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Relevant bibliographies by topics / Fish Disease Detection

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Academic literature on the topic 'Fish Disease Detection'

Author: Grafiati

Published: 5 June 2025

Last updated: 25 June 2025

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Journal articles on the topic "Fish Disease Detection"

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K P, Aswathi, Gopika M, Mohammed Afkar, Ajith K, and Manoj M. "Fish Disease Detection to Sustain Hatchery and Pond Production System." June 2023 5, no. 2 (2023): 144–53. http://dx.doi.org/10.36548/jaicn.2023.2.005.

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Diagnosis of fish disease in aquaculture is a necessary process and needs an exceptionally high level of competency to sustain hatchery and pond production systems. Developing an system to overcome the challenges faced by fish farmers in stopping the spreading the disease that leads to economic loss, is a crucial task. A crucial initial phase in preventing the spread of disease is early identification of diseases in fish. The fish disease usually propagates quickly through the water, affecting large numbers of fish and causing financial loss to the farmers. Since tilapia aquaculture is one of the methods for producing food that is expanding the quickest and has the highest export value, we’d like to know more about the fish disease that affects this sector. The research uses the pathogen-infected fish. System is developed by working perfect image processing and machine learning techniques together. The proposed work has two phase. Image pre-processing has been used in the first phase to, respectively, reduce distortion and magnify the image. In the second section, the system extracts the relevant information and uses machine learning approaches for recognising the diseases. A trained machine learning model has been deployed to the first fraction's processed images. Then, using the chosen fish image dataset to study the fish disease, the research integrates an extensive experiment combining different methods.

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Afridiansyah, Rahmanda, and De Rosal Ignatius Moses Setiadi. "Comparison of EfficientNetB1 Model Effectiveness in Identifying Fish Diseases in South Asian Fish Diseases and Salmon Fish Diseases." Journal of Applied Informatics and Computing 8, no. 2 (2024): 453–62. https://doi.org/10.30871/jaic.v8i2.8677.

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The purpose of this study is to evaluate the effectiveness of the EfficientNetB1 model in identifying fish diseases across two distinct datasets: South Asian Fish Diseases and Salmon Fish Diseases. The South Asian Fish Diseases dataset includes seven categories: red bacterial disease, aeromoniasis, gill bacterial disease, fungal saprolegniasis, parasitic disease, and white tail viral disease. The Salmon dataset is divided into two parts: FreshFish and InfectedFish. Using the EfficientNetB1 algorithm, each dataset was separately trained and tested to predict species and disease. Results showed an accuracy of 98.14% for the South Asian Fish Diseases dataset and 99.18% for the Salmon Diseases dataset. These findings support the argument that the model possesses sufficient capability to detect diseases affecting various fish species. This suggests that the model could be a valuable tool in the aquaculture industry for disease management and detection strategies.

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Son, Hyun Seung, Han-kyu Lim, and Han Suk Choi. "A Study on Disease Prediction of Paralichthys Olivaceus using Deep Learning Technique." Korean Institute of Smart Media 11, no. 4 (2022): 62–68. http://dx.doi.org/10.30693/smj.2022.11.4.62.

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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.

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Tiwari, Ayush. "Aqua Health Vision." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46963.

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Abstract— The "Aqua Health Vision" is aimed at forecasting fish disease diagnosis and to overcome various challenges in it has still not achieved any success. Conventional techniques for detecting diseases in fish, such as visual observations and laboratory analysis, are frequently less efficient and labor-intensive. With the evolution gained through technology, Artificial Intelligence (AI) and Machine Learning (ML) are becoming powerful tools to enhance detection and management of fish diseases.This paper discusses how these technologies are used to detect the fish diseases and presents various means of recovering from these diseases. In aquaculture, AI system can be used to analyze vast amounts of data, identify patterns and make decisions or predictions. Keywords— Fish Disease ,Deep Learning ,Aquaculture ,YOLO-V9.

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Tamut, Hayin, Robin Ghosh, Kamal Gosh, and Md Abdus Salam Siddique. "Enhancing Disease Detection in the Aquaculture Sector Using Convolutional Neural Networks Analysis." Aquaculture Journal 5, no. 1 (2025): 6. https://doi.org/10.3390/aquacj5010006.

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The expansion of aquaculture necessitates innovative disease detection methods to ensure sustainable production. Fish diseases caused by bacteria, viruses, fungi, and parasites result in significant economic losses and threaten food security. Traditional detection methods are labor-intensive and time-consuming, emphasizing the need for automated approaches. This study investigates the application of convolutional neural networks (CNNs) for classifying freshwater fish diseases. Such CNNs offer an efficient and automated solution for fish disease detection, reducing the burden on aquatic health experts and enabling timely interventions to mitigate economic losses. A dataset of 2444 images was used across seven classes—bacterial red disease, bacterial Aeromoniasis disease, bacterial gill disease, fungal disease, parasitic diseases, white tail disease, and healthy fish. The CNNs model incorporates convolutional layers for feature extraction, max-pooling for down-sampling, dense layers for classification, and dropout for regularization. Categorical cross-entropy loss and the Adam optimizer were used over 50 epochs, with continuous training and validation performance monitoring. The results indicated that the model achieved an accuracy of 99.71% and a test loss of 0.0119. This study highlights the transformative potential of artificial intelligence in aquaculture for enhancing food security.

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Li, Daoliang, Xin Li, Qi Wang, and Yinfeng Hao. "Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review." Animals 12, no. 21 (2022): 2938. http://dx.doi.org/10.3390/ani12212938.

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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.

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Coutinho, Catarina D., Charlotte E. Ford, Joseph D. Trafford, Ana Duarte, Rui Rebelo, and Gonçalo M. Rosa. "Non-Lethal Detection of Ranavirus in Fish." Viruses 15, no. 2 (2023): 471. http://dx.doi.org/10.3390/v15020471.

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Emergent infectious diseases have an increasing impact on both farmed animals and wildlife. The ability to screen for pathogens is critical for understanding host–pathogen dynamics and informing better management. Ranavirus is a pathogen of concern, associated with disease outbreaks worldwide, affecting a broad range of fish, amphibian, and reptile hosts, but research has been limited. The traditional screening of internal tissues, such as the liver, has been regarded as the most effective for detecting and quantifying Ranavirus. However, such methodology imposes several limitations from ethical and conservation standpoints. Non-lethal sampling methods of viral detection were explored by comparing the efficacy of both buccal swabbing and fin clipping. The study was conducted on two Iberian, threatened freshwater fish (Iberochondrostoma lusitanicum and Cobitis paludica), and all samples were screened using qPCR. While for C. paludica both methods were reliable in detecting Ranavirus, on I. lusitanicum, there was a significantly higher detection rate in buccal swabs than in fin tissue. This study, therefore, reports that fin clipping may yield false Ranavirus negatives when in small-bodied freshwater fish. Overall, buccal swabbing is found to be good as an alternative to more invasive procedures, which is of extreme relevance, particularly when dealing with a threatened species.

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Nadja, K. K., I. Jaya, M. Iqbal, and A. Santika. "Detection and Quantification of Motile Aeromonas Septicemia (MAS) Disease in Common carp Using Deep Learning." IOP Conference Series: Earth and Environmental Science 1359, no. 1 (2024): 012079. http://dx.doi.org/10.1088/1755-1315/1359/1/012079.

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Abstract Common carp (Cyprinus carpio) is one of the freshwater fisheries commodities that has important economic value, so it is widely cultured. However, common carp farming is susceptible to diseases, such as Motile Aeromonas Septicemia (MAS), which can lead to mortality. MAS disease is visually detectable, but continuous monitoring challenges arise for farmers. This research employs the YOLOv8 and DeepSORT algorithms to automatically detect common carp and wounds caused by Aeromonas hydrophila bacterial infection, obtaining wound area and swimming speed data. Training was conducted on a dataset with two different labels: infected fish wound areas and the entire fish body, using consecutive epochs of 3000 and 1000. The training results show wound label accuracy reached 91,49% for disease concentration of 107 cfu/mL and 88,68% for disease concentration of 108 cfu/mL, respectively, while the accuracy for fish label reached 96,61% for disease concentration of 107 cfu/mL and 93,44% for disease concentration of 108 cfu/mL. The coefficient value of predicted wound area against actual wound area approximates 1, indicating a close match between the two variables. The obtained fish swimming speed estimate reflects the lethargic behavior of fish due to MAS. The results demonstrate that the model is effective in detecting fish, wound areas, and swimming speed.

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Nguyen, Quang Hoan, Hong Quang Doan, Van Hung Tran, Vu Thi Tuyet Nhung, and Duc Anh Duong. "An Improved YOLOv8 Model for Fish Classification and Disease Detection." Journal of Measurement, Control, and Automation 29, no. 2 (2025): 64–72. https://doi.org/10.64032/mca.v29i2.279.

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Fish classification and disease detection are crucial for sustainable aquaculture, necessitating accurate and efficient vision models. This study introduces FISH-YOLOV8, an enhanced YOLOv8 variant, incorporating: (1) SPD-Conv for optimized feature extraction and reduced computational load; (2) BiFormer Attention for enhanced small object detection and occlusion management; (3) dynamic IoU-threshold NMS to minimize false positives. This Article states that, evaluated on 15,162 images, FISH-YOLOV8 attains a mAP@50 of 0.990 and a mAP@50:95 of 0.859, outperforming baseline YOLOv8 and advanced models such as YOLOv11, at 45 fps, supports effective real-time aquaculture monitoring.

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Tarasova, A. S., A. V. Perchun, and V. P. Melnikov. "Polymerase chain reaction for detection of some highly dangerous viral fish disease agents." Veterinary Science Today, no. 1 (March 30, 2020): 11–16. http://dx.doi.org/10.29326/2304-196x-2020-1-32-11-16.

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Today viral fish diseases cause major losses in the world aquaculture. Pathogen spread often occurs during the transportation of fish from infected farms to the disease-free ones. Therefore, the import of fish stocking material to Russia from countries with a different epidemic situation requires risk-based monitoring and forecasting. Diagnostics is of primary importance in the complex of measures to prevent the spread of viral infections in fish. To date, laboratory diagnostics of viral fish diseases is based on pathogen isolation and its identification using serological methods which require a lot of time and are performed only in large research institutes with specialized laboratories. Molecular diagnostic methods are more sensitive and high-performance. The article presents the results of using reverse transcription polymerase chain reaction to detect a number of highly dangerous viral diseases of fish (Salmonidae). As a result of this work, primers were selected and the temperature and time conditions of the reaction were optimized for the identification of infectious hematopoietic necrosis, viral hemorrhagic septicemia and infectious salmon anemia. The results obtained during the research allowed us to establish that this diagnostic method is highly specific with analytical sensitivity to infectious salmon anemia virus of 2.5 lg TCD50/сm3, to infectious hematopoietic necrosis – 2.9 lg TCD50/сm3 and to viral hemorrhagic septicemia – 4.2 lg TCD50/ сm3. The described method was used to identify reference and field strains available at the FGBI ARRIAH Reference Laboratory for Aquaculture Diseases and isolated in different years in fish farms in the territory of the Russian Federation. The research data correlated with the results obtained from virus neutralization in cell culture and ELISA performed using commercial kits. The proposed method of RT-PCR allows to detect pathogens both in fish with pronounced clinical signs and in latent virus carriers.

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Dissertations / Theses on the topic "Fish Disease Detection"

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Manfredi, Eugene Trent. "Immunodiagnostic methods for the detection of bacterial kidney disease in salmonid fishes /." Thesis, Connect to this title online; UW restricted, 1986. http://hdl.handle.net/1773/5282.

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Good, Christopher Michael. "Factors associated with the detection of bacterial pathogens in the Ontario provincial fish disease surveillance program." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0005/MQ43166.pdf.

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Wurdeman, Bret Mark. "The evaluation and development of diagnostic tools for the detection of ichthyophonus hoferi in fish host tissue samples." University of the Western Cape, 2019. http://hdl.handle.net/11394/7868.

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Magister Scientiae (Biodiversity and Conservation Biology) - MSc (Biodiv and Cons Biol)<br>Ichthyophonus hoferi is a highly pathogenic histozoic parasite that has low host specificity capable of producing mass mortalities of epizootic proportions in marine commercial fish populations. Currently in Southern Africa, I. hoferi has been reported from flathead mullet (Mugil cephalus) from the Kowie lagoon and from multiple species on exhibit at the Two Oceans Aquarium. Since epizootiologists rely on accurate assessments of prevalence to establish patterns of morbidity and mortality within populations, using the most accurate diagnostic techniques for accurate assessments of infection is imperative. Currently, several diagnostic techniques have been employed to detect I. hoferi in infected fish hosts. These include macroscopic examination of tissues, microscopic examinations of wet-mount squash preparations of tissue, histological examination of tissue sections, in vitro culture of tissue explants, the polymerase chain reaction (PCR) using I. hoferi-specific primers and real-time quantitative PCR (qPCR) using I. hoferi-specific primers and a hydrolysis probe.

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Ghosh, B. "Oral immunoprophylaxis using microencapsulated antigens as a disease-management strategy in farmed finfish populations." Thesis, 2015. https://eprints.utas.edu.au/23170/1/Ghosh_whole_thesis.pdf.

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The increase in intensification of aquaculture production has been accompanied by an increase in impacts of pathogenic diseases. Large populations of stock maintained in close proximity have left much of the aquaculture industry susceptible to major economic losses caused by disease. As a consequence, and due to the variety of negative impacts associated with chemotherapeutics, development of immunoprophylaxis has received great impetus as a preventive disease management strategy for finfish. The most commonly used methods for fish immunoprophylaxis - injection and immersion - are generally associated with high procedural cost and access constraints. Oral immunoprophylaxis strategies for finfish, the main focus of this thesis, are able to effectively obviate these constraints and have therefore been proposed as an ideal approach for fish health management. Due to performance inconsistencies linked to digestive degradation of orally administered antigens, oral immunoprophylaxis strategies have not been widely implemented in aquaculture. This thesis examined the feasibility of alginate microcapsules manufactured using a low impact technology and reagents to protect orally delivered immunogens for immunoprophylaxis of finfish. The microencapsulation method developed was found to be well suited for oral immunoprophylaxis of fish, as demonstrated by successful uptake of microcapsules and systemic distribution of contents ex vivo and in vivo, as well as the ability to affect controlled release of contents in target environmental conditions. The method also demonstrated no adverse impact on the integrity of the encapsulated substance, implying applicability to a broad range of immunoprophylactic materials. The microencapsulation method was adapted for use with live microbial cells, and its viability as a disease management strategy was assessed against pathogenic disease of temperate and coldwater fish, bacterial coldwater disease (BCWD), which is caused by Flavobacterium psychrophilum. The protective efficacy of a putative probiotic Enterobacter species (C6-6) against BCWD was examined when administered as an alginate microencapsulated oral treatment. A similar trial was performed to test the effectiveness of an orally administered, alginate microencapsulated live-attenuated vaccine (B17) against BCWD. In both trials, the modified method was successfully used to microencapsulate the live cell antigens while maintaining their viability. Though achieving significantly better fish survival than in untreated controls, oral administration of C6-6 was not as effective as intraperitoneal (IP) injection in protecting fish against BCWD. In contrast, orally administered B17 achieved similar serum antibody titres and survival as IP administered B17, with survival in both groups significantly better than untreated controls. An elevated challenge pressure made it difficult to draw clear conclusions regarding efficacy, though the similarities in treatment outcomes suggested that orally administered B17 could potentially approach the effectiveness of IP injected administration. Yersinia ruckeri, the causative agent of yersiniosis in fish, is is a ubiquitous finfish pathogen affecting a broad range of species, and has been responsible for severe mortality in fish stocks globally. It is also capable of establishing and maintaining asymptomatic infections in apparently healthy fish, which act as reservoirs of infection within populations. A non-destructive technique for reliable detection of low levels of Y. ruckeri is necessary for effective management of the disease. A highly sensitive quantitative real-time PCR-based assay targeting the Y. ruckeri 16S-ribosomal gene was developed, capable of reliably detecting single-cell presence of the pathogen in spleen and faecal samples. The assay was able to detect Y. ruckeri in faecal samples at levels lower than previously possible, presenting the possibility of screening populations for asymptomatic infection without the need for invasive sampling. Yersiniosis is conventionally managed by immersion immunisation in small fish and injected vaccines for larger fish. Large-scale Y. ruckeri infections have observed in fish smaller than typical minimum size immunised in the industry. Consequently, protecting fry at early developmental stages is important, and the effectiveness of an alginate-microencapsulated vaccine orally administered to first feeding fry was investigated. Significant protection following pathogenic challenge indicated considerable potential, though the treatments did not affect establishment rate of asymptomatic infection in survivors. The lack of typical adaptive immune responses made it difficult to draw clear conclusions regarding the mechanisms responsible for the protection observed. The work presented in this thesis establishes the feasibility of oral immunoprophylaxis for finfish. A versatile, low-impact alginate microencapsulation-based method for oral administration of a variety of immunogens is presented. Its potential as a health management strategy is demonstrated against known finfish diseases, though further optimisation of the approach will be greatly aided by an increased understanding of mucosal immune responses in finfish.

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Lin, Shiue-Lian, and 林學廉. "Establishment and Characterization of Ornamental Fish Cell Lines and Development of Methods for Detection of Koi Herpesvirus Disease." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/83400287477881642317.

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博士<br>國立臺灣大學<br>漁業科學研究所<br>102<br>Establishing ornamental fish cell lines is essential for researching the viral diseases that affect these ornamental fish, particularly ornamental fish species that can be artificially bred. Typically, because of high-density breeding and the flow of international trade, these fishes cause rapid viral disease spread and transmission, resulting in severe economic losses. This study reports the first successful cultivation of cell lines from the fin tissue of the marine ornamental fish species Apolemichthys trimaculatus, that is, the three-spot angelfish (TSAF). We successively cultivated 7 Carassius auratus or red carp (RC) cell lines from the fin (F), gill (G), heart (H), head kidney (HK), spleen (S), and ovum (O) tissues, yielding RCF, RCG, RCH, RCK, RCHK, RCS, and RCO cell lines. An RCHK macrophage (RCHKM) cell line was also cultivated from RC head kidney tissues. We also established 5 Cyprinus carpio (Koi) cell lines (KF, KG, KH, KB, and KO) from the fin, gill, heart, air bladder, and ovum tissues of koi. These cell lines can be continually reproduced and grown in simple cultivation conditions of 20°C-30°C and 10% FBS-L15. TSAF cell lines were highly susceptible to the eel herpesvirus in Formosa (EHVF), which ideally grows in 30°C environments, and to the infectious pancreatic necrosis virus (IPNV), endogenous viral elements (EVEs), and hippeastrum chlorotic ringspot virus (HCRV), which ideally grow in 18°C environments. Therefore, TSAF cell lines are extremely appropriate as virus detectors for cold and warm water fishes. The RCO, RCHKM, KF, KG, and KB cell lines were all susceptible to koi herpesvirus (KHV), generating the cytopathic effect commonly presented in herpesvirus. Hence, these cell lines can be used to mass-proliferate KHV. Accordingly, cell lines are the optimal tools for researching fish virology, pathology, molecular biology, and conducting environmental testing. A mutated KHV cell line, named NKV2, was identified at a koi breeding ground in Southern Taiwan. NKV2 cannot be detected using various KHV primers, carp interstitial nephritis and gill necrosis virus (CNGV) primers, thymidine kinase (TK) primers, and Gray primers, but can be detected using the carp pox cyprinid herpesvirus 1 (CyHV-1) helicase primer and CyHV-1 triplex primers, which are used to detect KHV. We designed and developed two new primers (i.e., the NKV2-2K and NKV2-1.5K primers) based on KHV primer. The proposed primers can be employed to detect both KHV and NKV2. An additional primer (i.e., the NKV2-TK primer) was designed and developed based on TK primer. This primer can be used to detect both KHV and NKV2 according to the introns of NKV2 are larger compared with those of KHV. The developed primers can be adopted in polymerase chain reaction (PCR) to detect KHV and NKV2 in the same time.

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Books on the topic "Fish Disease Detection"

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H, Amos Kevin, and American Fisheries Society. Fish Health Section., eds. Procedures for the detection and identification of certain fish pathogens. 3rd ed. Fish Health Section, American Fisheries Society, 1985.

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Byers, Helen K. Diagnosis and identification of Aeromonas salmonicida and detection of latent infections in carrier fish: Final report. CSIRO, 2000.

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Book chapters on the topic "Fish Disease Detection"

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Gupta, Nidhi, Anil Kumar, Chandra Bhushan Kumar, Neeraj Sood, and Gaurav Rathore. "Applications of Monoclonal Antibodies for Detection of Fish Pathogens." In Laboratory Techniques for Fish Disease Diagnosis. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4620-3_19.

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Mohanty, Debasmita, Punam Kumari, Asit Kumar Bera, et al. "Emerging Challenges of Extended-Spectrum β-Lactamase Producing Pathogen: Laboratory Strategies for Detection." In Laboratory Techniques for Fish Disease Diagnosis. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4620-3_24.

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Krishnan, Rahul, and Devika Pillai. "Detection and Quantification of Tilapia Lake Virus (TiLV) and Tilapia Parvovirus (TiPV) by Real-Time PCR." In Laboratory Techniques for Fish Disease Diagnosis. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4620-3_21.

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Nguyen, Hien Van, Thinh Quoc Huynh, Nhat Minh Nguyen, Anh Kim Su, and Hai Thanh Nguyen. "Comparative Analysis of Fine-Tuned MobileNet Versions on Fish Disease Detection." In Innovative Mobile and Internet Services in Ubiquitous Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64766-6_20.

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Harun, Nor Hazlyna, Mohamad Ghozali Hassan, Juhaida Abu Bakar, Fazli Azzali, and Nur Syafiqah Suhiman. "Underwater Image Segmentation for Early Detection of White Spot Disease in Fish: Evaluating Multiple Techniques for Enhanced Aquaculture Management." In Information Systems Engineering and Management. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-91485-0_36.

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Olshanskaya, Yu V., I. A. Demidova, N. G. Tiurina, et al. "Combined Cytogenetic, FISH and RT-PCR Technique in Detection of t(15;17) and Monitoring of Minimal Residual Disease in Acute Promyelocytic Leukemia." In Haematology and Blood Transfusion / Hämatologie und Bluttransfusion. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-18156-6_7.

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Huang, Youhua, Shaowen Wang, Xiaohong Huang, Jingguang Wei, and Qiwei Qin. "Characterization, Pathogenesis, and Immuno-Biological Control of Singapore Grouper Iridovirus (SGIV)." In Ranaviruses. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64973-8_5.

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AbstractSingapore grouper iridovirus (SGIV) was first isolated from diseased cultured groupers in Singapore and has been identified as a novel species within the genus Ranavirus (family Iridoviridae; subfamily Alphairidovirinae). SGIV infection causes considerable morbidity and mortality in many economically important fish species, such as grouper and seabass. In this chapter, we describe virus isolation in cell culture, virion purification, ultrastructural analysis, virion morphogenesis, and molecular identification of SGIV. SGIV has been molecularly characterized based on the SGIV genome, transcriptome, proteome, and viral miRNAs. Various aspects of pathogenesis resulting from SGIV infection were investigated, including cytopathology, virus entry and transport, paraptosis, autophagy, and signaling pathways. Functions of host immune and metabolism-related genes during SGIV infection are evaluated and discussed. Immuno-biological control strategies, including antibody-based flow cytometry and microfluidic chip detection technology, loop-mediated isothermal amplification (LAMP), and nucleic acid aptamer detection methods, were developed. Efficient SGIV vaccines have also been developed. These research approaches provide the basis for a better understanding of the pathogenesis of SGIV and other ranaviruses and offer technical support to control fish ranaviruses.

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Marschang, Rachel E., Jonathan I. Meddings, Thomas B. Waltzek, et al. "Ranavirus Distribution and Host Range." In Ranaviruses. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64973-8_6.

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AbstractRanaviruses are globally distributed pathogens in amphibian, fish, and reptile communities that appear to be emerging. Cases of ranavirus infection or disease have been confirmed in at least 177 amphibian species (25 families), 49 fish species (25 families), and 37 reptile species (17 families). Transmission of individual strains between animal classes has been documented. While ranaviruses are frequently associated with mass die-offs, host susceptibility differs among species, with some species harboring subclinical infections and likely serving as reservoirs for the virus and other highly susceptible species amplifying the virus. Currently, there are seven recognized species of ranavirus, with stark differences in pathogenicity between strains and hosts. Several strains among these species have been named, and changes in taxonomy in this genus can lead to some confusion. Frog virus 3 is the best studied species of the genus Ranavirus and appears to be the most globally distributed species, with viruses of this species infecting ectothermic vertebrates across three vertebrate classes. International commerce involving infected ectothermic vertebrates undoubtedly has contributed to the global distribution, diversity, and emergence of ranaviruses. Herein, we describe the global distribution of ranaviruses in amphibians, fish, and reptiles, host range of the different Ranavirus species, the implications of interclass transmission, and the impact of trade on ranavirus distribution. The Global Ranavirus Reporting System (GRRS), which documents global detections of ranaviruses, is also presented.

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Samiappan, Sumathi Chettipalayam, Sampathkumar Palanisamy, Mythili Ravichandran, Balamuralikrishnan Balasubramanian, Utthapon Issara, and Vijaya Anand Arumugam. "Common Bacterial Fish Diseases and Approaches on Molecular Techniques for Characterization and Early Detection of Pathogens." In Aquaculture Science and Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0817-0_8.

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Syahidah, Dewi, and Bernadetta Rina Hastilestari. "Machine Learning Approach for Early Detection of Plant and Fish Diseases Security." In Machine Learning Algorithms for Intelligent Data Analytics. Technoarete Research And Development Association, 2022. http://dx.doi.org/10.36647/mlaida/2022.12.b1.ch010.

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The information technologies currently used in plant and fish farming are largely based on equipment and mechanism, image processing, and pattern acknowledgement, computerized modelling, geographical information systems, expert systems (Pakar), data supervision, artificial intelligence (AI), decision maker devices, and care centres or links. The use of advanced technologies eases the prediction and prevention of parasite infestation and other disease outbreaks. The food productivity of the food sources, including plants and fish, is limited by diseases. The early detection of the disease’s infection by naked eyes is somehow difficult. Therefore, early detection through different image processing tools has been introduced widely. Due to the increasing number of reported paper on the potential use of data quarrying and types of machine learning (ML) for plant and fish disease prediction, this chapter consolidates and presents scientific information on the application of data mining and ML in both types of diseases and discussed how imaging technology can be applied to study the diseases and the method in the detection, with comprehensions on the different encounters and prospects. In addition, the potential application of ML in terms of plant and fish disease discoveries in Indonesia are put forward.

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Conference papers on the topic "Fish Disease Detection"

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Isaac, Ssekitto, Oyoka Daniel, Mwebembezi Grivin, Jonah Mubuuke Kyagaba, Emmanuel Lule, and Ggaliwango Marvin. "Explainable Machine Vision Techniques for Fish Disease Detection with Deep Transfer Learning." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10689742.

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Bhuria, Ruchika. "Smart Aquaculture: Fine-Tuned ResNet50 for Precision Fish Disease Detection and Sustainable Health Management." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10985132.

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Bhuria, Ruchika. "Smart Aquaculture: Fine-Tuned ResNet50 for Precision Fish Disease Detection and Sustainable Health Management." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10985708.

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Nath, Shodorson, Sadia Rahman Ani, Fahim Arefin, et al. "Towards Fish Disease Detection using the Vision Transformer Model and Convolutional Neural Network Model." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11022030.

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Saravanan, Harini, M. Rajesh, and Rajiv Vincent. "Fusion of C-Means Fuzzy Logic and Deep Neural Networks for Enhanced Fish Disease Detection." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724765.

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A, Vasumathi, Prathik Singh Rathore, Siva Bharathi E, Vignesh N, and Harsith S. "Fish Disease Detection Using Machine Learning." In 2024 International Conference on Science Technology Engineering and Management (ICSTEM). IEEE, 2024. http://dx.doi.org/10.1109/icstem61137.2024.10560522.

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Lazareva, O. I., I. N. Merzlyakov, L. V. Merzlyakova, and S. N. Kazarinov. "DETECTION OF TRYPANOSOMES (PROTOZOA: KINETOPLASTEA) IN FISH FROM THE KAMA RESERVOIR." In THEORY AND PRACTICE OF PARASITIC DISEASE CONTROL. VNIIP – FSC VIEV, 2025. https://doi.org/10.31016/978-5-6053355-1-1.2025.26.170-174.

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Data were obtained on commercial fish infection with haematophagous parasites of the genus Trypanosoma in the Kama Reservoir. Ichthyological material was collected in September-December 2024 from different districts of the Reservoir. A total of 40 specimens of three fish species were examined. Parasitological studies were carried out according to common methods using the VisionAssist automatic system (Austria) and Vision microscopy automation software from Medica Product (Russia). Fish leeches Piscicola geometra were found in fish from the upper district of the Kama Reservoir. High leech infection prevalence and intensity were found in the bream Abramis brama (80%, 3–15 specimens; average, 7 specimens). The prevalence in the pike perch Sander lucioperca was 8% and the intensity was 3 specimens. Protozoa of the Kinetoplastea class, genus Trypanosoma were found in the blood of the bream. Based on morphological features, the protozoa were classified as Trypanosoma carassii. The prevalence of trypanosomes in breams was 40%. Fish from the central and lower districts of the Kama Reservoir were free of parasitic flagellates and their carriers. Trypanosomes had not been previously recorded in fish from the Kama Reservoir.

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Bonina, O. M., E. A. Udaltsov, and M. S. Bortsova. "DETECTION OF POSTHODIPLOSTOMUM CUTICOLA (NORDMANN, 1832) IN FISH IN THE WATER BODIES OF THE NOVOSIBIRSK REGION." In THEORY AND PRACTICE OF PARASITIC DISEASE CONTROL. All-Russian Scientific Research Institute for Fundamental and Applied Parasitology of Animals and Plant – a branch of the Federal State Budget Scientific Institution “Federal Scientific Centre VIEV”, 2023. http://dx.doi.org/10.31016/978-5-6048555-6-0.2023.24.100-104.

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The article presents data on infection of fish of the cyprinid family (ide, dace, roach) with metacercariae of trematode Posthodiplostomum cuticola. Fish for the study were caught in the following water bodies of the Novosibirsk Region: the Chulym, Karakan, Inya, and Makhalikha Rivers, as well as from the left (Sharapovsky Bay) and right (Tulkinsky Bay) banks of the Novosibirsk Reservoir. Fish were studied by the compressor method generally accepted in parasitology and by partial helminthological dissection. A total of 270 fish specimens (71 ides, 82 daces, and 117 roaches) were studied. To analyze the fish infection, such indicators as the invasion prevalence and intensity, as well as the abundance index were used. The research results showed that the overall level of infection of cyprinids with P. cuticola metacercariae in the reservoirs of the Novosibirsk Region was 13.0%. The highest invasion prevalence of 35.2% was observed in ides; in daces and roaches this value is much lower and amounts to 7.3% and 3.4%, respectively. The invasion intensity was low, ranging from 1 to 7 parasite specimens per fish. The highest average invasion intensity of 3.3 and 3.1 specimens was recorded in daces from the Karakan River and ides from the Chulym River.

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Sanjay Kumaar, A., A. Vishnu Vignesh, and K. Deepak. "FishNet Freshwater Fish Disease Detection using Deep Learning Techniques." In 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2024. http://dx.doi.org/10.1109/incacct61598.2024.10550979.

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Sujatha, K., N. P. G. Bhavani, D. Kirubakaran, et al. "Fish disease detection using a novel feature extraction algorithm." In FIFTH INTERNATIONAL CONFERENCE ON APPLIED SCIENCES: ICAS2023. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0198179.

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Reports on the topic "Fish Disease Detection"

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Bercovier, Herve, and Ronald P. Hedrick. Diagnostic, eco-epidemiology and control of KHV, a new viral pathogen of koi and common carp. United States Department of Agriculture, 2007. http://dx.doi.org/10.32747/2007.7695593.bard.

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Original objectives and revisions-The proposed research included these original objectives: field validation of diagnostic tests (PCR), the development and evaluation of new sensitive tools (LC-PCR/TaqManPCR, antibody detection by ELISA) including their use to study the ecology and the epidemiology of KHV (virus distribution in the environment and native cyprinids) and the carrier status of fish exposed experimentally or naturally to KHV (sites of virus replication and potential persistence or latency). In the course of the study we completed the genome sequence of KHV and developed a DNA array to study the expression of KHV genes in different conditions. Background to the topics-Mass mortality of koi or common carp has been observed in Israel, USA, Europe and Asia. These outbreaks have reduced exports of koi from Israel and have created fear about production, import, and movements of koi and have raised concerns about potential impacts on native cyprinid populations in the U.S.A. Major conclusions-A suite of new diagnostic tools was developed that included 3 PCR assays for detection of KHV DNA in cell culture and fish tissues and an ELISA assay capable of detecting anti-KHV antibodies in the serum of koi and common carp. The TKPCR assay developed during the grant has become an internationally accepted gold standard for detection of viral DNA. Additionally, the ELISA developed for detecting serum anti-KHV antibodies is now in wide use as a major nonlethal screening tool for evaluating virus status of koi and common carp populations. Real time PCR assays have been able to detect viral DNA in the internal organs of survivors of natural and wild type vaccine exposures at 1 and 10³ genome equivalents at 7 months after exposure. In addition, vaccinated fish were able to transmit the virus to naive fish. Potential control utilizing hybrids of goldfish and common carp for production demonstrated they were considerably more resistant than pure common carp or koi to both KHV (CyHV-3). There was no evidence that goldfish or other tested endemic cyprinids species were susceptible to KHV. The complete genomic sequencing of 3 strains from Japan, the USA, and Israel revealed a 295 kbp genome containing a 22 kbp terminal direct repeat encoding clear gene homologs to other fish herpesviruses in the family Herpesviridae. The genome encodes156 unique protein-coding genes, eight of which are duplicated in the terminal repeat. Four to seven genes are fragmented and the loss of these genes may be associated with the high virulence of the virus. Viral gene expression was studies by a newly developed chip which has allowed verification of transcription of most all hypothetical genes (ORFs) as well as their kinetics. Implications, both scientific and agricultural- The results from this study have immediate application for the control and management of KHV. The proposal provides elements key to disease management with improved diagnostic tools. Studies on the ecology of the virus also provide insights into management of the virus at the farms that farmers will be able to apply immediately to reduce risks of infections. Lastly, critical issues that surround present procedures used to create “resistant fish” must be be resolved (e.g. carriers, risks, etc.). Currently stamping out may be effective in eradicating the disease. The emerging disease caused by KHV continues to spread. With the economic importance of koi and carp and the vast international movements of koi for the hobby, this disease has the potential for even further spread. The results from our studies form a critical component of a comprehensive program to curtail this emerging pathogen at the local, regional and international levels.

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Anderson, Donald M., Lorraine C. Backer, Keith Bouma-Gregson, et al. Harmful Algal Research & Response: A National Environmental Science Strategy (HARRNESS), 2024-2034. Woods Hole Oceanographic Institution, 2024. http://dx.doi.org/10.1575/1912/69773.

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Harmful and toxic algal blooms (HABs) are a well-established and severe threat to human health, economies, and marine and freshwater ecosystems on all coasts of the United States and its inland waters. HABs can comprise microalgae, cyanobacteria, and macroalgae (seaweeds). Their impacts, intensity, and geographic range have increased over past decades due to both human-induced and natural changes. In this report, HABs refers to both marine algal and freshwater cyanobacterial events. This Harmful Algal Research and Response: A National Environmental Science Strategy (HARRNESS) 2024-2034 plan builds on major accomplishments from past efforts, provides a state of the science update since the previous decadal HARRNESS plan (2005-2015), identifies key information gaps, and presents forward-thinking solutions. Major achievements on many fronts since the last HARRNESS are detailed in this report. They include improved understanding of bloom dynamics of large-scale regional HABs such as those of Pseudo-nitzschia on the west coast, Alexandrium on the east coast, Karenia brevis on the west Florida shelf, and Microcystis in Lake Erie, and advances in HAB sensor technology, allowing deployment on fixed and mobile platforms for long-term, continuous, remote HAB cell and toxin observations. New HABs and impacts have emerged. Freshwater HABs now occur in many inland waterways and their public health impacts through drinking and recreational water contamination have been characterized and new monitoring efforts have been initiated. Freshwater HAB toxins are finding their way into marine environments and contaminating seafood with unknown consequences. Blooms of Dinophysis spp., which can cause diarrhetic shellfish poisoning, have appeared around the US coast, but the causes are not understood. Similarly, blooms of fish- and shellfish-killing HABs are occurring in many regions and are especially threatening to aquaculture. The science, management, and decision-making necessary to manage the threat of HABs continue to involve a multidisciplinary group of scientists, managers, and agencies at various levels. The initial HARRNESS framework and the resulting National HAB Committee (NHC) have proven effective means to coordinate the academic, management, and stakeholder communities interested in national HAB issues and provide these entities with a collective voice, in part through this updated HARRNESS report. Congress and the Executive Branch have supported most of the advances achieved under HARRNESS (2005-2015) and continue to make HABs a priority. Congress has reauthorized the Harmful Algal Bloom and Hypoxia Research and Control Act (HABHRCA) multiple times and continues to authorize the National Oceanic and Atmospheric Administration (NOAA) to fund and conduct HAB research and response, has given new roles to the US Environmental Protection Agency (EPA), and required an Interagency Working Group on HABHRCA (IWG HABHRCA). These efforts have been instrumental in coordinating HAB responses by federal and state agencies. Initial appropriations for NOAA HAB research and response decreased after 2005, but have increased substantially in the last few years, leading to many advances in HAB management in marine coastal and Great Lakes regions. With no specific funding for HABs, the US EPA has provided funding to states through existing laws, such as the Clean Water Act, Safe Drinking Water Act, and to members of the Great Lakes Interagency Task Force through the Great Lakes Restoration Initiative, to assist states and tribes in addressing issues related to HAB toxins and hypoxia. The US EPA has also worked towards fulfilling its mandate by providing tools and resources to states, territories, and local governments to help manage HABs and cyanotoxins, to effectively communicate the risks of cyanotoxins and to assist public water systems and water managers to manage HABs. These tools and resources include documents to assist with adopting recommended recreational criteria and/or swimming advisories, recommendations for public water systems to choose to apply health advisories for cyanotoxins, risk communication templates, videos and toolkits, monitoring guidance, and drinking water treatment optimization documents. Beginning in 2018, Congress has directed the U.S. Army Corps of Engineers (USACE) to develop a HAB research initiative to deliver scalable HAB prevention, detection, and management technologies intended to reduce the frequency and severity of HAB impacts to our Nation’s freshwater resources. Since the initial HARRNESS report, other federal agencies have become increasingly engaged in addressing HABs, a trend likely to continue given the evolution of regulations(e.g., US EPA drinking water health advisories and recreational water quality criteria for two cyanotoxins), and new understanding of risks associated with freshwater HABs. The NSF/NIEHS Oceans and Human Health Program has contributed substantially to our understanding of HABs. The US Geological Survey, Centers for Disease Control and Prevention, and the National Aeronautics Space Administration also contribute to HAB-related activities. In the preparation of this report, input was sought early on from a wide range of stakeholders, including participants from academia, industry, and government. The aim of this interdisciplinary effort is to provide summary information that will guide future research and management of HABs and inform policy development at the agency and congressional levels. As a result of this information gathering effort, four major HAB focus/programmatic areas were identified: 1) Observing systems, modeling, and forecasting; 2) Detection and ecological impacts, including genetics and bloom ecology; 3) HAB management including prevention, control, and mitigation, and 4) Human dimensions, including public health, socio-economics, outreach, and education. Focus groups were tasked with addressing a) our current understanding based on advances since HARRNESS 2005-2015, b) identification of critical information gaps and opportunities, and c) proposed recommendations for the future. The vision statement for HARRNESS 2024-2034 has been updated, as follows: “Over the next decade, in the context of global climate change projections, HARRNESS will define the magnitude, scope, and diversity of the HAB problem in US marine, brackish and freshwaters; strengthen coordination among agencies, stakeholders, and partners; advance the development of effective research and management solutions; and build resilience to address the broad range of US HAB problems impacting vulnerable communities and ecosystems.” This will guide federal, state, local and tribal agencies and nations, researchers, industry, and other organizations over the next decade to collectively work to address HAB problems in the United States.

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