Recently, population growth and resource depletion have been matched by a growing demand for self-sustaining communities. Numerous studies promote sustainable solutions to the concerns of climate change and food scarcity. This study aims at creating an automated Economic Land Utilization Optimization Model (ELUOM) that identifies sustainable and cost-effective agricultural practices. Soil, water & climatic characteristics of over 400 crops are gathered in a relational database to build the model. Evolutionary algorithms are utilized to filter the database based on user input. Optimization process is then performed on all possible utilization plans of the filtered crops to maximize the 20-year return while minimizing water consumption. The model is verified on a case study in Giza, Egypt where it shows the potential of increasing the return/m³ of water by 370% versus current practices. This research also studies the application of ELOUM on a vacant plot in the American university in Cairo, Egypt.

The challenges of climate change, water and food scarcity have created the need for planning tools that assist in the agricultural decision-making process. This study aims to propose a multi-objective automated optimization model that uses an embedded comprehensive database to maximize the economic return whilst ensuring minimal water consumption. The suggested model capitalizes on big data in farming and greenhouses to filter all viable scenarios for a given soil, climate properties, and water availability. Evolutionary and genetic algorithms are employed to assess the long-term production, profitability, and water consumption of the scenarios. This study describes how the automated model operates through applying it to a case study to optimize the use of a land plot in Giza, Egypt, for farming purposes. The research opens the door for further application of the proposed model in different contexts both regionally and internationally, thus playing a vital role in the water and food nexus.