged from 0.50 to 0.70 and for structural soundness traits from 0.07 to 0.31. Longevity and lifetime reproductive trait heritability estimates ranged from 0.14 to 0.17 when REML was used. Unfavorable genetic correlations were obtained for DAYS with LT, RP, LNB, LBA, and PD% and for LRF with PD%. However, LMA was favorably associated with LT, RP, and LNB. Moderate to high correlations were obtained for BL and BRS with all longevity and lifetime reproductive traits. Correlations of BWD with LT and RP were moderate. Associations for leg soundness traits with longevity and lifetime reproductive traits were mainly low and nonsignificant (P ≥ 0.10). However, RLP was moderately correlated with LBA/LT and PD%. Current results indicate that selection for fewer DAYS has an antagonistic effect on lifetime performance. Furthermore, great BL, flat BRS, narrow BWD, and upright RLP seem detrimental to sow longevity and lifetime reproduction. ABSTRACT: The objective of this study was to estimate genetic associations for gilt growth, compositional, and structural soundness with sow longevity and lifetime reproduction. Performance and pedigree information from 1,447 commercial females from 2 genetic lines were included in the data analyzed. Growth was expressed as days to 113.5 kg BW (DAYS) and compositional traits included loin muscle area (LMA), 10th rib backfat (BF10), and last rib backfat (LRF). Structural soundness traits included body structure traits [length (BL), dep...

Background: Identification and culling of replacement gilts with poor skeletal conformation and feet and leg (FL) unsoundness is an approach used to reduce sow culling and mortality rates in breeding stock. Few candidate genes related to soundness traits have been identified in the pig.

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Tracking individual animals in a group setting is a exigent task for computer vision and animal science researchers. When the objective is months of uninterrupted tracking and the targeted animals lack discernible differences in their physical characteristics, this task introduces significant challenges. To address these challenges, a probabilistic tracking-by-detection method is proposed. The tracking method uses, as input, visible keypoints of individual animals provided by a fully-convolutional detector. Individual animals are also equipped with ear tags that are used by a classification network to assign unique identification to instances. The fixed cardinality of the targets is leveraged to create a continuous set of tracks and the forward-backward algorithm is used to assign ear-tag identification probabilities to each detected instance. Tracking achieves real-time performance on consumer-grade hardware, in part because it does not rely on complex, costly, graph-based optimizations. A publicly available, human-annotated dataset is introduced to evaluate tracking performance. This dataset contains 15 half-hour long videos of pigs with various ages/sizes, facility environments, and activity levels. Results demonstrate that the proposed method achieves an average precision and recall greater than 95% across the entire dataset. Analysis of the error events reveals environmental conditions and social interactions that are most likely to cause errors in real-world deployments.

Escalating replacement rates and production costs warrant attention on sow productive life (SPL). Increasing average SPL by one-tenth of 1 parity would result in an annual revenue increase of over $15 million in the United States. Research in model organisms has revealed conserved genes and gene pathways that lead to longer lifespan. The most prominent gene pathways are those involved in growth, most notably genes in the IGF pathway that serve to mimic the response of caloric restriction. The objective of this research was to test the hypothesis that these well conserved genes and gene pathways could also play a role in SPL, even though the productive life of sows is both a measure of longevity and their reproductive performance. Preliminary research on 3 distinct populations of over 2,000 animals suggested that several genes were associated with components of SPL. Genetic markers were then analyzed against the corresponding records of the sows for reproductive and longevity traits using a validation population of 2,000 commercial females. Right censored data were used to test associations of genetic markers with survival to defined time points. Three distinct models of survival analysis were implemented using nonparametric estimates of the survival distribution in a sequential order, using a parametric accelerated failure time model with a Weibull distribution of the error term, and a Cox proportional hazards model, which is a semiparametric model that uses an unspecified baseline hazard function. The genetic markers CCR7 and CPT1A were significantly associated (P < 0.05) with survival using the nonparametric model and tended (P < 0.1) toward significance using the parametric and semiparametric models with significantly different effects (P < 0.05) between some genotype classes. Genetic markers for MBL2, IGFBP3, and WARS2 also tended (P < 0.1) toward significance for survival traits, but were not consistent. Mixed model analyses were used to determine the associations of these genetic markers with reproductive traits. The genetic markers for IGFBP1, MBL2, CPT1A, CCR7, SLC22A5, and ACE were significantly (P < 0.05) associated with at least 1 reproductive trait. These results show that molecular markers should be considered for use in marker-assisted selection to improve SPL. ABSTRACT: Escalating replacement rates and production costs warrant attention on sow productive life (SPL). Increasing average SPL by one-tenth of 1 parity would result in an annual revenue increase of over $15 million in the United States. Research in model organisms has revealed conserved genes and gene pathways that lead to longer lifespan. The most prominent gene pathways are those involved in growth, most notably genes in the IGF pathway that serve to mimic the response of caloric restriction. The objective of this research was to test the hypothesis that these well conserved genes and gene pathways could also play a role in SPL, even though the productive life of sows is both a measure of longevity and their reproductive perfor...