An essential vegetable that is grown all over the world and eaten in a variety of ways is the onion (Allium cepa L.). A common condiment used to improve food flavor is onion. Around the world, red onion seed (A. fistulosum) is cultivated in a variety of temperate and tropical settings. It is grown in China and Japan, among other places, worldwide. A. fistulosum is grown across Ethiopia in various regions. In 2012, 3,281,574 tons of output were obtained from 30,478 hectares of coverage. Allium fistulosum covers the Amhara area over 8000 hectors, which is 26% of our country. For export, red onion seed is separated based on quality. Red onion seed quality separation or categorization is essential to the trade process. It aids in making people marketable. In Ethiopia, this procedure is carried out manually, which has a number of drawbacks like being less effective, inconsistent, and prone to subjectivity. To address this problem, we use pre-trained transfer learning model VGG, GoogleNet, and ResNet50 for quality classification of red onion seed. The main procedures include image preprocessing, resizing, data augmentation, and prediction. The model trained on 470 datasets collected from different agricultural fields in south Gondar libo kemkem and fogera woreda. We use various augmentation strategies to expand the dataset. Ten percent of the dataset was set aside for testing, ten percent for validation, and eighty percent for training. For VGG19, VGG16, GoogleNet, and ResNet, the model's classification accuracy for the input image is 99%, 100%, 100%, and 86%, respectively.
| Published in | Machine Learning Research (Volume 10, Issue 1) |
| DOI | 10.11648/j.mlr.20251001.15 |
| Page(s) | 44-52 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Allium Fitsulosum, Red Onion Seed, Visual Geometry Group, GoogleNet, ResNet, Pretrained Models, Transfer Learning
| [1] | C. W. Gathambiri, W. O. Owino, S. Imathiu, and J. N. Mbaka, “Postharvest Losses Of Bulb Onion (Allium Cepa L.) In Selected Sub-Counties Of Kenya,” African J. Food, Agric. Nutr. Dev., vol. 21, no. 2, pp. 17529–17544, 2021, |
| [2] | M. A. Zaki, S. Narejo, M. Ahsan, S. Zai, M. R. Anjum, and N. Din, “Image-based Onion Disease (Purple Blotch) Detection using Deep Convolutional Neural Network,” vol. 12, no. 5, pp. 448–458, 2021. |
| [3] | T. Project, S. Horticulture, F. Empowerment, and M. Agriculture, “የቀይ ሽንኩርት አመራረት,” ["Onion Production,"]2019. |
| [4] | N. A. P. Lestari, R. Dijaya, and N. L. Azizah, “Identification Growth Quality of Red Onion during Planting Period using Support Vector Machine,” J. Phys. Conf. Ser., vol. 1764, no. 1, 2021, |
| [5] | M. H. Ahmed, “College of Natural Sciences Automatic Soybean Quality Grading Using Image Processing and Supervised Learning Algorithms,” no. October, 2021. |
| [6] | S. Afaq et al., “Smart Agricultural Technology Automatic and fast classification of barley grains from images: A deep learning approach,” Smart Agric. Technol., vol. 2, no. December 2021, p. 100036, 2022, |
| [7] | M. Habtewold, C. Dane, A. Ayza, N. Resource, M. Directorate, and A. Agricultural, “Participatory Evaluation and Demonstration of Onion Spacing in Irrigated Agriculture at Kencho Kebele in Uba Debre Tsehay Woreda, Southern Ethiopia,” vol. 31, no. 2, pp. 105–114, 2021. |
| [8] | N. Semman, G. Etana, and T. Mulualem, “Adaptability and yield performance evaluation of onion (Allium cepa L.) varieties in Jimma zone, Southwestern Ethiopia.,” vol. 9, no. 4, pp. 405–409, 2019, |
APA Style
Yirdaw, T. W., Tadesse, E. M., Hiwote, E., Mebrate, A., Mulatu, A. (2025). Red Onion Seed Quality Classification Using Transfer Learning Approaches. Machine Learning Research, 10(1), 44-52. https://doi.org/10.11648/j.mlr.20251001.15
ACS Style
Yirdaw, T. W.; Tadesse, E. M.; Hiwote, E.; Mebrate, A.; Mulatu, A. Red Onion Seed Quality Classification Using Transfer Learning Approaches. Mach. Learn. Res. 2025, 10(1), 44-52. doi: 10.11648/j.mlr.20251001.15
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.mlr.20251001.15,
author = {Tarekegn Walle Yirdaw and Ermias Melku Tadesse and Endalkachew Hiwote and Abebaw Mebrate and Ambaw Mulatu},
title = {Red Onion Seed Quality Classification Using Transfer Learning Approaches
},
journal = {Machine Learning Research},
volume = {10},
number = {1},
pages = {44-52},
doi = {10.11648/j.mlr.20251001.15},
url = {https://doi.org/10.11648/j.mlr.20251001.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20251001.15},
abstract = {An essential vegetable that is grown all over the world and eaten in a variety of ways is the onion (Allium cepa L.). A common condiment used to improve food flavor is onion. Around the world, red onion seed (A. fistulosum) is cultivated in a variety of temperate and tropical settings. It is grown in China and Japan, among other places, worldwide. A. fistulosum is grown across Ethiopia in various regions. In 2012, 3,281,574 tons of output were obtained from 30,478 hectares of coverage. Allium fistulosum covers the Amhara area over 8000 hectors, which is 26% of our country. For export, red onion seed is separated based on quality. Red onion seed quality separation or categorization is essential to the trade process. It aids in making people marketable. In Ethiopia, this procedure is carried out manually, which has a number of drawbacks like being less effective, inconsistent, and prone to subjectivity. To address this problem, we use pre-trained transfer learning model VGG, GoogleNet, and ResNet50 for quality classification of red onion seed. The main procedures include image preprocessing, resizing, data augmentation, and prediction. The model trained on 470 datasets collected from different agricultural fields in south Gondar libo kemkem and fogera woreda. We use various augmentation strategies to expand the dataset. Ten percent of the dataset was set aside for testing, ten percent for validation, and eighty percent for training. For VGG19, VGG16, GoogleNet, and ResNet, the model's classification accuracy for the input image is 99%, 100%, 100%, and 86%, respectively.
},
year = {2025}
}
TY - JOUR T1 - Red Onion Seed Quality Classification Using Transfer Learning Approaches AU - Tarekegn Walle Yirdaw AU - Ermias Melku Tadesse AU - Endalkachew Hiwote AU - Abebaw Mebrate AU - Ambaw Mulatu Y1 - 2025/04/29 PY - 2025 N1 - https://doi.org/10.11648/j.mlr.20251001.15 DO - 10.11648/j.mlr.20251001.15 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 44 EP - 52 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20251001.15 AB - An essential vegetable that is grown all over the world and eaten in a variety of ways is the onion (Allium cepa L.). A common condiment used to improve food flavor is onion. Around the world, red onion seed (A. fistulosum) is cultivated in a variety of temperate and tropical settings. It is grown in China and Japan, among other places, worldwide. A. fistulosum is grown across Ethiopia in various regions. In 2012, 3,281,574 tons of output were obtained from 30,478 hectares of coverage. Allium fistulosum covers the Amhara area over 8000 hectors, which is 26% of our country. For export, red onion seed is separated based on quality. Red onion seed quality separation or categorization is essential to the trade process. It aids in making people marketable. In Ethiopia, this procedure is carried out manually, which has a number of drawbacks like being less effective, inconsistent, and prone to subjectivity. To address this problem, we use pre-trained transfer learning model VGG, GoogleNet, and ResNet50 for quality classification of red onion seed. The main procedures include image preprocessing, resizing, data augmentation, and prediction. The model trained on 470 datasets collected from different agricultural fields in south Gondar libo kemkem and fogera woreda. We use various augmentation strategies to expand the dataset. Ten percent of the dataset was set aside for testing, ten percent for validation, and eighty percent for training. For VGG19, VGG16, GoogleNet, and ResNet, the model's classification accuracy for the input image is 99%, 100%, 100%, and 86%, respectively. VL - 10 IS - 1 ER -
Information System Department, Kombolcha Institute of Technology, Wollo University, Kombolcha, Ethiopia
Department of Information Technology, Kombolcha Institute of Technology, Wollo University, Kombolcha, Ethiopia
Information