Keywords
ResNet50
CNN
ensemble deep learning
data augmentation
How to Cite
Abstract
Image classification is a fundamental task in vision recognition that aims to understand and categorize an image under a specific label. Image classification needs to produce a quick, economical, and reliable result. Convolutional Neural Networks (CNN) have proven effective for image analysis. However, problems arise due to factors such as the model’s quality, unbalanced training data, overfitting, and layers’ complexity. ResNet50 is a transfer learning-based convolutional neural network model frequently used in many areas, including Lepidopterology. Studies have shown that ResNet50 performs with lower accuracy than other models for classifying butterflies. Therefore, this study aims to optimise the accuracy of ResNet50 using an augmentation approach and ensemble deep learning for butterfly image classification. This study used a public dataset of butterflies from Kaggle. The dataset contains 75 different butterfly species, 9.285 training images, 375 testing images, and 375 validation images. A sequence of transformation functions was applied. The ensemble deep learning was constructed by incorporating ResNet50 with CNN. To measure ResNet50 optimisation, the experimental results of the original dataset and the proposed methods were compared and analysed using evaluation metrics. The research revealed that the proposed method provided better performance with an accuracy of 95%.
References
R. P. P. Fathimathul et al., “A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models,” Electron., vol. 11, no. 13, pp. 1–20, 2022.
F. Rohman, M. A. Efendi, and L. R. Andrini, BIOEKOLOGI, 1st ed. Malang: FMIPA UNM, 2019.
J. An and S.-W. Choi, “Butterflies as an indicator group of riparian ecosystem assessment,” J. Asia. Pac. Entomol., vol. 24, 2020.
N. Ismail, A. Awg Abdul Rahman, M. Mohamed, M. F. Abu Bakar, and L. Tokiman, “Butterfly as bioindicator for development of conservation areas in Bukit Reban Kambing, Bukit Belading and Bukit Tukau, Johor, Malaysia,” Biodiversitas J. Biol. Divers., vol. 21, pp. 334–344, 2020.
S. Chowdhury et al., “Insects as bioindicator: A hidden gem for environmental monitoring,” Front. Environ. Sci., vol. 11, 2023.
A. M. Shephard, A. M. Zambre, and E. C. Snell-Rood, “Evaluating costs of heavy metal tolerance in a widely distributed, invasive butterfly,” Evol. Appl., vol. 14, no. 5, pp. 1390–1402, 2021.
N. N. Kamaron Arzar, N. Sabri, N. F. Mohd Johari, A. Amilah Shari, M. R. Mohd Noordin, and S. Ibrahim, “Butterfly Species Identification Using Convolutional Neural Network (CNN),” 2019 IEEE Int. Conf. Autom. Control Intell. Syst. I2CACIS 2019 - Proc., no. June, pp. 221–224, 2019.
K. R. L. Van Der Burg and R. D. Reed, “ScienceDirect Seasonal plasticity : how do butterfly wing pattern traits evolve environmental responsiveness ?,” Curr. Opin. Genet. Dev., vol. 69, no. Figure 1, pp. 82–87, 2021.
A. Ghosh, M. S. Abedin, A. J. Howlader, and M. M. Hossain, “Molecular identification and phylogenetic relationships of seven Satyrinae butterflies in Bangladesh using Cytochrome c oxidase subunit I gene,” Jahangirnagar Univ. J. Biol. Sci., vol. 8, no. 1, pp. 67–74, 2019.
A. Paramita, M. Syazali, and M. Erfan, “Identifikasi Spesies Kupu- Kupu di Taman Narmada Lombok Barat,” J. Sci. Educ., vol. 02, no. 1, pp. 22–25, 2022.
A. S. Almryad and H. Kutucu, “Automatic identification for field butterflies by convolutional neural networks,” Eng. Sci. Technol. an Int. J., vol. 23, no. 1, pp. 189–195, 2020.
H. M. Geyle et al., “Butterflies on the brink: identifying the Australian butterflies (Lepidoptera) most at risk of extinction,” Austral Entomol., vol. 60, no. 1, pp. 98–110, 2021.
M. Chikkamath, D. Dwivedi, R. B. Hirekurubar, and R. Thimmappa, “Benchmarking of Novel Convolutional Neural Network Models for Automatic Butterfly Identification,” in Computer Vision and Robotics, 2023, pp. 351–364.
C. Cunha, H. Narotamo, A. Monteiro, and M. Silveira, “Detection and measurement of butterfly eyespot and spot patterns using convolutional neural networks,” PLoS One, vol. 18, no. 2, pp. 1–15, 2023.
A. Ghosh, A. Sufian, F. Sultana, A. Chakrabarti, and D. De, Fundamental concepts of convolutional neural network, vol. 172, no. June. 2020.
D. Prasetyawan and S. ’Uyun, “Penentuan Emosi pada Video dengan Convolutional Neural Network,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 5, no. 1, pp. 23–35, 2020.
E. Lashgari, D. Liang, and U. Maoz, “Data Augmentation for Deep-Learning-Based Electroencephalography,” J. Neurosci. Methods, vol. 346, pp. 1–55, 2020.
S. T. Krishna and H. K. Kalluri, “Deep Learning and Transfer Learning Approaches for Image Classification,” Int. J. Recent Technol. Eng., vol. 7, no. 5, pp. 427–432, 2019.
M. A. Ganaie, M. Hu, A. K. Malik, M. Tanveer, and P. N. Suganthan, “Ensemble deep learning: A review,” Eng. Appl. Artif. Intell., vol. 115, 2022.
M. Mujahid, F. Rustam, R. Álvarez, J. Luis Vidal Mazón, I. de la T. Díez, and I. Ashraf, “Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network,” Diagnostics, vol. 12, no. 5, pp. 1–16, 2022.
S. H. Amrullah, Hilda, and R. F. Rusli, “Identifikasi lebah dan kupu polinator di Hutan Billa Battang Kota Palopo,” J. Din., vol. 09, no. 2, pp. 1–12, 2018.
L. Prudhivi, M. Narayana, C. Subrahmanyam, and M. Gopi Krishna, “Animal species image classification,” Mater. Today Proc., no. xxxx, 2021.
Micheal and E. Hartati, “Klasifikasi Spesies Kupu Kupu Menggunakan Metode Convolutional Neural Network,” MDP Student Conf. 2022, pp. 569–577, 2022.
T. Zhou, H. Lu, Z. Yang, S. Qiu, B. Huo, and Y. Dong, “The ensemble deep learning model for novel COVID-19 on CT images.,” Appl. Soft Comput., vol. 98, p. 106885, Jan. 2021.
P. Gurunath Shivakumar and P. Georgiou, “Transfer learning from adult to children for speech recognition: Evaluation, analysis and recommendations,” Comput. Speech Lang., vol. 63, p. 101077, 2020.
S. Ruder, M. E. Peters, S. Swayamdipta, and T. Wolf, “Transfer Learning in Natural Language Processing,” in Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorials, 2019, pp. 15–18.
Z. Lv, F. Poiesi, Q. Dong, J. Lloret, and H. Song, “Deep Learning for Intelligent Human–Computer Interaction,” Appl. Sci., vol. 12, no. 22, 2022.
C. Stoean et al., “Unsupervised Learning as a Complement to Convolutional Neural Network Classification in the Analysis of Saccadic Eye Movement in Spino-Cerebellar Ataxia Type 2,” 2019, pp. 26–37.
R. Stoean, C. Stoean, A. Samide, and G. Joya, “Convolutional Neural Network Learning Versus Traditional Segmentation for the Approximation of the Degree of Defective Surface in Titanium for Implantable Medical Devices,” 2019, pp. 871–882.
A. Samide, C. Stoean, and R. Stoean, “Surface study of inhibitor films formed by polyvinyl alcohol and silver nanoparticles on stainless steel in hydrochloric acid solution using Convolutional Neural Networks,” Appl. Surf. Sci., vol. 475, 2018.
O. Kembuan, G. Caren Rorimpandey, and S. Milian Tompunu Tengker, “Convolutional Neural Network (CNN) for Image Classification of Indonesia Sign Language Using Tensorflow,” 2020 2nd Int. Conf. Cybern. Intell. Syst. ICORIS 2020, no. 26, 2020.
D. Bhatt et al., “CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope,” Electronics, vol. 10, no. 20, 2021.
Q. Ji, J. Huang, W. He, and Y. Sun, “Optimized deep convolutional neural networks for identification of macular diseases from optical coherence tomography images,” Algorithms, vol. 12, no. 3, pp. 1–12, 2019.
Z. Zahisham, C. P. Lee, and K. M. Lim, “Food Recognition with ResNet-50,” in 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 2020, pp. 1–5.
V. Rupapara, F. Rustam, H. F. Shahzad, A. Mehmood, I. Ashraf, and G. S. Choi, “Impact of SMOTE on Imbalanced Text Features for Toxic Comments Classification Using RVVC Model,” IEEE Access, vol. 9, pp. 78621–78634, 2021.
J. Kakarla, B. V. Isunuri, K. S. Doppalapudi, and K. S. R. Bylapudi, “Three-class classification of brain magnetic resonance images using average-pooling convolutional neural network,” Int. J. Imaging Syst. Technol., vol. 31, no. 3, pp. 1731–1740, 2021.
D. Theckedath and R. R. Sedamkar, “Detecting Affect States Using VGG16, ResNet50 and SE-ResNet50 Networks,” SN Comput. Sci., vol. 1, no. 2, p. 79, 2020.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.