Banana Yield Prediction Using Random Forest, Integrating Phenology Data, Soil Properties, Spectral Technology, and UAV Imagery in the Ecuadorian Littoral Region

Abstract

Accurate banana yield prediction is essential for optimizing agricultural management and ensuring food security in tropical regions, yet traditional estimation methods remain labor-intensive and error prone. This study developed a predictive model for banana yield in Buena Fé, Ecuador, using Random Forest integrated with phenological data, soil properties, spectral technology, and UAV imagery. Data were collected from a 75.2 ha banana farm divided into 26 lots, combining multispectral drone imagery, soil physicochemical analyses, and banana agronomic measurements (height, diameter, bunch weight). A rigorous variable selection process identified six key predictors: NDVI, plant height, plant diameter, soil nitrogen, porosity, and slope. Three machine learning algorithms were compared through 5-fold cross-validation with systematic hyperparameter optimization. Random Forest demonstrated superior performance, with R2 = 0.956 and RMSE=1164.9 kg ha−1, representing only CV = 2.79% of mean production. NDVI emerged as the most influential predictor (importance = 0.212), followed by slope (0.184) and plant structural variables. Local sensitivity analysis revealed distinct response patterns between low- and high-production scenarios, with plant diameter showing the greatest impact (+74.9 boxes ha−1) under limiting conditions, while NDVI dominated (−140.4 boxes ha−1) under optimal conditions. The model provides a robust tool for precision agriculture applications in tropical banana production systems.