Efficient Multi-Class Image-Based Rosemary Variety Verification and Classification Model Using Deep Learning: A Scientific Investigational Study

Abstract Artificial intelligence (AI) has a subfield called computer vision that allows systems and computers to extract replacement data from digital photos and videos. It is used in many fields, including agriculture, health care, education, self-driving cars, and daily living. In Ethiopia, rosemary is a well-known aromatic and therapeutic plant. It is an evergreen herb that belongs to the shrub family and it is widely used in Ethiopia with three varieties: WG rosemary I, WG rosemary II, WG rosemary III. Botanists, researchers, herbal industries, pharmacists and domain experts are facing challenges to classify appropriate varieties. And there is a lack of research and technology for identifying and classifying those varieties in Ethiopia. To address this gap, the proposed study employs supervised machine learning and multi class image classification. Specially, this study is conducted using a convolutional neural network (CNN) employing a SoftMax activation function in the last layer is used to develop the classification models. In this study, five cutting-edge models: convolutional neural network, Inception V3 and exception have been selected. After a comprehensive review of the best-performing models. The 80/20 percentage split was used to evaluate the model, and classification metrics were used to evaluate and compare the models. The pre-trained Inception V3 model outperforms well, achieving training and validation accuracy of 98.8% and 97.7%, respectively.