Abstract
Plants are the basis of all living things on earth, supplying us with oxygen, food, shelter, medicine, and preserving the planet from dam-ages that could face climate changes. Concerning their medicinal abilities, limited access to proper medical centers in many rural areas and developing countries made traditional medicine preferable by the community. In addition, their lower side effect and affordability also plays a big role. More than half of the population uses medicinal plants directly and indirectly for animals and personal use in Ethiopia. However, accurate medicinal plant identification has always been a challenge for manual identification and automatic recognition systems mainly because the knowledge transfer between the knowledge holders (traditional physicians, elderly) and modern science have a huge gap. Several studies addressed an automatic plant recognition system using different feature extraction methods and classification algorithms. In this paper, a novel dataset, which was based on Ethiopian medicinal plants, that use the leaf part of the plant, as a medicine was used to automatically classify the plants accordingly using their leaf image. An attempt has been made to collect leaf images of medicinal plants in Ethiopia, to train, test collected dataset images, and classify those images using convolutional neural network models like GoogleNet and AlexNet. The proposed convolutional neural networks were fine-tuned with the adjustment of hyper-parameters like learning rate, the number of epochs, optimizers to the models. Image augmentation is also implemented to enlarge the dataset. The experimental result for the augmented dataset and more training epoch gave better performance and accuracy in the classification of the images. From the two selected convolutional neural network models, the best model is then determined based on the result in accuracy and loss; from an experiment conducted, the best model, which is GoogLeNet with an accuracy of 96.7 % chosen to develop a web-based automatic medicinal plant classification system.
S. Lu—Contributed equally to this work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cope, J.S., Corney, D., Clark, J.Y., Remagnino, P., Wilkin, P.: Plant species identification using digital morphometrics: a review. Expert Syst. Appl. 39(8), 7562–7573 (2012)
Lambert, J., Srivastava, J., Vietmeyer, N.: Medicinal Plants: Rescuing a Global Heritage, vol. 355. World Bank Publications (1997)
Thirumalai, T., Kelumalai, E., Senthilkumar, B., David, E.: Ethnobotanical study of medicinal plants used by the local people in Vellore District, Tamilnadu, India. Ethnobotanical Leaflets 13(10), 1302–1311 (2009)
Musila, W., Kisangau, D., Muema, J.: Conservation status and use of medicinal plants by traditional medical practitioners in Machakos District, Kenya. Nat. Mus. Kenya 22, 12–18 (2002)
Demisew, S., Dagne, E.: Basic and applied research on medicinal plants of Ethiopia. In: Proceedings of the National Workshop on Biodiversity Conservation and Sustainable Use of Medicinl Plants in Ethiopia, Addis Ababa (2001)
Giday, M., Teklehaymanot, T.: Ethnobotanical study of plants used in management of livestock health problems by Afar people of Ada’ar District, Afar Regional State, Ethiopia. J. Ethnobiol. Ethnomed. 9(1), 1–10 (2013)
Lulekal, E., Asfaw, Z., Kelbessa, E., Van Damme, P.: Ethnomedicinal study of plants used for human ailments in Ankober District, North Shewa Zone, Amhara region, Ethiopia. J. Ethnobiol. Ethnomed. 9(1), 1–13 (2013)
Du, J.X., Wang, X.F., Zhang, G.J.: Leaf shape based plant species recognition. Appl. Math. Comput. 185(2), 883–893 (2007)
Herdiyeni, Y., Wahyuni, N.K.S.: Mobile application for Indonesian medicinal plants identification using fuzzy local binary pattern and fuzzy color histogram. In: 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 301–306 (2012)
Priya, C.A., Balasaravanan, T., Thanamani, A.S.: An efficient leaf recognition algorithm for plant classification using support vector machine. In: International Conference on Pattern Recognition, Informatics and Medical Engineering, pp. 428–432, March 2012
Liu, J., Yang, S., Cheng, Y., Song, Z.: Plant leaf classification based on deep learning. In: 2018 Chinese Automation Congress, pp. 3165–3169 (2018)
Lee, S.H., Chan, C.S., Wilkin, P., Remagnino, P.: Deep-plant: plant identification with convolutional neural networks. In: 2015 IEEE International Conference on Image Processing, pp. 452–456, September 2015
dos Santos Ferreira, A., Freitas, D.M., da Silva, G.G., Pistori, H., Folhes, M.T.: Weed detection in soybean crops using ConvNets. Comput. Electron. Agric. 143, 314–324 (2014)
Varghese, B.K., Augustine, A., Babu, J.M., Sunny, D., Cherian, S.: Plant recognition using convolutional neural networks. In: proceedings of the Fourth International Conference on Computing Methodologies and Communication (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Berihu, M., Fang, J., Lu, S. (2022). Automatic Classification of Medicinal Plants of Leaf Images Based on Convolutional Neural Network. In: Liao, X., et al. Big Data. BigData 2021. Communications in Computer and Information Science, vol 1496. Springer, Singapore. https://doi.org/10.1007/978-981-16-9709-8_8
Download citation
DOI: https://doi.org/10.1007/978-981-16-9709-8_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9708-1
Online ISBN: 978-981-16-9709-8
eBook Packages: Computer ScienceComputer Science (R0)
