Lettuce Yield Prediction: ElasticNet Regression Model (ElNetRM) for Indoor Aeroponic Vertical Farming System

Abstract

Indoor aeroponic vertical farming systems have revolutionized agriculture by allowing efficient use of space and resources, eliminating the need for soil. These systems improve crop productivity and growth rates. However, accurately predicting lettuce yield in aeroponic environments remains a complex task due to the intricate interactions between environmental, nutrient, and growth parameters. This work aims to address these issues by integrating advanced sensor technologies with ElasticNet Regression Model (ElNetRM) for its hybrid L1 and L2 regularization capabilities, handling multicollinearity and feature selection problems effectively in order to develop a reliable yield prediction framework. The predictive results showcases that the ElNetRM model forecasts lettuce yield with high accuracy of 92% and less error score (RMSE) of 2.28 using a comprehensive dataset from a sensor-equipped indoor aeroponic system. Also, the results demonstrate the superior predictive power of ElNetRM in capturing complex variable relationships, enhancing yield prediction reliability.