To meet challenges such as crop disease, weed management, pest control, and irrigation, Agricultural Engineering Research Institute (AEnRI) sought to increase agricultural output in different regions all over Egypt.
QSIT implemented an AI and remote-sensing spatial analysis solution to establish a dynamic, seasonal digital inventory of crop patterns. It integrates satellite imagery, field observations, data feeds and geospatial AI (GeoAI) capabilities to support AEnRI in maximising crop yields from limited resources. QSIT has built AI models to detect agricultural field boundaries and classify patterns at the field level.
The detection of already planted crops enables more accurate yield estimation as well as better monitoring of crop production and distribution. It also enables better water consumption estimation and water-loss control, and it optimises water use.
Fadl Abdelhamid Hashem, executive director of Climate Change Information Centre, Agriculture Research Centre, Ministry of Agriculture and Land Reclamation, Egypt, said, “AI is being utilised for the first time in the agriculture sector in Egypt and implemented to manage farms in a strategic way. The project is just the first step, opening doors towards a series of other AI implementations aiming to support the farming industry in Egypt.”
Sherif Awad, CEO of QSIT, said, “The project extends to include the implementation of multiple practices in the field of precision agriculture, with a view to achieving optimised water consumption and enhanced crop yield and distribution. The bigger picture is to take this implementation from Egypt as a pilot country to the rest of Africa, supporting sustainable development goals in the continent.”