A Deep Learning Approach for Herbal Plant Detection and Recognition

In this research, TensorFlow, a powerful deep learning toolkit, is utilized in conjunction with the Python programming language to conduct a comprehensive analysis of plant names and specifications. The research involves compiling a diverse dataset containing various plant species and their corresponding characteristics. Prior to processing, the dataset is meticulously cleaned and standardized to ensure consistency. By leveraging TensorFlow and natural language processing techniques, features are extracted from the plant names, enabling the identification of patterns and relationships. Deep learning models based on TensorFlow are then trained to recognize these patterns and associate them with specific plant attributes. The inclusion of a user-friendly interface facilitates seamless communication and allows users to input plant names for analysis. The research aims to enhance the understanding of plant taxonomy by providing valuable insights with implications for fields such as botany, agriculture, and pharmaceutical research. The project focuses on creating a robust herbal plant detection system using Convolutional Neural Networks (CNNs) with a target accuracy of 90%. CNNs enable precise herbal plant classification through automatic feature extraction from input.