Designing an Automated Multi-Objective Optimization Model for Integrated and Sustainable Farming

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.