Analytical techniques are crucial in aquaculture for assessing water quality, fish health, and feed efficiency. Data analysis helps identify trends, forecast production rates, and assess environmental impact. Techniques such as spectrometry, chromatography, and PCR testing are used to detect pathogens and contaminants in water. Additionally, sensors provide real-time data on temperature, pH, and oxygen levels, which are essential for maintaining optimal growing conditions. Advanced data analysis tools, including machine learning, analyze large datasets to identify patterns that improve efficiency and predict disease outbreaks. Data-driven insights aid farmers in making informed decisions, optimizing resources, and maintaining sustainable aquaculture systems that benefit both producers and the environment.
Title : The horizontal integration of a Shellfish farm in a broader business model
Perry Raso, Matunuck Oyster Farm, United States
Title : Role of artificial intelligence and remote sensing in remediation of aquatic pollution and development of Numerical Oceanic Climate Prediction Models (NOCPM)
Virendra Goswami, Indian Institute of Technology (IIT), India
Title : Aquaculture education challenges: Integrating sustainable practices into schools
J L Giovanna Hesley, CropKing, Inc. Curriculum Development, United States
Title : Can we farm eelgrass as a high protein sustainable marine grain for aquaculture?
Timothy C Visel, Retired Aquaculture Educator, United States
Title : Impact of antibiotic application on microbiome present in Catfish gut and water
Luxin Wang, University of California Davis, United States
Title : Smart sensors and real-time monitoring: Revolutionizing aquatic pollution management
Vidya Padmakumar, EcoDiversity Lab, Canada