Leaf-Type Image Classification Using Deep Learning Method Convolution Neural Network
Keywords:
Convolutional Neural Network, Classification, Deep Learning, Supervised LearningAbstract
One of the most important parts of an ecosystem is a plant, Plants life has given us many benefits from food, oxygen, and medicine. There are many species of plant each with its unique benefits and utilities. In this paper, we try to identify plants by their leaf using deep learning. For this research, we use the convolution neural network architecture Xception to classify 5 different types of leaves. We used 1075 images of leaves that can be classified into 5 different types of leaves. the classification model achieved an overall accuracy score of 74%. We hoped that the result of our research can help people's life by helping them to identify plants that they have so that they can use them for their benefit.
References
Chollet, F. (2016, 10). Xception: Deep Learning with Depthwise Separable Convolutions. doi:10.1109/CVPR.2017.195
Christenhusz, M., & Byng, J. (2016, 5). The number of known plants species in the world and its annual increase. Phytotaxa, 261(3), 201-217. doi:https://doi.org/10.11646/phytotaxa.261.3.1
Diallo, A. (2022). Artificial Intelligence-Based Medical PredictionS. Retrieved from https://uniselinus.education/sites/default/files/2022-06/abdoulaye%20diallo.pdf
Foody, G. (2023, 10). Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient. PLoS ONE, 18(10 October). doi:https://doi.org/10.1371/journal.pone.0291908
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022, 12). A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999-7019. doi:10.1109/TNNLS.2021.3084827
Liu, J., Yang, S., Cheng, Y., & Song, Z. (2018). Plant Leaf Classification Based on Deep Learning. 2018 Chinese Automation Congress (CAC), (pp. 3165-3169). China. doi:http://dx.doi.org/10.1109/CAC.2018.8623427
Morris, J., Puttick, M., Clark, J., Edwards, D., Kenrick, P., Pressel, S., . . . Donoghue, P. (2018, 3). The timescale of early land plant evolution. Proceedings of the National Academy of Sciences of the United States of America, 115(10), E2274-E2283. doi:https://doi.org/10.1073/pnas.1719588115
Nwankpa, C., Ijomah, W., Gachagan, A., & Marshall, S. (2018, 11). Activation Functions: Comparison of trends in Practice and Research for Deep Learning. doi:https://doi.org/10.48550/arXiv.1811.03378
Peter, W., Shengping, Z., Sharat, C., Stefan A., L., Scott L., W., & Thomas Serre. (2016). Computer vision cracks the leaf code. Proceedings of the National Academy of Sciences, 113(12), 3305-3310. doi:https://doi.org/10.1073/pnas.1524473113
Pimm, S. L., & Joppa, L. N. (2015). How Many Plant Species are There, Where are They, and at What Rate are They Going Extinct? Annals of the Missouri Botanical Garden, 100(3), 170-176. doi:10.3417/2012018
Ramachandran, P., Zoph, B., & Le, Q. (2017, 10). Searching for Activation Functions. doi:https://doi.org/10.48550/arXiv.1710.05941
Sarker, I. (2021, 11). Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science, 2(6). doi:https://doi.org/10.1007/s42979-021-00815-1
Shorten, C., & Khoshgoftaar, T. (2019, 12). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1). doi:https://doi.org/10.1186/s40537-019-0197-0
Yu, Y. (2022, 10). Deep Learning Approaches for Image Classification. ACM International Conference Proceeding Series, 1494-1498. doi:10.1109/ICDSBA.2018.00101
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Mikael Reichi Sopany, Teny Handhayani
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
