Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced

Investigations of factors that influence oestrus expression in dairy cattle

Oestrus expression and detection are keyin the reproductive management ofdairy cows where AI is routinely used. Over the past 50 years, the percentage of dairy cows in oestrus that stand to be mounted has declined from 80% to 50% and the duration of oestrus has fallen from15 h to 5 h. Furthermore, many cows show only the secondary signs of oestrus or do not show behavioural signs(silent oestrus). The first study was designed to determine whether cowtime budgets were affected by behavioural and silent oestrus in lactating dairy cows. Of the 40 behavioural oestrus events that were detected, the number of steps wereincreased(P<0.001) compared to three days before and three days after oestrus, whilst the percentage of lying time, the number of lying bouts, DMI, feeding duration and the number of visits to feed were reduced (P<0.001). On the day of silent predicted oestrus, only the durationof feeding was reduced(P<0.03).The second study was designed to investigate factors affecting the strength of oestrus expression in dairy cows. The duration of oestrus was shorter (P=0.051) in 1stoestruspostpartum (PP)with a lower intensity of oestrus expression on the day of oestrus compared to 2ndand ≥3rdoestrus PP. More steps and a lower lying (P<0.001) time with a longer oestrus duration (P=0.004) were recordedwhen three cows ormore were in oestrus (SG3+) simultaneously comparedto onecow (SG1) in oestrus. Also a highernumberof steps (P<0.001) were takenwhen two cows (SG2) were in oestrus comparedto SG1. More steps(P<0.001) were recordedin body condition score (BCS)2.75 cows compare to BCS ≤2.5 and BCS ≥3. On the day of oestrus,more steps but a lower lying time and fewer lying bouts (P<0.001) were recorded with a longer oestrus for cows of parity ≤2. The number of stepstaken wasincreasedwhile lying time,and lying bouts decreased (P<0.001)with increase locomotion score (LS). Oestrus duration was longer with a higher(P<0.001) intensity in cows that had locomotion score one (LS1). This study also found cows spent more time (P<0.001) walking withalonger oestrus duration insummer compared to other seasons. To further investigate the factors that affect oestrus, the third study was designed to determine the relationship between milk oestradiol (E2)concentration and oestrus activity. Of the 39 oestruses detected from milk progesterone (P4)concentrations,28 oestruses were behavioural and 11 were silent. Of the 28 behavioural oestruses,milk E2 concentrationsincreased from 2.0±0.5 pg/mL to 8.2±1.1 pg/mL on the day of oestrus. Milk E2 concentrations were significantly lower 1.3±0.2 pg/mL during silent oestrus compared to behavioural oestrus. Overall there was a positive relationship between milk E2 concentrations and the number of steps taken (r2=0.73; P<0.001). AbstractiiThe fourth study was designed to determine the milk fatty acid profileof dairy cows during oestrus and day 14 of the dioestrus period and their relationship with oestrus activity. Milk samples were analysedfor fatty acid concentrationsusing gas GC. On the day of oestrus, the concentrationof acetic acid (P<0.001), valeric acid (P=0.016), caproic acid (P<0.001) and myristoleic (P=0.035) werehigher in milk compared to day14after oestrus. However, on day 14 after oestrus,arachidonic acid concentrations in milk were higher(P=0.004)compared to the day of oestrus. In conclusion, from all these studies,approximately 59.9% of cows showedbehavioural oestrus. Time budgets of the cowsshowing behavioural oestrus were disrupted with a lowerlying time, feeding time but a higher number of steps per day. In cows undergoing silent oestrus,just feeding time was affected. Factors that affect oestrus intensity include the numberof oestrus post-partum,SG, BCS, LS, parity, season and E2 concentrations. Concentrations of some milk FAwere also affected.Further research is needed to determinewhether these could become part of our oestrus detection arsenal

Automated body condition scoring of dairy cattle : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Manawatū, New Zealand

This research demonstrates the development and implementation of an automatic body condition scoring system for dairy cattle that can operate in a real-world environment. Body condition scoring is a subjective method used for measuring changes in energy reserves in many animals, including dairy cattle. These energy reserves can be measured by analysing specific regions on the cow to estimate the amount of fat the animal is carrying. This information allows for greater management of the herd by adjusting the feeding strategies to ensure that each cow is at an optimal condition score. Maintaining an optimal condition throughout the year has implications for milk yield, reproductive performance, animal welfare, and overall farm profits. Current condition scoring methods are manual and are highly subjective, time consuming, expensive, and require a high level of training and competency. These limitations have created a demand for an accurate and objective scoring system. This research presents an automated system that utilises a single camera to be placed above the path of the cow at the entrance or exit to a milking platform or weigh scale. When the cow passes in view of the camera, the features are automatically extracted and converted to a conditions score. Tests have shown that the system successfully predicted the condition score within half a point of the true score for 83% of the 710 cows scored, and 96% within one point

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

Exploring the potential of Precision Livestock Farming technologies to help address farm animal welfare

The rise in the demand for animal products due to demographic and dietary changes has exacerbated difficulties in addressing societal concerns related to the environment, human health and animal welfare. As a response to this challenge, Precision Livestock Farming (PLF) technologies are being developed to monitor animal health and welfare parameters in a continuous and automated way, offering the opportunity to improve productivity and detect health issues at an early stage. However, ethical concerns have been raised regarding their potential to facilitate the management of production systems that are potentially harmful to animal welfare, or to impact the human-animal relationship and farmers’ duty of care. Using the Five Domains Model (FDM) as a framework, the aim is to explore the potential of PLF to help address animal welfare and to discuss potential welfare benefits and risks of using such technology. A variety of technologies are identified and classified according to their type (sensors, bolus, image or sound based, Radio Frequency Identification (RFID)), their development stage, the species they apply to, and their potential impact on welfare. While PLF technologies have promising potential to reduce the occurrence of diseases and injuries in livestock farming systems, their current ability to help promote positive welfare states remains limited, as technologies with such potential generally remain at earlier development stages. This is likely due to the lack of evidence related to the validity of positive welfare indicators as well as challenges in technology adoption and development. Finally, the extent to which welfare can be improved will also strongly depend on whether management practices will be adapted to minimize negative consequences and maximize benefits to welfare

New Generation Indonesian Endemic Cattle Classification: MobileNetV2 and ResNet50

Cattle are an essential source of animal food globally, and each country possesses unique endemic cattle races. However, categorizing cattle, especially in countries like Indonesia with a large cattle population, presents challenges due to costs and subjectivity when using human experts. This research utilizes Computer Vision (CV) for image data classification to address this urgent need for automatic categorization. The main objective of this study is to develop a mobile-friendly model using CV techniques that can accurately detect and classify Indonesian endemic cattle races, such as Limosin, Madura, Pegon, and Simental. To achieve this, an object localization approach is employed to extract multiple features from distinct regions of each cattle image, including the head, ear, horn, and muzzle areas. The authors evaluate two CV algorithms, ResNet50 and MobileNetV2, to assess their performance in cattle race classification. The dataset used is facial photos of 147 cows. The results indicate that ResNet50 outperforms MobileNetV2, achieving a training data accuracy of 83.33% compared to MobileNetV2's 77.08%. Moreover, the validation accuracy of ResNet50 (76.92%) significantly surpasses MobileNetV2's (38.46%). The novel contribution of this research lies in developing a cost-effective and efficient solution for identifying endemic cattle breeds in Indonesia. The mobile-friendly model based on ResNet50 demonstrates superior accuracy, enabling cattle farmers and researchers to categorize cattle races with higher precision, reducing manual effort, and minimizing costs. In conclusion, this research provides a valuable advancement in automatic cattle categorization using CV techniques. By offering a practical and accurate model that considers Indonesia's specific cattle breeding conditions, this study contributes to the sustainable management and conservation of endemic cattle races while enhancing the efficiency of cattle farming practices

Detecting cow behaviours associated with parturition using computer vision

Monitoring of dairy cows and their calf during parturition is essential in determining if there are any associated problems for mother and offspring. This is a critical period in the productive life of the mother and offspring. A difficult and assisted calving can impact on the subsequent milk production, health and fertility of a cow, and its potential survival. Furthermore, an alert to the need for any assistance would enhance animal and stockperson wellbeing. Manual monitoring of animal behaviour from images has been used for decades, but is very labour intensive. Recent technological advances in the field of Computer Vision based on the technique of Deep Learning have emerged, which now makes automated monitoring of surveillance video feeds feasible. The benefits of using image analysis compared to other monitoring systems is that image analysis relies upon neither transponder attachments, nor invasive tools and may provide more information at a relatively low cost. Image analysis can also detect and track the calf, which is not possible using other monitoring methods. Using cameras to monitor animals is commonly used, however, automated detection of behaviours is new especially for livestock. Using the latest state-of-the-art techniques in Computer Vision, and in particular the ground-breaking technique of Deep Learning, this thesis develops a vision-based model to detect the progress of parturition in dairy cows. A large-scale dataset of cow behaviour annotations was created, which included over 46 individual cow calvings and is approximately 690 hours of video footage with over 2.5k of video clips, each between 3-10 seconds. The model was trained on seven different behaviours, which included standing, walking, shuffle, lying, eating, drinking, and contractions while lying. The developed network correctly classified the seven behaviours with an accuracy of between 80 to 95%. The accuracy in predicting contractions while lying down was 83%, which in itself can be an early warning calving alert, as all cows start contractions one to two hours before giving birth. The performance of the model developed was also comparable to methods for human action classification using the Kinetics dataset

The Response of Beef Cattle to Disturbances from Unmanned Aerial Vehicles (UAVs)

Unmanned aerial vehicles (UAVs) are increasingly becoming common in animal agriculture. However, research regarding the impact of UAV disturbance on animal wellbeing is lacking or limited. The goal of this study was to investigate the effect of UAV flights on beef cattle by measuring cattle heart and movement rate when introduced to single or multiple UAV flights. A total of 16 -18 crossbred beef heifers were introduced to different flights patterns at between 5 and 9 m above ground level (AGL) at approximately 1 to 2 m/s horizontal velocity for 4 weeks with flights repeated 3 days per week. Results from the study showed that single UAV flights conducted in (i) circular and (ii) grid pattern flights had no significant effect on heifer heart and movement rate. However, multiple (i) circular pattern and (ii) approach style flights increased heifer heart rate when first introduced to UAVs, but repeated flights resulted in habituation. Moreover, heifers first introduced to circular pattern flights were likely to flee but became habituated after repeated flights. However, heifers introduced to approach style flights showed more fleeing behavior even after repeated flights. The findings of this study will provide information for safely using UAVs in cattle health and behavior monitoring