Revolutionising Sri Lankan shrimp farms with real-time water quality monitoring, smart AI feeding, disease detection, and an intelligent assistant.
Our research addresses four critical domains in modern aquaculture, each powered by cutting-edge artificial intelligence methodologies.
pH, oxygen, ammonia, temperature—24/7 real-time monitoring with instant alerts.
Learn moreAI-powered feeding reduces waste by up to 50% and optimises schedules for growth.
Learn moreEarly AI detection of diseases and pathogens—95% accuracy before visible symptoms.
Learn moreSri Lanka's shrimp farming industry faces significant challenges including unpredictable water quality fluctuations, late disease detection leading to massive farm losses, suboptimal feeding practices causing environmental degradation, and lack of data-driven growth management tools. Traditional methods are reactive rather than predictive, resulting in economic losses exceeding $3 billion annually in the global aquaculture industry.
Key academic milestones and deliverables throughout our research journey.
Initial proposal presentation including research problem identification, literature review, and proposed AI-driven solution architecture.
Demonstration of 50% project completion — data collection, initial model training, and prototype system architecture.
Demonstration of 90% project completion — refined models, system integration, and preliminary testing results.
Publication of research findings in a peer-reviewed conference or journal in the fields of AI and aquaculture.
Complete project demonstration, viva voce examination, and final evaluation by academic panel and industry experts.
Access all project-related academic documents, presentations, and publications.
Initial project topic assessment form submitted during the project registration phase.
Comprehensive project charter outlining scope, objectives, deliverables, and timeline.
Detailed research proposal with literature review, methodology, and expected outcomes.
First status document reflecting 50% progress — containing data analysis and initial results.
Second status document with 90% completion — model evaluations and system integration status.
Published research paper detailing novel contributions, experiments, and findings.
Complete academic final report covering all aspects of the research and implementation.
Viva voce presentation slides for the final academic evaluation and defense.
A carefully selected combination of cutting-edge tools and frameworks powering the AquaNext platform.
We are a dedicated team of undergraduate students from SLIIT Malabe, specialising in Information Technology and driven to modernise aquaculture with real-time monitoring, AI-powered decision-making, and smart automation.
To transform shrimp farming in Sri Lanka through advanced AI technology and sustainable innovation.
Our diverse skill sets—spanning machine learning, IoT, full-stack development, and UI/UX—fuel our shared vision for profitable, sustainable, and efficient shrimp farms across Sri Lanka.
The principles that guide every line of code and every design decision we make.
Pushing the frontiers of AI and IoT to solve real aquaculture challenges.
Building solutions that protect marine ecosystems for future generations.
Delivering measurable results for farmers: higher yields, lower costs, safer harvests.
Working alongside farmers, researchers, and industry to co-create real solutions.
Have questions about our research, datasets, or interested in collaboration? We'd love to hear from you.
aquanext@my.sliit.lk
Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
Faculty of Computing