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

Danilo Yánez-Cajo (), Gregorio Vásconez-Montúfar, Ronald Oswaldo Villamar-Torres, Luis Godoy-Montiel, Seyed Mehdi Jazayeri, Fernando Pérez-Porras and Francisco Mesas-Carrascosa
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Danilo Yánez-Cajo: Facultad de Ciencias Pecuarias y Biológicas, Universidad Técnica Estatal de Quevedo, Campus La María, Quevedo P.O. Box EC120550, Ecuador
Gregorio Vásconez-Montúfar: Facultad de Ciencias Pecuarias y Biológicas, Universidad Técnica Estatal de Quevedo, Campus La María, Quevedo P.O. Box EC120550, Ecuador
Ronald Oswaldo Villamar-Torres: Facultad de Ciencias Pecuarias y Biológicas, Universidad Técnica Estatal de Quevedo, Campus La María, Quevedo P.O. Box EC120550, Ecuador
Luis Godoy-Montiel: Facultad de Ciencias Pecuarias y Biológicas, Universidad Técnica Estatal de Quevedo, Campus La María, Quevedo P.O. Box EC120550, Ecuador
Seyed Mehdi Jazayeri: ERIT PSII—Plant Science, Interactions and Innovation, Institut Agrosciences, Environnement, et Santé (AgES), Avignon Université, 84029 Avignon, France
Fernando Pérez-Porras: Department of Graphic Engineering and Geomatics, University of Cordoba, Campus de Rabanales, 14014 Córdoba, Spain
Francisco Mesas-Carrascosa: Department of Graphic Engineering and Geomatics, University of Cordoba, Campus de Rabanales, 14014 Córdoba, Spain

Sustainability, 2025, vol. 17, issue 22, 1-20

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 R 2 = 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.

Keywords: banana; UAV; Random Forest; Ecuadorian littoral region; soil properties; yield (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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