UAE-built AI feeder tracks and feeds wildlife automatically

Dub Dev Technology’s Mghzlan Smart Feeder uses computer vision and IoT to monitor species, manage feed quantities, and cut human disturbance at conservation sites

Staff Writer
UAE smart feeder processes 15 million images to monitor wildlife feeding
Image: Supplied

Article summary

AI Generated

A Dubai company has built an AI-powered wildlife feeder that identifies species, monitors consumption and adjusts feed quantities remotely. Dub Dev Technology's Mghzlan system has processed over 15 million images and dispensed more than 50 tonnes of feed across deployments in three countries.

Key points

  • System identifies animals with 80–97% confidence using computer vision
  • More than 50 tonnes of feed dispensed across three-plus countries
  • Deployed at Al Marmoom Desert Conservation Reserve in Dubai

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A UAE company has developed an AI-powered wildlife feeder that identifies animal species, tracks population numbers and consumption, and adjusts feed quantities automatically based on real-time conditions.

The Mghzlan Smart Feeder, built by Dubai-based Dub Dev Technology, combines computer vision, IoT sensors and solar power to enable remote management of wildlife feeding operations.

The system has processed more than 15 million images in a year, dispensed more than 50 tonnes of feed, and is deployed across more than three countries, including at Al Marmoom Desert Conservation Reserve in Dubai.

When animals approach a unit, day-and-night cameras capture their species and numbers, log arrival times, and feed that data into a central platform that adjusts dispensing quantities accordingly.

The system also monitors remaining stock, ambient temperature and humidity, and sends operators alerts when replenishment is needed or when people or vehicles are detected near active sites.

Ali Khalfan Al Gaz Al Falasi, Director of Dub Dev Technology, told the Emirates News Agency (WAM) that the device “automatically dispenses calculated quantities of feed at scheduled times, reducing the need for daily human presence and limiting unnecessary disturbance to wildlife.” He said the entire AI stack, including the species-recognition algorithms, data platform and control dashboard, was developed in the UAE.

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Field tests recorded animal classification confidence levels of between 80 and 97 percent across a range of species including gazelles, oryx, antelopes and birds, including in crowded or low-light conditions. A bird-specific version of the feeder is also available.

The company says the platform’s ability to consolidate wildlife, feed and environmental data in one place is designed to support more sustainable habitat management while reducing operational costs tied to on-site staffing.

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