7,297 research outputs found
Automatic cattle location tracking using image processing
Behavioural scientists track animal behaviour patterns through the construction of ethograms which detail the activities of cattle over time. To achieve this, scientists currently view video footage from multiple cameras located in and around a pen, which houses the animals, to extract their location and determine their activity. This is a time consuming, laborious task, which could be automated. In this paper we extend the well-known Real-Time Compressive Tracking algorithm to automatically determine the location of dairy and beef cows from multiple video cameras in the pen. Several optimisations are introduced to improve algorithm accuracy. An automatic approach for updating the bounding box which discourages the algorithm from learning the background is presented. We also dynamically weight the location estimates from multiple cameras using boosting to avoid errors introduced by occlusion and by the tracked animal moving in and out of the field of view
Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review
Animals play a profoundly important and intricate role in our lives today.
Dogs have been human companions for thousands of years, but they now work
closely with us to assist the disabled, and in combat and search and rescue
situations. Farm animals are a critical part of the global food supply chain,
and there is increasing consumer interest in organically fed and humanely
raised livestock, and how it impacts our health and environmental footprint.
Wild animals are threatened with extinction by human induced factors, and
shrinking and compromised habitat. This review sets the goal to systematically
survey the existing literature in smart computing and sensing technologies for
domestic, farm and wild animal welfare. We use the notion of \emph{animal
welfare} in broad terms, to review the technologies for assessing whether
animals are healthy, free of pain and suffering, and also positively stimulated
in their environment. Also the notion of \emph{smart computing and sensing} is
used in broad terms, to refer to computing and sensing systems that are not
isolated but interconnected with communication networks, and capable of remote
data collection, processing, exchange and analysis. We review smart
technologies for domestic animals, indoor and outdoor animal farming, as well
as animals in the wild and zoos. The findings of this review are expected to
motivate future research and contribute to data, information and communication
management as well as policy for animal welfare
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
Underwater Fish Detection with Weak Multi-Domain Supervision
Given a sufficiently large training dataset, it is relatively easy to train a
modern convolution neural network (CNN) as a required image classifier.
However, for the task of fish classification and/or fish detection, if a CNN
was trained to detect or classify particular fish species in particular
background habitats, the same CNN exhibits much lower accuracy when applied to
new/unseen fish species and/or fish habitats. Therefore, in practice, the CNN
needs to be continuously fine-tuned to improve its classification accuracy to
handle new project-specific fish species or habitats. In this work we present a
labelling-efficient method of training a CNN-based fish-detector (the Xception
CNN was used as the base) on relatively small numbers (4,000) of project-domain
underwater fish/no-fish images from 20 different habitats. Additionally, 17,000
of known negative (that is, missing fish) general-domain (VOC2012) above-water
images were used. Two publicly available fish-domain datasets supplied
additional 27,000 of above-water and underwater positive/fish images. By using
this multi-domain collection of images, the trained Xception-based binary
(fish/not-fish) classifier achieved 0.17% false-positives and 0.61%
false-negatives on the project's 20,000 negative and 16,000 positive holdout
test images, respectively. The area under the ROC curve (AUC) was 99.94%.Comment: Published in the 2019 International Joint Conference on Neural
Networks (IJCNN-2019), Budapest, Hungary, July 14-19, 2019,
https://www.ijcnn.org/ , https://ieeexplore.ieee.org/document/885190
Video tracking of dairy cows for assessing mobility scores
Lameness afflicts a large proportion of dairy herds, but could be considerably reduced by automated monitoring by CCTV. Key to this is reliable, robust detection and tracking of individual cows in crowded video sequences. We introduce a novel detection and tracking method, based on the Viola-Jones detector. We show that animals can be tracked and their overall gait patterns and speed automatically extracted from video sequences. Preliminary work on identification of individual animals through principal component analysis and SIFT feature matching is also described
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
New Generation Indonesian Endemic Cattle Classification: MobileNetV2 and ResNet50
Cattle are an essential source of animal food globally, and each country possesses unique endemic cattle races. However, categorizing cattle, especially in countries like Indonesia with a large cattle population, presents challenges due to costs and subjectivity when using human experts. This research utilizes Computer Vision (CV) for image data classification to address this urgent need for automatic categorization. The main objective of this study is to develop a mobile-friendly model using CV techniques that can accurately detect and classify Indonesian endemic cattle races, such as Limosin, Madura, Pegon, and Simental. To achieve this, an object localization approach is employed to extract multiple features from distinct regions of each cattle image, including the head, ear, horn, and muzzle areas. The authors evaluate two CV algorithms, ResNet50 and MobileNetV2, to assess their performance in cattle race classification. The dataset used is facial photos of 147 cows. The results indicate that ResNet50 outperforms MobileNetV2, achieving a training data accuracy of 83.33% compared to MobileNetV2's 77.08%. Moreover, the validation accuracy of ResNet50 (76.92%) significantly surpasses MobileNetV2's (38.46%). The novel contribution of this research lies in developing a cost-effective and efficient solution for identifying endemic cattle breeds in Indonesia. The mobile-friendly model based on ResNet50 demonstrates superior accuracy, enabling cattle farmers and researchers to categorize cattle races with higher precision, reducing manual effort, and minimizing costs. In conclusion, this research provides a valuable advancement in automatic cattle categorization using CV techniques. By offering a practical and accurate model that considers Indonesia's specific cattle breeding conditions, this study contributes to the sustainable management and conservation of endemic cattle races while enhancing the efficiency of cattle farming practices
Quality control and product tracing in ERP systems
Food safety and quality are keys to companies' business survival and great efforts and
resources are devoted to them. This is an on-going challenge, demanding the best control systems and day-
to-day vigilance on farms, in processing plants and throughout the distribution system. The product quality
of the Hungarian meet industry meets the high level international standards, because the Hungarian meet
industry is an export oriented sector. However, the application of computers and information systems still
haven’t got enough emphasis in the food sector, although the majority of companies use ERP systems. IT
budgets of Hungarian companies are smaller than of the ones in industrialized countries. They spend 0.49%
of their return from sales on IT operation and development. We find different rates among Hungarian
owners and foreign owners. The Hungarian ones spend less (0.36%), but foreigners spend twice this amount
(0.61) on informatics. Quality control is conducted at several stages of the production flow. The most
important targets are basic materials coming from partners, purchased and processed products and foods.
We have to be able to identify and determine what ingredients there are in the end-products and what the
production and distribution processes were. Sometimes this refers to a process backwards that we have to
conduct when we discover a mistake in the production flow or in the quality of the end-product. Back-
tracing is a six stage flow in the system. Our paper and lecture describes how the ERP system is built-in
food tracing functions and experiences in Hungary
Beef Cattle Instance Segmentation Using Mask R-Convolutional Neural Network
Maintaining the cattle farm along with the wellbeing of every heifer has been the major concern in dairy farm. A robust system is required which can tackle the problem of continuous monitoring of cows. the computer vision techniques provide a new way to understand the challenges related to the identification and welfare of the cows. This paper presents a state-of-art instance segmentation mask RCNN algorithm to train and build a model on a very challenging cow dataset that is captured during the winter season. The dataset poses many challenges such as overlapping of cows, partial occlusion, similarity between cows and background, and bad lightening. An attempt is made to improve the accuracy of the segmenter and the performance is measured after fine tuning the baseline model. The experiment result shows that fine tuning the mask RCNN algorithm helps in significantly improving the accuracy of instance segmentation of cows. this work is a contribution towards the real time monitoring of cows in cattle farm environment with the purpose of behavioural analysis of the cattle
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