The proposal aims to apply machine learning algorithms to develop robust classification, estimation, and prediction models for lettuce crops thus enhancing production efficiency and food safety while minimizing the environmental impact. The project has four specific objectives: (1) To develop mathematical models to assess plant nutritional and water status from spectral data; (2) To irrigate lettuce plants using egg-washing wastewater as sustainable water and natural fertilizer sources; (3) To investigate the challenges and opportunities of using egg-washing wastewater for lettuce irrigation; (4) To analyze and model the spatio-temporal dynamics of mineral element composition in lettuce leaves during plant development. 

The project aims to implement a cohesive methodology and strategy to assess the societal implications of introducing and easing the acceptance of clean energy tractors by engaging all potential interested parties (producers, engineers, business and industry representatives, educators, policymakers, and consumers) from the beginning of the innovation processes while considering social, cultural, economic, legal, educational, and environmental aspects. 

The project focuses on automating drip irrigation systems for lettuce production using soil sensors and machine learning to conserve water and nutrients while maintaining plant health. Water and nutrient balances are quantified to enhance conservation in controlled environments. Energy efficiency is addressed through innovative solar modules for greenhouses, maximizing solar radiation, generating electricity, and boosting crop yields. Additionally, the team conducts outreach and educational activities, integrating these advancements into coursework and training programs for students.