Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks
Due to the computational power and memory of modern computers, computer vision techniques and neural networks can be used to develop a visual inspection system of agricultural products to satisfy product quality requirements. This chapter employs artificial vision techniques to classify seeds in RGB...
- Autores:
-
Suarez, Oscar J.
Macias-Garcia, Edgar
Vega, Carlos J.
Peñaloza, Yersica C.
Hernández Díaz, Nicolás
Garrido, Victor M.
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12322
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12322
- Palabra clave:
- Object Detection;
Deep Learning;
IOU
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks |
| title |
Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks |
| spellingShingle |
Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks Object Detection; Deep Learning; IOU LEMB |
| title_short |
Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks |
| title_full |
Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks |
| title_fullStr |
Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks |
| title_full_unstemmed |
Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks |
| title_sort |
Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks |
| dc.creator.fl_str_mv |
Suarez, Oscar J. Macias-Garcia, Edgar Vega, Carlos J. Peñaloza, Yersica C. Hernández Díaz, Nicolás Garrido, Victor M. |
| dc.contributor.author.none.fl_str_mv |
Suarez, Oscar J. Macias-Garcia, Edgar Vega, Carlos J. Peñaloza, Yersica C. Hernández Díaz, Nicolás Garrido, Victor M. |
| dc.subject.keywords.spa.fl_str_mv |
Object Detection; Deep Learning; IOU |
| topic |
Object Detection; Deep Learning; IOU LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
Due to the computational power and memory of modern computers, computer vision techniques and neural networks can be used to develop a visual inspection system of agricultural products to satisfy product quality requirements. This chapter employs artificial vision techniques to classify seeds in RGB images. As a first step, an algorithm based on pixel intensity threshold is developed to detect and classify a set of different seed types, such as rice, beans, and lentils. Then, the information inferred by this algorithm is exploited to develop a neural network model, which successfully achieves learning classification and detection tasks through a semantic-segmentation scheme. The applicability and satisfactory performance of the proposed algorithms are illustrated by testing with real images, achieving an average accuracy of 92% in the selected set of classes. The experimental results verify that both algorithms can directly detect and classify the proposed set of seeds in input RGB images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
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2023 |
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2023-07-21T16:21:09Z |
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2023-07-21T16:21:09Z |
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2023 |
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2023 |
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Suarez, O. J., Macias-Garcia, E., Vega, C. J., Peñaloza, Y. C., Díaz, N. H., & Garrido, V. M. (2022, July). Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks. In IEEE Colombian Conference on Applications of Computational Intelligence (pp. 1-17). Cham: Springer Nature Switzerland. |
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https://hdl.handle.net/20.500.12585/12322 |
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10.1007/978-3-031-29783-0_1 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Suarez, O. J., Macias-Garcia, E., Vega, C. J., Peñaloza, Y. C., Díaz, N. H., & Garrido, V. M. (2022, July). Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks. In IEEE Colombian Conference on Applications of Computational Intelligence (pp. 1-17). Cham: Springer Nature Switzerland. 10.1007/978-3-031-29783-0_1 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12322 |
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eng |
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eng |
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17 páginas |
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Suarez, Oscar J.b4e56031-cf82-4988-acc0-d6b98038e418Macias-Garcia, Edgarbdab05ee-11d6-46a1-af9e-6b1784dfa2acVega, Carlos J.1b4832f5-df5b-47fa-a9e4-b51914850ea4Peñaloza, Yersica C.f1fa3225-5d02-4d24-a1ae-29295105c3e6Hernández Díaz, Nicolás63e39a63-8a3f-4573-bde7-cd014effbf78Garrido, Victor M.f32cfdd6-b0c1-4f80-bba4-29bceab1320a2023-07-21T16:21:09Z2023-07-21T16:21:09Z20232023Suarez, O. J., Macias-Garcia, E., Vega, C. J., Peñaloza, Y. C., Díaz, N. H., & Garrido, V. M. (2022, July). Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks. In IEEE Colombian Conference on Applications of Computational Intelligence (pp. 1-17). Cham: Springer Nature Switzerland.https://hdl.handle.net/20.500.12585/1232210.1007/978-3-031-29783-0_1Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarDue to the computational power and memory of modern computers, computer vision techniques and neural networks can be used to develop a visual inspection system of agricultural products to satisfy product quality requirements. This chapter employs artificial vision techniques to classify seeds in RGB images. As a first step, an algorithm based on pixel intensity threshold is developed to detect and classify a set of different seed types, such as rice, beans, and lentils. Then, the information inferred by this algorithm is exploited to develop a neural network model, which successfully achieves learning classification and detection tasks through a semantic-segmentation scheme. The applicability and satisfactory performance of the proposed algorithms are illustrated by testing with real images, achieving an average accuracy of 92% in the selected set of classes. The experimental results verify that both algorithms can directly detect and classify the proposed set of seeds in input RGB images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.17 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Communications in Computer and Information ScienceDesign of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networksinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Object Detection;Deep Learning;IOULEMBCartagena de IndiasBen-Daya, M., Hassini, E., Bahroun, Z., Banimfreg, B.H. The role of internet of things in food supply chain quality management: A review (2020) Quality Management Journal, 28 (1), pp. 17-40. Cited 38 times. https://www.tandfonline.com/doi/full/10.1080/10686967.2020.1838978 doi: 10.1080/10686967.2020.1838978Schweizer, M., Kolar, J.W. Design and implementation of a highly efficient three-level T-type converter for low-voltage applications (2013) IEEE Transactions on Power Electronics, 28 (2), art. no. 6213134, pp. 899-907. Cited 5093 times. doi: 10.1109/TPEL.2012.2203151Constante, P., Gordon, A., Chang, O., Pruna, E., Acuna, F., Escobar, I. Artificial Vision Techniques to Optimize Strawberrys Industrial Classification (2016) IEEE Latin America Transactions, 14 (6), art. no. 7555221, pp. 2576-2581. Cited 29 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9907 doi: 10.1109/TLA.2016.7555221Dana, W., Ivo, W. Computer image analysis of seed shape and seed color for flax cultivar description (2008) Computers and Electronics in Agriculture, 61 (2), pp. 126-135. Cited 85 times. doi: 10.1016/j.compag.2007.10.001Schweizer, M., Kolar, J.W. Design and implementation of a highly efficient three-level T-type converter for low-voltage applications (2013) IEEE Transactions on Power Electronics, 28 (2), art. no. 6213134, pp. 899-907. Cited 5093 times. doi: 10.1109/TPEL.2012.2203151Galdelli, A., D’imperio, M., Marchello, G., Mancini, A., Scaccia, M., Sasso, M., Frontoni, E., (...), Cannella, F. A Novel Remote Visual Inspection System for Bridge Predictive Maintenance (2022) Remote Sensing, 14 (9), art. no. 2248. Cited 6 times. https://www.mdpi.com/2072-4292/14/9/2248/pdf?version=1651926784 doi: 10.3390/rs14092248Golnabi, H., Asadpour, A. Design and application of industrial machine vision systems (2007) Robotics and Computer-Integrated Manufacturing, 23 (6), pp. 630-637. 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A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products (Open Access) (2022) Frontiers in Nutrition, 9, art. no. 901342. Cited 14 times. journal.frontiersin.org/journal/nutrition doi: 10.3389/fnut.2022.901342Misra, N., Dixit, Y., Al-Mallahi, A., Bhullar, M., Upadhyay, R., Martynenko, A. IoT, big data and artificial intelligence in agriculture and food industry (2020) IEEE Internet of Things. Cited 42 times.Negrete, J. Artificial vision in Mexican agriculture, a new technology for increase food security (2018) Manag. Econ. J, pp. 381-398. Cited 3 times.Newman, T.S. Survey of automated visual inspection (Open Access) (1995) Computer Vision and Image Understanding, 61 (2), pp. 231-262. Cited 530 times. doi: 10.1006/cviu.1995.1017Ravikumar, S., Ramachandran, K.I., Sugumaran, V. Machine learning approach for automated visual inspection of machine components (Open Access) (2011) Expert Systems with Applications, 38 (4), pp. 3260-3266. 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