Abu Dhabi Agriculture and Food Safety Authority (ADAFSA) in collaboration with The Abu Dhabi Digital Authority and Emirates ICT Innovation Center (EBTIC) have successfully developed a model for UAE Food and Animal Feed Import Prediction using the artificial intelligence and machine learning technologies, to build a future vision of the direction and value of food imports to the UAE, and analysis of the most imported strategic foodstuffs, their sources and quantities, in order to identify potential diversification opportunities for imports and reducing  waste.

The model was built through the development of an interactive pictorial dashboard by analyzing a series of historical data on the quantities and values of imports and re-exports of animal, agricultural and feed food products. The panel shows interactive information on countries from which food and feed are imported and the quantities and values imported with the aim of creating a machine learning model to predict the amount of imports of plant and animal food and animal feed, to the UAE and determining the size and cost of imports and the countries from which imports are imported, the model also analyzes food waste in the UAE, thereby identifying potential opportunities for re-export.

Aysha Al Naili Al Shamisi Statistics & Analysis Division Director at the Abu Dhabi Agriculture and Food Safety Authority, said that developing  a model  for predicting food imports and understanding the trends of  food and feed import helps to develop a clear picture  of future levels of food imports,  enable the development of policies, regulations and plans to ensure the continuity of the State's food supply, as well as to assist in the development of accurate response plans in times of disaster and crisis, or in the event of any food supply disturbances, by knowing the quantities currently imported  in the country.

Shamsi  explained that the development of  the model was based on data on the country's food imports from 2015 to 2020, where data on animal food imports were relied upon for products such as eggs, frozen chicken meat and fresh chicken meat, as well as agricultural products such as cucumber tomatoes, watermelons, feed and hay to understand  the level of dependence  in meeting the needs of consumers for each product and its country of origin, thereby determining  the nature of potential risks in terms  of supply, prices and opportunities to find alternatives in times of crisis and emergency or in the event of a lack of supply or high prices, noting that  the model provides Detailed information  on waste rates for each product within the supply  chain, which helps to  strengthen accounting mechanisms for food waste and create re-export opportunities.

Al Shamsi pointed out that the model allows the possibility of adding new updates to keep up with future requirements, noting that future updates and planned additions to the models developed include the addition of new food products to analyze their data with the same mechanism of analysis of product data currently available, in addition to setting additional criteria for the development of the performance of the import forecast model, such as seasonal changes of product data, and will develop a model to predict local production, to determine the amount and value of expected production of local food products, depending on the seasonal changes of products data with artificial intelligence and machine learning techniques.