Evaluasi Komparatif Model Transferlearning untuk Klasifikasi Tanaman Aquascape
DOI:
https://doi.org/10.37253/telcomatics.v11i1.12440Keywords:
Aquascape, Image Classification, CNN, Transfer Learning, Image ProcessingAbstract
The popularity of aquascaping has increased significantly in recent years. However, beginners often face difficulties in identifying aquatic plant species due to their highly similar visual characteristics, which may lead to improper plant care. This study evaluates and compares the performance of three Convolutional Neural Network (CNN) architectures, namely MobileNetV3 Large, ResNet18, and EfficientNet-B0, for classifying six aquascape plant species: Anubias, Bucephalandra, Cryptocoryne Wendtii, Floaters, Hornwort, and Vallisneria Spiralis. The dataset consists of 1,998 images resized to 224 × 224 pixels and enhanced through data augmentation techniques, including rotation, horizontal flip, color jitter, and Gaussian blur, to improve model generalization. The models were trained using the PyTorch framework with transfer learning, fine-tuning based on ImageNet pretrained weights, the AdamW optimizer, class weighting, and an early stopping strategy. Experimental results show that ResNet18 achieved the highest test accuracy of 92.7%, followed by EfficientNet-B0 with 90.3% and MobileNetV3 Large with 88.7%. These findings indicate that the residual learning architecture of ResNet18 is particularly effective for aquatic plant classification on the proposed dataset, while MobileNetV3 Large remains a suitable alternative for deployment on resource-constrained devices.
Downloads
References
M. R. Dileep and P. N. Pournami, "AyurLeaf: A Deep Learning Approach for Classification of Medicinal Plants," in Proc. TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), Kochi, India, 2019, pp. 321-325.
M. Bansal, M. Kumar, M. Sachdeva, and A. Mittal, "Transfer learning for image classification using VGG19: Caltech-101 image data set," Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 4, pp. 3609-3620, 2023.
Y. Liu, D. Zhou, F. Tang, Y. Meng, and W. Dong, "Flower Classification via Convolutional Neural Network," in Proc. 2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications, 2016, pp. 110-116.
S. Bhat, P. Dsouza, S. K., Shreeraksha, Tejasvini, and A. Ananth, "Classification of Plant Leaves of Western Ghats using Deep Learning," in Proc. 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), 2021, pp. 18-23.
M. S. I. Musyaffa, N. Yudistira, M. A. Rahman, A. H. Basori, A. B. F. Mansur, and J. Batoro, "IndoHerb: Indonesia medicinal plants recognition using transfer learning and deep learning," Heliyon, vol. 10, no. 23, Art. no. e40606, 2024.
J. M. Reyes, J. C. Velasco, and M. A. Villaverde, "Deep Learning-Based Identification of Invasive Aquatic Plant Species Using Residual Network-50," Eng. Proc., vol. 134, no. 1, Art. no. 45, 2026.
C. P. Lee, K. M. Lim, Y. X. Song, and A. Alqahtani, "Plant-CNN-ViT: Plant Classification with Ensemble of Convolutional Neural Networks and Vision Transformer," Plants, vol. 12, no. 14, Art. no. 2642, 2023.
J. Wäldchen and P. Mäder, "Plant species identification using computer vision techniques: A systematic literature review," Arch. Computat. Methods Eng., vol. 25, no. 2, pp. 507–543, Apr. 2018.
N. Kumar, P. N. Belhumeur, A. Biswas, D. W. Jacobs, W. J. Kress, I. C. Lopez, and J. V. B. Soares, "Leafsnap: A computer vision system for automatic plant species identification," in Computer Vision – ECCV 2012, A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, and C. Schmid, Eds., Lecture Notes in Computer Science, vol. 7573. Berlin, Heidelberg: Springer, 2012, pp. 502–516.
S. R. G. Reddy, G. P. Saradhi Varma, and R. L. Davuluri, "Optimized convolutional neural network model for plant species identification from leaf images using computer vision," Int. J. Speech Technol., vol. 24, no. 4, pp. 869–881, Dec. 2021.
H. Gao, R. Li, Q. Shen, Y. Yao, Y. Shao, Y. Zhou, W. Li, J. Li, Y. Zhang, and M. Liu, "Deep-learning-based automatic extraction of aquatic vegetation from Sentinel-2 images: A case study of Lake Honghu," Remote Sens., vol. 16, no. 5, Art. no. 867, 2024.
J. E. Perrin, S. R. Jernigan, J. D. Thayer, A. W. Howell, J. K. Leary, and G. D. Buckner, "Sensor fusion with deep learning for autonomous classification and management of aquatic invasive plant species," Robotics, vol. 11, no. 4, Art. no. 68, 2022.
S. Egli and M. Höpke, "CNN-based tree species classification using high resolution RGB image data from automated UAV observations," Remote Sens., vol. 12, no. 23, Art. no. 3892, 2020.
R. Satya Rajendra Singh and R. K. Sanodiya, "Zero-Shot Transfer Learning Framework for Plant Leaf Disease Classification," IEEE Access, vol. 11, pp. 143861-143880, 2023, doi: 10.1109/ACCESS.2023.3343759.
H. A. Shehu, A. Ackley, M. Marvellous, and O. E. Eteng, "Early Detection of Tomato Leaf Diseases Using Transformers and Transfer Learning," European Journal of Agronomy, vol. 168, Art. no. 127625, 2025, doi: 10.1016/j.eja.2025.127625.
W. Maximilliano and N. Rachmat, "Comparative Analysis of MobileNetV3-Large and Small for Corn Leaf Disease Classification," Brilliance: Research of Artificial Intelligence, vol. 5, no. 1, pp. 325-332, 2025, doi: 10.47709/brilliance.v5i1.6259.
H. Pebrian and E. Hartati, "Potato Leaf Disease Classification Using MobileNetV3 Architecture with Adam and Stochastic Gradient Descent Optimizers," Green Intelligent Systems and Applications, vol. 6, no. 1, pp. 81-95, 2026, doi: 10.53623/gisa.v6i1.1063.
W. Shafik, A. Tufail, L. C. De Silva, and R. A. A. H. M. Apong, "Lightweight Vision Transformer and ResNet-9 Models for Real-Time Plant Disease Detection and Pest Classification With SHAP Explainability," BMC Plant Biology, vol. 26, Art. no. 838, 2026, doi: 10.1186/s12870-026-08667-8.
Y. A. Viknesh G. S., "Leaf Classification for Plant Recognition Using EfficientNet Architecture," International Journal of Engineering Research & Technology (IJERT), vol. 10, no. 11, pp. 68-72, Nov. 2021.
H. Ali, N. Shifa, R. Benlamri, A. A. Farooque, and R. Yaqub, "A Fine-Tuned EfficientNet-B0 Convolutional Neural Network for Accurate and Efficient Classification of Apple Leaf Diseases," Scientific Reports, vol. 15, Art. no. 25732, 2025, doi: 10.1038/s41598-025-04479-2.
Z. Yang, R. O. Sinnott, J. Bailey, and Q. Ke, "A Survey of Automated Data Augmentation Algorithms for Deep Learning-Based Image Classification Tasks," ACM Comput. Surveys, vol. 56, no. 3, Art. no. 68, 2023, doi: 10.1145/3597301.
M. N. Razali, N. Arbaiy, P. C. Lin, and S. Ismail, "Optimizing Multiclass Classification Using Convolutional Neural Networks with Class Weights and Early Stopping for Imbalanced Datasets," Electronics, vol. 14, no. 4, Art. no. 705, 2025, doi: 10.3390/electronics14040705.
U. Amin, M. I. Shahzad, A. Shahzad, M. Shahzad, U. Khan, and Z. Mahmood, "Automatic Fruits Freshness Classification Using CNN and Transfer Learning," Applied Sciences, vol. 13, no. 14, Art. no. 8087, 2023, doi: 10.3390/app13148087.
Published
Issue
Section
License
Copyright (c) 2026 Telcomatics
This work is licensed under a Creative Commons Attribution 4.0 International License.
