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 : Variations in nutritional and bioactive properties of north atlantic sea cucumber (cucumaria frondosa): role of seasonality, location, and processing
Amit Das, Memorial University of Newfoundland, Canada
Title : A preliminary investigation into the possibility of domestication of solafunmi (sierrathrissa leonensis) as an aquarium fish for ornamental purpose
Olayimika, Federal University of Technology, Niger
Title : Relationship between shapes and glass thicknesses on water holding capacity of 60 litres aquaria
Olayimika, Federal University of Technology, Niger
Title : Climate change adaptation among fishers in the gulf of kutch: experiences and insights
Monika Makwana , Indian Institute of Technology, India
Title : Site suitability analysis for sea cucumber mariculture in the coastal area of Bangladesh
Muhammad Mizanur Rahman, Shahjalal University of Science and Technology, Bangladesh
Title : Sustainable fisheries management through community based monitoring of iuu fishing along the sindh coast, arabian sea in north indian ocean, Pakistan
Muhammad Naeem Khan, University of the Punjab, Pakistan