Implementación de Modelos Predictivos para la Gestión del Proceso de Polinización y Germinación de Semillas en la Empresa Ecuagenera

Abstract

The study develops and implements a predictive system based on machine learning techniques for managing orchid pollination and germination processes at the Ecuadorian company Ecuagenera. These processes exhibit high biological and seasonal variability, which has historically forced the company to rely on manual records and empirical experience, generating operational uncertainty and production losses. The research adopts a quantitative and experimental approach, using historical data collected in the laboratory and applying feature engineering techniques to incorporate temporal, biological, and operational variables. Different supervised learning algorithms were evaluated, including Random Forest, XGBoost, and LightGBM, selecting the models with the best predictive performance according to metrics such as MAE, RMSE, and coefficient of determination (R²). The results show that Random Forest offers a high level of accuracy in predicting germination time, while XGBoost performs better in predicting the times associated with the pollination and germination process.