Prediction of coffee plant growth using machine learning based on climatic and agronomic variables
DOI:
https://doi.org/10.18779/cyt.v19i2.1149Keywords:
coffee, algorithm, gradient boosting, agriculture, growth prediction, agroclimatic factorsAbstract
Coffee cultivation in subtropical regions faces increasing challenges due to climate variability and the need for more precise agronomic decision-making. Considering this, machine learning techniques are emerging as a crucial tool to support agricultural management.This study presents the development and integration of a gradient boosting model to predict plant growth in terms of height and stem diameter for coffee varieties Manabí 01 and Sarchimor, using climatic and agronomic variables collected through sensors installed in the Sacha Wiwa sector, La Maná.The model, with a coefficient of determination of R² = 0.554 for height and R² = 0.535 for diameter, showed adequate performance in predicting coffee plant growth based on climatic and agronomic variables. Although these results can be improved through algorithm optimization and the inclusion of larger datasets, they demonstrate the potential of machine learning as a tool for agronomic decision-making.As a practical application, the model was later integrated into a mobile application to enable real-time predictions and facilitate field monitoring of plant development. Although some margins of error were observed due to the complexity of the agricultural environment, the results showed strong potential for improvement through the use of more robust datasets and algorithm optimization.Ultimately, this research aims to bring machine learning closer to the field, strengthening agronomic decision-making based on concrete data.
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