• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.48 no.4
• /
• pp.469-478
• /
• 2012

To manage, sort, and grade fishery resources, it is necessary to measure their morphometric characteristics. This labor-intensive task involves performing repetitive operations on land and on a research vessel. To reduce the amount of labor required, a vision-based automatic measurement system (VAMS) for the measurement of morphometric characteristics of flatfish, such as total length (TL), body width (BW), and body height (BH), has been developed as part of a database management system for fishery resources management. This system can also measure the mass (M) of flatfish. In the present study, we describe a morphological image processing algorithm for the measurement of certain characteristics of flatfish. This algorithm, which involves preprocessing, edge pattern matching, and edge point detection, is effective in cases where the flatfish being measured has a deformed tail and is randomly oriented. The satisfactory performance of the proposed algorithm is also demonstrated by means of experiments involving the measurement of the BW, TL and BH of a flatfish when it is straightened (BW : 117mm, TL : 329mm, BH : 24.5mm), when its tail is deformed, and when it is randomly oriented.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.909-919
• /
• 2023

Benthic marine invertebrates, the invertebrates living on the bottom of the ocean, are an essential component of the marine ecosystem, but excessive reproduction of invertebrate grazers or pirate creatures can cause damage to the coastal fishery ecosystem. In this study, we compared and evaluated You Only Look Once Version 7 (YOLOv7), the most widely used deep learning model for real-time object detection, and detection tansformer (DETR), a transformer-based model, using underwater images for benthic marine invertebratesin the coasts of South Korea. YOLOv7 showed a mean average precision at 0.5 (mAP@0.5) of 0.899, and DETR showed an mAP@0.5 of 0.862, which implies that YOLOv7 is more appropriate for object detection of various sizes. This is because YOLOv7 generates the bounding boxes at multiple scales that can help detect small objects. Both models had a processing speed of more than 30 frames persecond (FPS),so it is expected that real-time object detection from the images provided by divers and underwater drones will be possible. The proposed method can be used to prevent and restore damage to coastal fisheries ecosystems, such as rescuing invertebrate grazers and creating sea forests to prevent ocean desertification.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.42 no.1
• /
• pp.15-19
• /
• 2009

A simple and sensitive method for erythromycin quantification by liquid chromatography electrospray mass spectrometry (LC-MS/MS) in fishery products was developed. Samples were extracted by liquid-liquid extraction using 70% acetonitrile. Lipids were removed by acetonitrile saturated hexane. LC separation was performed on a Shiseido UG C-18 column ($150\;mm{\times}2.0\;mm$ internal diameter.) with a gradient system of 0.2% acetic acid-acetonitrile containing 0.2% acetic acid as a mobile phase at flow rate of 0.2 mL/min. The mass spectrometer was operated in selected reaction monitoring with positive electro-spray interface. Transitions were monitored a m/z $734{\to}577$ and $734{\to}158$, with m/z $734{\to}577$ chosen for quantification. Recovery of erythromycin from fish and shrimp fortified at the 10 ng/mL, 50 ng/mL and 100 ng/mL were 91.6-109.4%, 84.4-111.2% and 98.8-109.6% with high precision, respectively. Limits of quantification and limits of detection of erythromycin in both fish and shrimp were 10.0 ng/mL and 1.0 ng/mL, respectively. This analysis method for erythromycin has been proposed for registration in the Korean Official Methods of Food Analysis and has been utilized for fishery products analysis by the Korea Food and Drug Adminstration and the National Fisheries Products Quality Inspection Service.

• Journal of Life Science
• /
• v.18 no.2
• /
• pp.284-286
• /
• 2008

Octopus minor (O. minor) is widely distributed along the coastal regions of Korea, but most of them are caught in southern waters which are associated with one of the important fisheries stock. At present, O. minor from China has been introduced to the fishery markets in Korea. Here, we attempt to discriminate their origin for Korea or China using molecular techniques. Based on the O. minor mitochondrial DNA sequence, we developed a PCR-based origin discrimination system. The assay specificity was assessed by testing four individuals of O. minor from Sangdong, China, as well as 20 additional O. minor from Namhae, Muan, Yeosu and Jindo, Korea. Only four isolates of O. minor originated from China tested as positive in our distinction system. All PCR-positive products yielded identical sequences from Chinese O. minor, whereas Korean O. minor appeared to be PCR amplification. This result suggested that the primers used in this study are O. minor species specific, especially originated from China. The detection system appeared to be positive results in the use of 0.1 ng of Chinese O. minor DNA as template, however, the Korean O. minor even using $1{\mu}g$ of DNA showed no amplification. Consequently, the assay provides a simple, rapid and accurate method for the detection of Chinese O. minor.

• Food Science of Animal Resources
• /
• v.44 no.4
• /
• pp.873-884
• /
• 2024

Flunixin is a veterinary nonsteroidal anti-inflammatory agent whose residues have been investigated in their original form within tissues such as muscle and liver. However, flunixin remains in milk as a metabolite, and 5-hydroxy flunixin has been used as the primary marker for its surveillance. This study aimed to develop a quantitative method for detecting flunixin and 5-hydroxy flunixin in milk and to strengthen the monitoring system by applying to other livestock and fishery products. Two different methods were compared, and the target compounds were extracted from milk using an organic solvent, purified with C₁₈, concentrated, and reconstituted using a methanol-based solvent. Following filtering, the final sample was analyzed using liquid chromatography-tandem mass spectrometry. Method 1 is environmentally friendly due to the low use of reagents and is based on a multi-residue, multi-class analysis method approved by the Ministry of Food and Drug Safety. The accuracy and precision of both methods were 84.6%-115% and 0.7%-9.3%, respectively. Owing to the low matrix effect in milk and its convenience, Method 1 was evaluated for other matrices (beef, chicken, egg, flatfish, and shrimp) and its recovery and coefficient of variation are sufficient according to the Codex criteria (CAC/GL 71-2009). The limits of detection and quantification were 2-8 and 5-27 ㎍/kg for flunixin and 2-10 and 6-33 ㎍/kg for 5-hydroxy flunixin, respectively. This study can be used as a monitoring method for a positive list system that regulates veterinary drug residues for all livestock and fisheries products.

• The Journal of Korea Robotics Society
• /
• v.8 no.1
• /
• pp.51-57
• /
• 2013

Recently, the number of jellyfish has been rapidly grown because of the global warming, the increase of marine structures, pollution, and etc. The increased jellyfish is a threat to the marine ecosystem and induces a huge damage to fishery industries, seaside power plants, and beach industries. To overcome this problem, a manual jellyfish dissecting device and pump system for jellyfish removal have been developed by researchers. However, the systems need too many human operators and their benefit to cost is not so good. Thus, in this paper, the design, implementation, and experiments of autonomous jellyfish removal robot system, named JEROS, have been presented. The JEROS consists of an unmanned surface vehicle (USV), a device for jellyfish removal, an electrical control system, an autonomous navigation system, and a vision-based jellyfish detection system. The USV was designed as a twin hull-type ship, and a jellyfish removal device consists of a net for gathering jellyfish and a blades-equipped propeller for dissecting jellyfish. The autonomous navigation system starts by generating an efficient path for jellyfish removal when the location of jellyfish is received from a remote server or recognized by a vision system. The location of JEROS is estimated by IMU (Inertial Measurement Unit) and GPS, and jellyfish is eliminated while tracking the path. The performance of the vision-based jellyfish recognition, navigation, and jellyfish removal was demonstrated through field tests in the Masan and Jindong harbors in the southern coast of Korea.

• Journal of Advanced Navigation Technology
• /
• v.25 no.6
• /
• pp.433-440
• /
• 2021

The problem of maritime accidents due to the carelessness of fishing vessels, which is affected by the aging of fishing vessel operators. And there is navigation, communication and fish finder that is installed inside the narrow bridge of a fishing vessel. Therefore these system are monitors as many as of each terminal, which is bad influence on obscuring view of front sea from a fishing vessel bridge. In addition a large problem, it is occurs to reduce of the information recognition ability due to the confusion, which is can not check the display information each of screen equipments. Therefore, there has been demand to simply integrated the equipment, and it has wanted the integrated support system of these equipment. The display must be provided on a fishing vessels such as electronic charts, communications equipments and fish detection into one case. In this paper, the integrated system will be installed the GPS plotter, AIS, VHF-DSC, V-pass, fish finder and power supply in the narrow wheelhouse on a fishing vessel, which is configured in one case and operated by multi function display (MFD). The MFD is integrated to simplify for several multi terminals and provided necessary information on a single screen. This integration fishery support system will has improved in sea safety operation and fishery environment of fishing vessels by this implementation.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1643-1652
• /
• 2022

Deposited Marine Debris(DMD) can negatively affect marine ecosystems, fishery resources, and maritime safety and is mainly detected by sonar sensors, lifting frames, and divers. Considering the limitation of cost and time, recent efforts are being made by integrating underwater images and artificial intelligence (AI). We conducted a comparative study of You Only Look Once Version 5 (YOLOv5) and You Only Look Once Version 7 (YOLOv7) models to detect DMD from underwater images for more accurate and efficient management of DMD. For the detection of the DMD objects such as glass, metal, fish traps, tires, wood, and plastic, the two models showed a performance of over 0.85 in terms of Mean Average Precision (mAP@0.5). A more objective evaluation and an improvement of the models are expected with the construction of an extensive image database.

• Journal of Intelligence and Information Systems
• /
• v.20 no.3
• /
• pp.93-108
• /
• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Smart Media Journal
• /
• v.11 no.4
• /
• pp.62-68
• /
• 2022

To prevent the spread of disease in aquaculture, it is a need for a system to predict fish diseases while monitoring the water quality environment and the status of growing fish in real time. The existing research in predicting fish disease were image processing techniques. Recently, there have been more studies on disease prediction methods through deep learning techniques. This paper introduces the research results on how to predict diseases of Paralichthys Olivaceus with deep learning technology in aquaculture. The method enhances the performance of disease detection rates by including data augmentation and pre-processing in camera images collected from aquaculture. In this method, it is expected that early detection of disease fish will prevent fishery disasters such as mass closure of fish in aquaculture and reduce the damage of the spread of diseases to local aquaculture to prevent the decline in sales.