Intelligence technologies play an important role in increasing product quality and production efficiency in digital aquaculture. Automatic fish detection will contribute to achieving intelligent production and scientific management in precision farming. Due to the availability and ubiquity of modern information technology, such as the internet of things, big data, and camera devices, computer vision techniques, as an essential branch of artificial intelligence, have emerged as a powerful tool for achieving automatic fish detection. At present, it has been widely used in fish species identification, counting, and behavior analysis. Nevertheless, computer vision modeling used for fish detection is riddled with many challenges, such as varies in illumination, low contrast, high noise, fish deformation, frequent occlusion, and dynamic background. Hence, this paper provides a comprehensive review of the computer vision model for fish detection under unique application scenarios. Firstly, the image acquisition system based on 2D and 3D is discussed. Further, many fish detection techniques are categorized as appearance-based, motion-based, and deep learning. In addition, applications of fish detection and public open-source datasets are also presented in the literature. Finally, the prominent findings and the directions of future research are addressed toward the advancement in the aquaculture field throughout the discussion and conclusion section.

Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A Review

Marine scientists use remote underwater video recording to survey fish species in their natural habitats. This helps them understand and predict how fish respond to climate change, habitat degradation, and fishing pressure. This information is essential for developing sustainable fisheries for human consumption, and for preserving the environment. However, the enormous volume of collected videos makes extracting useful information a daunting and time-consuming task for a human. A promising method to address this problem is the cutting-edge Deep Learning (DL) technology.DL can help marine scientists parse large volumes of video promptly and efficiently, unlocking niche information that cannot be obtained using conventional manual monitoring methods. In this paper, we provide an overview of the key concepts of DL, while presenting a survey of literature on fish habitat monitoring with a focus on underwater fish classification. We also discuss the main challenges faced when developing DL for underwater image processing and propose approaches to address them. Finally, we provide insights into the marine habitat monitoring research domain and shed light on what the future of DL for underwater image processing may hold. This paper aims to inform a wide range of readers from marine scientists who would like to apply DL in their research to computer scientists who would like to survey state-of-the-art DL-based underwater fish habitat monitoring literature.

/pdf/computer-vision-and-deep-learning-for-fish-classification-in-25b88chw.pdf

Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey

Assessing the quality of seafood both in retail and during packaging at the production side must be carried out minutely in order to avoid spoilage which causes severe human health problems and also economic loss. Since the illnesses and decay in seafood presents distinct symptoms in different species, primarily the classification of species is required. In this field, the inadequacy of the current laborious and slow traditional methods can be overcome with systems based on machine learning and image processing, which present fast and precise results. In order design such systems, practical and suitable datasets are required. However, most of the publicly available datasets are not fit for the mentioned purpose. These datasets either contain images taken underwater or consist of seafood which is generally not (widely) consumed. In this study, a practical and large dataset containing nine distinct seafood widely consumed in the Aegean Region of Turkey is formed. Additionally, comprehensive experiments based on different classification approaches are performed to analyze the usability of this collected dataset. Experimental results demonstrate very promising outcomes; therefore, this dataset will be made publicly available for further investigations in this research domain.

A Large-Scale Dataset for Fish Segmentation and Classification

Smart aquaculture is nowadays one of the sustainable development trends for the aquaculture industry in intelligence and automation. Modern intelligent technologies have brought huge benefits to many fields including aquaculture to reduce labor, enhance aquaculture production, and be friendly to the environment. Machine learning is a subdivision of artificial intelligence (AI) by using trained algorithm models to recognize and learn traits from the data it watches. To date, there are several studies about applications of machine learning for smart aquaculture including measuring size, weight, grading, disease detection, and species classification. This review provides and overview of the development of smart aquaculture and intelligent technology. We summarized and collected 100 articles about machine learning in smart aquaculture from nearly 10 years about the methodology, results as well as the recent technology that should be used for development of smart aquaculture. We hope that this review will give readers interested in this field useful information.

Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

Simple Summary The automatic diagnosis of fish diseases is an important task in smart aquaculture, and plays a key role in detecting fish conditions, understanding disease signs, and improving fish welfare and health. This paper reviews the latest advances in intelligent fish disease diagnosis techniques for aquatic applications. To acquire high-quality images to improve the accuracy of fish disease diagnosis, the paper reviews, in the second paragraph, how image-processing techniques can be used to acquire high-quality images. The third paragraph of this paper reviews the methods of intelligent fish disease diagnosis, including expert system detection, camera image detection, microscope image detection, spectral image detection, fluorescence image detection, ultrasonic image detection, and sensor detection methods. Additionally, the advantages and disadvantages of each detection method are summarized, including whether they cause damage to the fish body. Finally, we put forward the prospect of the intelligent detection of fish diseases and summarize the methods that may be used in the intelligent diagnosis of fish disease. Abstract Aquatic products, as essential sources of protein, have attracted considerable concern by producers and consumers. Precise fish disease prevention and treatment may provide not only healthy fish protein but also ecological and economic benefits. However, unlike intelligent two-dimensional diagnoses of plants and crops, one of the most serious challenges confronted in intelligent aquaculture diagnosis is its three-dimensional space. Expert systems have been applied to diagnose fish diseases in recent decades, allowing for restricted diagnosis of certain aquaculture. However, this method needs aquaculture professionals and specialists. In addition, diagnosis speed and efficiency are limited. Therefore, developing a new quick, automatic, and real-time diagnosis approach is very critical. The integration of image-processing and computer vision technology intelligently allows the diagnosis of fish diseases. This study comprehensively reviews image-processing technology and image-based fish disease detection methods, and analyzes the benefits and drawbacks of each diagnostic approach in different environments. Although it is widely acknowledged that there are many approaches for disease diagnosis and pathogen identification, some improvements in detection accuracy and speed are still needed. Constructing AR 3D images of fish diseases, standard and shared datasets, deep learning, and data fusion techniques will be helpful in improving the accuracy and speed of fish disease diagnosis.

/pdf/advanced-techniques-for-the-intelligent-diagnosis-of-fish-37cjw6oy.pdf

Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review

Underwater fish species recognition has gained importance due to the emerging researches in marine science. Automating the fish species identification using technology would help the marine science to evolve further. Image classification tasks have seen a rise with the introduction of deep learning techniques. In this paper, we have proposed a hybrid Convolutional Neural Network (CNN) framework that uses CNN for feature extraction and Support Vector Machine (SVM) and K-Nearest Neighbour (k-NN) for classification. Both the proposed frameworks are tested on Fish4Knowledge dataset. Our experimental results show that our framework gives better results than most of the traditional as well as existing deep learning techniques.

Underwater Fish Species Recognition Using Deep Learning Techniques

Cloud computing is one of the fast-growing technologies in recent times. People are adopting cloud services often and they do not possess any other substitute for its services. At the same time, users have to be aware of privacy and security issues in the cloud environment. Due to the distributed nature of cloud computing, multi-domain support, and multi-user platform, the cloud-based system is more vulnerable to security threats. Security threats can be distributed denial of service attacks and intrusion prospects. Thus, organizations need to have techniques like intrusion detection as well as prevention, firewalls, encryption, authentication, etc. for securing the stored information on the cloud. Intruders attempt to identify loopholes to break security. For that, organizations are adopting the system for intrusion detection and prevention to provide privacy and security in the cloud environment. Attacks whether internal or external must be prevented and thus it is significant to adopt the technique of preventing and detection system for identifying intrusion. Therefore, this research intends to study the prevention and detection of intrusion in the cloud environment.

Prevention and detection of intrusion in cloud using hidden markov model

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Underwater Fish Species Recognition Using Deep Learning Techniques

Prevention and detection of intrusion in cloud using hidden markov model