Semi-wildlife gait patterns classification using Statistical Methods and Artificial Neural Networks

Several studies have focused on classifying behavioral patterns in wildlife and captive species to monitor their activities and so to understanding the interactions of animals and control their welfare, for biological research or commercial purposes. The use of pattern recognition techniques, statistical methods and Overall Dynamic Body Acceleration (ODBA) are well known for animal behavior recognition tasks. The reconfigurability and scalability of these methods are not trivial, since a new study has to be done when changing any of the configuration parameters. In recent years, the use of Artificial Neural Networks (ANN) has increased for this purpose due to the fact that they can be easily adapted when new animals or patterns are required. In this context, a comparative study between a theoretical research is presented, where statistical and spectral analyses were performed and an embedded implementation of an ANN on a smart collar device was placed on semi-wild animals. This system is part of a project whose main aim is to monitor wildlife in real time using a wireless sensor network infrastructure. Different classifiers were tested and compared for three different horse gaits. Experimental results in a real time scenario achieved an accuracy of up to 90.7%, proving the efficiency of the embedded ANN implementation.Junta de Andalucía P12-TIC-1300Ministerio de Economía y Competitividad TEC2016-77785-

Utilization of information and communication technologies to monitor grazing behaviour in sheep

This thesis is a contribution on the study of feeding behaviour of grazing sheep and its general goal was to evaluate the effectiveness of a tri-axial accelerometer based sensor in the discrimination of the main activities of sheep at pasture, the quantification of the number of bites and the estimation of intake per bite. Based on the literature, it has been observed that feed intake at pasture is a difficult parameter to measure with direct observation, for this reason automated systems for monitoring the activities of free-ranging animals have became increasingly important and common. Among these systems, tri-axial accelerometers showed a good precision and accuracy in the classification of behavioural activities of herbivores, but they do not yet seem able to discriminate jaw movements, which are of great importance for evaluating animal grazing strategies in different pastures and for estimating the daily herbage intake. Thus, the main objective of this research was to develop and test a tri-axial accelerometer based sensor (BEHARUM) for the study of the feeding behaviour of sheep and for the estimation of the bite rate (number of bites per min of grazing) on the basis of acceleration variables. The thesis is organized in 4 main chapters. Chapter 1. This introduction section reports a literature review on the importance of studying the feeding behaviour of ruminants and on the measuring techniques developed over the years for its detection, with specific emphasis on accelerometer based sensors, which showed a good precision and accuracy in the classification of behavioural activities of herbivores. Chapter 2. This chapter describes the results of short tests performed in grazing conditions to discriminate three behavioural activities of sheep (grazing, ruminating and resting) on the base of acceleration data collected with the BEHARUM device. The multivariate statistical analysis correctly assigned 93.0% of minutes to behavioural activities. Chapter 3. This part evaluates the effectiveness of the BEHARUM in discriminating between the main behaviours (grazing, ruminating and other activities) of sheep at pasture and to identify the epoch setting (5, 10, 30, 60, 120, 180 and 300 s) with the best performance. Results show that a discriminant analysis can accurately classify important behaviours such as grazing, ruminating and other activities in sheep at pasture, with a better performance in classifying grazing behaviour than ruminating and other activities for all epochs; the most accurate classification in terms of accuracy and Coehn’s k coefficient was achieved with the 30 s epoch length. Chapter 4. This section illustrates the results of a study that aimed to derive a model to predict sheep behavioural variables like number of bites, bite mass, intake and intake rate, on the basis of variables calculated from acceleration data recorded by the BEHARUM. The experiment was carried out using micro-swards of Italian ryegrass (Lolium multiflorum L.), alfalfa (Medicago sativa L.), oat (Avena sativa L.), chicory (Cichorium intibus L.) and a mixture (Italian ryegrass and alfalfa). The sheep were allowed to graze the micro-swards for 6 minutes and the results show that the BEHARUM can accurately estimate with high to moderate precision (r2=0.86 and RMSEP=3%) the number of bites and the herbage intake of sheep short term grazing Mediterranean forages. Finally, the dissertation ends with a summary of the main implications and findings, and a general discussion and conclusions

Identification of reindeer fine-scale foraging behaviour using tri-axial accelerometer data

Animal behavioural responses to the environment ultimately affect their survival. Monitoring animal fine-scale behaviour may improve understanding of animal functional response to the environment and provide an important indicator of the welfare of both wild and domesticated species. In this study, we illustrate the application of collar-attached acceleration sensors for investigating reindeer fine-scale behaviour. Using data from 19 reindeer, we tested the supervised machine learning algorithms Random forests, Support vector machines, and hidden Markov models to classify reindeer behaviour into seven classes: grazing, browsing low from shrubs or browsing high from trees, inactivity, walking, trotting, and other behaviours. We implemented leave-one-subject-out cross-validation to assess generalizable results on new individuals. Our main results illustrated that hidden Markov models were able to classify collar-attached accelerometer data into all our pre-defined behaviours of reindeer with reasonable accuracy while Random forests and Support vector machines were biased towards dominant classes. Random forests using 5-s windows had the highest overall accuracy (85%), while hidden Markov models were able to best predict individual behaviours and handle rare behaviours such as trotting and browsing high. We conclude that hidden Markov models provide a useful tool to remotely monitor reindeer and potentially other large herbivore species behaviour. These methods will allow us to quantify fine-scale behavioural processes in relation to environmental events

Calving and estrus detection in dairy cattle using a combination of indoor localization and accelerometer sensors

Accelerometers (neck- and leg-mounted) and ultra-wide band (UWB) indoor localization sensors were combined for the detection of calving and estrus in dairy cattle. In total, 13 pregnant cows and 12 cows with successful insemination were used in this study. Data were collected two weeks before and two weeks after delivery for calving. Similarly, data were collected two weeks before and two weeks after artificial insemination (AI) for estrus. Different cow variables were extracted from the raw data (e.g., lying time, number of steps, ruminating time, travelled distance) and used to build and test the detection models. Logistic regression models were developed for each individual sensor as well as for each combination of sensors (two or three) for both calving and estrus. Moreover, the detection performance within different time intervals (24 h, 12 h, 8 h, 4 h, and 2 h) before calving and AI was investigated. In general, for both calving and estrus, the performance of the detection within 2-4 h was lower than for 8 h24 h. However, the use of a combination of sensors increased the performance for all investigated detection time intervals. For calving, similar results were obtained for the detection within 24 h, 12 h, and 8 h. When one sensor was used for calving detection within 24-8 h, the localization sensor performed best (Precision (Pr) 73-77%, Sensitivity (Se) 57-58%, Area under curve (AUC) 90-91%), followed by the leg-mounted accelerometer (Pr 67-77%, Se 54-55%, AUC = 88-90%) and the neck-mounted accelerometer (Pr 50-53%, Se 47-48%, AUC = 86-88%). As for calving, the results of estrus were similar for the time intervals 24 h-8 h. In this case, similar results were obtained when using any of the three sensors separately as when combining a neck- and a leg-mounted accelerometers (Pr 86-89%, Se 73-77%). For both calving and estrus, the performance improved when localization was combined with either the neck- or leg-mounted accelerometer, especially for the sensitivity (73-91%). Finally, for the detection with one sensor within a time interval of 4 h or 2 h, the Pr and Se decreased to 55-65% and 42-62% for estrus and to 40-63% and 33-40% for calving. However, the combination of localization with either leg or neck-mounted accelerometer as well as the combination of the three sensors improved the Pr and Se compared to one sensor (Pr 72-87%, Se 63-85%). This study demonstrates the potential of combining different sensors in order to develop a multi-functional monitoring system for dairy cattle

Feature Extraction and Random Forest to Identify Sheep Behavior from Accelerometer Data

Sensor technologies play an essential part in the agricultural community and many other scientific and commercial communities. Accelerometer signals and Machine Learning techniques can be used to identify and observe behaviours of animals without the need for an exhaustive human observation which is labour intensive and time consuming. This study employed random forest algorithm to identify grazing, walking, scratching, and inactivity (standing, resting) of 8 Hebridean ewes located in Cheshire, Shotwick in the UK. We gathered accelerometer data from a sensor device which was fitted on the collar of the animals. The selection of the algorithm was based on previous research by which random forest achieved the best results among other benchmark techniques. Therefore, in this study, more focus was given to feature engineering to improve prediction performance. Seventeen features from time and frequency domain were calculated from the accelerometer measurements and the magnitude of the acceleration. Feature elimination was utilised in which highly correlated ones were removed, and only nine out of seventeen features were selected. The algorithm achieved an overall accuracy of 99.43% and a kappa value of 98.66%. The accuracy for grazing, walking, scratching, and inactive was 99.08%, 99.13%, 99.90%, and 99.85%, respectively. The overall results showed that there is a significant improvement over previous methods and studies for all mutually exclusive behaviours. Those results are promising, and the technique could be further tested for future real-time activity recognition

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Assessing sheep behavior through low-power microcontrollers in smart agriculture scenarios

Automatic animal monitoring can bring several advantages to the livestock sector. The emergence of low-cost and low-power miniaturized sensors, together with the ability of handling huge amounts of data, has led to a boost of new intelligent farming solutions. One example is the SheepIT solution that is being commercialized by iFarmtec. The main objectives of the solution are monitoring the sheep’s posture while grazing in vineyards, and conditioning their behaviour using appropriate stimuli, such that they only feed from the ground or from the lower branches of the vines. The quality of the monitoring procedure has a linear correlation with the animal condition capability of the solution, i.e., on the effectiveness of the applied stimuli. Thus, a Real-Time mechanism capable of identifying animal behaviour such as infraction, eating, walking or running movements and standing position is required. On a previous work we proposed a solution based on low-power microcontrollers enclosed in collars wearable by sheep. Machine Learning techniques have been rising as a useful tool for dealing with big amounts of data. From the wide range of techniques available, the use of Decision Trees is particularly relevant since it allows the retrieval of a set of conditions easily transformed in lightweight machine code. The goal of this paper is to evaluate an enhanced animal monitoring mechanism and compare it to existing ones. In order to achieve this goal, a real deployment scenario was availed to gather relevant data from sheep’s collar. After this step, we evaluated the impact of several feature transformations and pre-processing techniques on the model learned from the system. Due to the natural behaviour of sheep, which spend most of the time grazing, several pre-processing techniques were tested to deal with the unbalanced dataset, particularly resorting on features related with stateful history. Albeit presenting promising results, with accuracy over 96%, these features resulted in unfeasible implementations. Hence, the best feasible model was achieved with 10 features obtained from the sensors’ measurements plus an additional temporal feature. The global accuracy attained was above 91%. Howbeit, further research shall assess a way of dealing with this kind of unbalanced datasets and take advantage of the insights given by the results achieved when using the state’s history.publishe

Prediction of poor health in small ruminants and companion animals with accelerometers and machine learning

Global warming is one of the biggest challenge of our times, and significant efforts are being undertaken by academics, industries and other actors to tackle the problem. In the agricultural field precision farming is part of the solution to environmental sustainability and has been researched increasingly in recent years. Indeed, it has the potential to effectively increase livestock yield and decrease production carbon footprint while maintaining welfare. The thesis begins with a review of developments in automated animal monitoring and then moves on to a case study of a health monitoring system for small-ruminant in South Africa. As a demonstration and validation of the potential use case of the system, the method we propose is then applied to another study which aims to study feline health. Lower and Middle Income countries will be strongly affected by the changing climate and its impacts. We devise our method based on two South African small scale sheep and goat farms where assessment of the health status of individual animals is a key step in the timely and targeted treatment of infections, which is critical in the fight against anthelmintic and antimicrobial resistance. The FAMACHA scoring system has been used successfully to detect anaemia caused by infection with the parasitic nematode Haemonchus contortus in small ruminants and is an effective way to identify individuals in need of treatment. However, assessing FAMACHA is labour-intensive and costly as individuals must be manually examined at frequent intervals. Here, we used accelerometers to measure the individual activity of extensively grazed small ruminants exposed to natural Haemonchus contortus worm infection in southern Africa over long time scales (13+ months). When combined with machine learning for missing data imputation and classification, we find that this activity data can predict poorer health as well as those individuals that respond to treatment, with precision up to 80%. We demonstrate that these classifiers remain robust over time. Interpretation of trained classifiers reveals that poorer health can be predicted mainly by the night-time activity levels in the sheep. Our study reveals behavioural patterns across two small ruminant species, which low-cost biologgers can exploit to detect subtle changes in animal health and enable timely and targeted intervention. This has real potential to improve economic outcomes and animal welfare as well as limit the use of anthelmintic drugs and diminish pressures on anthelmintic resistance in both commercial and resource-poor communal farming. The validation of the proposed techniques with a different study group will be discussed in the latter part of the thesis. We used the accelerometry data of indoor cats equipped with wearable accelerometers in conjunction with their health status to detect signs of degenerative joint disease, and adapted our machine-learning pipeline to analyse bursts of high activity in the cats. We were able to classify high-activity events with precision up to 70% despite the relatively small dataset adding further evidence to the viability of animal health monitoring with accelerometers