Authors - Bhagyashree D. Lambture, Madhavi A. Pradhan Abstract - Phytochemical qualities, geographic information, environmental conditions, and traditional medicinal knowledge are some of the sources of information that are incorporated into this research project, which presents a comparative examination of machine learning (ML) algorithms for the qualitative evaluation of medicinal plants. In order to categorize and forecast the medicinal value of plants based on multi-modal data, the purpose of this study is to investigate the effectiveness of various machine learning algorithms. For the purpose of determining which method is the most effective for evaluating complicated and diverse datasets, a full evaluation is carried out utilizing well-known machine learning models. These models include decision trees, random forests, support vector machines, and deep learning algorithms. Key criteria including as accuracy, precision, recall, F1-score, and computing efficiency are utilized in order to evaluate the levels of performance achieved by each method. For the purpose of gaining a deeper comprehension of the role that each data source plays in determining the medicinal potential of plants, the value of features and their interpretability are also investigated. A basis for the ongoing development of AI-driven tools in pharmacological research and plant-based drug discovery is provided by the findings of this comparative analysis, which offer vital insights into the usefulness of machine learning for medicinal plant assessment. Contributing to the expanding fields of computational botany and natural product science, the purpose of this study is to improve the precision and effectiveness of the evaluation of medicinal plants.
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