Predictive Models for the Management of the Pollination and Germination Process of Orchids Seeds

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. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back