Application of Digital Image Processing for Orchid Image Segmentation in Morphological Plant Analysis
https://doi.org/10.35877/454RI.asci3772
Keywords:
Digital Image Processing, Image Segmentation, Orchid, K-means
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Abstract
The deployment of digital image processing in orchid image segmentation for plant morphological analysis is investigated in this study. The goal of this study is to increase the accuracy of orchid species identification using color-based segmentation approaches using 90 photos of three different orchid species—Cattleya, Dendrobium, and Onchidium—that were retrieved from Kaggle. Pre-processing is the first step in the process, which involves shrinking the size of the photos, separating them into RGB components, and converting them to HSV color space for additional analysis. Segmentation is done using the K-Means technique, which clusters pixels according to the color features that have been retrieved. Centroid updates are made until convergence is reached. With an identification accuracy of 92%, the binary and RGB segmentation results show how well this method works to distinguish the flower item from the backdrop. By advancing image processing methods in botany, this study aids in the identification of rare orchid species and conservation initiatives.
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[2] S. Artuso, A. Gamisch, Y. M. Staedler, J. Schönenberger, and H. P. Comes, “Evidence for an evo-devo-derived hypothesis on three-dimensional flower shape modularity in a tropical orchid clade,” Evolution (N. Y)., vol. 76, no. 11, pp. 2587–2604, 2022, doi: 10.1111/evo.14621.
[3] Andi Asrida Reskinah. D, M. Najib, and Muhammad Ashdaq, “Sentiment Analysis on Erspo Jersey in X Using Machine Learning Algorithms,” SAGA J. Technol. Inf. Syst., vol. 2, no. 3, pp. 288–294, Jan. 2025, doi: 10.58905/saga.v2i3.334.
[4] A. Zizka, D. Silvestro, P. Vitt, and T. Knight, “Automated conservation assessment of the orchid family with deep learning,” Conserv. Biol., vol. 35, Nov. 2020, doi: 10.1111/cobi.13616.
[5] 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 October, pp. 1–27, 2021, doi: 10.1371/journal.pone.0259036.
[6] Fajhar Muhammad, Agung Triayudi, and Eri Mardiani, “Implementation of Face Recognition for Lecturer Attendance Using Deep Learning CNN Algorithm,” SaNa J. Blockchain, NFTs Metaverse Technol., vol. 2, no. 2, pp. 123–130, Sep. 2024, doi: 10.58905/sana.v2i2.275.
[7] L. Lufila, Septyan Eka Prastya, and Finki Dona Marleny, “Image Segmentation Of Ornamental Plants Typical Of South Kalimantan Using The Convolutional Neural Network Method,” Install Inf. Syst. Technol. J., vol. 1, no. 1, pp. 15–22, 2024, doi: 10.33859/install.v1i1.544.
[8] J. Lu, L. Tan, and H. Jiang, “Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification,” Agriculture, vol. 11, no. 8, 2021, doi: 10.3390/agriculture11080707.
[9] N. Chaudhary et al., “ORCHID: A Comprehensive Oral Cancer Histology Image Database for Histopathological Analytics and Diagnostics,” medRxiv, vol. 11, no. 1, pp. 1–14, 2023.
[10] A. Nisruti, B. Naga Jogayya, Kothakota , Sheerin, and S. Ampolu, “Identification Of Orchidaceae Species by Using Various Methods: A Review,” J. Emerg. Technol. Innov. Res., vol. 06, no. 4, pp. 7–20, 2020.
[11] A. Andrio, D. Diana, M. Marhalim, and P. Pahrizal, “Application Of The Template Matching Method In Orchid Image Classification In Bengkulu,” J. Komputer, Inf. dan Teknol., vol. 3, no. 1, pp. 151–158, 2023, doi: 10.53697/jkomitek.v3i1.1197.
[12] D. Apriyanti, L. Spreeuwers, and P. J. Lucas, “Deep neural networks for explainable feature extraction in orchid identification,” Appl. Intell., vol. 53, pp. 1–16, Aug. 2023, doi: 10.1007/s10489-023-04880-2.
[13] 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, 2022, doi: 10.3390/app12094770.
[14] R. Ismayanti, F. Alameka, D. Mirwansyah, N. Sari, A. Rahim, and Riyayatsyah, Identification of Pests on Black Orchid Plants Using Naïve Bayes Method Based on Leaf Image Texture. 2022. doi: 10.1109/ICSINTESA56431.2022.10041471.
[15] C. A. Post, “Multilabel Classification of Orchid Features based on Deep Learning,” pp. 1–10, 2020.
[16] P. Andono, E. Rachmawanto, N. Herman, and K. Kondo, “Orchid types classification using supervised learning algorithm based on feature and color extraction,” Bull. Electr. Eng. Informatics, vol. 10, pp. 2530–2538, Oct. 2021, doi: 10.11591/eei.v10i5.3118.
[17] D. S. Dewantara, R. Hidayat, H. Susanto, and A. Arymurthy, CNN with Multi Stage Image Data Augmentation Methods for Indonesia Rare and Protected Orchids Classification. 2020. doi: 10.1109/ICOSICA49951.2020.9243174.
[18] N. Ideastari, A. Sari, E. Faisal, Z. Arifin, A. Krismawan, and M. Muslih, “An optimum hyperparameters of restnet-50 for orchid classification based on convolutional neural network,” J. Soft Comput. Explor., vol. 5, pp. 55–66, Mar. 2024, doi: 10.52465/joscex.v5i1.297.
[19] T. Kattenborn, J. Leitloff, F. Schiefer, and S. Hinz, “Review on Convolutional Neural Networks (CNN) in Vegetation Remote Sensing,” ISPRS J. Photogramm. Remote Sens., vol. 173, pp. 24–49, Mar. 2021, doi: 10.1016/j.isprsjprs.2020.12.010.
[20] L.-B. Chen, G.-Z. Huang, X.-R. Huang, and W.-C. Wang, “A Self-Supervised Learning-Based Intelligent Greenhouse Orchid Growth Inspection System for Precision Agriculture,” IEEE Sens. J., vol. 22, pp. 24567–24577, Dec. 2022, doi: 10.1109/JSEN.2022.3221960.
[21] S. Ahmed et al., “Use of remote sensing and image processing for identification of wild orchids,” Front. Environ. Sci., vol. 12, Apr. 2024, doi: 10.3389/fenvs.2024.1371445.
[22] M. Forero, C. Beltrán, A. Troncoso, and C. Gonzalez Santos, “Classification of Cattleya Trianae and Its Varieties by Using Colorimetry,” 2020, pp. 35–44. doi: 10.1007/978-3-030-49076-8_4.
[23] B. Tugrul, E. Elfatimi, and R. Eryigit, “Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review,” Agriculture, vol. 12, no. 8, 2022, doi: 10.3390/agriculture12081192.
[24] S. Khandelwal, A. Raut, H. Vyawahare, D. Theng, and S. Dhande, “Optimizing Performance in Mango Plant Leaf Disease Classification through Advanced Machine Learning Techniques,” Eng. Technol. Appl. Sci. Res., vol. 14, no. 6, pp. 18476–18480, Dec. 2024, doi: 10.48084/etasr.8220.
[25] J. Gao, A. French, M. Pound, Y. He, T. Pridmore, and J. Pieters, “Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields,” Plant Methods, vol. 16, Mar. 2020, doi: 10.1186/s13007-020-00570-z.
Published
2025-04-30
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How to Cite
Riza, B. S., Rosnelly, R., & Haryanto S., E. V. (2025). Application of Digital Image Processing for Orchid Image Segmentation in Morphological Plant Analysis. Journal of Applied Science, Engineering, Technology, and Education, 7(1), 94-101. https://doi.org/10.35877/454RI.asci3772
Copyright (c) 2025 Bob Subhan Riza, Rika Rosnelly, Edy Victor Haryanto S. (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
