Introduction

Although diverse, Small Island Developing States (SIDS) share many challenges in their journeys toward sustainable development1. Inherent constraints like remoteness and small size amplify the impacts from natural disasters2, disease outbreaks3, and climate change4,5,6,7,8. The unique and disproportionate nature of these vulnerabilities was internationally recognized in 19929, which has driven considerable efforts to develop solutions in the decades since10,11,12.

These vulnerabilities are apparent in all Pacific SIDS13,14,15, where coastal fisheries are central to sustainable development for food security16. Fish, broadly defined and include invertebrates, provide critical livelihoods and food in the 22 countries and territories in the region17. In all Pacific SIDS, but particularly in the atoll countries of Micronesia, fish consumption rates are among the highest in the world and fish are the dominant animal source food, providing essential macro- and micronutrients18. Pacific Island peoples acquire fish from a variety of sources and with a diverse array of exchange mechanisms, ranging from home production through to cash-based markets12,19.

The central role of coastal fisheries in the lives and economies of Pacific peoples, which include nearshore commercial and subsistence fisheries, means that sustainability of fish catches is an important national policy priority in all jurisdictions in the region20,21. The importance of ensuring the sustainable supply of fish is also reflected in several international instruments22, including the Sustainable Development Goals of the United Nations (UN) 2030 Agenda23,24.

Agencies and organizations tasked with ensuring the sustainability of small-scale coastal fisheries often face historically insurmountable challenges25,26. Many of the constraints recognized in oceanic fisheries in Pacific SIDS have present day analogues in their coastal fisheries27,28,29, making conventional fisheries management challenging30,31,32. These are exacerbated by the lack of centralized and mandated coordination of scientific investigation and the diversity of coastal fisheries themselves in terms of individual stocks and fishing methods33. Consequently, Pacific coastal fisheries are usually categorized as data deficient despite many initiatives aimed at improving management and research to overcome these constraints34,35.

A major obstacle in addressing these challenges is the high cost of scalable methods to monitor the status of fisheries and performance of management36,37. Many of the methods used in the region were developed using twentieth century approaches for large scale fisheries38,39. The absence of affordable and accessible methods and centralized coordinated approaches creates a disconnect between local fisheries management, national policy development and global understandings of fishery status40,41.

The persistence of technical and financial barriers regularly inhibits expansion of science-based governance of fisheries in the Pacific. The real-world costs of collecting data in difficult, dynamic field conditions, means such science-based programs that depend on methodological rigor are often unattainable without highly trained personnel. The reliance on complex technical approaches limits the ability of people to adopt and action management independently, so that general involvement and acceptance of fisheries management remains poor42. In practice, resource-constrained fisheries managers often grapple with balancing the collection of reliable data with the human and financial resources required to effectively collect, curate and analyze data to manage their fishery43. Compounding this are the considerable time lags between data collection, analysis, reporting and application, which mean data are often not available, or made available too late, for it to effectively inform adaptive management44.

The high demand for rapid, easy-to-implement assessments that inform management of small-scale fisheries, particularly subsistence and artisanal fisheries, is reflected in many available toolkits, statistical methods, and field approaches41,45,46. These methods rely on relatively simple yet informative data with which to make broad management inferences, for example, comparing mean catch length with information on a species’ size at maturity. The utility of many such ‘rule of thumb’ methods however, still rely on the capacity of agencies and communities to collect accurate data at scales needed to make meaningful management decisions.

Digital technologies hold potential for transforming fishery livelihoods47, particularly in the culturally diverse and dispersed island context of the Pacific, where telecommunications infrastructure rollout is extending to ever more remote places. Policymakers, the private sector, scientists, and international development partners are increasingly co-leveraging digital technologies in the Pacific socio-economic development sector48. When these advances are paired with rapid evolution of computational, computer vision and artificial intelligence (AI) technologies there is an opportunity to automate repetitive time-consuming aspects of fishery evaluation. In doing so, information gathering exponentially expands and catalyzes a revolution in how coastal fisheries are managed49.

AI applications in fisheries have to date been predominantly applied in large-scale commercial fisheries or aquaculture, where machine learning approaches are used to identify growth or disease in products50,51, predict population connectivity patterns52, track oceanic vessel movements53, identify species from commercial fishing catches54,55,56,57,58, or extract classifications from images uploaded by enlisted recreational fishers59,60. In these cases the scale and resolution at which AI is applied is such that these systems are not suited to monitoring subsistence and artisanal fisheries in small islands developing states61. For example, AI technologies designed to track large commercial vessels or automatically identify fish species based on commercial purse-seine catches are not designed to handle the diverse species, fishing methods, and landing practices characteristic of small-scale fisheries. These programs, while appropriate and useful within their respective domains, must include the simultaneous capture of information needed to inform management of complex multispecies coastal fisheries, including method-disaggregated catch per unit effort, specimen length, market or landing dynamics, catch methods, and other fishery associated socio-economic data. Considering the above, most AI applications in recreational or coastal fisheries are primarily serving as a tool to describe or characterize fisheries, rather than utilizing it as an integrated part of comprehensive analysis platforms that fisheries require in order to adapt and adjust management according to change.

In this paper we describe the development of state-of-the-art digital fishery monitoring processing system for Pacific SIDS powered by AI and computer vision technology. This system provides an opportunity to design and build solutions that bridge conventional data collection programs and governance structures with people’s local (traditional) use of fishery resources62. While AI’s transformative impacts are growing exponentially across diverse fields63, its application in coastal fisheries is in its infancy. AI offers promising avenues to address current limitations in management of small-scale coastal fisheries. AI-powered systems demonstrate the potential to automate data collection and analysis, improving efficiency and cost-effectiveness64. The work presented here builds on this emerging trend, introducing an AI-based system specifically tailored for coastal fisheries monitoring. Unlike existing approaches, this system prioritizes affordability, versatility, and accessibility, to ensure usability in (and by) remote Pacific communities. Moreover, the system distinguishes itself in its capacity to process and integrate diverse monitoring data from multispecies coastal fisheries, including catch rates, fishing locations, and socio-economic information. Its additional compatibility with smaller existing programs currently using computer vision technologies, offers growth for learning and expansion of application as technology advances. The platform is managed by the peak regional technical agency the Pacific Community (SPC) headquarters, with access by national fisheries agencies, non-government organizations, and communities in SPC member countries (Fig. 1).

Fig. 1
figure 1

The basic construct of the AI-enabled coastal fisheries monitoring system behind Ikasavea, showing flow of raw data from various field contexts and countries (e.g. landing sites, communities, and/or markets) to a central computing facility hosted at SPC. Automated analyses of imagery extract information that is then fed back to inform management practice.

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Structural architecture of the AI fisheries monitoring system

The architecture of the AI monitoring system is comprised of four main stages: data acquisition (photo imagery and survey data) and upload, AI enabled image processing and data extraction, data analysis, and reporting. CV technology facilitates the extraction of data from images, and deep learning algorithms were applied to train the system to make decisions based on extracted information (e.g., image standardization, image and specimen orientations, specimen taxonomic classifications, and measurement types). These processes are integrated and advanced through an automated software pipeline to ensure data consistency and an efficient workflow.

Data acquisition (field methods)

Data and imagery are collected and uploaded in a variety of ways during fishery surveys in the region. Although the system can accommodate manual entry of data from paper records and images uploaded to a web browser from, for example, a camera SD card, the focus of data acquisition is through a smartphone or tablet-based application. The Android application, Ikasavea, was developed in collaboration with national agency partners (ika-savea means ‘fish-survey’ in Polynesian languages). Ikasavea can be customized by users through a web portal to adapt the survey design to their needs. Users can, for example, set administrative regions, assign spatial management areas to sampling hierarchies (e.g., locally managed marine areas in Fiji), and set specific input metrics and data fields used for a wide range of fishery assessments. This information is then synchronized to Ikasavea to facilitate structured flows of data input.

Given the diversity in artisanal and subsistence fishing practices, the system allows for various forms of data input through a range of modules in the tablet application and on the web-accessible portal, including data collection from landing sites, community fisheries, and fish markets. Enumerators intercepting fish catches in any of these contexts use Ikasavea to collect information on a range of catch attributes, such as fishing methods, market details, and socio-economic dimensions (See Fig. S1 Supplementary materials). A central attribute of the system is the use of photographs taken of the catch, which are then processed centrally. The use of photographs significantly reduces the level of taxonomic knowledge and time required at the point of data acquisition and reduces disruption to fishers and retailers, thereby reducing refusals.

Two categories of photograph or ‘measurement type’ can be processed – either a single specimen on a calibrated measuring board, with or without a digital scale (hereafter ‘board’), or multiple specimens arranged on a standardized, calibrated mat (hereafter ‘mat’). Specific identifier patterns on boards (numbers) and mats (symbols) were used to calibrate measurements and orient images (see Fig. S2 Supplementary materials). Mats proved more appropriate for fishers taking part in community-based fisheries monitoring programs or by fisheries officers when expediting landing surveys. Once data are uploaded to a central online database, automated image processing and data analyses derive information useful for management (See Fig. S3 in Supplementary material). This information is then sent back to the field for application by managers, in near real-time.

In instances when both length and weight were required, the board was attached to a scale system. Length and weight information allowed the development of length–weight relationships useful in quantitative fishery assessments. In this paper, weight was estimated to the nearest gram. Once sufficient length–weight information is gathered for each species, the relationship may be used to estimate weight from lengths measured in the photographs.

Fulton’s condition factor (K) and Tukey’s outlier detection on K methods were used in combination to remove outliers in body condition to improve LWR estimates65,66,67 e.g., Fig. 7. Fulton’s condition factor is a standard method to identify individuals that deviate significantly from a base population55. K was calculated using Eq. (1).

$$K =100 * W / {L}^{3}$$
(1)

Where 100 is scaling constant to increase K to manageable units, L is length in mm and W is weight in grams. Once K was calculated, Tukey’s IQR method68,69 was used to detect outliers. The calculation of K and evaluation of outliers served three purposes. Firstly, it identified individuals that had a disproportionate influence on the predictive model over and above what a healthy individual would have for that species’ population. Secondly, it identified specimens that were unsuitable for inclusion in AI model training or for further data analysis. Thirdly, calculation of K allowed tracking of body condition of fished species populations between geographic areas and through time70. Once local LWR models are established, predicted specimen weights can be used to estimate production volumes of a fishery. Allometric relationships for many coastal fisheries species can vary seasonally and spatially, so using locally developed LWRs rather than those from global data repositories such as FishBase71 increase the accuracy of length-based stock assessments72.73. All analyses were implemented in R core program and RStudio using a range of plotting and statistics packages74,75,76,77,78,79.

AI enabled image processing and data extraction

Raw images are processed through a series of AI-enabled steps via an automated image processing pipeline (Fig. 2), each involving model trainings prior to model deployment for consequent data extraction. As images are uploaded, they undergo automated corrections, are classified, and partitioned into various categories. Every subsequent step through the processing phases uses a model developed specifically for the classification level of partitioned imagery (stored in a model library). This method of multi-stage classification decreased the number of distinct types of imagery that each model must classify, which helped avoid overfitting the models. Below we summarize this workflow and the model’s validation and evaluation processes used to assess each of the models’ performance.

Fig. 2
figure 2

The multistage process for analyzing and classifying specimen images using convolutional neural network algorithms. Custom C# application accesses and updates the SQL database, fetches original images, creates image outputs, and runs YOLOv4/Darknet53 models through a wrapper and C +  + /CUDA implementation of YOLOv4 darknet and OpenCV libraries. It also runs ResNet101 models using Microsoft ML.NET. In Step 1, YOLOv4 is used to identify images, categorize them, and correct orientation. In Step 2, multiple YOLOv4 and OpenCV models adjust image properties and calibrate pixels to known dimensions. In Step 3, multiple YOLOv4 models detect and classify specimens based on a specimen's visible attributes. In Step 4, YOLOv4 extracts measurements for each taxonomic subclassification. In Step 5, a dual-stage process comparing YOLOv4 and ResNet101 detects the species name from a species detection model library, where the most accurate output is used.

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Storage, analysis, and validation of specimen images are integrated into an ASP.NET web application and SQL Server backend. When photos are uploaded manually or during synchronization from Ikasavea, they are stored on the web server and registered for processing in the SQL Server database as photo tables. Using a custom C-sharp (C#) application on a machine equipped with a GPU, the application triggers the automated processing of images every 15 min. The AI detection and automatic measurement system drew from open-source libraries and models such as Open-Source Computer Vision (OpenCV)80, ResNet10181, and YOLOv482,83. OpenCV provides tools and algorithms for image processing, including image enhancement and feature detection59. This open-source package offered a comprehensive set of functions for image processing, such as contrast enhancement, thresholding, histogram equalization, and adaptive histogram equalization84. These functions, triggered by the C# application when images are uploaded, were used to preprocess images to improve quality before being transferred to deep learning models to perform further analysis. ResNet101 is a deep convolutional neural network architecture that is widely used for image classification tasks. It is a variant of the Residual Network (ResNet) architecture, which involves the training of very deep networks, and is widely used as a backbone in CV classification tasks. YOLOv4, also widely used in CV applications, is a faster single-pass object detection algorithm, which divides the input image into a grid, and predicts bounding boxes and class probabilities for each grid cell.

The C# application processes images by initiating YOLOv4/Darknet53 models via a C +  + /CUDA implementation for object detection, and ResNet101 models via Microsoft ML.NET for tasks like image classification or feature extraction. It generates image outputs, stores them on an SQL file server, and updates the SQL Server database with processing status and results. The application uses flags and output data fields, such as image type, predicted species, and bounding box coordinates, to filter images for further processing. For instance, fish-on-mat detection is triggered by the C# application once the OpenCV model has successfully calibrated an image containing a mat photo. Model training and validation involved just over 32 000 images of reef associated finfish and invertebrates from 13 Pacific SIDS and territories. The initial stages of development used images from Samoa, New Caledonia, Tonga, Papua New Guinea, Fiji, Kiribati, and Vanuatu. As images were uploaded and additional photographs were validated by trained observers, the models underwent retraining through a process of transfer learning85. This involved using validated, corrected, and ‘failed’ images (where ‘failed’ images were corrected and annotated) to retrain the models. Adding failed images to the training dataset improved fish detection or calibration models on a range of images of varying quality. Data augmentation was used to accelerate model training, minimize overfitting, and enhance accuracy for species with fewer photographs63. This method of periodic training and re-evaluation was needed to maintain accuracy and precision when dealing with highly variable imagery from the countries and programs using the system.

Preprocessing phase

The pre-processing phase proceeds as a series of steps powered by YOLOv4 and OpenCV to prepare images for data extraction. First, YOLOv4’s object detection capability is used to identify whether the image was from a board or a mat, and to categorize it accordingly. Using a combination of multiple YOLOv4 and OpenCV models, each image went through quality enhancement and calibration to normalize image projection to known real-world dimensions.

Lastly, a library of YOLOv4 models were used to identify, correct, and classify specimens based on their position, orientation, and broad taxonomic type. The ‘taxonomic type’ classification is used to group morphologically similar species into a single classification as either finfish or for grouping invertebrates into lobsters, crabs, bivalves, or gastropods. This step provides critical information about specimen characteristics, aiding subsequent data extraction processes. Once the taxonomic type of the specimen is identified, the images are placed in their respective categories.

Data extraction phase

This two-step phase utilizes deep learning to build algorithms to automate data extraction. First, YOLOv4 is used to extract accurate lengths from a calibrated image after detecting the snout and fork or caudal fin margin of fishes. For invertebrates, learned morphometric features are used. For photographs with an electronic scale, digits are recognized and recorded to provide a weight of the specimen, while for photographs using mats, only the lengths of each of the specimens are estimated.

For the calibrated measuring board method, images were standardized in size and position, starting at zero, vertically centered, and scaled to 2 pixels per mm. The red lines on the image correspond to the theoretical position of the center line and black lines every 10 cm to check that calibration is correct (see Fig. S4 in supplementary material). The fish’s fork position on the board was used to determine its length. A YOLOv4 model, trained on 1600 images, normalized to 416 × 416 input for 80 epochs, was used to detect the fork, providing a bounding box centered on the fork. The vertical position of this bounding box center yielded the fork length of the fish.

For the calibrated mat method, the size and position of specimen images were standardized. A YOLOv4 model, trained on 1334 images and normalized as 512 × 512 input for approximately 287 epochs, was first applied to detect fish bounding boxes on the mat. Each detected specimen’s image was cropped according to the bounding box with an extra 10% margin, then processed through a YOLOv4 fish orientation detection model to standardize the orientation of the fish. This model was trained on 30,528 images, normalized as 416 × 416 input for approximately 33 epochs.

Measurement of the specimen was done using a YOLOv4 fish snout/fork detection model (Fig. 2), using the distance between the center of the snout bounding box and the center of the fork bounding box of each detected specimen (see Fig. S5 in supplementary material). This model was trained on 4818 specimens, normalized as 512 × 512 input for approximately 80 epochs. During the processing, the system may detect multiple potential snouts or forks within a single image so a final step to disambiguate multiple specimens in the image processing pipeline ensured each detected fish was associated with only one snout and one fork. This process continues until each specimen is associated with a single snout and fork, a crucial step for ensuring the accuracy of measurements and overall effectiveness of the AI measurement system. The results are displayed to the user for validation or correction if required, with the user interface allowing editing of the measurement line and adding segments for length measurement along a curve. In the next step, pre-trained models for each measurement type and taxonomic group (e.g., mat or board having either a fish or invertebrate) extract a species classification of a specimen using both ResNet101 and YOLOv4 architecture. The species detection model library is used to assign a species name. This involves a dual-stage parallel process where the C# coded system compares both the YOLOv4 and ResNet101 outputs and reports only the most accurate classification in the user interface. This is achieved by comparing the model’s confidence score from 0 to 100, with zero having no confidence and 100 being highly confident.

Model validation and addressing bias

Performance of AI length and weight detection

We tested the accuracy and precision of 869 AI-measured lengths from a suite of randomly selected specimen species and sizes sold at markets in Tonga. Depending on the species’ morphology either the total or fork length of a specimen was used to validate the AI measurements and collected in situ using the same board. An observer recorded the measurement beneath the posterior margin of the intersecting lobes of the caudal fin (fork length) or furthest margin of a straight-line distance from snout to tail (total length).

A linear regression model was used to evaluate the AI-enabled system’s ability to replicate a human measurement. The model was fitted between paired AI (independent variable) and human (dependent variable) measured lengths. We chose the dependent variable based on the need to evaluate the accuracy of the AI measurement (the predictor) to predict a real-world human measurement. The dataset covered lengths ranging from 130 to 720 mm. Results of this analysis indicated that AI measurements were consistent with human measurements for both measurement types. The near one to one accuracy is verified by a strong positive linear relationship (R2 = 0.99) (Fig. 3a). In six cases during this validation assessment the AI detection demonstrated that it could detect erroneous measurements by human observers, which were corrected post-hoc.

Fig. 3
figure 3

(a) Comparison of fish length measurements estimated by an AI system and those by a human observer. Each point (n = 869) represents an individual fish, with the x-coordinate being the AI measurement and the y-coordinate being the human measurement. The blue line represents the fitted linear regression and the orange shaded area represents the bootstrapped 95% confidence interval for the fit (please note due to the extremely close relationship the 95% CI is narrow). (b) Bland–Altman plot showing the agreement between fish length measurements taken by an AI system and a human observer for the mat measurement type. The x axis shows the mean of the AI and human measurements, and the y axis shows the difference between the two measurements. The solid blue line indicates the mean difference (bias), while the dashed gray lines represent the upper and lower 95% confidence interval and the grey shaded region indicates the 95% confidence interval of the fitted linear model, (c) the comparable Bland–Altman plot for the board measuring type.

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A Bland–Altman plot was constructed to assess size-specific and measurement type bias that was not detected by the linear regression86,87. Overall, 92% of the AI measured lengths were within 5 mm and 98 percent of lengths were within 10 mm of the human measured lengths (Fig. 3b). The overall mean absolute error was 3.4 mm and the root mean square error was 4.9 mm. The mean square error for the board and mat measurement types was 2.7 and 4.2 mm, which equates to a 1.2 and 1.8% error, respectively. There was minimal positive bias apparent with increasing length. The mean bias was approximately 5 mm for specimens greater than 600 mm on both measurement types (slope coefficient b = 0.01 for both mat and board), which was considered acceptable.

We assessed how often enumerators either validated or changed the length estimated by the AI system in a random selection of 80,446 finfish (Fig. 4). The Studentized deleted residuals method69 was used to identify outliers in both x and y variables. Observer validated AI produced length estimates in all but 0.4% of instances. The relationship between these values showed a near perfect fit, where the model coefficients were near zero or one for the intercept and slope, respectively. This high degree of agreement indicated that the AI length measurement system was robust over a range of lengths, species and contexts. We identified 278 (0.35%) instances where lengths were changed on specimens, mostly because the AI failed from poor quality images (e.g., poor color, lighting, and/or orientation—three dimensionality). In addition, observers either changed a correct AI identification or length measurement (See Fig. S6 in Supplementary material).

Fig. 4
figure 4

Human validation of AI length estimates. Blue circles indicate validated length estimates and red circles indicate estimates identified as outliers using Studentized deleted residuals method. The orange dashed line indicates the 1:1 relationship and the black dashed line indicates the fitted linear model.

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Performance of AI species identification

Correctly identifying species in multi-species fisheries requires specialist knowledge that is rarely available in the context of communities in the Pacific region. This limitation is more evident in SIDS where species names are often in local languages, and where many species are grouped into a single local name. Automating the identification process will significantly improve the likelihood that a sampling program will correctly identify species88.

We evaluated four computer vision models (m20, m21, m22, and m23) developed in successive years on their ability to identify species from 51,800 specimen images. The models’ performance was measured by their recall accuracy89,90, which is the rate of correctly identifying a species (true positives) and the rate of missing a species that was present (false negatives).

Each model was trained in a different year, with an increasing number of species and images. Specifically, m20 was trained on 21 species using 1,817 images, m21 on 64 species using 5210 images, m22 on 111 species using 10,191 images, and m23 on 264 species using 32,818 images. The training data for each model included images from its respective year and all preceding years, with the training datasets comprising 90% of the testing dataset.

Each model was tasked with classifying images of each species and the number of correct classifications were tallied and the proportion of correct classifications were calculated. To maintain a balanced learning system and to make meaningful assessments of each model’s performance, only species with more than twenty images were included. This criterion reduced the total number of finfish species that have been identified by the AI classification system from 612 to 264. The number of training images were capped at two hundred specimens per species to reduce the risk of overfitting. We found that models tend to perform better when trained on 150 to 200 unique specimens (Fig. S7 in supplementary material). When a model is overfitted, it may misclassify images by focusing on characteristics that are common between species but not related to the specimen’s taxonomy, such as the presence of a red pectoral fin margin, which therefore could lead to an increase in misclassifications.

Each model was able to recall species classifications with high accuracy, but only when applied to images taken in its development and preceding years (Fig. 5). New species or conspecifics with varied colorations or markings that were uploaded, resulted in a decline in performance. For example, model m22 (trained on imagery up to December 2022) was able to recall and accurately classify species 94%, 95%, 91%, and 35% of the time when assessed against images from 2019 to 2023, respectively (Fig. 5). Model m23 had a 79% successful recall rate on 264 species as opposed to m22’s recall rate of approximately 30% on the same dataset. Model m22 correctly identified species in images uploaded in 2022 at a rate 97%, a drastic improvement over Model m21’s accuracy of 35% when evaluated against the same imagery (Fig. 5).

Fig. 5
figure 5

Recall performance (accuracy) of four models (m20 to m23) over 4 years (2020–2023). Each model was trained on imagery collected preceding 31st December of that year and preceding years. Individual data points within each panel represent the proportion of correct classifications (i.e., the number of correct specimen classifications/total number of specimens) for each species in the training dataset for that year. Only species that were represented by twenty or more images were included. Grey points represent the mean ± standard error of the mean number of correct classifications for each model. The number of species (n) in the training and performance evaluation datasets are annotated on each panel.

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Model m23’s recall performance over imagery uploaded in its development year was lower than expected at 79% but when evaluated against preceding years imagery (2020 to 2022) the average recall rate of m23 was over 91% (Fig. 5). In contrast, the recall score of models trained on imagery in their same or preceding years was over 90%. Model m23’s lower recall rate is due to the upload of 153 new species with only 20 to 40 specimen images, whereas a maximum of only 47 new species were introduced in 2022 and 43 new species in 2021. The m23 model therefore had to learn to classify over three times as many species with too few images. This indicates that when a model was applied to new imagery over time, they were exposed to new species that were not part of their training, or distinct species that shared common characteristics leading to misidentification. This signals that as the number of species increased, which included distinct species with similar characteristics, a resulting increase in misclassifications occurred (i.e., false positives).

Application and uptake across Pacific SIDS

The Ikasavea monitoring program has seen rapid uptake in the region. Following the first upload of images collected in Kiribati as part of landing surveys, other countries’ fisheries management authorities expressed interest. This initiated a deliberate effort to apply modifications to accommodate the tailored needs of Pacific SIDS programs. As programs were integrated, the volume of images increased. By March 2021, new and established fisheries monitoring programs across all three Pacific subregions of Micronesia, Melanesia, and Polynesia were integrated, thereby accelerating uptake towards what is now a regional AI-supported monitoring system. Larger countries like Papua New Guinea (PNG) have more recently contributed to the data pool, emphasizing the system’s usability, utility, and performance in diverse and extensive geographical contexts. Active participation by diverse national fisheries management agencies demonstrates the system’s ability to manage large-scale data, thereby validating its scalability. As of March 2024, 11 national fisheries authorities are using the system, with the Cook Islands, Nauru and Palau currently being onboarded for their national programs (Fig. 6). By December 2023, over 80,000 images had been uploaded, containing over 180,000 specimens (Fig. 7).

Fig. 6
figure 6

Extent of uptake by Pacific Island Countries and Territories (PICTs) of the Ikasavea system, allowing AI-supported data collection of key invertebrate and finfish landed by fishers or sold at markets. Three letter ISO codes as: American Samoa (ASM), Cook Islands (COK), Federated States of Micronesia (FSM), Fiji (FJI), French Polynesia (PYF), Guam (GUM), Kiribati (KIR), Marshall Islands (MHL), Nauru (NRU), New Caledonia (NCL), Niue (NIU), Northern Mariana Islands (MNP), Palau (PLW), Papua New Guinea (PNG), Pitcairn Islands (PCN), Samoa (WSM), Solomon Islands (SLB), Tokelau (TKL), Tonga (TON), Tuvalu (TUV), Vanuatu (VUT), and Wallis and Futuna (WLF). Extent of EEZs indicative only.

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Fig. 7
figure 7

Cumulative count of fish species identifications and length measurements made by the AI system using images uploaded to the SPC web portal by national fisheries authorities. Red points indicate when a new country first uploaded images to the SPC web portal. Count does not include images from mats obtained by SPC partners. Three letter ISO codes for PICTs as indicated in Fig. 6.

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In the four years since the system became operational, over 50 finfish and invertebrate species have sufficient data for length–weight predictions across the region. This is a critical metric that can be used in analyses of life histories, population growth, and in comparative analyses between different populations from different regions, habitats and/or environmental conditions. Three species were selected here to demonstrate how the LWR models were developed using AI detection (Fig. 8). Of the 7885 measurements made, 313 (not all shown) were detected as outliers using Tukey’s IQR method on K, and were the result of deficient images (e.g., gutted, damaged or malformed specimens) or incorrect lengths or weight validations by observers (e.g., AI’s detected weight being overridden by a human observer) (Fig. 8a). As the number of uploaded images with boards and scales increase, the number of locally derived allometric LWRs are also expected to increase, providing much needed allometric data across the region. Generalized linear model regression on log10 transformed data was used to build predictive LWR models91.

Fig. 8
figure 8

Allometric length–weight relationships of three species from Samoa (WS) and Papua New Guinea (PNG). These relationships were derived from automated length classification and weight readings using CV technologies. Outliers (shown for Lutjanus gibbus as red circles) were identified and removed using Tukey’s outlier method on Fulton’s condition factor (K). The predicted weights, represented by red lines, were obtained through generalized linear regression on log-transformed data.

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Implications for fisheries management

The system serves as a comprehensive platform that integrates the collection of multiple lines of fisheries data necessary for informing the sustainable management of fisheries, rather than as a single-purpose tool for automated species identification and/or length measurements. The unique ability of this system to collect and integrate AI-automated morphometric data simultaneously with other relevant fisheries data, over a broad range of coastal fisheries, enhances monitoring beyond conventional paper-based or single species stock assessment approaches92. As such, the system facilitates data collection to estimate volumes and pricing of fish products traded across market networks, volumes of landed catches, and catch per unit effort that is appropriately categorized for artisanal and commonly used subsistence fishing methods. In addition to data collection from fish markets and landings, the system integrates monitoring programs from coastal communities implementing community-based fisheries management93. Together these data support a broad range of fishing activities94, economic dynamics of the sector18, and management needs within coastal Pacific communities13.

In the absence of tailored fisheries management tools, often broader standardized tools and indices are applied43. Data for context-specific determination of LWR are, for the first time, being collected at regional, national, and subnational scales, thereby explicating the spatial and temporal variabilities in complex multispecies coastal fisheries. Such measures of LWR can be used to more accurately assess local population condition and stock status. The temporal and spatial scales of data collected, including both length and weight, have important implications for the management of fish populations in and across Pacific SIDS. The continuous and autonomous tracking of body condition, using indices such as Fulton’s Factor (K) and Le Cren’s modification on K to relative Kn together with other length-based stock assessment indices (e.g., Length-Based Spawning Potential Ratio—LBSPR), can provide an indication of changes in stock status67,72,73,95. Collecting weight data in addition to length data can further improve these LBSPR indices. It can also help track changes in body condition and detect spawning individuals55.

The platform’s principal function is to assist with ‘data poor’ fisheries management, specifically in Pacific SIDS. It responds to the need for access, compatibility and functionality, both within the region and between regional and global systems. Regarding the former, it offers authenticated users with tailored data exports, while for the latter it offers options for customized Application Programming Interfaces (APIs) to connect with alternative platforms aligned with SDG 14.4.1. Among others, this includes, for example the United Nations, Food and Agriculture Organization’s Virtual Research Environment (VRE)96. Such interfaces allow state of the art length-based stock assessment methods for SSF97 to integrate into the AI data collection system, generating models and predictive models as needed and addressing the enduring and critical challenge of timely stock assessment reporting in coastal fisheries98. These efficiencies thus enable better data collection and promote integrated assessment and advice in small-scale fisheries42,99.

While scientific support for decisions concerning highly migratory fish stocks in the Pacific SIDS region has expanded since 2004 under the formal mandate provided by the Western and Central Pacific Fisheries Commission (WCPFC) Convention, a critical gap remains. Unlike the WCPFC’s managed operations in the broader Pacific region, which drive informed policy development through science100, there exists no comparable regional framework for the science and management of coastal fisheries in Pacific SIDS101. Despite the latter being a critical global indicator of improved fisheries management102. This institutional void extends to the literature on applied science guiding national and regional policy development on artisanal and coastal fisheries103. Consequently, there is relatively limited attention from national and international policymakers and funding bodies directed toward the national and regional challenges faced by Pacific coastal fisheries104. The efficient means of collecting scientific evidence for fisheries management offered through this regional system, stands to decrease the uncertainty in the status and dynamics of Pacific coastal fisheries47.

The co-design of the monitoring system has strengthened collaborations among communities and national and regional organizations engaged in Pacific coastal fisheries100. The development and rollout of the monitoring system has strengthened decentralization ambitions within national programs for data collection, while also integrating them into regional coordination efforts. Domestically, such collaborations can be leveraged to tailor harvesting strategies as part of national requirements for coastal fisheries management104,105,106. Applying information, they collect and own, leveraged through regional partnerships, enables greater collaboration between fisheries authorities and communities107. This allows national fisheries agencies, for example, to more efficiently gauge the effectiveness of their strategies within the context of local practices and traditions108. Particularly in the context of community fisheries, where since 2015 regional policy and management has focused on enabling scaling community-based fishery management through cost-effective support measures109, this system functions to catalyze the kind of decentralized management solution that is required93. Uninterrupted data flows between the centralized support platform and remote fisheries offices and/or villages and fishers, enables the essential rapid return of results that has thus far challenged local adaptive management13.

Limitations in practice

A noteworthy limitation in later models trained with more than 140 new species, was the reduction in maximum recall. This is a common occurrence in CV deep learning applications in large datasets110,111. While further training is expected to overcome this decline in precision, caution is needed as ‘excessive’ training can also lead to specificity and misclassifications (false positives), especially when species share similar characteristics. These issues can be overcome112,113,114,115 as the system and AI technologies rapidly develop. For example, current trials in integrating CSPDarkNet-53 backbone architecture for species detection116,117 promises to improve recall, and thus accuracy in species detection. Recognizing the need for retraining, models in the system underwent multiple retraining following large influxes of new species or those conspecifics with varied visible characteristics. Detection rates were further improved by using multiple model algorithms simultaneously (e.g., YOLOv4, ResNet101). Other measures that integrated multiple approaches to preprocessing, like splitting classifications into grouped classification chains (Fig. 2) were also applied to improve recall and accuracy. This included, for example, partitioning input imagery into morphologically or geographically partitioned groups and trainings and comparing multiple model architectures in each group. As the system matures over time it is critical that new model capabilities are continually integrated into the system to further enhance its applicability, efficiency and useability118.

A major obstacle to any such system is the need to continuously upgrade software and hardware. Both these needs have resourcing implications (e.g. budget and technical skills) that must be recognized for these endeavors to continuously deliver capacity enhancements. The growing magnitude of data input from users into the system, for example, resulted in the need to upgrade the infrastructure and invest in new and more powerful computing capacity. While these costs were low relative to those incurred from manual methods at this scale, implementation and support to these systems must consider ongoing costs, and with that have sustainable resourcing mechanisms in place.

This system has created much needed efficiencies in data collection and has closed a critical capacity gap by improving access to technology and monitoring science in artisanal and subsistence fisheries. However, there remains the need for continued investment in national fisheries monitoring budgets and the human resources to collect these data on the ground119. Good data collection is quite simply contingent on the time and effort invested by fisheries authorities and community enumerators, so without such functional programs in countries monitoring could not occur, regardless of the improvement in technology. The projected upshift in the frequency of assessment and reporting of the status of key coastal fisheries and ensuring provision of regular informed policy briefs to decision makers, should help strengthen investment in these areas.

Conclusions

This paper describes the evolution, structural components and functionality of a comprehensive AI-enabled monitoring system that serves national and regional coastal fishery management needs. With the system’s development being integrated in national fisheries programs, it is making significant contributions to adaptive management cycles at various scales and demonstrates the value of digital and AI technology in addressing the enduring challenge of delivering scientific evidence for fisheries managers of tropical SSF. The system’s ability to incorporate a broad range of approaches in the fisheries management cycle and its versatility to integrate other programs, promises to revolutionize how institutions and communities communicate and use the information collected120.

The AI-enabled fisheries monitoring system represents a first on several fronts. Firstly, it enables near real time transfer of data to diverse, data-poor coastal fisheries management contexts for evidence-based adaptive management. Secondly, it allows the application of state-of-the-art technology in remote fisheries contexts, empowering local actors with information needed to made good management decisions. Thirdly, it supports the development of management metrics and tools that are tailored by and for Pacific people to specific conditions and geographies (e.g., country specific LWRs). Fourthly, the system’s compatibility to interface with other learning platforms facilitates the continued improvement and refinement of supporting models, thereby keeping up with technical advances in the field. In addition to these benefits, the experience of co-developing and implementing the system with multiple stakeholders has strengthened cross border collaboration of Pacific SIDS in the Western, Central and South Pacific Region.

Commitments by national fisheries authorities and stakeholders engaging with the system demonstrate the level of buy-in into the fisheries monitoring support platform. Its evolution and integration at scale serves to illustrate how this approach can be replicated among SIDS globally, thereby addressing critical barriers to achieving adaptive management44. The development of this innovation is a result of a technology implemented, tested, and refined as part of policy and practice—this ensures institutional fit by embeddedness as it matures, that it responds to existing and emerging needs, that it challenges, with evidence, entrenched management practices and approaches121, and that Pacific Island nations are on the forefront of the rapid advances in AI technologies.

The strategic design and execution of the Ikasavea system responds to an urgent need for timely management of fish stocks in a changing world44,122. Fish remain a critical resource of people in SIDS, many of whom live in remote communities far from capital cities and national fishery agencies. Projecting into the future, Ikasavea offers potential to develop into an exchange platform for fishery-related knowledge among connected but geographically distant communities and their supporting agencies123.