DISubNet: Depthwise Separable Inception Subnetwork for Pig Treatment Classification Using Thermal Data

Thermal imaging is increasingly used in poultry, swine, and dairy animal husbandry to detect disease and distress. In intensive pig production systems, early detection of health and welfare issues is crucial for timely intervention. Using thermal imaging for pig treatment classification can improve animal welfare and promote sustainable pig production. In this paper, we present a depthwise separable inception subnetwork (DISubNet), a lightweight model for classifying four pig treatments. Based on the modified model architecture, we propose two DISubNet versions: DISubNetV1 and DISubNetV2. Our proposed models are compared to other deep learning models commonly employed for image classification. The thermal dataset captured by a forward-looking infrared (FLIR) camera is used to train these models. The experimental results demonstrate that the proposed models for thermal images of various pig treatments outperform other models. In addition, both proposed models achieve approximately 99.96–99.98% classification accuracy with fewer parameters.

High Precision Classification of Resting and Eating Behaviors of Cattle by Using a Collar-Fitted Triaxial Accelerometer Sensor

Cattle are less active than humans. Hence, it was hypothesized in this study that transmitting acceleration signals at a 1 min sampling interval to reduce storage load has the potential to improve the performance of motion sensors without affecting the precision of behavior classification. The behavior classification performance in terms of precision, sensitivity, and the F1-score of the 1 min serial datasets segmented in 3, 4, and 5 min window sizes based on nine algorithms were determined. The collar-fitted triaxial accelerometer sensor was attached on the right side of the neck of the two fattening Korean steers (age: 20 months) and the steers were observed for 6 h on day one, 10 h on day two, and 7 h on day three. The acceleration signals and visual observations were time synchronized and analyzed based on the objectives. The resting behavior was most correctly classified using the combination of a 4 min window size and the long short-term memory (LSTM) algorithm which resulted in 89% high precision, 81% high sensitivity, and 85% high F1-score. High classification performance (79% precision, 88% sensitivity, and 83% F1-score) was also obtained in classifying the eating behavior using the same classification method (4 min window size and an LSTM algorithm). The most poorly classified behavior was the active behavior. This study showed that the collar-fitted triaxial sensor measuring 1 min serial signals could be used as a tool for detecting the resting and eating behaviors of cattle in high precision by segmenting the acceleration signals in a 4 min window size and by using the LSTM classification algorithm.

Opportunities to monitor animal welfare using the five freedoms with precision livestock management on rangelands

Advances in technology have led to precision livestock management, a developing research field. Precision livestock management has potential to improve sustainable meat production through continuous, real-time tracking which can help livestock managers remotely monitor and enhance animal welfare in extensive rangeland systems. The combination of global positioning systems (GPS) and accessible data transmission gives livestock managers the ability to locate animals in arduous weather, track animal patterns throughout the grazing season, and improve handling practices. Accelerometers fitted to ear tags or collars have the potential to identify behavioral changes through variation in the intensity of movement that can occur during grazing, the onset of disease, parturition or responses to other environmental and management stressors. The ability to remotely detect disease, parturition, or effects of stress, combined with appropriate algorithms and data analysis, can be used to notify livestock managers and expedite response times to bolster animal welfare and productivity. The “Five Freedoms” were developed to help guide the evaluation and impact of management practices on animal welfare. These freedoms and welfare concerns differ between intensive (i.e., feed lot) and extensive (i.e., rangeland) systems. The provisions of the Five Freedoms can be used as a conceptual framework to demonstrate how precision livestock management can be used to improve the welfare of livestock grazing on extensive rangeland systems.

A Case Study Using Accelerometers to Identify Illness in Ewes Following Unintentional Exposure to Mold-Contaminated Feed

Sensor technologies can identify modified animal activity indicating changes in health status. This study investigated sheep behavior before and after illness caused by mold-contaminated feed using tri-axial accelerometers. Ten ewes were fitted with HerdDogg biometric accelerometers. Five ewes were concurrently fitted with Axivity AX3 accelerometers. The flock was exposed to mold-contaminated feed following an unexpected ration change, and observed symptomatic ewes were treated with a veterinarian-directed protocol. Accelerometer data were evaluated 4 days before exposure (d −4 to −1); the day of ration change (d 0); and 4 days post exposure (d 1 to 4). Herddogg activity index correlated to the variability of minimum and standard deviation of motion intensity monitored by the Axivity accelerometer. Herddogg activity index was lower (p < 0.05) during the mornings (0800 to 1100 h) of days 2 to 4 and the evening of day 1 than days −4 to 0. Symptomatic ewes had lower activity levels in the morning and higher levels at night. After accounting for symptoms, activity levels during days 1 to 4 were lower (p < 0.05) than days −4 to 0 the morning after exposure. Results suggest real-time or near-real time accelerometers have potential to detect illness in ewes.

Back to Nature With Fenceless Farms—Technology Opportunities to Reconnect People and Food

The development and application of the fence was one of the earliest forms of agricultural technology in action. Managing the supply of animal protein required hunter gatherer communities to be able to domesticate and contain wild animals. Over the ages the fence has become ingrained in the very fabric of society and created a culture of control and ownership. Garett Hardin's article titled “The Tragedy of the Commons” suggested that shared land, typified by access to a fenceless common resource, was doomed to failure due to a human instinct for mistrust and exploitation. Perhaps the fence has created an ingrained societal cultural response. While natural ecosystems do have physical boundaries, these are based on natural environmental zones. Landscapes are more porous and resilience is built up through animal's being able to respond to dynamic changes. This paper explores the opportunity for remote monitoring technologies to create open fenceless landscapes and how this might be integrated into the growing need for humans to access animal protein.

A Pilot Study Using Accelerometers to Characterise the Licking Behaviour of Penned Cattle at a Mineral Block Supplement

Simple Summary

Quantifying mineral block supplement intake by individual beef cattle is a challenging task but may enable improved efficiency of supplement use particularly in a grazed system. Estimating time spent licking when cattle access the mineral block supplement can be useful for predicting intake on an individual basis. The advancement of sensor technology has facilitated collection of individual data associated with ingestive behaviours such as feeding and licking duration. This experiment was intended to investigate the effectiveness of wearable tri-axial accelerometers fitted on both neck-collar and ear-tag to identify the licking behaviour of beef cattle by distinguishing it from eating, standing and lying behaviours. The capability of tri-axial accelerometers to classify licking behaviour in beef cattle revealed in this study would offer the possibility of measuring time spent licking and further developing a practical method of estimating mineral block supplement intake by individual grazing cattle.

Abstract

Identifying the licking behaviour in beef cattle may provide a means to measure time spent licking for estimating individual block supplement intake. This study aimed to determine the effectiveness of tri-axial accelerometers deployed in a neck-collar and an ear-tag, to characterise the licking behaviour of beef cattle in individual pens. Four, 2-year-old Angus steers weighing 368 ± 9.3 kg (mean ± SD) were used in a 14-day study. Four machine learning (ML) algorithms (decision trees [DT], random forest [RF], support vector machine [SVM] and k-nearest neighbour [kNN]) were employed to develop behaviour classification models using three different ethograms: (1) licking vs. eating vs. standing vs. lying; (2) licking vs. eating vs. inactive; and (3) licking vs. non-licking. Activities were video-recorded from 1000 to 1600 h daily when access to supplement was provided. The RF algorithm exhibited a superior performance in all ethograms across the two deployment modes with an overall accuracy ranging from 88% to 98%. The neck-collar accelerometers had a better performance than the ear-tag accelerometers across all ethograms with sensitivity and positive predictive value (PPV) ranging from 95% to 99% and 91% to 96%, respectively. Overall, the tri-axial accelerometer was capable of identifying licking behaviour of beef cattle in a controlled environment. Further research is required to test the model under actual grazing conditions.

Legal Complexities of Animal Welfare in Australia: Do On-Animal Sensors Offer a Future Option?

Simple Summary

‘Good animal welfare’ has evolved in recent decades to recognise behavioural, physiological and health factors, acknowledging that an animal may have good clinical health and be productive, though their welfare may be poor. The five freedoms and domains of animal welfare provide internationally recognised frameworks against which to evaluate practices to shape evidence-based standards which recognise both the physical and mental health needs of animals to provide a balanced view of an animal’s ability to cope in its environment. Whilst there are many techniques to measure animal welfare, the challenge lies with how best to align these with future changes in definitions and expectations, advances in science, legislative requirements and technology improvements. Substantial literature discusses the use of technology for improving animal monitoring, management and productivity on and off farm, though little has been published in relation to using such technologies to support legislative compliance and drive overall improvements in animal welfare. This article discusses the current legislation around animal welfare (with a focus on the Australian red meat sector); the impact of public expectation of welfare standards and production practices; and the current and future opportunity for on-animal sensors to support animal welfare, monitoring, management and compliance.

Abstract

The five freedoms and, more recently, the five domains of animal welfare provide internationally recognised frameworks to evaluate animal welfare practices which recognise both the physical and mental wellbeing needs of animals, providing a balanced view of their ability to cope in their environment. Whilst there are many techniques to measure animal welfare, the challenge lies with how best to align these with future changes in definitions and expectations, advances in science, legislative requirements, and technology improvements. Furthermore, enforcement of current animal welfare legislation in relation to livestock in Australia and the reliance on self-audits for accreditation schemes, challenges our ability to objectively measure animal welfare. On-animal sensors have enormous potential to address animal welfare concerns and assist with legislative compliance, through continuous measurement and monitoring of an animal’s behavioural state and location being reflective of their wellbeing. As reliable animal welfare measures evolve and the cost of on-animal sensors reduce, technology adoption will increase as the benefits across the supply chain are realised. Future adoption of on-animal sensors by producers will primarily depend on a value proposition for their business being clear; algorithm development to ensure measures are valid and reliable; increases in producer knowledge, willingness, and trust in data governance; and improvements in data transmission and connectivity.

Towards sensor-based calving detection in the rangelands: a systematic review of credible behavioral and physiological indicators

Calving is a critical point in both a cow and calf’s life, when both become more susceptible to disease and risk of death. Ideally, this period is carefully monitored. In extensive grazing systems, however, it is often not economically or physically possible for producers to continuously monitor animals, and thus, calving frequently goes undetected. The development of sensor systems, particularly in these environments, could provide significant benefits to the industry by increasing the quantity and quality of individual animal monitoring. In the time surrounding calving, cows undergo a series of behavioral and physiological changes, which can potentially be detected using sensing technologies. Before developing a sensor-based approach, it is worthwhile considering these behavioral and physiological changes, such that the appropriate technologies can be designed and developed. A systematic literature review was conducted to identify changes in the dam’s behavioral and physiological states in response to a calving event. Articles (n = 104) consisting of 111 independent experiments were assessed following an intensive search of electronic databases. Commonly reported indicators of parturition (n = 38) were identified, and temporal trend graphs were generated for 13 of these changes. The results compare trends in behavioral and physiological changes across a variety of animal-related factors and identifies several reliable indicators of parturition for detection with sensors, namely calf grooming behavior, changes in rumination duration, and lying bouts. This synthesis of literature suggests that variability exists between individuals and thus, combining several calving indicators may result in a more broadly applicable and accurate detection of parturition.

Remote Identification of Sheep with Flystrike Using Behavioural Observations

Simple Summary

Flystrike in sheep is a common condition in Australia where parasitic flies lay eggs on soiled wool or open wounds; and the resulting maggots feed off the flesh. Identification of ‘flystruck’ individuals is crucial for treatment; but requires labour-intensive physical examination of every animal. The aim of this study was to investigate the behaviour of sheep; while they remained in the paddock; to try and visually distinguish those suffering from flystrike. Observers who were blinded to the flystrike status of the sheep were asked to score the animal’s body language from video footage. These scores were then compared with the condition of the wool and whether the sheep were flystruck. The observers found that the flystruck sheep exhibited behavioural characteristics that corresponded to the flystrike severity and the condition of the wool around the tail (breech) of the sheep. We therefore conclude that behavioural monitoring of sheep in the paddock could be used to identify animals that had flystrike.

Abstract

Flystrike is a major problem affecting sheep in Australia. Identification of ‘flystruck’ individuals is crucial for treatment; but requires labour-intensive physical examination. As the industry moves toward more low-input systems; there is a need for remote methods to identify flystruck individuals. The aim of this study was to investigate the behaviour of sheep with breech flystrike within a paddock setting. Video footage of sixteen Merino sheep; eight later confirmed with flystrike and eight without; was collected as they moved freely within the paddock with conspecifics. Quantitative behavioural measurements and a qualitative behavioural assessment (QBA) were conducted and compared to their breech conditions (i.e., faecal/urine staining; flystrike severity). Both qualitative and quantitative assessments indicated behavioural differences between flystruck and non-flystruck animals. Flystruck sheep had a behavioural profile characterised by restless behaviour; abnormal postures and reduced grazing time (p < 0.05). Furthermore; flystruck sheep were scored to have a more ‘exhausted/irritated’ demeanour using QBA (p < 0.05). The behavioural responses also corresponded to the flystrike severity scores and condition of the breech area. We conclude that remotely assessed behaviour of flystruck sheep diverges markedly from non-flystruck sheep; and thus could be a low-input method for identifying and treating affected animals.