Cannabis Seed Variant Detection using Faster R-CNN
This work addresses the need for precision breeding and regulatory compliance in the agriculture industry, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of detecting cannabis seed variants using Faster R-CNN on a dataset from Thailand with 17 classes, achieving a mAP score of 94.08% and an F1 score of 95.66%.
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.