Published Paper Details:

This paper presents a comprehensive review of machine learning techniques for the identification of medicinal plants. Despite several existing studies, there are still challenges in automating the identification of plant species accurately. To determine the model's effectiveness, it must first be trained, then validated, and ultimately tested. The ML model is evaluated using measures including accuracy, precision, and recall. For this reason, the model was able to correctly identify medicinal leaves at an accuracy rate of 98.3%. This system has been implementing a technique for medicinal plant identification using Fuzzy C means, a clustering machine learning algorithm based on color, texture and geometrical features. Then reduced feature vectors are inputted into the classification model. The proposed system utilizes adverse dataset of high-resolution leaf images representing various medicinal plant species like Image preprocessing techniques, including normalization and feature extraction. Several machine learning algorithms, such as Convolutional Neural Networks (CNNs) and Multi Logistic Regression, are investigated to discern their effectiveness in accurately identifying medicinal plant species.