Leveraging ResNet-50 for Precision Toxicity Classification in Plants: A Vision-Based Approach to Safeguard Public Health

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Authors

Aria Makhija

Abstract

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.

DOI

https://doi.org/10.32942/X28W4K

Subjects

Environmental Engineering, Plant Sciences

Keywords

Poisonous plants, Non-poisonous plants, ResNet-50, Plant classification, Deep learning, transfer learning, accuracy

Dates

Published: 2024-10-04 18:25

License

CC BY Attribution 4.0 International

Additional Metadata

Language:
English

Data and Code Availability Statement:
Available on Kaggle