Deduction of Medicinal Plant through Supervise Machine Learning.
Keywords:
Medicinal Plant Classification, Supervised Machine Learning, Image Recognition, Plant Feature ExtractionAbstract
A promising method for improving the identification and classification of medicinal plants with alleged therapeutic properties lies in the deduction of medicinal plants through supervised machine learning (ML) techniques. This study looks at the use of ML models, including supervised learning algorithms, to analyze plant feature qualities, for example, leaf shape, texture, color, and other morphological characteristics. We train various ML models (e.g. Support Vector Machines (SVM), Random Forests, Convolutional Neural Networks (CNN) ) on a labelled plant image dataset along with their medicinal properties to classify plants according to their medicinal value. In this process we do preprocessing of raw data, feature extraction, and applying suitable machine learning algorithms to construct prediction models that predict medicinal plants with high precision. The results show that supervised ML methods can greatly enhance the efficiency and accuracy of plant classification than the manual methods. Additionally, making reliable predictions of medicinal qualities from visual plant features contributes to the development of pharmacognosy and the search for novel natural medicine. To the growing field of digital ethnobotany, this study contributes computational methods that aid the preservation and identification of medicinal plants and expedite the discovery of drugs and the development of nature based healthcare solutions.
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