Medicinal Plants Detection Using Convolutional Neural Networks: ConvNet

The use of convolutional neural networks (CNNs) for processing medicinal plant images is suggested by this study. The goal is to create a reliable system that can recognize and categorize different species of medicinal plants from photos. Preprocessing picture data, training CNN models, and assessing classification accuracy are all part of the implementation process. The outcomes show how well CNNs work at automating the identification procedure, which supports studies on medicinal plants and efforts to conserve them. The proposed model achieves 94.7 percent accuracy during testing phase. Loss also calculated during training phase. ConvNet outperform all other existing methods. The performance metrics such as accuracy, specificity, sensitivity, F1-Score and AUC Curve are calculated and compared with existing approaches.