Identification of Long Bean Seed Varieties Using Digital Image Processing Coupled With Neural Network Analysis
DOI:
https://doi.org/10.32528/ias.v1i2.164Keywords:
Artificial Neural Network, Computer Vision, Image Processing, Seed VarietyAbstract
Identification of long bean seed varieties can be used to save plant variety and intellectual property rights. Using digital image processing combined with artificial neural networks (ANN) has a possibility to recognize the seed morphology. The purpose of this research is to identify the image variables that can be used to identify long bean seed varieties so that the best algorithm of artificial neural networks can be arranged and the level of accuracy in expecting the long bean varieties. The samples used in this study were long bean seeds of parade tavi, kanton tavi, branjangan, and petiwi varieties. For each variety, 400 samples were taken for training data and 200 samples for testing data, so the total sample was 2400 long bean seeds. The research stages include image acquisition, image retrieval, image variable estimation, image processing program development, data analysis, ANN training, long bean variety identification program preparation, and program validation. The results showed that ANN with 10 hidden layers is the best model to develop a long bean seed identification. The identification program of long bean seed varieties resulting from the integration of image processing with artificial neural networks has an accuracy of 99.75%.
Downloads
References
REFERENCES
I. statistic, “Luas Area, Produksi, Dan Produktivitas Kentang Di Indonesia Pada Tahun 2015-2019,” Www.Bps.Go.Id, vol. 2019, p. 2019, 2020, [Online]. Available: https://www.pertanian.go.id/home/?show=page&act=view&id=61.
K. Nooprom and Q. Santipracha, “Effect of Varieties on Growth and Yield of Yard Long Bean under Songhkla Conditions, Southern Thailand,” Mod. Appl. Sci., vol. 9, no. 13, p. 247, 2015, doi: 10.5539/mas.v9n13p247.
A. B. Endres, “Revising Seed Purity Laws to Account for the Adventitious Presence of Genetically Modified Varieties: A First Step towards Coexistence,” J. Law Policy, vol. 1, no. 1, pp. 131–163, 2005.
A. Jordaan, D. C. J. Wessels, and H. Kruger, “Morphology, ontogeny and functional anatomy of the seeds of Colophospermum mopane,” South African J. Bot., vol. 67, no. 2, pp. 214–229, 2001, doi: 10.1016/S0254-6299(15)31122-4.
S. Kujawa and G. Niedbała, “Artificial neural networks in agriculture,” Agric., vol. 11, no. 6, pp. 1–6, 2021, doi: 10.3390/agriculture11060497.
Y. Sun, Z. Ren, and W. Zheng, “Research on Face Recognition Algorithm Based on Image Processing,” Comput. Intell. Neurosci., vol. 2022, pp. 1–11, Mar. 2022, doi: 10.1155/2022/9224203.
S. SankarNath and P. Rakshit, “A Survey of Image Processing Techniques for Emphysema Detection,” Int. J. Comput. Appl., vol. 114, no. 15, pp. 7–13, 2015, doi: 10.5120/20052-1983.
K. Gayatri, R. D. Kanti, V. C. Sekhar Rao Rayavarapu, B. Sridhar, and V. Rama Gowri Bobbili, “Image processing and Pattern Recognition based Plant Leaf diseases Identification and Classification,” J. Phys. Conf. Ser., vol. 1804, no. 1, p. 012160, Feb. 2021, doi: 10.1088/1742-6596/1804/1/012160.
P. G. J. Barten, “Effect of Color Saturation and Hue on Image Quality,” Soc. Imaging Sci. Technol. Image Process. Image Qual. Image Capture, Syst. Conf., pp. 16–21, 2003.
S. Chen, D. F. Laefer, J. Byrne, and A. S. Natanzi, “The effect of angles and distance on image-based, three-dimensional reconstructions,” in Safety and Reliability – Theory and Applications, Jun. 2017, no. June, pp. 399–399, doi: 10.1201/9781315210469-350.
C. Li, X. Jia, H. Li, L. Deng, and X. Shi, “Digital image processing technology applied in level measurement and control system,” Procedia Eng., vol. 24, pp. 226–231, 2011, doi: 10.1016/j.proeng.2011.11.2631.
C. M. Sabliov, D. Boldor, K. M. Keener, and B. E. Farkas, “Image processing method to determine surface area and volume of axi-symmetric agricultural products,” Int. J. Food Prop., vol. 5, no. 3, pp. 641–653, 2002, doi: 10.1081/JFP-120015498.
C. Gupta, V. K. Tewari, R. Machavaram, and P. Shrivastava, “An image processing approach for measurement of chili plant height and width under field conditions,” J. Saudi Soc. Agric. Sci., vol. 21, no. 3, pp. 171–179, 2022, doi: 10.1016/j.jssas.2021.07.007.
M. S. Gharajeh, “To Measure the Perimeter of an Ellipse Using Image Processing and Mathematical Reasoning,” Int. J. Res. Stud. Comput. Sci. Eng., vol. 4, no. 4, pp. 15–21, 2017, doi: 10.20431/2349-4859.0404002.
D. F. Williamson, R. A. Parker, and J. S. Kendrick, “The box plot: A simple visual method to interpret data,” Ann. Intern. Med., vol. 110, no. 11, pp. 916–921, 1989, doi: 10.7326/0003-4819-110-11-916.
F. Malard, L. Danner, E. Rouzies, J. G. Meyer, and E. Lescop, “EpyNN : Educational python for Neural Networks,” 2021.
S. V. Razavi, M. Z. Jumaat, and A. H. EI-Shafie, “Using feed-forward back propagation (FFBP) neural networks for compressive strength prediction of lightweight concrete made with different percentage of scoria instead of sand,” Int. J. Phys. Sci., vol. 6, no. 6, pp. 1325–1331, 2011, doi: 10.5897/IJPS11.204.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Wahyu Nurkholis Hadi Syahputra, , Dandi Citra Nugraha, Abdul Jalil, Chatchawan Chaichana
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
