The use of artificial neural networks to diagnose mastitis in dairy cattle

The use of milk sample categorization for diagnosing mastitis using Kohonen's self-organizing feature map (SOFM) is reported. Milk trait data of 14 weeks of milking from commercial dairy cows in New Zealand was used to train and test a SOFM network. The SOFM network was useful in discriminating data patterns into four separate mastitis categories. Several other artificial neural networks were tested to predict the missing data from the recorded milk traits. A multi-layer perceptron (MLP) network proved to be most accurate (R² = 0.84, r = 0.92) when compared to other MLP (R² = 0.83, r = 0.92), Elman (R² = 0.80, r = 0.92), Jordan (R² = 0.81, r = 0.92) or linear regression (R² = 0.72, r = 0.85) methods. It is concluded that the SOFM can be used as a decision tool for the dairy farmer to reduce the incidence of mastitis in the dairy herd

Sperm quality, semen production, and fertility in young Norwegian Red bulls

Ved bruk av genomisk seleksjon i storfeavlen blir eliteokser selektert basert på deres estimerte genomiske avlsverdier i stedet for ved avkomsgransking. Oksene er derfor yngre når de blir tatt i bruk i sædproduksjon enn tidligere. Hovedmålet med denne avhandlingen var å identifisere nye indikatorer for når sædproduksjonen er i gang hos unge Norsk Rødt Fe okser, og som kan måles i løpet av testperioden og gi informasjon om oksenes potensielle fremtidige sædproduksjon, aksept for semin-stasjonen samt fruktbarhet i felt. I Artikkel 1 ble flowcytometri og Computer-Aided Sperm Analysis brukt til å analysere ulike spermiekvalitetsparametere i ejakulater fra 65 okser i alderen 9-13 måneder. Sædprøver ble utsatt for stresstester og kryokonservering. Oksene ble klassifisert i tre grupper med ulik respons på spermie-stresstester. Ved å benytte spermie-stresstester, kryokonservering og morfologianalyse tidlig i testperioden, kan en få verdifull innsikt i når oksene er tilstrekkelig utviklet for sædproduksjon. Med denne tilnærmingen vil en kunne ta i bruk yngre okser i sæduttak og -produksjon, og dermed bidra til redusert generasjonsintervall og økt genetisk framgang. I Artikkel 2 ble det fokusert på å undersøke potensialet til insulin-like factor 3 som en biomarkør for å predikere når sædproduksjonen starter hos unge Norsk Rødt Fe okser. Det ble tatt blodprøver og samtidig utført målinger av skrotumomkrets på 142 okser på fire tidspunkt mellom 2 og 12 måneders alder. Studien hadde som mål å belyse sammenhenger mellom nivået av insulin-like factor 3, skrotumomkrets og ulike sædparametere. Det ble funnet en positiv korrelasjon mellom insulin-like factor 3 og skrotumomkretsen, men det ble ikke funnet signifikante sammenhenger mellom skrotumomkretsen og sædparametere. På grunn av betydelige individuelle variasjoner i den undersøkte norske okse-populasjonen, er insulin-like factor 3 foreløpig ikke en egnet biomarkør til å kunne predikere når sædproduksjonen starter hos denne rasen. I Artikkel 3 presenteres en automatisert metode for å måle skrotumomkretsen hos Norsk Rødt Fe okser ved hjelp av 3D-bilder og konvolusjonelle nevrale nettverk. 3D-bilder ble tatt samtidig som manuelle målinger av skrotumomkretsen ble utført på oksene, noe som ble gjentatt ved ulike aldere. Studien sammenlignet de manuelle og automatiserte målingene oppnådd ved semantisk segmentering. Det ble vist at de automatiserte målingene av skrotumomkretsen ga tilsvarende resultater som de manuelle målingene. Gjennomsnittlig prediksjonsfeil varierte med oksenes alder og kvaliteten på 3D-bildene. Denne nye målemetoden har potensiale til å kunne implementeres i breeding soundness evaluation ved testings- og seminstasjoner, og kan gi en rask og effektiv vurdering av skrotumomkretsen.Abstract. With the application of genomic selection in dairy cattle breeding, the choice of elite sires is based on their estimated genomic breeding values instead of progeny testing. Consequently, bulls are introduced into semen production at a younger age than previously. The main aim of this thesis was to identify novel early indicators of sperm production onset and maturity status of young Norwegian Red bulls during their performance test period, to provide insight into their potential future semen production, acceptance for the AI station, and field fertility. In Paper 1, flow cytometry and computer-aided sperm analysis were used to analyse various sperm quality parameters in ejaculates collected from 65 bulls aged 9-13 months. Semen samples were subjected to stress tests and cryopreservation. The bulls were classified into three clusters with different responses to sperm stress tests. By incorporating sperm stress tests, cryopreservation, and early morphology analysis, valuable insights into the maturity of bulls for sperm production could be gained. This approach would allow for the integration of younger bulls into semen collection, facilitating reduced generation interval and increased genetic gain. The focus in Paper 2 is on investigating the potential of insulin-like factor 3 as a biomarker for predicting the onset of sperm production in young Norwegian Red bulls. Blood samples and scrotal circumference measurements were collected from 142 bulls at four time-points between 2 and 12 months of age. The aim of the study was to determine the relationship between insulin-like factor 3, scrotal circumference, and semen characteristics. While a positive correlation was found between insulin-like factor 3 and scrotal circumference, no significant correlations were observed between scrotal circumference and semen characteristics. Due to the substantial interindividual variability in the Norwegian Red bull population, insulin-like factor 3 is currently not a reliable biomarker for predicting the onset of sperm production in this breed. In Paper 3 an automated method for measuring scrotal circumference of Norwegian Red bulls using 3D images and convolutional neural networks is presented. 3D images were captured, and manual scrotal circumference measurements made of bulls at different ages. The study compared the manual and automated measurements obtained through semantic segmentation. The results showed that the automated scrotal circumference measurements were similar to manual measurements. Mean prediction error varied depending on bull age and image quality. This novel measurement method has the potential to be implemented in bull breeding soundness evaluations at performance test stations and semen collection centers, providing a fast and efficient approach for assessing scrotal circumference.publishedVersio

Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks.

Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1) to assess if milk MIRS data could improve DMI predictions of Canadian Holstein cows using artificial neural networks (ANN); (2) to investigate the ability of different ANN architectures to predict unobserved DMI; and (3) to validate the robustness of developed prediction models. A total of 7,398 milk samples from 509 dairy cows distributed over Canada, Denmark, and the United States were analyzed. Data from Denmark and the United States were used to increase the training data size and variability to improve the generalization of the prediction models over the lactation. For each milk spectra record, the corresponding weekly average DMI (kg/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d), metabolic body weight (MBW), age at calving, year of calving, season of calving, days in milk, lactation number, country, and herd were available. The weekly average DMI was predicted with various ANN architectures using 7 predictor sets, which were created by different combinations MY, FY, PY, MBW, and MIRS data. All predictor sets also included age of calving and days in milk. In addition, the classification effects of season of calving, country, and lactation number were included in all models. The explored ANN architectures consisted of 3 training algorithms (Bayesian regularization, Levenberg-Marquardt, and scaled conjugate gradient), 2 types of activation functions (hyperbolic tangent and linear), and from 1 to 10 neurons in hidden layers). In addition, partial least squares regression was also applied to predict the DMI. Models were compared using cross-validation based on leaving out 10% of records (validation A) and leaving out 10% of cows (validation B). Superior fitting statistics of models comprising MIRS information compared with the models fitting milk, fat and protein yields suggest that other unknown milk components may help explain variation in weekly average DMI. For instance, using MY, FY, PY, and MBW as predictor variables produced a predictive accuracy (r) ranging from 0.510 to 0.652 across ANN models and validation sets. Using MIRS together with MY, FY, PY, and MBW as predictors resulted in improved fitting (r = 0.679-0.777). Including MIRS data improved the weekly average DMI prediction of Canadian Holstein cows, but it seems that MIRS predicts DMI mostly through its association with milk production traits and its utility to estimate a measure of feed efficiency that accounts for the level of production, such as residual feed intake, might be limited and needs further investigation. The better predictive ability of nonlinear ANN compared with linear ANN and partial least squares regression indicated possible nonlinear relationships between weekly average DMI and the predictor variables. In general, ANN using Bayesian regularization and scaled conjugate gradient training algorithms yielded slightly better weekly average DMI predictions compared with ANN using the Levenberg-Marquardt training algorithm

Leveraging large scale beef cattle genomic data to identify the architecture of polygenic selection and local adaptation

Includes vita.Since the invention of the first array-based genotyping assay for cattle in 2008, millions of animals have been genotyped worldwide. Leveraging these genotypes offers exciting opportunities to explore both basic and applied research questions. Commercial genotyping assays are of adequate variant density to perform well in prediction contexts but are not sufficient for mapping studies. Using reference panels made up of individuals genotyped at higher densities, we can statistically infer the missing variation of low-density assays through the process of imputation. Here, we explore the best practices for performing routine imputation in large commercially generated genomic datasets of U.S. beef cattle. We find that using a large multi-breed imputation reference maximizes accuracy, particularly for rare variants. Using three of these large, imputed datasets, we explore two major population genetics questions. First, we map polygenic selection in the bovine genome, using Generation Proxy Selection Mapping (GPSM). This identifies hundreds of regions of the genome actively under selection in cattle populations. Using a similar approach, we identify dozens of genomic variants associated with environments across the U.S., likely involved local adaptation. Understanding the genomic basis of local adaptation in cattle will enable select and breed cattle better suited to a changing climate.Includes bibliographical references (pages 203-228

Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management

The role of Fourier-transform mid-infrared spectroscopy in improving the prediction of new and existing traits in New Zealand dairy cattle : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Science at Massey University, AL Rae Centre, Hamilton, New Zealand

Bovine milk is a rich source of dietary nutrients that are important to human health. Market demand for bovine milk is driven by its nutritional value, price, processability, and consumer expectations and perceptions about food production systems. The ability to quantify traits associated with milk quality, processability, animal health and environmental impact is critical for selective breeding and thus highly valuable to the dairy industry. However, obtaining direct measurements of such traits can be difficult and expensive. Estimation of major milk components using Fourier-transform mid-infrared (FT-MIR) spectroscopy is common practice, and spectral-based predictions of these traits are already widely used in milk payment and animal evaluation systems. Applications using FT-MIR spectra to predict other traits have increased in popularity over the last decade, and are attractive alternatives to directly measuring phenotypes because the FT-MIR spectra are readily available as a by-product of routine milk testing. The objectives of this thesis were to improve understanding of the phenotypic and genetic characteristics of FT-MIR spectra, and assess the role that such data can play in predicting new traits or improving the prediction of existing traits in New Zealand dairy cattle. We assessed different strategies for improving the quality of spectral data and demonstrated that there are limitations in predicting traits such as pregnancy status, due to confounding effects such as stage of lactation. From a genetics perspective, we reviewed the evolving role of spectral data in the improvement of dairy cattle by selection and discussed opportunities for consolidating spectral datasets with other genomic and molecular data sources. We conducted GWAS on individual FT-MIR wavenumbers and demonstrated that the individual wavenumbers provided stronger association effects and improved power for identifying candidate causal variants, compared to conducting GWAS on FT-MIR predicted traits. We also demonstrated the potential utility of spectral data for predicting and incorporating fatty acids and protein traits into breeding programs, but showed that even when genetic correlations between directly measured and FT-MIR predicted traits were high, the detectable QTL underpinning these traits were not always the same. Although there are many potential applications for FT-MIR spectral datasets, there are still challenges to developing robust prediction equations and understanding the genetic relationships between traits of interest and their FT-MIR predictions. Addressing these challenges will provide opportunities to improve the prediction of new and existing traits in dairy cattle milk production systems and breeding programs into the future

Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras

Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in transport. Skin temperature (°C) from head features were extracted from infrared thermal videos (IRTV) using automated tracking algorithms. Two parameter engineering procedures from RGB videos were performed to assess Heart Rate (HR) in beats per minute (BPM) and respiration rate (RR) in breaths per minute (BrPM): (i) using changes in luminosity of the green (G) channel and (ii) changes in the green to red (a) from the CIELAB color scale. A supervised machine learning (ML) classification model was developed using raw RR parameters as inputs to classify cutoff frequencies for low, medium, and high respiration rate (Model 1). A supervised ML regression model was developed using raw HR and RR parameters from Model 1 (Model 2). Results showed that Models 1 and 2 were highly accurate in the estimation of RR frequency level with 96% overall accuracy (Model 1), and HR and RR with R = 0.94 and slope = 0.76 (Model 2) without statistical signs of overfitting

Application of knowledge discovery and data mining methods in livestock genomics for hypothesis generation and identification of biomarker candidates influencing meat quality traits in pigs

Recent advancements in genomics and genome profiling technologies have lead to an increase in the amount of data available in livestock genomics. Yet, most of the studies done in livestock genomics have been following a reductionist approach and very few studies have either followed data mining or knowledge discovery concepts or made use of the wealth of information available in the public domain to gain new knowledge. The goals of this thesis were: (i) the adoption of existing analysis strategies or the development of novel approaches in livestock genomics for integrative data analysis following the principles of data mining and knowledge discovery and (ii) demonstrating the application of such approaches in livestockgenomics for hypothesis generation and biomarker discovery. A pig meat quality trait termed androstenone measurement in backfat was selected as the target phenotype for the experiments. Two experiments were performed as a part of this thesis. The first one followed a knowledge driven approach merging high-throughput expression data with metabolic interaction network. Based on the results from this experiment, several novel biomarker candidates and a hypothesis regarding different mechanisms regulating androstenone synthesis in porcine testis samples with divergent androstenone measurements in back fat were proposed. The model proposed that the elevated levels of androstenone synthesis in sample population could be due to the combined effect of cAMP/PKA signaling, elevated levels of fatty acid metabolism and anti lipid peroxidation activity of members of glutathione metabolic pathway. The second experiment followed a data driven approach and integrated gene expression data from multiple porcine populations to identify similarities in gene expression patterns related to hepatic androstenone metabolism. The results indicated that one of the low androstenone phenotype specific co-expression cluster was functionally enriched in pathways related to androgen and androstenone metabolism and that the members of this cluster exhibited weak co-expression in high androstenone phenotype. Based on the results from this experiment, this co-expression cluster was proposed as a signature cluster for hepatic androstenone metabolism in boars with low androstenone content in back fat. The results from these experiments indicate that integrative analysis approaches following data mining and knowledge discovery concepts can be used for the generation of new knowledge from existing data in livestock genomics. But, limited data availability in livestock genomics is a hindrance to the extensive use such analysis methods in livestock genomics field for gaining new knowledge. In conclusion, this study was aimed at demonstrating the capabilities of data mining and knowledge discovery methods and integrative analysis approaches to generate new knowledge in livestock genomics using existing datasets. The results from the experiments hint the possibilities of further exploring such methods for knowledge generation in this field. Although the application of such methods is limited in livestock genomics due to data availability issues at present, the increase in data availability due to evolving high throughput technologies and decrease in data generation costs would aid in the wide spread use of such methods in livestock genomics in the coming future