Vision-Based Classification of Significant Varieties of Immuno-Herbal Plant Leaves Using Modified Convolutional Model
DOI:
https://doi.org/10.52783/jns.v14.3457Keywords:
Immuno-herbal leaf, Deep learning model, Batch normalization, ClassificationAbstract
This study introduces an advanced AI-based model named Deep Leaf Convv designed to identify and classify medicinal plants such as Argula, Giloy, Taro, and Ground Ivy. Using an enhanced version of the VGG16 deep learning architecture, the model is optimized with 41 layers, including Batch Normalization and Dropout techniques, to ensure stability and accuracy while avoiding overfitting. By efficiently analysing plant features, this system offers a trustworthy and autonomous solution for distinguishing between different plant species. This paper has the potential to support healthcare, agriculture, and ethnobotanical research by streamlining the process of identifying plants with medicinal properties. The overall accuracy of 93.39% is achieved.
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