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 : Application of Artificial Intelligence and NISAR satellite to study the air sea CO2 exchange and aquatic toxicology to develop ‘Aquatic Pollution Remediation Technologies’(PART)
Virendra Kumar Goswami, Indian Institute of Technology, India
Title : Conditionally pathogenic microparasites (Microsporidia and Myxosporea) of mullet fish potential objects of mariculture in the Black and Azov Seas
Violetta M Yurakhno, A. O. Kovalevsky Institute of Biology of the Southern Seas of Russian Academy of Sciences, Russian Federation
Title : New approaches to assessing and managing the multispecies fishery in the Gulf of Thailand
Pavarot Noranarttragoon, Department of Fisheries, Thailand
Title : Integrating art, science and rural development: The multifaced role of aquarium keeping
T V Anna Mercy, Kerala University of Fisheries and Ocean Studies, India
Title : Seaweed aquaculture policy gap analyses in Indonesia, Kenya, and Tanzania
Megan Considine, The Nature Conservancy, Puerto Rico
Title : Utilizing art to enhance learning STEM subjects required for aquaculture
J L Giovanna Hesley, Education Emerita, CropKing Inc., United States