Hydroponic using Deep Water Culture for Lettuce Farming using Random Forest Compared with Decision Tree Algorithm

This research aims to optimize lettuce cultivation in a Deep Water Culture (DWC) hydroponic system by leveraging machine learning techniques. By analyzing various parameters such as temperature, pH, and nutrient concentration, the study seeks to identify optimal conditions for maximizing lettuce growth and yield. A Random Forest algorithm is employed to predict optimal parameter settings and compare its performance with a Decision Tree algorithm. The Random Forest’s ensemble approach is expected to enhance prediction accuracy and robustness. By optimizing hydroponic parameters through machine learning, this research contributes to sustainable and efficient agriculture, reducing resource consumption and maximizing crop yield.