Smart Aeroponic Farming System: Using IoT with LCGM-Boost Regression Model for Monitoring and Predicting Lettuce Crop Yield

Abstract

Aeroponics is a popular soilless crop cultivation technology that integrates plant nutrition, physiology, and ecological control. It offers automated monitoring, protected cultivation, improved growth mechanisms, better yield and requires less maintenance. Here, to predict the crop yield, two systems are available: manual and automated. Manual systems often fail to produce better prediction results, leading to substantial crop losses whereas, the automated systems use machine intelligence for growth monitoring. This article proposes a lettuce crop growth monitoring-boost (LCGM-Boost) regression model for lettuce yield forecasting in aeroponic vertical farming system. This model is highly robust to outliers, produces better prediction results of 95.86% and lower error rates of 0.36 (MAE), 0.40 (MSE), and 0.63 (RMSE) than other machine learning models namely, support vector, random forest and XGBoost regressors. Hence, it is preferable for growth monitoring and yield prediction of the lettuce crop in the real-time aeroponics system.