feeding or timing to administer it, resulting in wastage and water contamination that impacts production. We present “IntelliFeed,” a digital twin-based multi-modal AI platform that leverages real-time sensor fusion, edge reinforcement learning and virtual pond simulations to automate feeding. Preliminary simulations indicate that the amount of wasted feed can be cut in half and a better grown ratio feed is achieved. IntelliFeed provides a scalable and cost-effective route for profitable and sustainable Blue Transformation in Indian aquaculture.
Aquaculture is the backbone of rural livelihood in India, but feeding inefficiency remains a primary cause of financial loss and environmental pressure. Feed represents >60–70% of input costs and lack of responsive, inexpensive automation has led to 30–40% feed wastage and chronically unhealthy ponds. Even though automatic feeder systems based on simple internet-of-things (IoT) schemes do exist, most of them are too costly, closed source or not tailored enough to the existing local condition. To achieve India’s "Blue Growth" a new generation of intelligent feeding solutions, both improving the environmental footprint and increasing growth and efficiency is urgently required.
Existing aquaculture automation R&D, as well as commercial products are predominantly: Static or timer based feeders; Simple IoT-devices; Vendor lock-in hardware platform solutions. Few recent works use low-resolution sensors or cloud analytics for estimating appetite, but there is a delay in the response and little adaptability. Reinforcement learning (RL) offers promising avenues for dynamic scheduling in both agriculture and manufacturing, while digital twins are becoming powerful enablers of safe, low-cost testing and optimization. Yet, most of the existing Indian deployments do not adequately exploit multimodal sensor integration, edge analytics or sim-based RL training—constraining their effectiveness and accessibility for edgy farmers.
Proposed Work
Early digital twin–based RL studies 16 show that IntelliFeed is able to reduce wastage and increase yield when compared to timer-based and simple vendor IoT feeders. Their simulated deployment based on Andhra Pradesh pond parameters brought the water quality metrics into equilibrium and resulted in a clear reduction in FCR that could sustain pond health while reducing input costs. They are now in the process of scheduling full-scale field validation with small shrimp and fish farms within Taiwan to compare IntelliFeed performance-to-cost ratios against existing industry feeds.
IntelliFeed is the first Indian use case of a digital twin-centred reinforcement learning platform for aquaculture feed management. The strategy ensures remarkable economic and environmental benefits resulted from less waste, improved FCR and better management of ponds. Through providing farmers with affordable, flexible and data-driven solutions, IntelliFeed allows mass expansion of sustainable aquaculture and fits national and global Blue Transformation policies agendas. The next step will be full farmer validation, scalability techniques to make the tool more robust and integration with Government digital agriculture initiatives.