Self-pollinated cannabis seeds lead to less variation in shape: a technological approach of potential commercial interest

Our research demonstrates that 2D geometric morphometrics can effectively distinguish and trace feminized and self-pollinated cannabis seeds. These seeds exhibit the least morphological variation, enabling accurate identification and providing a reliable foundation for practical applications. The Random Forest classifier's high performance confirms the effectiveness of using morphological traits for seed discrimination. These results open the door for advanced machine-learning techniques aimed to improve scalability and automation.