Big Data Platform for Smart Aquaculture
Step 1: Define the scope of the Big Data Platform
This step establishes the boundaries of the platform and determines which aquaculture systems, production environments, and value chain components will be included in data collection, monitoring, analytics, and decision-support activities.
A clear scope definition is essential because the technical requirements, data needs, governance arrangements, monitoring indicators, and analytical approaches of a Big Data Platform vary significantly depending on the species, farming systems, geographical context, and value chain functions covered by the platform.
The scope and coverage of the platform should be defined across three core dimensions:
Species and area coverage
Defines the biological and geographical boundaries of the platform. The platform should specify aquaculture species or sectors included, or integrated multi-species systems. It should also determine its geographical extent, which may range from individual farming zones, production clusters and watersheds to coastal regions, provinces or national territories, depending on national priorities and available resources. Environmental conditions, disease risks, regulatory frameworks, and monitoring requirements differ substantially between species and geographical contexts. For example, coastal shrimp farming systems may require intensive monitoring of salinity, dissolved oxygen, and disease outbreaks, while freshwater cage culture systems may prioritize hydrodynamic conditions, temperature profiles, and carrying capacity assessments.
Production system coverage
Defines the types of aquaculture systems and farming intensities included in the platform. Aquaculture production systems vary widely in their level of intensification, infrastructure, management practices, and degree of digitalization. The platform may focus on extensive, semi-intensive, or intensive production systems, and may include ponds, cages, hatcheries, recirculating aquaculture systems, biofloc systems, or integrated farming systems. Low-intensity systems may rely primarily on manual reporting, mobile applications, and periodic environmental monitoring, while intensive systems may integrate continuous IoT sensor networks, automated feeding systems, real-time alerts, and AI-supported forecasting models. Defining production system coverage helps determine the level of technological complexity and operational support required by the platform.
Value chain coverage
Defines the actors and operational stages included in the platform. Some platforms may focus primarily on farm-level production monitoring, while others may integrate broader functions to support traceability, food safety, logistics, certification and national biosecurity systems. Depending on the objectives, the platform may include hatcheries, feed suppliers, grow-out farms, diagnostic laboratories, processing facilities, transport operators, exporters, extension services, financial institutions, certification bodies and regulatory agencies. Defining value chain coverage is important because many smart aquaculture services depend on data sharing and coordination across multiple actors. Disease surveillance, production forecasting, traceability and food safety monitoring all require integration beyond the farm level.
Use the checklist below to verify that all key elements of this step have been addressed or refer to the overall checklist to ensure all recommended actions are planned or completed: