OBM-RFEcv: An adaptive ensemble model for monitoring key growth indicators of Gerbera using multi-spectral image fusion features

Abstract

This study aims to address the challenge of monitoring Plant Height (PH), SPAD, Leaf Area Index (LAI), and Above-Ground Biomass (AGB) in Gerbera under greenhouse cultivation conditions. We initially gathered multi-spectral images and corresponding ground truth data of these parameters at various growth stages using a low-altitude UAV. From the collected images, we derived five Vegetation Indices (VIs): NDVI, GNDVI, LCI, NDRE, and OSAVI, and extracted their textural features as fusion features. An adaptive ensemble model, OBM-RFEcv, was then developed by integrating six base models (Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost Regressor, and Support Vector Regressor) with Recursive Feature Elimination (RFE) to predict the key growth indicators. The results indicate that the OBM-RFEcv model outperforms the other models when using the fusion of the five VIs, particularly in the test dataset, where it achieved the highest accuracy for PH (NDVI), SPAD (GNDVI), LAI (GNDVI), and AGB (NDRE) with R2 values of 0.92, 0.90, 0.89, and 0.93, respectively. The root mean square error (RMSE) values were 0.04, 0.07, 0.08, and 0.07, respectively, showing improvements over the best individual model by 0.01, 0.03, 0.01, and 0.09 in R2, and reductions in RMSE by 0.01, 0.07, 0.08, and 0.03, respectively. These findings confirm that the OBM-RFEcv model, based on multi-spectral image fusion, effectively monitors key growth indicators in Gerbera, providing a non-invasive and precise method for greenhouse crop monitoring.