Machine Learning Techniques to Predict Rain Tomorrow for Automated Plant Watering System

This research analyzes weather classification to improve crop watering automation. Artificial intelligence algorithms such as Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression are implemented to perform weather classification based on rainfall event data. A dataset of 63,677 data was used, and the K-fold cross-validation method was used to select the best model from the three algorithms based on the value of f1, accuracy, precision, and recall. The results showed that the Support Vector Machine algorithm with Linear kernel produced the highest accuracy of 71.99% at 10-fold. Naïve Bayes algorithm with the Bernoulli model produced the highest accuracy of 76.10% at 10-fold. Meanwhile, Logistic Regression produced the highest accuracy of 72.45% at a 10-fold. This research provides an overview of how artificial intelligence algorithms can be applied to classify weather in improving plant watering automation. It provides a basis for effective and accurate decision-making in plant care.