AI-Powered Cannabis Seed Detection and Classification: A Machine Learning Approach for Precision Agriculture

Cannabis consumption and related research have accelerated globally as a result of recent legalization policy trends. Cannabis poses unique challenges in obtaining proper classification and standardization as it is the second most widely used psychoactive substance in the world. Accurate cannabis seed classification is crucial for precision agriculture since it has a direct impact on industrial regulation, genetic integrity, and crop productivity. This study uses a curated dataset of 3,434 seed images from 17 different varieties to investigate a machine-learning-based method for cannabis seed detection and classification. A Support Vector Machine (SVM) classifier was employed in this study for the classification of grayscale image features extracted after resizing and preprocessing the images. The SVM classifier achieved an impressive classification accuracy of 93.98% across certain varieties—such as AK47_photo, Hang Kra Rog KU, and Thaistick Foi Thong —exhibiting perfect classification performance. The macro-average F1-score of 0.93 and the weighted-average F1-score of 0.94 both show that the classification is strong, balanced, and reliable across all categories.These results confirm the usefulness of SVM-based grayscale feature modeling for automating cannabis seed classification and improving precision agriculture through efficient, scalable, and data-driven solutions. The study also points out areas for future research, like making the dataset more diverse and using advanced feature extraction and deep learning methods to make the model work better.