Classification Of Fish Species Research Articles

OMSACC-SGRAN: an implementation of hybrid Optimized Multi-Scale Atrous Convoluted CNN with Self Guided Residual Attention Network for fish species classification

OMSACC-SGRAN: an implementation of hybrid Optimized Multi-Scale Atrous Convoluted CNN with Self Guided Residual Attention Network for fish species classification

A New Workflow for Instance Segmentation of Fish with YOLO

The application of deep-learning technology for marine fishery resource investigation is still in its infancy stage. In this study, we applied YOLOv5 and YOLOv8 methods to identify and segment fish in the seabed. Our results show that both methods could achieve superior performance in the segmentation task of the DeepFish dataset. We also expanded the labeling of specific fish species classification tags on the basis of the original semantic segmentation dataset of DeepFish and completed the multi-class instance segmentation task of fish based on the newly labeled tags. Based on the above two achievements, we propose a general and flexible self-iterative fish identification and segmentation standard workflow that can effectively improve the efficiency of fish surveys.

Impact of Climate Change on Fish Species Classification Using Machine Learning and Deep Learning Algorithms

Impact of Climate Change on Fish Species Classification Using Machine Learning and Deep Learning Algorithms

Integrating Edge-Intelligence in AUV for Real-Time Fish Hotspot Identification and Fish Species Classification

Enhancing the livelihood environment for fishermen’s communities with the rapid technological growth is essential in the marine sector. Among the various issues in the fishing industry, fishing zone identification and fish catch detection play a significant role in the fishing community. In this work, the automated prediction of potential fishing zones and classification of fish species in an aquatic environment through machine learning algorithms is developed and implemented. A prototype of the boat structure is designed and developed with lightweight wooden material encompassing all necessary sensors and cameras. The functions of the unmanned boat (FishID-AUV) are based on the user’s control through a user-friendly mobile/web application (APP). The different features impacting the identification of hotspots are considered, and feature selection is performed using various classifier-based learning algorithms, namely, Naive Bayes, Nearest neighbors, Random Forest and Support Vector Machine (SVM). The performance of classifications are compared. From the real-time results, it is clear that the Naive Bayes classification model is found to provide better accuracy, which is employed in the application platform for predicting the potential fishing zone. After identifying the first catch, the species are classified using an AlexNet-based deep Convolutional Neural Network. Also, the user can fetch real-time information such as the status of fishing through live video streaming to determine the quality and quantity of fish along with information like pH, temperature and humidity. The proposed work is implemented in a real-time boat structure prototype and is validated with data from sensors and satellites.

Creation of Machine Learning-Based Fish Classification Systems Based on Morphometric and Mathematical Transform Data

The accurate classification of fish species is crucial for various ecological, conservation, and fisheries management applications. Traditional methods relying solely on manual observation or morphological characteristics can be time-consuming and prone to errors. In recent years, advancements in machine learning (ML) techniques coupled with morphometric and mathematical transform data have shown promising results in automating and enhancing fish species classification processes. This paper presents a comprehensive review of the development and application of ML-based fish classification systems utilizing morphometric measurements and mathematical transforms. The core of this paper focuses on the integration of morphometric and mathematical transform data with ML algorithms for fish classification. In this review commonly employed ML algorithms, including but not limited to support vector machines (SVM), artificial neural networks (ANN), random forests (RF), and convolutional neural networks (CNN). Furthermore, this work discuss feature selection techniques to optimize classification performance and reduce dimensionality. These may include the incorporation of multimodal data sources, transfer learning approaches, and the development of user-friendly tools for field biologists and conservationists. Overall, this paper serves as a comprehensive guide for researchers and practitioners interested in leveraging ML techniques for accurate and efficient fish species classification.

Fish Species Classification Using Deep Learning and Appearance-Based Feature Extraction

As aquatic ecosystems face increasing threats and challenges, the accurate and efficient classification of fish species has become a critical task for fisheries management, biodiversity conservation, and environmental monitoring. This research explores the application of deep learning techniques in conjunction with appearance-based feature extraction for the automated and precise identification of fish species from images. The study employs a comprehensive dataset of underwater fish images, encompassing various species, sizes, and environmental conditions. Convolutional Neural Networks (CNNs) are used for feature extraction and classification, allowing for the capture of intricate visual characteristics that distinguish different fish species. Moreover, appearance-based features, such as colour patterns, fin shapes, and other morphological attributes, are integrated into the model to enhance classification accuracy. Additionally, the study introduces Improved Sequential Forward Selection (ISFS) as a method to select relevant features for classification, further enhancing the efficiency and effectiveness of the model. The research evaluates the performance of the proposed methodology against existing classification methods, emphasizing the advantages of deep learning and appearance-based feature extraction in terms of classification accuracy, robustness, and efficiency. In addition, the study explores the potential for real-world applications in fisheries management, species monitoring, and ecological research. The findings of this research demonstrate that the combination of deep learning and appearance-based feature extraction provides a promising approach for fish species classification, offering a valuable tool for conservation efforts and ecological studies. This research contributes to the growing body of knowledge in the field of computer vision and biodiversity conservation and opens up new possibilities for automated fish species identification in aquatic environments.

A Metric-Based Few-Shot Learning Method for Fish Species Identification with Limited Samples.

Fish species identification plays a vital role in marine fisheries resource exploration, yet datasets related to marine fish resources are scarce. In open-water environments, various fish species often exhibit similar appearances and sizes. To solve these issues, we propose a few-shot learning approach to identifying fish species. Our approach involves two key components. Firstly, the embedding module was designed to address the challenges posed by a large number of fish species with similar phenotypes by utilizing the distribution relationships of species in the embedding space. Secondly, a metric function was introduced, effectively enhancing the performance of fish species classification and successfully addressing the issue of limited sample quantity. The proposed model is trained end to end on fish species public datasets including the Croatian fish dataset, Fish4Knowledge and WildFish. Compared with the prototypical networks, our method performs more effectively and improves accuracy by 2% to 10%; it is able to identify fish effectively in small samples sizes and complex scene scenarios. This method provides a valuable technological tool for the development of fisheries resources and the preservation of fish biodiversity.

Simultaneous, vision-based fish instance segmentation, species classification and size regression.

Overexploitation of fisheries is a worldwide problem, which is leading to a large loss of diversity, and affects human communities indirectly through the loss of traditional jobs, cultural heritage, etc. To address this issue, governments have started accumulating data on fishing activities, to determine biomass extraction rates, and fisheries status. However, these data are often estimated from small samplings, which can lead to partially inaccurate assessments. Fishing can also benefit of the digitization process that many industries are undergoing. Wholesale fish markets, where vessels disembark, can be the point of contact to retrieve valuable information on biomass extraction rates, and can do so automatically. Fine-grained knowledge about the fish species, quantities, sizes, etc. that are caught can be therefore very valuable to all stakeholders, and particularly decision-makers regarding fisheries conservation, sustainable, and long-term exploitation. In this regard, this article presents a full workflow for fish instance segmentation, species classification, and size estimation from uncalibrated images of fish trays at the fish market, in order to automate information extraction that can be helpful in such scenarios. Our results on fish instance segmentation and species classification show an overall mean average precision (mAP) at 50% intersection-over-union (IoU) of 70.42%, while fish size estimation shows a mean average error (MAE) of only 1.27 cm.

Species identification of fish shoals using coupled split-beam and multibeam echosounders and two scuba-diving observational methods

Species identification remains crucial for interpreting acoustic backscatter delivered by active acoustic methodologies. The study took place in a Marine Protected Area where highly restricted areas were present such as no take zones. We used an innovative methodology coupling split-beam and multibeam echosounders to detect and classify monospecific fish shoals (i.e. schools or aggregations). Species identifications were realised by underwater visual censuses made by scientific divers. Two experimental protocols, where the divers gave the identifications instantaneously thanks to a communication wireframe, were tested: three roving scuba divers locating the shoals or a towed scuba diver directly behind the vessel. Energy responses, 3-D morphological, shape indexes and spatial descriptive variables of multiple independent samples of 4 observed fish species shoals (Atherina sp., Boops boops, Chromis chromis and Spicara maena) were calculated from the acoustic data. According to their behaviour and feeding strategy, significant differences in the acoustic variables were found between species. The combined use of acoustic data from both echosounders significantly improved the fish species classification. They were well discriminated using a Linear Discriminant Analysis (LDA), including for B. boops, C. chromis and S. maena, which were all observed in aggregations. Finally, we used this LDA model to allocate species to unknown shoals monitored by acoustics methods in the studied site, highlighting the interest of our methodology to predict bentho-pelagic and pelagic fish distributions in shallow waters. We suggest that these acoustic methods to discriminate fish species could provide valuable insights for marine management and decision-making.

FishRNFuseNET: development of heuristic-derived recurrent neural network with feature fusion strategy for fish species classification

FishRNFuseNET: development of heuristic-derived recurrent neural network with feature fusion strategy for fish species classification

T-KD: two-tier knowledge distillation for a lightweight underwater fish species classification model

T-KD: two-tier knowledge distillation for a lightweight underwater fish species classification model

Attention ConvMixer Model and Application for Fish Species Classification

Exploring the ocean has always been one of the foremost challenges for humankind, and fish classification is one of the crucial tasks in this endeavor. Manual fish classification methods, although accurate, consume significant time, money, and effort, while computer-based methods such as image processing and traditional machine learning often fall short of achieving high accuracy. Recently, deep convolutional neural networks have demonstrated their capability to ensure both time efficiency and accuracy in this task. However, deep convolutional networks typically have a large number of parameters, requiring substantial training time, and the convolutional operations lack attentional mechanisms. Therefore, in this paper, we propose the AttentionConvMixer neural network with Priority Channel Attention (PCA) and Priority Spatial Attention (PSA). The proposed approach exhibits good performance across all three fish classification datasets without introducing any additional parameters, thus demonstrating the effectiveness of our proposed method.

Deep Blue Discoveries: Advancements in Fish Detection and Species Taxonomy Using Deep Learning in the Subaquatic Realm

Abstract: The Deep Learning Based Underwater Fish Detection and Species Classification project employs deep learning methodologies to accurately detect and classify underwater fish species using a dataset consisting of diverse fish images captured from different underwater locations. The project utilizes a Region-based Convolutional Neural Network (R-CNN) to extract image features, ensuring precise species identification. The model's performance is evaluated using accuracy metrics to assess its effectiveness in fish detection and species classification. This project holds practical implications for the field of fisheries management and conservation by enabling accurate identification and classification of fish species in underwater environments. Moreover, the project's outcomes have the potential to find applications in marine biology and ecology, serving as a valuable tool for studying fish behavior and ecological dynamics within their natural habitats. The project's successful implementation underscores the transformative potential of deep learning techniques in the domain of computer vision, specifically in tasks such as image classification, segmentation, and object detection. In conclusion, the Deep Learning Based Underwater Fish Detection and Species Classification project employs advanced deep learning techniques to accurately identify and categorize various fish species in underwater environments. The project's diverse dataset, encompassing fish images captured from different underwater locations, enhances its significance for the fields of fisheries management and conservation. The project's outcomes have the potential to significantly contribute to our understanding of underwater ecosystems and support endeavors aimed at their protection and preservation

Combination of K-NN and PCA Algorithms on Image Classification of Fish Species

To do fish farming, you need to know the types of fish to be cultivated. This is because the type of fish will affect how it is handled and managed. Therefore, this study aims to develop an image processing system for classifying fish species, especially cultivated fish, with a combination of the K-Nearest Neighbor (K-NN) algorithm and Principal Component Analysis (PCA). The feature extraction used is feature extraction based on its color and shape. The K-NN algorithm can group certain objects considering the shortest distance from the object. According to the best criteria, the PCA method is employed in the meanwhile to decrease and keep the majority of the relevant data from the original characteristics. On the basis of the test results, the accuracy value obtained is 85%. The use of a combination of the K-NN and PCA algorithms in the image classification of fish species in the research that has been done has been shown to be capable of increasing accuracy by 7.5% compared to only using the K-NN algorithm.

Classification of Fish Species Using Multispectral Data from a Low-Cost Camera and Machine Learning

This work creates a fish species identification tool combining a low-cost, custom-made multispectral camera called MultiCam and a trained classification algorithm for application in the fishing industry. The objective is to assess, non-destructively and using reflectance spectroscopy, the possibility of classifying the spectra of small fish neighborhoods instead of the whole fish for situations where fish are not completely visible, and use the classification to estimate the percentage of each fish species captured. To the best of the authors’ knowledge, this is the first work to study this possibility. The multispectral imaging device records images from 10 horse mackerel, 10 Atlantic mackerel, and 30 sardines, the three most abundant fish species in Portugal. This results in 48,741 spectra of 5 × 5 pixel regions for analysis. The recording occurs in twelve wavelength bands from 390 nm to 970 nm. The bands correspond to filters with the peculiarity of being highpass to keep the camera cost low. Using a Teflon tape white reference is also relevant to control the overall cost. The tested machine learning algorithms are k-nearest neighbors, multilayer perceptrons, and support vector machines. In general, the results are better than random guessing. The best classification comes from support vector machines, with a balanced accuracy of 63.8%. The use of Teflon does not seem to be detrimental to this result. It seems possible to obtain an equivalent accuracy with ten cameras instead of twelve.

Fish-TViT: A novel fish species classification method in multi water areas based on transfer learning and vision transformer

The classification of fish species has important practical significance for both the aquaculture industry and ordinary people. However, existing methods for classifying marine and freshwater fishes have poor feature extraction ability and do not meet actual needs. To address this issue, we propose a novel method for multi-water fish classification (Fish-TViT) based on transfer learning and visual transformers. Fish-TViT uses a label smoothing loss function to solve the problem of overfitting and overconfidence of the classifier. We also employ Gradient-weighted Category Activation Mapping (Grad-CAM) technology to visualize and understand the features of the model and the areas on which the decision depends, which guides the optimization of the model architecture. We first crop and clean fish images, and then use data augmentation to expand the number of training datasets. A pre-trained visual transformer model is used to extract enhanced features of fish images, which are subsequently cropped into a series of flat patches. Finally, a multi-layer perceptron is used to predict fish species. Experimental results show that Fish-TViT achieves high classification accuracy on both low-resolution marine fish data (94.33%) and high-resolution freshwater fish data (98.34%). Compared with traditional convolutional neural networks, Fish-TViT has better performance.

Robust features extraction from shape signature for fish images classification

Recently, the process of fish species classification has become one of the most challenging problems addressed by researchers. In this work, a robust scheme to classify fish images based on robust feature extraction from shape signatures is proposed. First, the image contour is fitted using one of the common approaches named radial basis function neural network (RBFNN) fitting to obtain image centroid. Afterward, prominent features from the shape signature are extracted. These features are representative of fish shapes because they can distinguish the characteristics of each class as well as being relatively robust to scale and rotation changes. Finally, for the classification process purpose, RBFNN is used again for image classification against one of the most commonly used classification techniques called support vector machine (SVM). The proposed paradigm has been applied to a standard fish dataset acquired from a live video dataset grouped into twenty-three clusters representing specific fish species. The resulting accuracy based on SVM and RBFNN was 90.41% and 98.04%, respectively.

Video Based Fish Species Detection Using Faster Region Convolution Neural Network

Fish recognition and classification represent significant challenges in marine biology and agriculture, promising fields for advancing research. Despite advancements in real-time data collection, underwater fish recognition and classification still require improvement due to challenges such as variations in fish size and shape, image quality issues, and environmental changes. Feature learning approaches, particularly utilizing convolutional neural networks (CNNs), have shown promise in addressing these challenges. This study focuses on video-based fish species classification, employing a feature learning-based extraction method through CNNs. The process involves two main stages: detection and classification. To address the detection and classification in video a Faster Region Convolutional Neural Network (RCNN) with transfer learning techniques are applied, achieving a mean average precision of 84% for detection and classification tasks. These techniques offer promising avenues for enhancing fish recognition and classification in diverse environments

Fish species classification using a collaborative technique of firefly algorithm and neural network

Monitoring various Fish Species and its distribution of the species obtains a primary significance in receiving the insights to marine ecological-system. After this, visual classification of those species would aid in tracing out the movement and yield the patterns and trends in fish activities, which provides in depth knowledge of the species. Unconstrained under-water images pose highly variations because of the fish orientation changes, Light-intensities, similarity in fish patterns and fish shapes. This would create the greater challenge for Image-processing techniques in accurate classification of Fish species or the Fish classes. Hence, for this reason, Underwater Image Enhancement is implemented in combination of Morphological-operations in pre-processing method. The pre-processed image is then subjected to feature extraction process by using Speed-up Robust Feature algorithm. This is followed by Firefly Algorithm, applied for optimization of Region of interest selection in the selected-features. For the categorization of Fish-species, PatternNet is a technique which is employed, in classifying 10,000 marine fish-images to five categories (Dascyllus reticulatus, Plectroglyphidodon dickii, Chromis chrysura, Amphiprion clarkii, and Chaetodon lunulatus). The Efficiency of the proposed-framework is performed in terms of Classification accuracy, execution time, precision value, F-measure and recall factors with respect to various categories of fish species. The comparison of the proposed-framework is also assessed with the other existing methods. 98% of accuracy rate in classification was produced by the evaluation results of the proposed framework with a lesser average computation time of 3.64 s upon different tested images. Thus, the higher efficiency of the proposed framework is proved by the outcomes of the study.

Novel modified convolutional neural network and FFA algorithm for fish species classification

Novel modified convolutional neural network and FFA algorithm for fish species classification