Herbal Leaf Recognization Using Backpropagation Artificial Neural Network Algorithm

This study presents an approach for herbal leaf recognization using Backpropagation Artificial Neural Network algorithm. The main purpose is to enhance the accuracy and efficiency of herbal leaf identification by leveraging the capabilities of BPANN. The proposed system will extract relevant features from herbal leaf images, which are then used as input data for training and testing the BPANN model. The algorithm’s learning process is simplified through backpropagation which allows the network to change and optimize its parameters to improve the classification performance. The experimental results demonstrate the effectiveness of the proposed method in achieving robust herbal leaf recognization, showing its potential for application in fields such as medicine, plant sciences, agriculture management, environmental monitoring and conservation.