Detecting the presence-absence of bluefin tuna by automated analysis of medium-range sonars on fishing vessels

Uranga, Jon; Arrizabalaga, Haritz; Boyra, Guillermo; Goñi, Nicolás; Arregui, Igor; Fernandes, José António; Yurramendi, Y.; Santiago, Josu

doi:10.1371/journal.pone.0171382

Cited by 18 publications

(7 citation statements)

References 37 publications

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“…In recent years, fisheries scientists have shown a growing interest in machine learning methods for the processing of both passive acoustic data (Roch et al, 2008;Zaugg et al, 2010;Noda et al, 2016;Malfante et al, 2018) and acoustic data collected by scientific echosounders (Fernandes, 2009;Robotham et al, 2010;Bosch et al, 2013). Despite this trend, very few studies have been conducted on the implementation of automated classification methods for analysing the extensive datasets collected by commercial vessels (Uranga et al, 2017). This paper presents a new methodology, based on machine learning, for processing the echosounder data collected from one of the main models of echosounder buoy used to equip DFADs worldwide (Moreno et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Machine learning for characterizing tropical tuna aggregations under Drifting Fish Aggregating Devices (DFADs) from commercial echosounder buoys data

et al. 2020

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The use of echosounder buoys deployed in conjunction with Drifting Fish Aggregating Devices (DFADs) has progressively increased in the tropical tuna purse seine fishery since 2010 as a means of improving fishing efficiency. Given the broad distribution of DFADs, the acoustic data provided by echosounder buoys can provide an alternative to the conventional CPUE index for deriving trends on tropical tuna stocks. This study aims to derive reliable indices of presence of tunas (and abundance) using echosounder buoy data. A novel methodology is presented which utilizes random forest classification to translate the acoustic backscatter from the buoys into metrics of tuna presence and abundance. Training datasets were constructed by cross-referencing acoustic data with logbook and observer data which reported activities on DFADs (tuna catches, new deployments and visits of DFADs) in the Atlantic and Indian Oceans from 2013 to 2018. The analysis showed accuracies of 75 and 85 % for the recognition of the presence/absence of tuna aggregations under DFADs in the Atlantic and Indian Oceans, respectively. The acoustic data recorded at ocean-specific depths (6-45 m in the Atlantic and 30-150 m in the Indian Ocean) and periods (4 a.m.-4 p.m.) were identified by the algorithm as the most important explanatory variables for detecting the presence of tuna. The classification of size categories of tuna aggregations showed a global accuracy of nearly 50 % for both oceans. This study constitutes a milestone towards the use of echosounder buoys data for scientific purposes, including the development of promising fisheriesindependent indices of abundance for tropical tunas. Highlights► Machine learning approach to process the acoustic data from commercial echosounder buoys into an indicator of the presence and abundance of tuna. ► The implementation of the approach in the Atlantic and Indian Oceans showed a high performance in detecting the presence-absence of tuna aggregations under drifting aggregating fish devices. ► Differences in the importance of the explanatory variables used to characterize fish aggregations were observed between the two oceans.Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site.

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Section: Introductionmentioning

confidence: 99%

Machine learning for characterizing tropical tuna aggregations under Drifting Fish Aggregating Devices (DFADs) from commercial echosounder buoys data

et al. 2020

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“…Despite the growth in the capacity to collect, store, and analyze data has increased, cost-effective data collection with industry for integration with scientific data from oceanographic surveys is sparse. , Besides, it is estimated that 80% of research time is consumed in data preparation, which is why it is important to digitalize the vessels and build repositories with interoperable and reusable data. , Large data sets analysis and application development in the bioeconomy sector can be accelerated by recent providers of Big Data, such as the Copernicus initiative and its Sentinel satellites for EO …”

Section: Concluding Discussionmentioning

confidence: 99%

Toward Digitalization of Fishing Vessels to Achieve Higher Environmental and Economic Sustainability

Uriondo,

Fernandes-Salvador,

Reite

et al. 2024

ACS Environ. Au

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Fishing vessels need to adapt to and mitigate climate changes, but solution development requires better information about the environment and vessel operations. Even if ships generate large amounts of potentially useful data, there is a large variety of sources and formats. This lack of standardization makes identification and use of key data challenging and hinders its use in improving operational performance and vessel design. The work described in this paper aims to provide cost-effective tools for systematic data acquisition for fishing vessels, supporting digitalization of the fishing vessel operation and performance monitoring. This digitalization is needed to facilitate the reduction of emissions as a critical environmental problem and industry costs critical for industry sustainability. The resulting monitoring system interfaces onboard systems and sensors, processes the data, and makes it available in a shared onboard data space. From this data space, 209 signals are recorded at different frequencies and uploaded to onshore servers for postprocessing. The collected data describe both ship operation, onboard energy system, and the surrounding environment. Nine of the oceanographic variables have been preselected to be potentially useful for public scientific repositories, such as Copernicus and EMODnet. The data are also used for fuel prediction models, species distribution models, and route optimization models.

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“…Sonar technology has been used to create waterborne fish observatories to generate videolike imagery or screenshots, even under highturbidity low-light environments. Coupled with semi-automated image processing algorithms, these sonar systems have been employed for stock assessment of commercially important fish, documenting fish aggregates, individual fish sizes, and monitoring biomass, abundance and distribution of invasive aquatic fauna (Wolff and Badri-Hoeher 2014, Uranga et al 2017, McCann et al 2018, Vatnehol et al 2018. Machine learning has shown promising success in feature extraction and background elimination, resulting in high detection accuracies (78-88%) for marine and coastal fish (Moniruzzaman et al 2017, Sung et al 2017, Salman et al 2019.…”

Section: Surveying Aquatic Species With Hydroacoustic Sensors and Remote Sensingmentioning

confidence: 99%

Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

et al. 2021

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A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30-yr horizon of the network. In contrast to NEON's extensive use of automated instruments to collect environmental data, NEON's biodiversity surveys are almost entirely conducted using traditional human-centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long-term costs. In this manuscript, we first review the current status of instrument-based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound-producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground-based measurements for plant biodiversity, and laboratory-based imaging for physical specimens and samples in the NEON biorepository. Through its data science-literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human-driven surveys.

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Detecting the presence-absence of bluefin tuna by automated analysis of medium-range sonars on fishing vessels

Cited by 18 publications

References 37 publications

Machine learning for characterizing tropical tuna aggregations under Drifting Fish Aggregating Devices (DFADs) from commercial echosounder buoys data

Machine learning for characterizing tropical tuna aggregations under Drifting Fish Aggregating Devices (DFADs) from commercial echosounder buoys data

Toward Digitalization of Fishing Vessels to Achieve Higher Environmental and Economic Sustainability

Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

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