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 : Seasonal habitat shifts and purse seine dependence of mene maculata in the Taiwan strait: Early indicators of climate driven ecosystem change
Ipsita Biswas, National Taiwan Ocean University, Taiwan
Title : National action plan for sustainable and resilient fisheries aquaculture system in Pakistan
Nazia Sher, National Institute of Maritime Affairs, Pakistan
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 : Trends in fish value chain improvement in Rift Valley Lakes and Lake Tana, Ethiopia
Kidanie Misganaw Bezabih, University of Gondar, Ethiopia