Cannabis Seed Variant Detection using Faster R-CNN

Object Detection
Agriculture
Deep Learning

Toqi Tahamid Sarker, Taminul Islam and Khaled R Ahmed, “Cannabis Seed Variant Detection using Faster R-CNN,” 2024 IEEE International Conference on Advanced Computing and Communication Systems

Authors
Affiliation

School of Computing, Southern Illinois University

Taminul Islam

School of Computing, Southern Illinois University

School of Computing, Southern Illinois University

Published

March 2024

Other details

Presented at 2024 IEEE International Conference on Advanced Computing and Communication Systems, India, March 2024.

Abstract

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.