  Analyzing and detecting cannabis seed variants is crucial for the agriculture
industry. It enables precision breeding, allowing cultivators to selectively
enhance desirable traits. Accurate identification of seed variants also ensures
regulatory compliance, facilitating the cultivation of specific cannabis
strains with defined characteristics, ultimately improving agricultural
productivity and meeting diverse market demands. This paper presents a study on
cannabis seed variant detection by employing a state-of-the-art object
detection model Faster R-CNN. This study implemented the model on a locally
sourced cannabis seed dataset in Thailand, comprising 17 distinct classes. We
evaluate six Faster R-CNN models by comparing performance on various metrics
and achieving a mAP score of 94.08\% and an F1 score of 95.66\%. This paper
presents the first known application of deep neural network object detection
models to the novel task of visually identifying cannabis seed types.
