Hybrid Deep Learning for Thai Cannabis Plant Classification: YOLO+CNN Approach

Cannabis is a plant species with various subspecies, and its classification is vital for both medical and regulatory purposes. In the context of Thailand, where cannabis has significant cultural and economic importance, accurate identification of different cannabis subspecies is of utmost importance. This work presents a novel approach for the classification of Thai cannabis plants using a hybrid deep learning model that combines You Only Look Once (YOLO) and Convolutional Neural Networks (CNN). The proposed model is evaluated against other deep learning models commonly used for image classification, and it demonstrates exceptional performance, achieving an impressive accuracy rate of 95.37%. Experimental results show that the YOLO+CNN hybrid model outperforms other state-of-the-art deep learning models, in terms of classification accuracy. The proposed model seems efficient due to the YOLO model’s ability to precisely localize cannabis plants in images and the CNN’s capability to extract fine-grained features for accurate classification. The high classification accuracy achieved by the hybrid model has significant implications for applications such as monitoring the growth of specific cannabis subspecies, ensuring product quality in the cannabis industry, and aiding law enforcement in identifying illegal cannabis plants. This research paves the way for the development of more efficient and accurate tools for cannabis classification, benefiting both the scientific community and society at large.