Automated Visual Fin Identification of Individual Great White Sharks

This paper discusses the automated visual identification of individual great white sharks from dorsal fin imagery. We propose a computer vision photo ID system and report recognition results over a database of thousands of unconstrained fin images. To the best of our knowledge this line of work establishes the first fully automated contour-based visual ID system in the field of animal biometrics. The approach put forward appreciates shark fins as textureless, flexible and partially occluded objects with an individually characteristic shape. In order to recover animal identities from an image we first introduce an open contour stroke model, which extends multi-scale region segmentation to achieve robust fin detection. Secondly, we show that combinatorial, scale-space selective fingerprinting can successfully encode fin individuality. We then measure the species-specific distribution of visual individuality along the fin contour via an embedding into a global `fin space'. Exploiting this domain, we finally propose a non-linear model for individual animal recognition and combine all approaches into a fine-grained multi-instance framework. We provide a system evaluation, compare results to prior work, and report performance and properties in detail.Comment: 17 pages, 16 figures. To be published in IJCV. Article replaced to update first author contact details and to correct a Figure reference on page

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

Microbiological, chemical and physical quality of drinking water for commercial turkeys: a cross-sectional study.

open9Drinking water for poultry is not subject to particular microbiological, chemical and physical requirements, thereby representing a potential transmission route for pathogenic microorganisms and contaminants and/or becoming unsuitable for water-administered medications. This study assessed the microbiological, chemical and physical drinking water quality of 28 turkey farms in North-Eastern Italy: 14 supplied with tap water (TW) and 14 with well water (WW). Water salinity, hardness, pH, ammonia, sulphate, phosphate, nitrate, chromium, copper and iron levels were also assessed. Moreover, total bacterial count at 22°C, presence and enumeration of Enterococcus spp. and E. coli, presence of Salmonella spp. and Campylobacter spp. were quantified. A water sample was collected in winter and in summer at 3 sampling sites: the water source (A), the beginning (B) and the end (C) of the nipple line (168 samples in total). Chemical and physical quality of both TW and WW sources was mostly within the limits of TW for humans. However, high levels of hardness and iron were evidenced in both sources. In WW vs. TW, sulphate and salinity levels were significantly higher, whilst pH and nitrate levels were significantly lower. At site A, microbiological quality of WW and TW was mostly within the limit of TW for humans. However, both sources had a significantly lower microbiological quality at sites B and C. Salmonella enterica subsp. enterica serotype Kentucky was isolated only twice from WW. Campylobacter spp. were rarely isolated (3.6% of farms); however, Campylobacter spp. farm-level prevalence by real-time PCR was up to 43% for both water sources. Winter posed at higher risk than summer for Campylobacter spp. presence in water, whereas no significant associations were found with water source, site, recirculation system, and turkey age. Low salinity and high hardness were significant risk factors for C. coli and C. jejuni presence, respectively. These results show the need of improving sanitization of drinking water pipelines for commercial turkeys.openDi Martino G., Piccirillo A., Giacomelli M., Comin D., Gallina A., Capello K., Buniolo F., Montesissa C., Bonfanti L.Di Martino, G.; Piccirillo, A.; Giacomelli, M.; Comin, D.; Gallina, A.; Capello, K.; Buniolo, F.; Montesissa, C.; Bonfanti, L

ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning

Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species

Detecting animals in African Savanna with UAVs and the crowds

Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods. They have readily been used to count birds, marine mammals and large herbivores in different environments, tasks which are routinely performed through manual counting in large collections of images. In this paper, we propose a semi-automatic system able to detect large mammals in semi-arid Savanna. It relies on an animal-detection system based on machine learning, trained with crowd-sourced annotations provided by volunteers who manually interpreted sub-decimeter resolution color images. The system achieves a high recall rate and a human operator can then eliminate false detections with limited effort. Our system provides good perspectives for the development of data-driven management practices in wildlife conservation. It shows that the detection of large mammals in semi-arid Savanna can be approached by processing data provided by standard RGB cameras mounted on affordable fixed wings UAVs