Machine Learning Model for Predicting the Suitability of Cultivating Alternative Crops in Lower Northern Thailand

Abstract

Intensive rice cultivation presents significant environmental and economic challenges. While crop diversification offers potential benefits for agricultural sustainability and financial resilience, farmers face considerable uncertainty when transitioning to alternative crops. This study assessed the prediction efficacy of machine learning (ML) models in identifying suitable crops for cultivation in a specific geographical area considering various factors influencing agricultural viability. Through comprehensive experimentation, a decision tree model, an artificial neural network (ANN), and a Naïve Bayes model were used for predictions and rigorously evaluated for various crops, including rubber, coconut, longan, durian, rambutan, and mangosteen. Various hyperparameter configurations were tested, and multiple evaluation indicators were employed to assess the prediction performance of the models. The results consistently demonstrated the superiority of the decision tree model, which exhibited high accuracy, precision, recall, and F-measure across most crops. Its ability to capture intricate patterns and relationships between crop attributes and suitability levels underscores its value as a decision-support tool in agriculture. While the ANN model performed well for coconut, its effectiveness varied across the other crops, highlighting the need for tailored model selection. This study provides valuable insights into the application of ML in agricultural decision-making processes, suggesting potential avenues for future optimization and enhancement of prediction accuracy.