Prediction of THC Levels in Cannabis Species Using Machine Learning Methods

In this study, machine learning algorithms were utilized to predict THC (tetrahydrocannabinol) levels based on the terpene profiles of hybrid, sativa, and indica cannabis species. The cannabis plant has gained popularity due to its uses in medical and industrial fields; therefore, understanding the characteristics of these species is of critical importance. In the study, the terpene components of cannabis species were identified as one of the significant factors affecting THC levels. The dataset includes terpene components of different cannabis species and the THC levels of these species. Models were developed using classical and deep learning algorithms, such as regression analysis, k-NN (k-Nearest Neighbors), SVM (Support Vector Machines), and ANN (Artificial Neural Networks). The performance metrics of each algorithm were evaluated. The results, with accuracy rates reaching 94%, indicate that terpene components play a significant role in predicting THC levels. This study demonstrates the applicability of machine learning methods in analyzing cannabis species and establishes a foundation for better understanding the relationships between terpene components and THC levels