Work

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

Year: 2025

Type: article

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.

Source: Sustainability

DOI: 10.3390/su172210098