Flora Recognizer and Botanical Traits using Machine Learning Techniques
DOI:
https://doi.org/10.15662/IJARCST.2025.0802008Keywords:
Medicinal plants, Deep Learning, Convolutional Neural Networks, Image Processing, Xception Model, Feature ExtractionAbstract
Machine learning (ML) and deep learning models have significantly advanced plant identification and classification, addressing the limitations of traditional manual methods. Existing approaches primarily focus on counting plants from 3D scans, limiting detailed botanical analysis. These methods lack the ability to extract intricate leaf structures, making accurate classification challenging. This paper introduces Florasense, an advanced plant recognition system that utilizes Convolutional Neural Networks (CNNs) and Xception features to classify medicinal plants based on leaf morphology. Unlike conventional techniques, Florasense focuses on both identification and medicinal trait analysis, making it a comprehensive tool for researchers and practitioners.The system automates plant identification, significantly improving accuracy, efficiency, and usability across various fields, including medicine, forestry, and agriculture. By leveraging deep learning and leaf vein segmentation, Florasense enhances recognition precision, even in complex environments. The model achieves an impressive classification accuracy of 94.1%, surpassing traditional machine learning methods Additionally, it integrates a medicinal knowledge base, providing users with detailed insights into plant properties, uses, and dosage recommendations.
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