Automatic plant image recognition is one of the most promising options for closing the botanical taxonomic gap, and it has garnered significant attention from both the botany and computing communities. As machine learning technology progresses, more sophisticated models for automatic plant identification have been developed. Medicinal plants are regaining popularity in the pharmaceutical sector due to their fewer side effects and lower costs compared to modern pharmaceuticals. Many researchers have expressed strong interest in the study of automatic medicinal plant recognition as a result of these facts. The goal of this research is to create a reliable classifier capable of categorizing medicinal plants in real time. Recent advancements in deep learning techniques for plant classification, particularly those using leaf images, have shown promising results. In this study, various effective deep learning classifiers, such as Convolutional Neural Networks (CNNs), are used to process leaf images and extract important leaf features. The leaf attributes, including shape, vein structure, and texture, are critical in accurately identifying plant species. By using CNNs, the system is able to recognize these key features and categorize plants with greater accuracy. This approach involves applying image processing methods to detect and extract significant leaf characteristics, which can then be used for plant identification.
The proposed system not only focuses on plant classification but also aims to provide detailed information about the medicinal herbs, such as their scientific names, uses, and descriptions. By integrating machine learning with image recognition, the system can provide a higher accuracy rate in identifying the correct plant and its medicinal uses. Based on experimental findings, the system shows an improved accuracy in classifying plants and retrieving information about their usage, making it a valuable tool for both researchers and healthcare professionals.
Keywords— Symptom-driven disease prediction, machine learning, plant image recognition, medicinal plants, deep learning, Convolutional Neural Networks (CNN), herb classification.