AI-Based Smart Identification of Medicinal Plants Using Vision Transformer and CatBoost for Biodiversity and Healthcare
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Abstract
In most countries, medicinal plants are crucial remedies for disease treatment. Even though the majority are edible, ingesting the incorrect herbal plant can have fatal consequences. It is essential to accurately identify these plants not only for safe usage by individuals but also for various real-time applications like aiding biodiversity conservation, supporting farmers in recognizing local herbs, and also preserving indigenous systems. Numerous automatic methods for identifying medicinal plants have been developed; however, most of them are severely limited, either by the relatively small number of plant species they support or by the fact that they rely on manual visual segmentation of plant leaf surfaces. This means that instead of being easily recognized in their natural environments, which frequently include complicated and chaotic backgrounds, they are snapped against a plain background. Deep learning-based techniques have advanced significantly in recent years. Still, they are trained on data that isn't always fully reflective of the intra-class and inter-class variances among the plant species in consideration. The paper approaches this issue by integrating the hybrid model of a pre-trained vision transformer with a CatBoost classifier tuned with Optuna. The vision transformer model is trained with the Indian medicinal plant dataset with the five most commonly used species. The hybrid model is compared with the deep learning models regarding precision, recall, F1-score, accuracy, and execution time on the same dataset. Our proposed model achieves a training phase accuracy of 93%, which shows the improvement for automating the identification of medicinal plants. In conclusion, our proposed hybrid model reveals enhanced accuracy, improved reliability, and reduced false positives in automating the identification of medicinal plants, contributing effectively to healthcare applications and biodiversity.
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