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

Development of an Automated Pain Facial Expression Detection System for Sheep (Ovis Aries).

The use of technology to optimize the production and management of each individual animal is becoming key to good farming. There is a need for the real-time systematic detection and control of disease in animals in order to limit the impact on animal welfare and food supply. Diseases such as footrot and mastitis cause significant pain in sheep, and so early detection is vital to ensuring effective treatment and preventing the spread across the flock. Facial expression scoring to assess pain in humans and non-humans is now well utilized, and the Sheep Pain Facial Expression Scale (SPFES) is a tool that can reliably detect pain in this species. The SPFES currently requires manual scoring, leaving it open to observer bias, and it is also time-consuming. The ability of a computer to automatically detect and direct a producer as to where assessment and treatment are needed would increase the chances of controlling the spread of disease. It would also aid in the prevention of resistance across the individual, farm, and landscape at both national and international levels. In this paper, we present our framework for an integrated novel system based on techniques originally applied for human facial expression recognition that could be implemented at the farm level. To the authors' knowledge, this is the first time that this technology has been applied to sheep to assess pain

Seeing is caring – automated assessment of resource use of broilers with computer vision techniques

Routine monitoring of broiler chickens provides insights in the welfare status of a flock, helps to guarantee minimum defined levels of animal welfare and assists farmers in taking remedial measures at an early stage. Computer vision techniques offer exciting potential for routine and automated assessment of broiler welfare, providing an objective and biosecure alternative to the current more subjective and time-consuming methods. However, the current state-of-the-art computer vision solutions for assessing broiler welfare are not sufficient to allow the transition to fully automated monitoring in a commercial environment. Therefore, the aim of this study was to investigate the potential of computer vision algorithms for detection and resource use monitoring of broilers housed in both experimental and commercial settings, while also assessing the potential for scalability and resource-efficient implementation of such solutions. This study used a combination of detection and resource use monitoring methods, where broilers were first detected using Mask R-CNN and were then assigned to a specific resource zone using zone-based classifiers. Three detection models were proposed using different annotation datasets: model A with annotated broilers from a research facility, model B with annotated broilers from a commercial farm, and model A+B where annotations from both environments were combined. The algorithms developed for individual broiler detection performed well for both the research facility (model A, F1 score > 0.99) and commercial farm (model A+B, F1 score > 0.83) test data with an intersection over union of 0.75. The subsequent monitoring of resource use at the commercial farm using model A+B for broiler detection, also performed very well for the feeders, bale and perch (F1 score > 0.93), but not for the drinkers (F1 score = 0.28), which was likely caused by our evaluation method. Thus, the algorithms used in this study are a first step to measure resource use automatically in commercial application and allow detection of a large number of individual animals in a non-invasive manner. From location data of every frame, resource use can be calculated. Ultimately, the broiler detection and resource use monitoring might further be used to assess broiler welfare

Precision Poultry Farming

This book presents the latest advances in applications of continuous, objective, and automated sensing technologies and computer tools for sustainable and efficient poultry production, and it offers solutions to the poultry industry to address challenges in terms of poultry management, the environment, nutrition, automation and robotics, health, welfare assessment, behavior monitoring, waste management, etc. The reader will find original research papers that address, on a global scale, the sustainability and efficiency of the poultry industry and explore the above-mentioned areas through applications of PPF solutions in poultry meat and egg productio

An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification

This study proposes a computer vision and machine learning (ML)-based approach to classify gender and breed in native chicken production industries with minimal human intervention. The supervised ML and feature extraction algorithms are utilized to classify eleven Indian chicken breeds, with 17,600 training samples and 4,400 testing samples (80:20 ratio). The gray-level co-occurrence matrix (GLCM) algorithm is applied for feature extraction, and the principle component analysis (PCA) algorithm is used for feature selection. Among the tested 27 classifiers, the FG-SVM, F-KNN, and W-KNN classifiers obtain more than 90% accuracy, with individual accuracies of 90.1%, 99.1%, and 99.1%. The BT classifier performs well in gender and breed classification work, achieving accuracy, precision, sensitivity, and F-scores of 99.3%, 90.2%, 99.4%, and 99.5%, respectively, and a mean absolute error of 0.7

Farmers' Perspectives of the Benefits and Risks in Precision Livestock Farming in the EU Pig and Poultry Sectors

Simple Summary Smart farming is a concept of agricultural innovation that combines technological, social, economic and institutional changes. It employs novel practices of technologies and farm management at various levels (specifically with a focus on the system perspective) and scales of agricultural production, helping the industry meet the challenges stemming from immense food production demands, environmental impact mitigation and reductions in the workforce. Precision Livestock Farming (PLF) systems will help the industry meet consumer expectations for more environmentally and welfare-friendly production. However, the overwhelming majority of these new technologies originate from outside the farm sector. The adoption of new technologies is affected by the development, dissemination and application of new methodologies, technologies and regulations at the farm level, as well as quantified business models. Subsequently, the utilization of PLF in the pig and especially the poultry sectors should be advocated (the latter due to the foreseen increase in meat production). Therefore, more significant research efforts than those that currently exist are mainly required in the poultry industry. The investigation of farmers' attitudes and concerns about the acceptance of technological solutions in the livestock sector should be integrally incorporated into any technological development.Abstract More efficient livestock production systems are necessary, considering that only 41% of global meat demand will be met by 2050. Moreover, the COVID-19 pandemic crisis has clearly illustrated the necessity of building sustainable and stable agri-food systems. Precision Livestock Farming (PLF) offers the continuous capacity of agriculture to contribute to overall human and animal welfare by providing sufficient goods and services through the application of technical innovations like digitalization. However, adopting new technologies is a challenging issue for farmers, extension services, agri-business and policymakers. We present a review of operational concepts and technological solutions in the pig and poultry sectors, as reflected in 41 and 16 European projects from the last decade, respectively. The European trend of increasing broiler-meat production, which is soon to outpace pork, stresses the need for more outstanding research efforts in the poultry industry. We further present a review of farmers' attitudes and obstacles to the acceptance of technological solutions in the pig and poultry sectors using examples and lessons learned from recent European projects. Despite the low resonance at the research level, the investigation of farmers' attitudes and concerns regarding the acceptance of technological solutions in the livestock sector should be incorporated into any technological development

Individuality of a group: detailed walking ability analysis of broiler flocks using optical flow approach

Impaired walking ability is one of the most important factors affecting broiler welfare. Routine monitoring of walking ability provides insights in the welfare status of a flock and assists farmers in taking remedial measures at an early stage. Several computer vision techniques have been developed for automated assessment of walking ability, providing an objective and biosecure alternative to the currently more subjective and time-consuming manual assessment of walking ability. However, these techniques mainly focus on assessment of averages at flock level using pixel movement. Therefore, the aim of this study was to investigate the potential of optical flow algorithms to identify flock activity, distribution and walking ability in a commercial setting on levels close to individual monitoring. We used a combination of chicken segmentation and optical flow methods, where chicken contours were first detected and were then used to identify activity, spatial distribution, and gait score distribution (i.e. walking ability) of the flock via optical flow. This is a step towards focusing more on individual chickens in an image and its pixel representation. In addition, we predicted the gait score distribution of the flock, which is a more detailed assessment of broiler walking ability compared to average gait score of the flock, as slight changes in walking ability are more likely to be detected when using the distribution compared to the average score. We found a strong correlation between predicted and observed gait scores (R2 = 0.97), with separate gait scores all having R2 > 0.85. Thus, the algorithm used in this study is a first step to measure broiler walking ability automatically in a commercial setting on a levels close to individual monitoring. These validation results of the developed automatic monitoring of flock activity, distribution and gait score are promising, but further validation is required (e.g. for chickens at a younger age, with very low and very high gait scores)

Precision livestock farming towards broiler welfare

Due to intensification of the livestock system the ratio between number of broilers and number of farmers have been increasing, making impossible the individualized attention to animals without the use of appropriate tools. Increasingly societal concern on broiler welfare requires farmers to find means to improve animal welfare level. Precision livestock farming (PLF) emerges as a possible solution as it enables the monitoring of animals and its environment 24/7. The present study aims to provide information on how PLF technologies can address broiler welfare and to evaluate reasons for their adoption (or non-adoption) by farmers. The results discussions and analysis are based in the three main pillars that guide the present research: animal welfare, PLF technologies and innovation adoption. Methodologically, the study consists of two different steps. Initially, a systematic review of the literature was carried out to identify which are the PLF technologies related to broiler welfare and to assess how they address birds ́ welfare. Results indicate that most PLF technologies are related to image analysis and mainly focused on broiler health improvements. In the second stage, an empirical research was carried out with broiler farmers in the Southern Brazil. From this survey, information on broiler farmers ́ opinions towards broiler welfare and PLF potentialities were assessed as well as on the determinants and limiting factors for technologies adoption. In general, Brazilian broiler farmers attribute great importance to broiler welfare and perceive the current level of welfare as high; however higher scores for importance than for perception indicate that there is room for welfare improvements. In broiler farmers ́ opinions, providing animals food/water and good housing and health conditions are more important than provide means for the animals to express their natural behaviors. Broiler farmers believe that technologies can help them on welfare improvements and are willing to adopt them even when no extra income come from this. Broiler farmers with less experience, producing chicken grillers, having other farm activity besides broiler production and presenting high beliefs on PLF potentialities regarding animal welfare improvements are more likely to adopt PLF technologies. Major limiting factors for PLF technologies adoption are regarding technology high prices, maintenance requirements and to possible financial consequences with technical problems. It is expected the present thesis to be useful to clarify about PLF technologies opportunities in the broiler farmers point of view and that the results obtained to be valuable to increase PLF adoption, which can potentially improve animal and farmers welfare alike.A intensificação do sistema produtivo aumentou a relação entre o número de frangos de corte e o número de trabalhadores rurais, impossibilitando a atenção individualizada aos animais sem o uso de ferramentas adequadas. Em paralelo, a sociedade pressiona os produtores a encontrarem meios para aumentar o nível bem-estar animal (BEA). Tecnologias da zootecnia de precisão (ZP)surgem como possívelsolução, pois possibilitam o monitoramento dos animais e de seu ambiente de forma contínua. O presente estudo objetiva fornecer informações sobre como as tecnologias da ZP abordam o bem-estar de frangos de corte e avaliar os fatores que influenciam a sua adoção pelos produtores. A discussão e a análise dos resultados baseiam-se em três pilares, a saber: BEA, tecnologias da ZP e adoção de inovações. Metodologicamente, o estudo é composto por duas etapas distintas. Inicialmente, uma revisão sistemática da literatura foi realizada para identificar quais são as tecnologias da ZP relacionadas ao bem-estar de frangos de corte e para avaliar como elas abordam o bem-estar das aves. Os resultados indicam que a maioria das tecnologias está relacionada à análise de imagens e principalmente focada na melhoria da saúde dos frangos. Na segunda etapa, foi realizada uma pesquisa empírica com produtores de frangos de corte no Sul do Brasil. A partir desta pesquisa, foram avaliadas informações sobre as opiniões dos criadores de frangos de corte em relação ao BEA e às potencialidades das tecnologias, bem como sobre os fatores determinantes e limitantes para adoção de tecnologias. Em geral, os avicultores brasileiros atribuem grande importância ao bem-estar dos frangos e consideram alto o nível atual de BEA; no entanto, maiores escores para importância do que para percepção indicam que há espaço para melhorias. Na opinião dos produtores, fornecer aos animais comida/água e boas condições de alojamento e saúde é mais importante do que fornecer meios para que os animais expressem seus comportamentos naturais. Os produtores acreditam que as tecnologias podem ajudá-los a aumentar o BEA e estão dispostos a adotá-las mesmo que isso não resulte em maior renda. Produtores com menos experiência, que produzem grillers, que possuem mais de uma atividade agropecuária e que acreditam nas potencialidades das tecnologias em melhorar o BEA são mais propensos a adotar tecnologias. Os principais fatores limitantes para a adoção de tecnologias são os preços elevados, as exigências de manutenção e as possíveis consequências financeiras com problemas técnicos. Espera-se que a presente tese seja útil para esclarecer sobre as oportunidades da ZP do ponto de vista dos produtores e que os resultados obtidos sejam valiosos para aumentar a adoção de tecnologias, as quais podem melhorar o BEA e o bem-estar dos produtores