Cannabis Seed Variant Detection Using Faster R-CNN

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 utilizing Faster R-CNN, a two-stage object detection model, to accurately detect and classify 17 distinct classes of cannabis seeds sourced locally in Thailand. We evaluate six Faster R-CNN models by comparing performance across key metrics and achieving a mAP score of 94.08% and an F1 score of 95.66%. To our knowledge, this paper presents the first known application of a deep neural network-based object detection model to the novel task of visually identifving variants of cannabis seeds.