Enhancing Sugar Beet Productivity and Quality Using Bio-Stimulants and Artificial Intelligence Techniques in Modern Irrigation Systems in New Lands

Climate change adversely affects agricultural land, water resources, crop productivity, and food security. In Egypt, sugar beet production has declined, increasing the country's reliance on imports. The current study aimed to enhance sugar beet productivity and quality through the application of a biological stimulant (CMS) and the integration of artificial intelligence (AI) techniques for optimizing irrigation management. Two field trials were conducted at the Experimental Station of the National Research Centre, Nubaria, Egypt, during the 2019/2020 and 2020/2021 winter seasons. CMS was applied at various concentrations (0.0, 5.0, 10.0, 15.0 g/L) as a foliar spray at 45 and 60 days after sowing.AI algorithms were employed to analyze real-time field data, including soil moisture, weather patterns, and crop growth parameters, enabling the development of a smart irrigation scheduling model. The use of AI-driven decision support systems enhanced irrigation water use efficiency and reduced stress on crops by adjusting irrigation timings based on predictive analytics.Results indicated that CMS significantly improved sugar beet growth and yield characteristics in both drip and sprinkler irrigation systems, with the highest yield and quality observed at the 15 g/L treatment, particularly under drip irrigation. AI-assisted irrigation scheduling contributed to increased irrigation efficiency, improved plant performance, and resource conservation. Notably, drip irrigation was superior in enhancing TSS%, sucrose%, and purity%, while some vegetative growth parameters were higher under sprinkler irrigation.The integration of biological stimulants with AI-based irrigation scheduling presents a promising approach to improving sugar beet productivity and sustainability under climate stress conditions.