An optimum hyperparameters of restnet-50 for orchid classification based on convolutional neural network

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Nukat Alvian Ideastari
Christy Atika Sari
Edi Faisal
Zaenal Arifin
Andi Danang Krismawan
Muslih Muslih

Abstract

There are many types of orchids in Indonesia, such as Phalaenopsis Amabilis (Moon Orchid), Cattleya, etc. Because the shape and color of each orchid flower looks the same, a system is needed that can classify orchid flowers. In this research, we will use a system using a Convolutional Neural Network with ResNet50 architecture to classify orchid species. There are 4 types of orchids that will be used, namely Moon Orchids, xDoritaenopsis Orchids, Cattleya Orchids, and Coelogyne Pandurata Orchids (1000 datasets for each type). The aim of this research is to implement deep learning using the Convolutional Neural Network method combined with the ResNet50 architecture and identifying the types of orchid flowers and calculating accuracy when identifying orchid flower types. This research uses 4000 orchid image datasets, with a data split of 80:20 so that 800 images are used as training data and 200 as test data. ResNet50 uses a confusion matrix evaluation, namely Accuracy, Precision, Recall, Specificity and F1-score with epochs 10, 20, 30, 40. From the research that has been carried out, it produces the highest accuracy on Test Data with the 30th epoch, reaching 98.87%. and the lowest accuracy on Test Data with the 10th epochs which produces an accuracy of 97.75%.

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[1]
N. Alvian Ideastari, C. Atika Sari, E. Faisal, Z. Arifin, A. Danang Krismawan, and M. Muslih, “An optimum hyperparameters of restnet-50 for orchid classification based on convolutional neural network”, J. Soft Comput. Explor., vol. 5, no. 1, pp. 55-66, Mar. 2024.
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References

H. Henri, L. Hakim, and J. Batoro, “The Potential of Flora and Fauna as Tourist Attractions in Biodiversity Park of Pelawan Forest, Central Bangka,” Biosaintifika J. Biol. Biol. Educ., vol. 9, no. 2, Jul. 2017, doi: 10.15294/biosaintifika.v9i2.9225.

D. S. Dewantara, R. Hidayat, H. Susanto, and A. M. Arymurthy, “CNN with Multi Stage Image Data Augmentation Methods for Indonesia Rare and Protected Orchids Classification,” in 2020 International Conference on Computer Science and Its Application in Agriculture (ICOSICA), IEEE, Sep. 2020, pp. 1–5. doi: 10.1109/ICOSICA49951.2020.9243174.

P. N. Andono, E. H. Rachmawanto, N. S. Herman, and K. Kondo, “Orchid types classification using supervised learning algorithm based on feature and color extraction,” Bull. Electr. Eng. Informatics, vol. 10, no. 5, pp. 2530–2538, Oct. 2021, doi: 10.11591/eei.v10i5.3118.

T.-W. Chang, W.-C. Wang, and R. Chen, “Intelligent Control System to Irrigate Orchids Based on Visual Recognition and 3D Positioning,” Appl. Sci., vol. 11, no. 10, p. 4531, May 2021, doi: 10.3390/app11104531.

D. H. Apriyanti, L. J. Spreeuwers, P. J. F. Lucas, and R. N. J. Veldhuis, “Automated color detection in orchids using color labels and deep learning,” PLoS One, vol. 16, no. 10, p. e0259036, Oct. 2021, doi: 10.1371/journal.pone.0259036.

I. A. Mohtar and A. A. Mohd Fadzil, “New Species Orchid Recognition System Using Convolutional Neural Network,” Math. Sci. Informatics J., vol. 2, no. 2, pp. 35–43, Nov. 2021, doi: 10.24191/mij.v2i2.14248.

D. H. Apriyanti, L. J. Spreeuwers, and P. J. F. Lucas, “Deep neural networks for explainable feature extraction in orchid identification,” Appl. Intell., vol. 53, no. 21, pp. 26270–26285, Nov. 2023, doi: 10.1007/s10489-023-04880-2.

W. Sarachai, J. Bootkrajang, J. Chaijaruwanich, and S. Somhom, “Orchid classification using homogeneous ensemble of small deep convolutional neural network,” Mach. Vis. Appl., vol. 33, no. 1, p. 17, Jan. 2022, doi: 10.1007/s00138-021-01267-6.

K. Xiao, L. Zhou, H. Yang, and L. Yang, “Phalaenopsis growth phase classification using convolutional neural network,” Smart Agric. Technol., vol. 2, p. 100060, Dec. 2022, doi: 10.1016/j.atech.2022.100060.

M. Das, C. K. Deb, R. Pal, and S. Marwaha, “A Machine Learning Approach for the Non-Destructive Estimation of Leaf Area in Medicinal Orchid Dendrobium nobile L.,” Appl. Sci., vol. 12, no. 9, p. 4770, May 2022, doi: 10.3390/app12094770.

C.-F. Tsai et al., “Intelligent image analysis recognizes important orchid viral diseases,” Front. Plant Sci., vol. 13, Dec. 2022, doi: 10.3389/fpls.2022.1051348.

P. S. Sundari and M. Khafidz Putra, “Optimization house price prediction model using gradient boosted regression trees (GBRT) and xgboost algorithm,” J. Student Res. Explor., vol. 2, no. 1, Sep. 2023, doi: 10.52465/josre.v2i1.176.

H. Hadiq, S. Solehatin, D. Djuniharto, M. A. Muslim, and S. N. Salahudin, “Comparison of the suitability of the otsu method thresholding and multilevel thresholding for flower image segmentation,” J. Soft Comput. Explor., vol. 4, no. 4, pp. 242–249, Dec. 2023, doi: 10.52465/joscex.v4i4.266.

Q. Fu, X. Zhang, F. Zhao, R. Ruan, L. Qian, and C. Li, “Deep Feature Extraction for Cymbidium Species Classification Using Global–Local CNN,” Horticulturae, vol. 8, no. 6, p. 470, May 2022, doi: 10.3390/horticulturae8060470.

L. Shuai, H. Liu, J. Li, and Y. Wang, FT-MIR combined with 3DCOS-ResNet model for rapid identification of wild and cultivated Gastrodia elata. 2023. doi: 10.21203/rs.3.rs-2855573/v1.

M. Seeland, M. Rzanny, D. Boho, J. Wäldchen, and P. Mäder, “Image-based classification of plant genus and family for trained and untrained plant species,” BMC Bioinformatics, vol. 20, no. 1, p. 4, Dec. 2019, doi: 10.1186/s12859-018-2474-x.

A. Hamid et al., “Decision Support System for Program Indonesia Pintar Recipients Using the Fuzzy Multi-Criteria Decision-Making Method,” J. Inf. Syst. Explor. Res., vol. 1, no. 2, Jun. 2023, doi: 10.52465/joiser.v1i2.157.

V. Praskatama, C. A. Sari, E. H. Rachmawanto, and N. Mohd Yaacob, “Pneumonia Prediction Using Convolutional Neural Network,” J. Tek. Inform., vol. 4, no. 5, pp. 1217–1226, Oct. 2023, doi: 10.52436/1.jutif.2023.4.5.1353.

X. Zhang et al., “Hierarchical bilinear convolutional neural network for image classification,” IET Comput. Vis., vol. 15, no. 3, pp. 197–207, Apr. 2021, doi: 10.1049/cvi2.12023.

J. Wang, H. Wang, Y. Long, and Y. Lan, “An Improved Classification Model Based on Feature Fusion for Orchid Species,” J. Electr. Eng. Technol., vol. 19, no. 3, pp. 1955–1964, Mar. 2024, doi: 10.1007/s42835-023-01705-7.

E. H. Rachmawanto et al., “Eggs Classification based on Egg Shell Image using K-Nearest Neighbors Classifier,” in 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), IEEE, Sep. 2020, pp. 50–54. doi: 10.1109/iSemantic50169.2020.9234305.

S. Arwatchananukul, K. Kirimasthong, and N. Aunsri, “A New Paphiopedilum Orchid Database and Its Recognition Using Convolutional Neural Network,” Wirel. Pers. Commun., vol. 115, no. 4, pp. 3275–3289, Dec. 2020, doi: 10.1007/s11277-020-07463-3.

D. Hindarto and N. Amalia, “Implementation of Flower Recognition using Convolutional Neural Networks,” Int. J. Softw. Eng. Comput. Sci., vol. 3, no. 3, pp. 341–351, Dec. 2023, doi: 10.35870/ijsecs.v3i3.1808.

A. Susanto, I. U. W. Mulyono, C. A. Sari, E. H. Rachmawanto, D. R. I. M. Setiadi, and M. K. Sarker, “Improved Javanese script recognition using custom model of convolution neural network,” Int. J. Electr. Comput. Eng., vol. 13, no. 6, p. 6629, Dec. 2023, doi: 10.11591/ijece.v13i6.pp6629-6636.

N. R. D. Cahyo, C. A. Sari, E. H. Rachmawanto, C. Jatmoko, R. R. A. Al-Jawry, and M. A. Alkhafaji, “A Comparison of Multi Class Support Vector Machine vs Deep Convolutional Neural Network for Brain Tumor Classification,” in 2023 International Seminar on Application for Technology of Information and Communication (iSemantic), IEEE, Sep. 2023, pp. 358–363. doi: 10.1109/iSemantic59612.2023.10295336.

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