Instance Segmentation with Mask R-CNN Applied to Loose-Housed Dairy Cows in a Multi-Camera Setting

With increasing herd sizes came an enhanced requirement for automated systems to support the farmers in the monitoring of the health and welfare status of their livestock. Cattle are a highly sociable species, and the herd structure has important impact on the animal welfare. As the behaviour of the animals and their social interactions can be influenced by the presence of a human observer, a camera based system that automatically detects the animals would be beneficial to analyse dairy cattle herd activity. In the present study, eight surveillance cameras were mounted above the barn area of a group of thirty-six lactating Holstein Friesian dairy cows at the Chamber of Agriculture in Futterkamp in Northern Germany. With Mask R-CNN, a state-of-the-art model of convolutional neural networks was trained to determine pixel level segmentation masks for the cows in the video material. The model was pre-trained on the Microsoft common objects in the context data set, and transfer learning was carried out on annotated image material from the recordings as training data set. In addition, the relationship between the size of the used training data set and the performance on the model after transfer learning was analysed. The trained model achieved averaged precision (Intersection over union, IOU = 0.5) 91% and 85% for the detection of bounding boxes and segmentation masks of the cows, respectively, thereby laying a solid technical basis for an automated analysis of herd activity and the use of resources in loose-housing

Precision technologies to address dairy cattle welfare: focus on lameness, mastitis and body condition

Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and realtime assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.FE1B-06B2-126F | Jos? Pedro Pinto de Ara?joN/

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

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

Automated cow body condition scoring using multiple 3D cameras and convolutional neural networks

DATA AVAILABITY STATEMENT: The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy concerns.Body condition scoring is an objective scoring method used to evaluate the health of a cow by determining the amount of subcutaneous fat in a cow. Automated body condition scoring is becoming vital to large commercial dairy farms as it helps farmers score their cows more often and more consistently compared to manual scoring. A common approach to automated body condition scoring is to utilise a CNN-based model trained with data from a depth camera. The approaches presented in this paper make use of three depth cameras placed at different positions near the rear of a cow to train three independent CNNs. Ensemble modelling is used to combine the estimations of the three individual CNN models. The paper aims to test the performance impact of using ensemble modelling with the data from three separate depth cameras. The paper also looks at which of these three cameras and combinations thereof provide a good balance between computational cost and performance. The results of this study show that utilising the data from three depth cameras to train three separate models merged through ensemble modelling yields significantly improved automated body condition scoring accuracy compared to a single-depth camera and CNN model approach. This paper also explored the real-world performance of these models on embedded platforms by comparing the computational cost to the performance of the various models.The Milk South Africa (MilkSA).https://www.mdpi.com/journal/sensorsAnimal and Wildlife SciencesElectrical, Electronic and Computer EngineeringSDG-02:Zero HungerSDG-09: Industry, innovation and infrastructur

Estimation of lamb weight using transfer learning and regression

Meat production needs of accurate measurement of livestock weight. In lambs, traditional scales are still used to weigh live animals, which is a tedious process for the operators and stressful for the animal. In this paper, we propose a method to estimate the weight of live lambs automatically, fast, non-invasive and affordably. The system only requires a camera like those that can be found in mobile phones. Our approach is based on the use of a known Convolutional Neural Network architecture (Xception) pre-trained on the ImageNet dataset. The acquired knowledge during training is used to estimate the weight, which is known as transfer learning. The best results are achieved with a model that receives the image, the sex of the lamb and the height from where the image is taken. A mean absolute error (MAE) of 0.58 kg and an R2 of 0.96 were obtained, improving on current techniques. Only one image and two values specified by the user (sex and height) allow to estimate with a minimum error the optimal weight of a lamb, maximising the economic profit

Advanced Sensors for Real-Time Monitoring Applications

It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications

A review of deep learning algorithms for computer vision systems in livestock.

In livestock operations, systematically monitoring animal body weight, bio-metric body measurements, animal behavior, feed bunk, and other difficult-to-measure phenotypes is manually unfeasible due to labor, costs, and animal stress. Applications of computer vision are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. However, the development of a computer vision system requires sophisticated statistical and computational approaches for efficient data management and appropriate data mining, as it involves mas-sive datasets. This article aims to provide an overview of how deep learning has been implemented in computer vision systems used in livestock, and how such implementation can be an effective tool to predict animal phe-notypes and to accelerate the development of predictive modeling for precise management decisions. First, we reviewed the most recent milestones achieved with computer vision systems and its respective deep learning algorithms implemented in Animal Science studies. Second, we reviewed the published research studies in Animal Science, which used deep learning algorithms as the primary analytical strategy for image classification, object detection, object segmentation, and feature extraction. The great number of reviewed articles published in the last few years demonstrates the high interest and rapid development of deep learning algorithms in computer vision systems across livestock species. Deep learning algorithms for computer vision systems, such as Mask R-CNN, Faster R-CNN, YOLO (v3 and v4), DeepLab v3, U-Net and others have been used in Animal Science research studies. Additionally, network architectures such as ResNet, Inception, Xception, and VGG16 have been implemented in several studies across livestock species. The great performance of these deep learning algorithms suggests an33improved predictive ability in livestock applications and a faster inference.34However, only a few articles fully described the deep learning algorithms and its implementation. Thus, information regarding hyperparameter tuning, pre-trained weights, deep learning backbone, and hierarchical data structure were missed. We summarized peer-reviewed articles by computer vision tasks38(image classification, object detection, and object segmentation), deep learn-39ing algorithms, species, and phenotypes including animal identification and behavior, feed intake, animal body weight, and many others. Understanding the principles of computer vision and the algorithms used for each application is crucial to develop efficient systems in livestock operations. Such development will potentially have a major impact on the livestock industry by predicting real-time and accurate phenotypes, which could be used in the future to improve farm management decisions, breeding programs through high-throughput phenotyping, and optimized data-driven interventions

Prediction of milk yield using visual images of cows through deep learning.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.The broad objective of the study was to determine, through deep learning, the predictability of milk yield from a cow's image data. The data size of 1238 image pairs (the side-view images and the rear-view images) from 743 Holstein cows within their first or second parity and the cows’ corresponding first lactation 305-day milk yield values were used to train a deep learning model. The data was first split into the training and testing data at the ratio of 80:20, respectively. The training data was then augmented four times more, then again split into training and validation data at the ratio of 80:20, respectively. Three principal analyses were done, i.e. the prediction of milk yield using rear-view images only, the prediction of milk yield using the side-view images only and the prediction of milk yield using a merge of the side-view and rear-view images (the combined-view images). In all three analyses, poor predictions were observed, i.e. R2 values of 0.32 for the model using the side-view image, 0.30 for the model using the rear-view images and 0.38 for the model using combined side and rear images. The mean absolute errors were 1146.4 kg, 1148.3 kg and 1112.9 kg for the side-view, the rear-view and the combined-view models, respectively. The root mean square error values were 1460.7 kg, 1480.5 kg and 1401.2 kg and the mean absolute error percentages were 17.6, 17.3 and 17.0 % for the side-view, rear-view and combined-view models, respectively. Hypotheses tests were also done to check whether there was any difference between these three prediction models. There was no significant difference in performance between all the prediction models (p>0.05), i.e. the side-view model, the rear-view model and the combinedview model. It was concluded that predicting 305-day milk yield of Holstein cows using either view has the same level of accuracy and no additional benefits are derived from using both the rear and the side views. Keywords: Computer vision; deep learning; linear conformation traits; 305-day milk yield; side-view images; rear-view images; combined-view images; Holstein cows

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