Smarter Feeding in Aquaculture: Advancing Precision Feeding and Better Farm Control

Syed Hamza Syed Saidoddin; Nikhil Madhavrao Salunke

Smarter Feeding in Aquaculture: Advancing Precision Feeding and Better Farm Control

Author(s)	Syed Hamza Syed Saidoddin, Nikhil Madhavrao Salunke
Country	India
Abstract	Feed is one of the largest operational inputs in aquaculture and has a direct influence on production cost, growth performance, feed conversion efficiency, water quality and environmental sustainability. Conventional feeding practices are frequently based on predetermined feeding rates, fixed schedules and farmer experience, which may not adequately account for changes in fish biomass, appetite, behaviour and environmental conditions. Smarter feeding represents a transition towards precision, responsive and data-driven feed management. It combines automatic feeders with real-time information obtained from water-quality sensors, biomass estimation, machine vision, acoustic technologies, Internet of Things (IoT) platforms and artificial intelligence (AI). These technologies enable feeding decisions to be adjusted according to the estimated nutritional demand and feeding response of cultured animals. Machine-vision systems can monitor fish activity, feeding behaviour, size and, in some applications, uneaten feed, while environmental sensors provide information on temperature, dissolved oxygen, pH, salinity and other variables relevant to feeding performance. Integration of these data streams can facilitate dynamic ration adjustment, demand-based feeding and closed-loop control. Such approaches have the potential to reduce feed wastage, improve feed utilization, stabilize water quality and reduce labour requirements. However, practical adoption is constrained by equipment cost, sensor reliability, biofouling, turbidity, species-specific behaviour, data quality, algorithm generalization and the need for farm-level validation. Smarter feeding should therefore be viewed as a decision-support and precision-management framework rather than simply as automated feed dispensing. Future systems are expected to integrate multimodal sensing, AI-based prediction, digital farm records and closed-loop control to achieve increasingly precise and sustainable aquaculture production.
Keywords	Aquaculture; precision feeding; smart feeding; automated feeder; artificial intelligence; machine vision; Internet of Things; feeding behaviour; feed conversion ratio; water quality; precision aquaculture; farm management
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-09-26

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Smarter Feeding in Aquaculture: Advancing Precision Feeding and Better Farm Control

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