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In this work a database from a chili pepper crop is used for training five classical machine learning models: Linear Regression (LR), Random Forest (RF), AdaBoost (ADA), Gradient Boosting (GDB), and K-Nearest Neighbors Regressor (KNN). The models are intended to predict soil humidity for the next day. The prediction of soil humidity is used to calculate the amount of water to apply to the crop. In the evaluation phase, Random Forest shows the best performance. Finally, in the deployment phase, AdaBoost shows the best performance.

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Exploration of Machine Learning Models in a Chili Pepper Irrigation System

  • 会议论文

  • 发表日期：

    2026年

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  • Andrés Bocanegra,
  • Tania Vallejos,
  • Nadia Rosero

摘要

In this work a database from a chili pepper crop is used for training five classical machine learning models: Linear Regression (LR), Random Forest (RF), AdaBoost (ADA), Gradient Boosting (GDB), and K-Nearest Neighbors Regressor (KNN). The models are intended to predict soil humidity for the next day. The prediction of soil humidity is used to calculate the amount of water to apply to the crop. In the evaluation phase, Random Forest shows the best performance. Finally, in the deployment phase, AdaBoost shows the best performance.