Leveraging ResNet-50 for Precision Toxicity Classification in Plants: A Vision-Based Approach to Safeguard Public Health
Aria Makhija · 2024
The classification of toxic and non-toxic plants plays an important role in ensuring public safety, especially in agriculture, food safety, and health. Correct identification of these plants can prevent accidental poisoning and promote ecological protection. In this paper, we investigate the application of the ResNet-50 model for the classification of toxic and non-toxic plants. Leveraging the powerful feature extraction techniques of the ResNet-50 architecture, the model achieved 89.6% accuracy, 87.4% precision, 91.1% recall, and an 89.2% F1 score, demonstrating the model’s effectiveness. Transfer learning proved effec-tive with limited data while maintaining high performance metrics in the classification task. Future research could focus on expanding the dataset to include more plant species and exploring other state-of-the-art models to improve classification accuracy. Addition-ally, integrating these models with mobile applications or monitoring systems could pro-vide solutions for business and public use, enhancing environmental protection and pub-lic safety.