AIMC Topic: Aquaculture

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A machine learning-driven early warning system for cryptocaryoniasis in marine aquaculture.

Parasites & vectors
BACKGROUND: Disease outbreaks, particularly cryptocaryoniasis caused by the ciliate Cryptocaryon irritans, pose significant barriers to sustainable marine fish aquaculture, undermining productivity, profitability, and biosecurity. Despite its impact,...

Advanced machine learning models for accurate water quality classification and WQI prediction: Implications for aquatic disease risk management.

The Science of the total environment
Accurate classification of water quality and precise prediction of the Water Quality Index (WQI) are essential for safeguarding aquatic ecosystems and mitigating disease risks in aquaculture. This study systematically evaluates multiple machine learn...

Machine learning for genomic prediction of growth traits in aquaculture: a case study of the Australasian snapper (Chrysophrys auratus).

BMC bioinformatics
BACKGROUND: Chrysophrys auratus (family: Sparidae), commonly known as Australasian snapper, is a warm-water species being developed as a candidate for aquaculture in New Zealand. Genomic selection of elite snapper offers significant potential to acce...

Lightweight deep deterministic policy gradient for edge computing in recirculating aquaculture systems: real-time feeding control with reduced computational requirements.

Scientific reports
The deployment of advanced reinforcement learning algorithms in edge computing environments presents significant challenges for real-time aquaculture management, particularly in resource-constrained recirculating aquaculture systems (RAS). Building u...

Developing highly accurate machine learning models for optimizing water quality management decisions in tilapia aquaculture.

Scientific reports
The optimization of water quality management is crucial for the success and sustainability of tilapia aquaculture. This study presents a novel approach for developing a decision-support system by comparing various machine learning models to predict o...

Hybrid deep learning framework for real-time DO prediction in aquaculture.

Scientific reports
Dissolved oxygen (DO) is a vital parameter in regulating water quality and sustaining the health of aquatic organisms in aquaculture environments. Therefore, estimation and control of DO levels are essential in aquaculture operations. However, tradit...

Design and Synthesis of Magnolol Derivatives Using Integrated CNNs and Pharmacophore Approaches for Enhanced Parasiticidal Activity in Aquaculture.

Journal of agricultural and food chemistry
Aquaculture is a rapidly growing sector of global food production, playing a vital role in poverty alleviation, food security, and income generation. However, it faces substantial challenges, particularly due to infections caused by the protozoan , l...

Microbiome determinants of productivity in aquaculture of whiteleg shrimp.

Applied and environmental microbiology
UNLABELLED: Aquaculture holds immense promise for addressing the food needs of our growing global population. Yet, a quantitative understanding of the factors that control its efficiency and productivity has remained elusive. In this study, we addres...

Metagenomics studies in aquaculture systems: Big data analysis, bioinformatics, machine learning and quantum computing.

Computational biology and chemistry
The burgeoning field of aquaculture has become a pivotal contributor to global food security and economic growth, presently surpassing capture fisheries in aquatic animal production as evidenced by recent statistics. However, the dense fish populatio...

Application of automatic image analysis using a Deep Learning Neural Network for assessing the growth of green algae containing carotenoids - importance for environment, health and aquaculture.

Annals of agricultural and environmental medicine : AAEM
Using deep learning and neural networks enables us to greatly speed-up quantitative studies and provide a useful tool for analyzing microscopic images. Studies conducted on selected algae and sp. confirm the feasibility of using the deep learning n...