Enhanced fish bending model for automatic tuna sizing using computer vision

[EN] This paper presents a non-invasive fully automatic procedure to obtain highly accurate fish length estimation in adult Bluefin Tuna, based on a stereoscopic vision system and a deformable model of the fish ventral silhouette. The present work takes a geometric tuna model, which was previously developed by the same authors to discriminate fish in 2D images, and proposes new models to enhance the capabilities of the automatic procedure, from fish discrimination to accurate 3D length estimation. Fish length information is an important indicator of the health of wild fish stocks and for predicting biomass using length-weight relations. The proposal pays special attention to parts of the fish silhouette that have special relevance for accurate length estimation. The models have been designed to best fit the rear part of the fish, in particular the caudal peduncle, and a width parameter has been added to better fit the silhouette. Moreover, algorithms have been developed to extract snout tip and caudal peduncle features, allowing better initialization of model parameters. Snout Fork Length (SFL) measurements using the different models are extracted from images recorded with a stereoscopic vision system in a sea cage containing 312 adult Atlantic Bluefin Tuna. The automatic measurements are compared with two ground truths: one configured with semiautomatic measurements of favourable selected samples and one with real SFL measurements of the tuna stock collected at harvesting. Comparison with the semiautomatic measurements demonstrates that the combination of improved geometric models and feature extraction algorithms delivers good results in terms of fish length estimation error (up to 90% of the samples bounded in a 3% error margin) and number of automatic measurements (up to 950 samples out of 1000). When compared with real SFL measurements of the tuna stock, the system provides a high number of automatic detections (up to 6706 in a video of 135¿min duration, i.e., 50 automatic measurements per minute of recording) and highly accurate length measurements, obtaining no statistically significant difference between automatic and real SFL frequency distributions. This procedure could be extended to other species to assess the size distribution of stocks, as discussed in the paper.This work was supported by funding from ACUSTUNA project ref. CTM2015-70446-R (MINECO/ERDF, EU). This project has been possible thanks to the collaboration of IEO (Spanish Oceanographic Institute). We acknowledge the assistance provided by the Spanish company Grup Balfego S.L. in supplying boats and divers to acquire underwater video in the Mediterranean Sea.Muñoz-Benavent, P.; Andreu García, G.; Valiente González, JM.; Atienza-Vanacloig, V.; Puig Pons, V.; Espinosa Roselló, V. (2018). Enhanced fish bending model for automatic tuna sizing using computer vision. Computers and Electronics in Agriculture. 150:52-61. https://doi.org/10.1016/j.compag.2018.04.005S526115

Automatic Bluefin Tuna sizing using a stereoscopic vision system

[EN] This article presents a non-invasive fully automatic procedure for Bluefin Tuna sizing, based on a stereoscopic vision system and a deformable model of the fish ventral silhouette. An image processing procedure is performed on each video frame to extract individual fish, followed by a fitting proce- dure to adjust the fish model to the extracted targets, adapting it to the bending movements of the fish. The proposed system is able to give accu- rate measurements of tuna snout fork length (SFL) and widths at five predefined silhouette points without manual intervention. In this work, the system is used to study size evolution in adult Atlantic Bluefin Tuna (Thunnus Thynnus) over time in a growing farm. The dataset is composed of 12 pairs of videos, which were acquired once a month in 2015, between July and October, in three grow-out cages of tuna aquaculture facilities on the west Mediterranean coast. Each grow out cage contains between 300 and 650 fish on an approximate volume of 20 000 m3.Measurements were au- tomatically obtained for the 4 consecutive months after caging and suggest a fattening process: SFL shows an increase of just a few centimetres (2%) while themaximum width (A1)shows arelative increaseofmorethan20%,mostlyinthe first 2months in farm. Moreover, a linear relation (with co- efficient of determination R2> 0.98) between SFL and widths for each month is deduced, and a fattening factor (F) is introduced. The validity of the measurements is proved by comparing 15 780 SFL measurements, obtained with our automatic system in the last month, versus ground truth data of a high percentage of the stock under study (1143 out of 1579), obtaining no statistically significant difference. This procedure could be extended to other species to assess the size distribution of stocks, as discussed in the article.This work was supported by funding from ACUSTUNA project ref. CTM2015-70446-R (MINECO/ERDF, EU). This project has been possible thanks to the collaboration of IEO (Spanish Oceanographic Institute).Muñoz-Benavent, P.; Andreu García, G.; Valiente González, JM.; Atienza-Vanacloig, V.; Puig Pons, V.; Espinosa Roselló, V. (2018). Automatic Bluefin Tuna sizing using a stereoscopic vision system. ICES Journal of Marine Science. 75(1):390-401. https://doi.org/10.1093/icesjms/fsx151S39040175

Esiselvitys 3D-kameratekniikan ja koneoppimisen hyödyntämisestä suomalaisessa kalan- kasvatuksessa

Tehokas kalankasvatus edellyttää tarkkaa ajantasaista tietoa kalojen lukumäärästä, aktiivisuudesta, terveydentilasta ja kasvusta (biomassasta). Tämä mahdollistaa erilaisten toimenpiteiden, kuten ruokinnan, lajittelun, kasvatustiheyksien ja lopulta perkuuajankohdan optimaalisen hallinnan. Videovalvonnan ja -ohjauksen käyttö on lisääntynyt merkittävästi kalankasvatuksen seurannassa ja tuotannonohjauksessa. Myös tietokoneavusteiset kuva-tai videoanalyysit ovat kehittyneet viimeisten kolmen vuosikymmenen aikana, ja ne ovat avainroolissa kasvatettavien kalastojen automaattisessa, ilman ihmistä tapahtuvassa mittaamisessa ja analysoinnissa. Tärkeimmät käytännön sovellukset liittyvät kasvatettavan kalaston ruokinnan ja biomassan seuraamiseen mutta myös kalaterveyteen ja välineiden kunnossapitoon. Kalankasvatusmarkkinoille on kehitetty jonkin aikaa muun muassa 3D-videoseurantaan perustuvia biomassalaskureita, mutta toistaiseksi niiden tarkkuus ei ole ollut välttämättä riittävä. Suomen sameissa rannikkovesissä laskureista ei ole raportoituja käyttökokemuksia, eivätkä sovellukset huomioi kotimaassa kasvatettavia kalalajeja. Laskurit ovat myös verrattain kalliita, jolloin kilpailukykyisten sovellusten kehittäminen voisi lisätä niiden käyttöä ja hyötyjä. Tähän hankeraporttiin koottiin esiselvitys: • Tämänhetkisistä seurantasovelluksista kalankasvatuksessa • Kolmiulotteisen (3D) kuvamateriaalin soveltuvuudesta ja jatkokehitysmahdollisuuksista kasvatettavien kalojen seurannassa kotimaiset erityisolosuhdevaatimukset ja tekoälyn omat reunaehdot huomioiden. Kuvauskokeiluissa ruokailevista kaloista (kirjolohi ja siika) saatiin liikedataa, josta pystyttiin toteamaan niiden aktiviteetin muutoksia. Datamäärät jäivät kuitenkin pieneksi varsinaista mallintamista ajatellen. Myös kuhakasvatusta kuvattiin, mutta samean veden ja kuhan passiivisen ruokailukäyttäytymisen takia niiden liikeaktiivisuutta ei havaittu. Kalojen pituudesta saatiin luotettavia metrisiä mittaustuloksia 3D-kameradatan avulla, joskaan näytemäärä ei ollut tässäkään tapauksessa suuri suoraan biomassan arviointiin. Tulosten perusteella 3D-kuvasta saatuja pituusmittoja voitaisiin käyttää kalojen kasvun tarkempaan arviointiin ilman invasiivisia (stressaavia) välimittauksia. Hanke on toteutettu yritysyhteistyönä Luken koordinoimassa ja Euroopan meri-ja kalatalousrahaston rahoittamassa vesiviljelyn innovaatio-ohjelmassa vuonna 2018.201

Precision fish farming: a new framework to improve production in aquaculture

Aquaculture production of finfish has seen rapid growth in production volume and economic yield over the last decades, and is today a key provider of seafood. As the scale of production increases, so does the likelihood that the industry will face emerging biological, economic and social challenges that may influence the ability to maintain ethically sound, productive and environmentally friendly production of fish. It is therefore important that the industry aspires to monitor and control the effects of these challenges to avoid also upscaling potential problems when upscaling production. We introduce the Precision Fish Farming (PFF) concept whose aim is to apply control-engineering principles to fish production, thereby improving the farmer's ability to monitor, control and document biological processes in fish farms. By adapting several core principles from Precision Livestock Farming (PLF), and accounting for the boundary conditions and possibilities that are particular to farming operations in the aquatic environment, PFF will contribute to moving commercial aquaculture from the traditional experience-based to a knowledge-based production regime. This can only be achieved through increased use of emerging technologies and automated systems. We have also reviewed existing technological solutions that could represent important components in future PFF applications. To illustrate the potential of such applications, we have defined four case studies aimed at solving specific challenges related to biomass monitoring, control of feed delivery, parasite monitoring and management of crowding operations

Implanted Antennas for Biomedical Applications

Body-Centric Wireless Communication (BCWC) is a central topic in the development of healthcare and biomedical technologies. Increasing healthcare quality, in addition to the continuous miniaturisation of sensors and the advancement in wearable electronics, embedded software, digital signal processing and biomedical technologies, has led to a new era of biomedical devices and increases possibility of continuous monitoring, diagnostic and/or treatment of many diseases. However, the major difference between BCWC, particularly implantable devices, and conventional wireless systems is the radio channel over which the communication takes place. The human body is a hostile environment from a radio propagation perspective. This environment is a highly lossy and has a high effect on the antenna elements, the radio channel parameters and, hence a dramatic drop in the implanted antenna performance. This thesis focuses on how to improve the gain of implanted antennas. In order to improve the gain and performance of implanted antennas, this thesis uses a combination of experimental and electromagnetic numerical investigations. Extensive simulation and experimental investigations are carried out to study the effects of various external elements on the performance improvement of implanted antennas. The thesis also shows the design, characterisation, simulation and measurements of four different antennas to work at ISM band and seventeen different scenarios for body wireless communication. A 3- layer (skin, fat and muscle) and a liquid homogenise phantom were used for human body modelling in both simulation and measurements. The results shows that a length of printed line and a grid can be used on top of the human skin in order enhance the performance of the implanted antennas. Moreover, a ring and a hemispherical lens can be used externally in order to enhance the performance of the implanted antenna. This approach yields a significant improvement in the antenna gain and reduces the specific absorption rate (SAR) in most cases and the obtained gain varies between 2 dB and 8 dB