Transfer Learning Approach to Seed Taxonomy: A Wild Plant Case Study
-
Published:2023-07-04
Issue:3
Volume:7
Page:128
-
ISSN:2504-2289
-
Container-title:Big Data and Cognitive Computing
-
language:en
-
Short-container-title:BDCC
Author:
Ibrahim Nehad M.1ORCID, Gabr Dalia G.2ORCID, Rahman Atta1ORCID, Musleh Dhiaa1, AlKhulaifi Dania1, AlKharraa Mariam1ORCID
Affiliation:
1. Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia 2. Botany and Microbiology Department, Faculty of Science (Girls Branch), Al Azhar University, Cairo 11651, Egypt
Abstract
Plant taxonomy is the scientific study of the classification and naming of various plant species. It is a branch of biology that aims to categorize and organize the diverse variety of plant life on earth. Traditionally, plant taxonomy has been performed using morphological and anatomical characteristics, such as leaf shape, flower structure, and seed and fruit characters. Artificial intelligence (AI), machine learning, and especially deep learning can also play an instrumental role in plant taxonomy by automating the process of categorizing plant species based on the available features. This study investigated transfer learning techniques to analyze images of plants and extract features that can be used to cluster the species hierarchically using the k-means clustering algorithm. Several pretrained deep learning models were employed and evaluated. In this regard, two separate datasets were used in the study comprising of seed images of wild plants collected from Egypt. Extensive experiments using the transfer learning method (DenseNet201) demonstrated that the proposed methods achieved superior accuracy compared to traditional methods with the highest accuracy of 93% and F1-score and area under the curve (AUC) of 95%, respectively. That is considerable in contrast to the state-of-the-art approaches in the literature.
Subject
Artificial Intelligence,Computer Science Applications,Information Systems,Management Information Systems
Reference45 articles.
1. Effect of glomerular change on the electrolyte reabsorption of the renal tubule in glomerulonephritis (author’s transl);Takamitsu;Jpn. J. Nephrol.,1978 2. Wani, J.A., Sharma, S., Muzamil, M., Ahmed, S., Sharma, S., and Singh, S. (2021). Machine Learning and Deep Learning Based Computational Techniques in Automatic Agricultural Diseases Detection: Methodologies, Applications, and Challenges, Springer. 3. ImageNet Classification with Deep Convolutional Neural Networks;Krizhevsky;Commun. ACM,2017 4. Simonyan, K., and Zisserman, A. (2015, January 7–9). Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA. 5. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|