Farm Animals’ Behaviors and Welfare Analysis with AI Algorithms: A Review

peer reviewedNumerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors

Comparison of Tree Method, Support Vector Machine, Naïve Bayes, and Logistic Regression on Coffee Bean Image

Coffee is one of the many favorite drinks of Indonesians. In Indonesia there are 2 types of coffee, namely Arabica & Robusta. The classification of coffee beans is usually done in a traditional way & depends on the human senses. However, the human senses are often inconsistent, because it depends on the mental or physical condition in question at that time, and only qualitative measures can be determined. In this study, to classify coffee beans is done by digital image processing. The parameters used are texture analysis using the Gray Level Coocurrence Matrix (GLCM) method with 4 features, namely Energy, Correlation, Homogeneity & Contrast. For feature extraction using a classification algorithm, namely Naïve Bayes, Tree, Support Vector Machine (SVM) and Logistic Regression. The evaluation of the coffee bean classification model uses the following parameters: AUC, F1, CA, precision & recall. The dataset used is 29 images of Arabica coffee beans and 29 images of Robusta beans. To test the accuracy of the model using Cross Validation. The results obtained will be evaluated using the confusion Matrix. Based on the results of testing and evaluation of the model, it is obtained that the SVM method is the best with the value of AUC = 1, CA = 0.983, F1 = 0.983, Precision = 0.983 and Recall = 0.983

Design of a Sky Camera-Based Cloud Monitoring Camera at the Agam Space and Atmospheric Observation Station, Bukit Kototabang

Indonesia is a center of convection and acts as a driving force for global atmospheric circulation due to its geographical position. Moreover, Kototabang Hill is one of the national strategic areas in the equatorial atmospheric observation room with limited cloud cover data so that tools and development are needed to meet these data needs. Sky Camera for the purpose of observing clouds (Cloud Camera) is urgently needed to complement the need for cloud cover data to support observation and research activities in the field of the atmosphere. The Cloud Camera design is done by modifying the CCD Camera with several supporting devices including fish eye, solar tracker, sun filter and dome. Evaluation of the urgency of these enhancements is discussed in this paper. Among the four combinations of using supporting instruments (dome and sun filter) for the Cloud Camera device, the best image obtained is the device that uses a sun filter and without a dome. Among the four combinations of using supporting instruments (dome and sun filter) for the Cloud Camera device, the best image obtained is the device that uses a sun filter and without a dome

Animal Welfare Assessment

This Special Issue provides a collection of recent research and reviews that investigate many areas of welfare assessment, such as novel approaches and technologies used to evaluate the welfare of farmed, captive, or wild animals. Research in this Special Issue includes welfare assessment related to pilot whales, finishing pigs, commercial turkey flocks, and dairy goats; the use of sensors or wearable technologies, such as heart rate monitors to assess sleep in dairy cows, ear tag sensors, and machine learning to assess commercial pig behaviour; non-invasive measures, such as video monitoring of behaviour, computer vision to analyse video footage of red foxes, remote camera traps of free-roaming wild horses, infrared thermography of effort and sport recovery in sport horses; telomere length and regulatory genes as novel biomarkers of stress in broiler chickens; the effect of environment on growth physiology and behaviour of laboratory rare minnows and housing system on anxiety, stress, fear, and immune function of laying hens; and discussions of natural behaviour in farm animal welfare and maintaining health, welfare, and productivity of commercial pig herds

An adaptive pig face recognition approach using convolutional neural networks

The evolution of agriculture towards intensive farming leads to an increasing demand for animal identification associated with high traceability, driven by the need for quality control and welfare management in agricultural animals. Automatic identification of individual animals is an important step to achieve individualised care in terms of disease detection and control, and improvement of the food quality. For example, as feeding patterns can differ amongst pigs in the same pen, even in homogenous groups, automatic registration shows the most potential when applied to an individual pig. In the EU for instance, this capability is required for certification purposes. Although the RFID technology has been gradually developed and widely applied for this task, chip implanting might still be time-consuming and costly for current practical applications. In this paper, a novel framework composed of computer vision algorithms, machine learning and deep learning techniques is proposed to offer a relatively low-cost and scalable solution of pig recognition. Firstly, pig faces and eyes are detected automatically by two Haar feature-based cascade classifiers and one shallow convolutional neural network to extra high-quality images. Secondly, face recognition is performed by employing a deep convolutional neural network. Additionally, class activation maps generated by grad-CAM and saliency maps are utilised to visually understand how the discriminating parameters have been learned by the neural network. By applying the proposed approach on 10 randomly selected pigs filmed in farm condition, the proposed method demonstrates the superior performance against the state-of-art method with an accuracy of 83% over 320 testing images. The outcome of this study will facilitate the real-application of AI-based animal identification in swine production

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

Automatic recognition of feeding and foraging behaviour in pigs using deep learning

Highlights• An automated detection method of pig feeding and foraging behaviour was developed.• The automated method is based on convolutional deep neural networks.• The automated method does not rely on pig tracking to estimate behaviours.• Detection of feeding behaviour is highly accurate (99.4%) and fast (0.02 sec/image).• The robust method can be applied under different husbandry/ management conditions.Automated, vision-based early warning systems have been developed to detect behavioural changes in groups of pigs to monitor their health and welfare status. In commercial settings, automatic recording of feeding behaviour remains a challenge due to problems of variation in illumination, occlusions and similar appearance of different pigs. Additionally, such systems, which rely on pig tracking, often overestimate the actual time spent feeding, due to the inability to identify and/or exclude non-nutritive visits (NNV) to the feeding area. To tackle these problems, we have developed a robust, deep learning-based feeding detection method that (a) does not rely on pig tracking and (b) is capable of distinguishing between feeding and NNV for a group of pigs. We first validated our method using video footage from a commercial pig farm, under a variety of settings. We demonstrate the ability of this automated method to identify feeding and NNV behaviour with high accuracy (99.4% ± 0.6%). We then tested the method's ability to detect changes in feeding and NNV behaviours during a planned period of food restriction. We found that the method was able to automatically quantify the expected changes in both feeding and NNV behaviours. Our method is capable of monitoring robustly and accurately the feeding behaviour of groups of commercially housed pigs, without the need for additional sensors or individual marking. This has great potential for application in the early detection of health and welfare challenges of commercial pigs

Bovine parturition: welfare and production implications of assistance and ketoprofen analgesia

Parturition is a necessary event for productive dairy cows (and their calves) and assisted parturition is common. Although difficult parturition is believed by farmers and veterinary surgeons to be painful and stressful for cows and their calves, data to support this view are limited. Previous studies typically analysed the effects of assistance or analgesia as individual effects but inclusion of both in a factorial design is rare, so the association between pain and parturition assistance is not certain. Furthermore, there is a paucity of studies investigating calf birth-related experiences in general, and available work typically focuses on health and productivity rather than more sensitive measures of welfare (e.g. behaviour). Differences in study design further challenge the interpretation and practical application of available data; most studies refer to ‘dystocia’, but definitions of this term vary widely and important differences between veterinary and farmer provided assistance are not always acknowledged. Accordingly, it is currently difficult to develop evidence based recommendations for farmers and veterinary surgeons regarding the value of analgesic provision to cows and calves around parturition. Farmers are recommended to closely monitor cows that may need assisted parturition to enable intervention to be optimised; however, this can be difficult to achieve particularly if staff availability is limited, and it is currently not possible to accurately predict when cows will give birth, or whether they are likely to need assistance. As such, some cows that experience difficult parturition may not receive timely assistance and conversely, some cows may be assisted unnecessarily — both are situations that may challenge welfare. The studies presented in this thesis aimed to investigate the effects of farmer-assisted parturition and administration of the non-steroidal anti-inflammatory drug ketoprofen on the welfare, health, and productivity of commercially managed Holstein dairy cows and calves (Bos taurus) using a 2 x 2 factorial study design. Further work aimed to support the findings of initial studies using accelerometer generated data to analyse behavioural patterns of cows and calves for up to 48 h postpartum. A final aim was to assess the potential for data generated by animal-worn accelerometers to detect cows that are likely to need farmer-provided assistance at parturition. Cows and calves subject to farmer-assisted and unassisted parturition were randomly assigned to receive either ketoprofen or saline within 3 h of parturition. Behaviour in the first 48 h postpartum was analysed using focal instantaneous sampling (visual observations) to investigate welfare outcomes. Detailed behavioural analysis was complemented with analysis of biomarkers indicative of health and welfare status (cortisol, creatine kinase [cows and calves]; L-lactate, plasma total protein [calves only]) in the first 7 d postpartum. Regardless of ketoprofen treatment, cows and calves subject to assisted birth showed behavioural differences consistent with a reduced welfare state (increased lateral recumbency [both] and reduced play [calves only]), compared to unassisted animals. Additionally, the plasma cortisol concentration of assisted cows was higher than unassisted cows immediately after parturition, suggesting assisted parturition is associated with heightened maternal stress. Irrespective of assistance status, cows and calves treated with ketoprofen engaged in behaviours consistent with pain and reduced welfare less than saline treated animals. Additionally, ketoprofen treated cows engaged in lying postures suggestive of improved comfort, and ketoprofen treated calves engaged in play behaviour more than saline treated cows and calves respectively (regardless of assistance status) — suggesting that all cows and calves experience pain after parturition that can be improved by ketoprofen. Results of further work using accelerometers to continuously monitor behaviour for 48 h after parturition corroborated these findings — ketoprofen treated cows and calves were more active than saline treated animals and ketoprofen treated calves engaged in increased play behaviour. Health and productivity data for cows and calves recruited in initial work were obtained from farm records: cow data were collected until the end of the subsequent lactation (approximately one year), calf data were collected until the end of the first lactation (approximately three years). Regardless of treatment status, parturition assistance was associated with increased postpartum disease and reduced maternal reproductive performance in the subsequent lactation. Birth assistance was associated with poorer growth of calves before first parturition and reduced reproductive performance in the first lactation (irrespective of treatment status). Ketoprofen treated cows had a 305 d mature equivalent milk yield 664 kg higher than saline treated cows, irrespective of assistance status. Ketoprofen treatment did not affect measures of calf productivity overall but ketoprofen treated assisted calves had a growth rate to weaning 0.1 kg/d higher than calves in the other assistance x treatment status interaction groups. Accelerometer generated data (primarily step count) showed potential for detection of cows more likely to need assistance, although a threshold for detection could not be established with high accuracy. Additionally, the number of lying bouts exhibited by cows in the last 12 h of gestation showed promise for predicting the timing of parturition. These data suggest that leg-worn accelerometers may be a valuable tool to aid pre-partum management of dairy cows, and the results presented here offer a starting point for the development of pre-partum specific algorithms for use in future remote devices. Collectively, the results presented in these studies indicate that parturition assistance is negatively associated with welfare and future productivity of cows and calves, and that ketoprofen administration immediately after parturition has beneficial effects on these outcomes. However, observed interaction effects were few, suggesting that a) farmer-assisted cows and calves experience challenges to welfare that extend beyond pain (i.e. challenges that cannot be manipulated using analgesia), and b) pain is experienced by all cows and calves after parturition, not just those that are assisted. These findings suggest that assistance at parturition should be provided judiciously and not be a routine management intervention. Furthermore, these results provide a robust basis on which inclusion of ketoprofen administration in parturition and newborn calf management protocols can be recommended to dairy farmers and veterinary surgeons seeking to optimise the welfare and productivity of Holstein cows and calves managed in a housed dairy system

The automatic classification of canine state

PhD ThesisOsteoarthritis is a prevalent disease among domestic dogs which, even when well managed, often causes bouts of chronic pain and a lesser quality of life. Despite a lack of training dog owners are relied upon to recognise the signs of pain or illness in their animals. This often leads to treatment being sought later than would be ideal, resulting in the unnecessary and avoidable suffering of their dogs. This can be further compounded by the qualitative nature of lameness assessment performed by veterin arians. The difficulty of which is further exacerbated when symptoms are subtle, and the disease is in its early stages. This thesis investigates the use of remote, animal borne, tri-axial accelerometers to supplement the welfare information available to both caregivers and veterinarians. Published acceleration-derived measures, of both the time and frequency domains, common within human and non-human animal acceler ometer research, are assessed for their potential as daily and weekly identifiers of os teoarthritic lameness. The suitability of identified measures was evaluated using both Principal Component Analysis based feature selection and logistic linear models. The results of this process highlighted a potential link between both the level and entropy of an animals overall weekly activity with the occurrence of osteoarthritis. It also provided insight into areas of further development and established the complexity of the task of recognising lameness from acceleration data. A behaviour-based methodology was established hybridising techniques used across wildlife ecology deployments, existing veterinary assessment of lameness and, the assessment of human gait impacted by both physical illness and neurodegeneration. This led to the development of a method ology focussing on the identification of behaviours, starting with canine postural state, to provide context as to the daily activities of the subject. Two distinct approaches to postural recognition were assessed both employing machine learning techniques with a focus on the interpretability of results. The first, examined the identification of 6 pos tural transitions, similar to methods established in human accelerometer assessments, using linear discriminant analyses at 3 different sliding window lengths. The inclusion of an empirical cumulative distribution function representation was also assessed. The results suggested that the isolation of transitional periods from among non-transitional periods was difficult and there was high confusion between the transitions themselves. The second examined the identification of the postures themselves alongside the oc currence of locomotion during the standing posture. Linear discrimination analyses were once again used due to the interpretability of the method and the simplicity of its implementation. The effects of pre-processing techniques and differing posture group ings were also explored. The findings suggested a binary decision tree approach was the most effective mechanism and that the application of pre-processing techniques to clean data caused a distinct negative impact that requires forethought as to the poten tial costs and benefits of their use. Standing was the most easily identified, perhaps due to its prominence, and the further classification of locomotion from among stand ing periods was ineffective. To further supplement the postural methods of identifying osteoarthritis an investigation of the remote monitoring of circadian rhythm was estab lished. This is of interest due to prior results highlighting the potential relationship of activity entropy and level with lameness and the reports of sleep disruption by human chronic pain sufferers. Features relating to the length and frequency of both resting and active bouts were used in logistic regression models to establish their relationship to the presence or absence of osteoarthritis. Minor disruption was observed to the amplitude of activity frequencies within osteoarthritic dogs consistent with prior find ings. However, further work is needed to disentangle this effect from that of advanced age, a possible confound. The potential of remote sensing technologies is shown but further development of methodologies is required. A combination of the described approaches, with the refinements highlighted within this thesis, could further improve their efficacy and should be investigated. A behaviour based, transparent and fully in terpretable monitor of lameness, pain, and/or welfare could prove valuable to the early and effective treatment of canine osteoarthritis and should be pursued furthe