Identification of Selected Ethiopian Traditional Medicinal Plants Using Digital Image Processing and Deep Learning Techniques

Abstract Ethiopia is rich in biodiversity, and its vast landscape offers a variety of medicinal plants with significant cultural and therapeutic importance. The study is conducted to identify medicinal plants using image processing and deep learning techniques. This study explores both pre-trained models (such as VGG-16 and ResNet-50) and CNN sequential for creating models from scratch. Experimental results show that the Medicinal Plant Identification (MPI) model constructed from scratch performs better with accuracy of 99.90% and an F-score of 99.00% performance as compared to pre-trained models VGG-16 andResNet-50.The proposed model achieves high accuracy in medicinal plant images classification by utilizing multiple convolutional layers that capture fine-grained patterns in images.