LEAFLET: A Web-Based Leaf Classification System Using Convolutional Neural Networks

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Paul Genre O. Lobaton, Jean Eileen C. Magtiay, Ven Gabriel E. Mendez, Guillan Jude G. Moster, Michael M. Casabuena, Maricel M. Gaspar, Philip V. Mojares


The Convolutional Neural Network (CNN), a crucial component of deep learning neural networks, is a network model frequently used in the classification of images, target recognition, and other applications. Utilizing this network model, this study aims to find the best CNN model compatible for the web-based application that can identify endemic trees found locally at FAITH College’s Serenity and Labyrinth Garden with 90% accuracy rate. Similar to this study, Salem et al. (2019) tested different classifiers with both Flavia and their own dataset and found that K-Nearest Neighbors (KNN) displayed the highest precision among all. The study focuses on a web application utilizing the five pre-trained models namely VGG-16, ResNet50, Inception V3, Xception and Sequential, and it was found that the best performing among those is the VGG-16 which was then used for leaf classification, achieving a remarkable 94% accuracy, surpassing the 90% objective. The application allows users to scan leaves of specific trees in a designated area, with accuracy not guaranteed beyond this zone. The application's performance is influenced by the user's device, internet connection, and image quality.

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Author Biography

Paul Genre O. Lobaton, Jean Eileen C. Magtiay, Ven Gabriel E. Mendez, Guillan Jude G. Moster, Michael M. Casabuena, Maricel M. Gaspar, Philip V. Mojares

1Paul Genre O. Lobaton

2Jean Eileen C. Magtiay

3Ven Gabriel E. Mendez 

4Guillan Jude G. Moster

5Michael M. Casabuena

6Dr. Maricel M. Gaspar

7Philip V. Mojares

1,2,3,4 Students, FAITH Colleges, Tanauan City, Batangas, Philippines 4233

5Faculty-Adviser, Laguna State Polytechnic University, Los Baños, Laguna, Philippines 4030

6,7Faculty-Advisers, FAITH Colleges, Tanauan City, Batangas, Philippines 4233

1s2020103952@firstasia.edu.ph, 2s2020102878@firstasia.edu.ph, 3s2020102316@firstasia.edu.ph, 4s2020102536@firstasia.edu.ph, 5 michaelcasabuena.dev@gmail.com, 6mlmalabanan@firstasia.edu.ph, 7pvmojares@firstasia.edu.ph

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Ahmed. (2022, October 18). The Motivation for Train-Test Split. Retrieved from https://medium.com/@nahmed3536/the-motivation-for-train-test-split-2b1837f596c3

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O. Santamaria, J., Fadhel, M. A., Al-Amidie, M., and Farhan, L. (2021, March). “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J. Big Data, vol. 8, no. 1, p. 53.

Azlah, M. A. F., Chua, L. S., Rahmad, F. R., Abdullah, F. I., & Wan Alwi, S. R. (2019). Review on Techniques for Plant Leaf Classification and Recognition. Computers, 8(4), 77. doi:10.3390/computers8040077

Collins, A.S. (2019, December 17). How the Sobel Operator Works. Retrieved from https://automaticaddison.com/how-the-sobel-operator-works/

Copeland, B.J. (2023, October 11). Artificial intelligence. Retrieved from https://www.britannica.com/technology/artificial-intelligence

Dabbadie, L. (2023, July 17). The revival of a damaged Philippines watershed is helping improve nutrition and livelihoods of communities. Retrieved from https://www.fao.org/news/countries-good-practices/article/en/c/1644557/

de Jesus, A. (2021, March 6). Why we need to plant native Philippine trees. Retrieved from https://business.inquirer.net/319013/why-we-need-to-plant-native-phi lippine-trees

Fan, X., Zhou, R., Tjahjadi, T. Choudhury, S. D. & Ye, Q. (2022). A Segmentation-Guided

Deep Learning Framework for Leaf Counting. Frontiers in Plant Science, 13(1), 1 - 13. https://doi.org/10.3389/fpls.2022. 844522

Huellman, T. (2022, November 16). Image processing: How do image classifiers work? levity.ai. Retrieved October 20, 2023, from https://levity.ai/blog/how-do-ima ge-classifiers-work

Keivani, M., Mazloum, J., Sedaghatfar, E., & Tavakoli, M. B. (2020). Automated Analysis of Leaf Shape, Texture, and Color Features for Plant Classification. https://www.academia.edu/download/88721176/25544.pdf

Nidhi & Yadav, J. (2020, July 26). Plant Leaf Classification Using Convolutional Neural Network. Recent Advances in Computer Science and Communications, 15(3), 421. https://doi.org/10.2174/2666255813999200904162029

Rizk, S. (2019). Plant Leaf Classification Using Dual Path Convolutional Neural Networks [Master’s thesis, Notre Dame University-Louaize]. Notre Dame University-Louaize Institutional Repository.

Rouse, M. (2023, October 11). Deep Neural Network. Retrieved from https://www.techopedia.com/definition/32902/deep-neural-network

Saha, S. (2023, April 8). A Comprehensive Guide to Convolutional Neural Networks —the ELI5 way. Retrieved from https://towardsdatascience.com/a-com prehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53

Saleem, G., Akhtar, M., Ahmed, N., & Qureshi, W. S. (2019). Automated analysis of visual leaf shape features for plant classification. Computers and Electronics in Agriculture, 157, 270–280. doi:10.1016/j.compag.2018.12.038

Urwin, M. (2023, May 24). 20 Deep Learning Applications You Should Know. Retrieved from https://builtin.com/artificial-intelligence/deep-learning-applications

Turkoglu, M. & Hanbay, D. (2019). Recognition of plant leaves: An approach with hybrid features produced by dividing leaf images into two and four parts. Applied Mathematics and Computation, 352, 1-14. https://doi.org/10.1016/j.amc.2019.01.054

Wang, B., & Wang, D. (2019). Plant Leaves Classification: A Few-Shot Learning Method Based on Siamese Network. IEEE Access, 7, 151754–151763. doi:10.1109/access.2019.2947510

Yang, K., Zhong W. & Li , F. (2020). Leaf Segmentation and Classification with a Complicated Background Using Deep Learning. Agronomy, 10(11), 1721. https://doi.org/10.3390/agronomy10111721

Zhuang, F. (2019). “A Comprehensive Survey on Transfer Learning”

Zhu, Y., Li, G., Wang, R., Tang, S., Su, H., & Cao, K. (2021). Intelligent fault diagnosis of hydraulic piston pump based on wavelet analysis and improved AlexNet. Sensors, 21(2), 549.