The Challenge of Big Data and Machine Learning Optimizing Banana Yields in Ecuador

Ecuador, the world's leading exporter of bananas, heavily relies on traditional farming practices that contribute significantly to environmental degradation. This paper investigates using big data and machine learning to optimize banana yields in Ecuador, aiming to align economic benefits with ecological sustainability. We developed a predictive regression model to determine the optimal use of water and fertilizers, incorporating a comprehensive dataset from the Ecuadorian National Census. Despite achieving some predictive capability, the Bagged Trees model used in this study explained 7% of the variance in banana yields, confirming this paper's exploring nature. The study highlights the potential of integrating additional environmental, genetic, and soil variables and employing advanced machine learning techniques such as neural networks and boosted trees to enhance model accuracy. Future work should focus on developing interpretable models and user-friendly decision support systems to aid farmers in making data-driven decisions, ultimately fostering more sustainable and productive agricultural practices.