Evaluation of infrared thermography combined with behavioral biometrics for estrus detection in naturally cycling dairy cows

Use of fidget and drinking behaviour in combination with facial infrared thermography for diagnosis of bovine respiratory disease in a spontaneous model

Bovine respiratory disease (BRD) is a highly prevalent multi pathogen infectious disease (70-80%) in newly received feedlot cattle, causing significant economic losses and reduced animal welfare. Current BRD diagnosis involves stressful and invasive methods that can increase the incidence and transmission of BRD. An alternative is the use of an automated infrared thermography (IR) platform that can monitor facial temperature and behaviour traits to diagnose BRD in a non-invasive manner. The objective of this study was to investigate the use of fidget and drinking behaviours in conjunction with facial temperature as method of BRD diagnosis in beef calves. Sixty-five weaned calves (N = 65) were monitored over a 21-d period after 6 h transportation to predispose calves to BRD infection. Data collected from an automated IR platform placed at a water station included the number of IR frames during drinking (Fidget), number of drinking visits (Drinking bouts), total drinking duration, average drinking duration, average cheek temperature (AVG temp), and maximum orbital temperature (Max temp). Fidget, drinking behaviours, and IR were compared to a clinical score assessment based on respiratory, digestive, and lethargy signs (visual observation) and haematology analysis using a receiver operating characteristics curve analysis to identify the accuracy of each metric and combinations of metrics for BRD diagnosis. The greater accuracies observed were Fidget, Youden's index (J): 0.25 J), Drinking bout (0.28 J), and Total drinking duration (0.22 J). The average IR temperature accuracy resulted in 0.88 J and Max temp 0.77 J. Thirty-five combinations of drinking behaviour and facial IR metrics were evaluated to identify BRD calves. Optimum accuracy (100%) was achieved when combining Fidget, Drinking bout, Average drinking duration, AVG temp, and Max temp 1.00 J. Similar evaluations were performed at 48 and 24 h before d 0 using the most accurate Fidget, Drinking behaviour, and IR combination, resulting in 0.44 J 48 h prior to d 0 and 0.45 J 24 h prior to d 0. Combining fidget and drinking behaviour metrics increased the sensitivity to detect the onset of BRD infection and the specificity to discriminate true positive BRD calves from true negative BRD calves.

Infrared Thermography as a Potential Non-Invasive Tool for Estrus Detection in Cattle and Buffaloes

The productivity of dairy animals has significantly increased over the past few decades due to intense genetic selection. However, the enhanced yield performance of milk animals caused a proportional increase in stress and compromised reproductive efficiency. Optimal reproductive performance is mandatory for the sustainable production of dairy animals. Reproductive efficiency is marked by proper estrus detection and precise breeding to achieve maximum pregnancies. The existing conventional methods of estrus detection are somewhat labor intensive and less efficient. Similarly, the modern automated methods that rely on detecting physical activity are expensive, and their efficiency is affected by factors such as type of housing (tie stall), flooring, and environment. Infrared thermography has recently emerged as a technique that does not depend on monitoring physical activity. Furthermore, infrared thermography is a non-invasive, user-friendly, and stress-free option that aids in the detection of estrus in dairy animals. Infrared thermography has the potential to be considered a useful non-invasive tool for detecting temperature fluctuations to generate estrus alerts without physical contact in cattle and buffaloes. This manuscript highlights the potential use of infrared thermography to understand reproductive physiology and practical implementation of this technique through discussing its advantages, limitations, and possible precautions.

Evaluating automated infrared thermography and vulva exposure tracking as components of an estrus detection platform in a commercial dairy herd

The primary objective of this study was to develop an automated infrared thermography platform (Estrus BenchMark) capable of measuring skin temperature and tail movements as a means of identifying cows in estrus. The secondary objective was to evaluate the accuracy of Estrus BenchMark to detect estrus compared to in-line milk progesterone (P₄) analysis (Herd Navigator System) in a commercial dairy herd managed under a robotic milking system. Data were collected on forty-six cows from 45 to 120 d after calving. Cows were flagged in estrus when milk P₄ fell below 5 ng/mL. The Estrus BenchMark true positive estrus alerts (Sensitivity; Se%) were compared to Herd Navigator System estrus alerts at different time-windows (±12 h, ±24 h, ±48 h, and ±72 h) relative to the Estrus BenchMark estrus alerts for all the estrus alerts (AE) and confidence-quality estrus (CQE; >80% quality) alerts identified by Herd Navigator System. The Estrus BenchMark captured skin temperature and tail movements resulting in vulva exposure (left tail movements, LTail; right tail movements, RTail; and pooled tail movements, PTail) for each milking event. Skin temperature tended to increase when the milk P₄ concentration (Least-Squares Means ± SE) dropped for AE (estrus day [d 0]; P₄; 3.51 ± 0.05 ng/mL, Skin temperature; 33.31 ± 2.38 °C) compared with d -7 (P₄; 20.22 ± 0.73 ng/mL; Skin temperature: 32.05 ± 3.77 °C). The increase in skin temperature, however, was significant in cows with CQE > 80% at d 0 (32.75 ± 0.29 °C) compared to d -7 (31.80 ± 0.28 °C). The prevalence of tail movements to expose vulva was greater (P = 0.01) in AE at d 0 (LTail: 62.50%; PTail; 68.75%; and RTail: 56.25%) compared with d -7 (LTail: 18.75%; PTail: 9.37%: and RTail: 9.37%), and d +4 (LTail: 9.37%; PTail: 9.37%; and RTail: 12.5%). Moreover, the higher prevalence of tail movements at d 0 was observed in cows with CQE > 80% (LTail; 65%, PTail; 80%, and RTail; 70%) compared to those with CQE < 80%. The highest Estrus BenchMark Youden index (YJ; 0.45), diagnostic odds ratio (DOR; 9.04), and Efficiency (0.77) were achieved for AE in a ±48 h window and at ±72 h window for CQE (YJ; 0.66, DOR; 25.29, and Efficiency 0.76) relative to Herd Navigator System estrus alerts. The highest Estrus BenchMark resulted in 58% estrus detection rates for AE and 80% for cows with CQE compared to the Herd Navigator System.

Automated Systems for Estrous and Calving Detection in Dairy Cattle

Purpose: The objective of this review is to describe the main technologies (automated activity monitors) available commercially and under research for the detection of estrus and calving alerts in dairy cattle. Sources: The data for the elaboration of the literature review were obtained from searches on the Google Scholar platform. This search was performed using the following keywords: reproduction, dairy cows, estrus detection and parturition, electronic devices. After the search, the articles found with a title related to the objective of the review were read in full. Finally, the specific articles chosen to be reported in the review were selected according to the method of identification of estrus and parturition, seeking to represent the different devices and technologies already studied for both estrus and parturition identification. Synthesis: Precision livestock farming seeks to obtain a variety of information through hardware and software that can be used to improve herd management and optimize animal yield. Visual observation for estrus detection and calving is an activity that requires labor and time, which is an increasingly difficult resource due to several others farm management activities. In this way, automated estrous and calving monitoring devices can increase animal productivity with less labor, when applied correctly. The main devices available currently are based on accelerometers, pedometers and inclinometers that are attached to animals in a wearable way. Some research efforts have been made in image analysis to obtain this information with non-wearable devices. Conclusion and applications: Efficient wearable devices to monitor cows’ behavior and detect estrous and calving are available on the market. There is demand for low cost with easy scalable technology, as the use of computer vision systems with image recording. With technology is possible to have a better reproductive management, and thus increase efficiency.

Progress on Infrared Imaging Technology in Animal Production: A Review

Infrared thermography (IRT) imaging technology, as a convenient, efficient, and contactless temperature measurement technology, has been widely applied to animal production. In this review, we systematically summarized the principles and influencing parameters of IRT imaging technology. In addition, we also summed up recent advances of IRT imaging technology in monitoring the temperature of animal surfaces and core anatomical areas, diagnosing early disease and inflammation, monitoring animal stress levels, identifying estrus and ovulation, and diagnosing pregnancy and animal welfare. Finally, we made prospective forecast for future research directions, offering more theoretical references for related research in this field.

Business analysis of IRT, Visual observation, and Ovsynch as breeding strategies in Alberta dairies

The dairy industry is searching for new technologies to address low (<50%) estrus detection. However, the lack of information on the potential economic benefits regarding new technology implementation has led some dairy producers to continue using conventional estrus detection methods (e.g. visual observation of standing to be mounted). The objective of this study was to compare the costs of infrared thermography (IRT), visual observation (VO) and ovulation synchronization (Ovsynch: OVS) as breeding strategies at different accuracy levels (Sensitivity [Se], Specificity [Sp]) and pregnancy rates (PR). The costs associated with Breeding, Feeding, Operation Costs, Return to Equity and Culling Risk per estrus detection rate (ER; 30-100%, conception rate for OVS; 30-100%), PR [PR per Parity group; 1-2 (50%), 3-4 (43%), and >4 (41%)], and ER accuracy determined the potential financial benefit of each breeding method for a representative farm. Breeding Cost results (Canadian dollars per cow; CAD/cow) showed a higher cost of OVS (138.99) as compared to VO (115.78) and IRT (127.69). Pregnancy Costs were affected by Breeding Cost; however, ER had a significant effect on PR expense for each method, IRT (ER; 30%: 210.38; 100%: 132.19), VO (ER; 30%: 205.93; 100%: 129.39), and OVS (ER; 30%: 247.21; 100%: 155.33). The minimum Se level with a positive Financial Effect for IRT and VO was 60% with a Sp of 100%, and for the OVS was Se 65% and Sp 100%. However, when the Se was 100% a positive Financial Effect was observed with a minimum Sp of 85% for IRT and 75% for VO. Culling Risk was reduced if ER increases differently depending on the parity group. Implementing of IRT as an estrus detection method yields a competitive breeding cost compared to VO and OVS. Further, breeding methods must accomplish at least ∼60% accuracy to have a positive net return.

Biometric Cloud Services for Web-Based Examinations

Biometric recognition may be used in conjunction with human authentication on a smartphone to improve accuracy, reliability, and simplicity, and to aid in fraud prevention and user authentication. While single biometric authentication addresses environmental degradation and sensor noise limitations, and the single point of failure scenario in biometric systems can result in more robust biometric systems, multimodal biometric authentication can improve the accuracy of identification and recognition. The purpose of this research is to propose a facial and speech authentication system that is cloud-based and supports a web-based examination approach. The system enables students' biometrics to be registered, students to be recognized, and student recognition results to be reported. The confusion matrix is used to compare the results of positive and negative detection in various ways, including accuracy score, precision value, and recall value. Adaptive multimodal biometric authentication should be designed and evaluated for further research using the optimal weights for each biometric.

Contribution of Precision Livestock Farming Systems to the Improvement of Welfare Status and Productivity of Dairy Animals

Although the effects of human–dairy cattle interaction have been extensively examined, data concerning small ruminants are scarce. The present review article aims at highlighting the effects of management practices on the productivity, physiology and behaviour of dairy animals. In general, aversive handling is associated with a milk yield reduction and welfare impairment. Precision livestock farming systems have therefore been applied and have rapidly changed the management process with the introduction of technological and computer innovations that contribute to the minimization of animal disturbances, the promotion of good practices and the maintenance of cattle’s welfare status and milk production and farms’ sustainability and competitiveness at high levels. However, although dairy farmers acknowledge the advantages deriving from the application of precision livestock farming advancements, a reluctance concerning their regular application to small ruminants is observed, due to economic and cultural constraints and poor technological infrastructures. As a result, targeted intervention training programmes are also necessary in order to improve the efficacy and efficiency of handling, especially of small ruminants.