AI-Powered Sustainable Decision Support System for Indoor Lettuce Farming

About the Project

Indoor hydroponic lettuce cultivation requires continual oversight, yet many instruments merely display raw sensor data, causing novice growers to find it difficult to determine necessary adjustments and potentially wasting water and nutrients. This initiative is an AI-driven decision support system for indoor Batavia lettuce cultivated using the Nutrient Film Technique. Three Random Forest models, developed using 39 actual daily records from the HydroGrowNet dataset, forecast water efficiency, nutrient efficiency, and an overall sustainability score. A five-page Streamlit dashboard offers straightforward recommendations and a what-if simulation that forecasting the impact of adjustments before implementation. The sustainability score model achieved R² = 0.833 on the test set and 0.854 with 5-fold cross-validation. The outcomes are initial due to the limited dataset. The system endorses SDG 12 and SDG 13.