Botanical vegetables recognition on Raspberry Pi using single shot detector (SSD)
Advancements in computer vision technologies have fueled research interest in automating object detection, particularly in agricultural contexts. Human eyes prone to error during the sorting process when differentiating the various types of botanical vegetables such as bell pepper (capsicum), chili,...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UTHM
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/115454/ http://psasir.upm.edu.my/id/eprint/115454/1/115454.pdf |
| _version_ | 1848866780698640384 |
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| author | Iqbal Mortadza, M. Shah Zainudin, M.N. Idris, M.I. Mohd Saad, W.H. Kamarudin, M.R. Napiah, Z.A.F.M. Nizam, Nurul Zarirah Muhammad, Sufri |
| author_facet | Iqbal Mortadza, M. Shah Zainudin, M.N. Idris, M.I. Mohd Saad, W.H. Kamarudin, M.R. Napiah, Z.A.F.M. Nizam, Nurul Zarirah Muhammad, Sufri |
| author_sort | Iqbal Mortadza, M. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Advancements in computer vision technologies have fueled research interest in automating object detection, particularly in agricultural contexts. Human eyes prone to error during the sorting process when differentiating the various types of botanical vegetables such as bell pepper (capsicum), chili, tomatoes, etc. Hence, the use an object detection method is believed could categorize this botanical vegetables precisely, allowing farmers to optimize their operations and reduce labor expenses. This study explores the identification of various botanical vegetables types using a Raspberry Pi and the Single Shot Detector (SSD). The proposed approach involves curating an extensive botanical vegetables dataset with detailed annotations to optimize training process. Implementing SSD on the Raspberry Pi capitalizes on its processing power and versatility. Our research demonstrates the system's effectiveness in detecting a wide range of botanical vegetables, including chili, capsicum, tomatoes, and vegetable leaf, achieving an average precision of 89% across diverse environmental conditions. Computational efficiency analysis showcases its real-time vegetable detection capabilities, rendering it suitable for agricultural applications such as automated sorting, inventory management, and quality monitoring. |
| first_indexed | 2025-11-15T14:26:03Z |
| format | Article |
| id | upm-115454 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:26:03Z |
| publishDate | 2024 |
| publisher | Penerbit UTHM |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1154542025-03-04T08:15:30Z http://psasir.upm.edu.my/id/eprint/115454/ Botanical vegetables recognition on Raspberry Pi using single shot detector (SSD) Iqbal Mortadza, M. Shah Zainudin, M.N. Idris, M.I. Mohd Saad, W.H. Kamarudin, M.R. Napiah, Z.A.F.M. Nizam, Nurul Zarirah Muhammad, Sufri Advancements in computer vision technologies have fueled research interest in automating object detection, particularly in agricultural contexts. Human eyes prone to error during the sorting process when differentiating the various types of botanical vegetables such as bell pepper (capsicum), chili, tomatoes, etc. Hence, the use an object detection method is believed could categorize this botanical vegetables precisely, allowing farmers to optimize their operations and reduce labor expenses. This study explores the identification of various botanical vegetables types using a Raspberry Pi and the Single Shot Detector (SSD). The proposed approach involves curating an extensive botanical vegetables dataset with detailed annotations to optimize training process. Implementing SSD on the Raspberry Pi capitalizes on its processing power and versatility. Our research demonstrates the system's effectiveness in detecting a wide range of botanical vegetables, including chili, capsicum, tomatoes, and vegetable leaf, achieving an average precision of 89% across diverse environmental conditions. Computational efficiency analysis showcases its real-time vegetable detection capabilities, rendering it suitable for agricultural applications such as automated sorting, inventory management, and quality monitoring. Penerbit UTHM 2024 Article PeerReviewed text en cc_by_nc_sa_4 http://psasir.upm.edu.my/id/eprint/115454/1/115454.pdf Iqbal Mortadza, M. and Shah Zainudin, M.N. and Idris, M.I. and Mohd Saad, W.H. and Kamarudin, M.R. and Napiah, Z.A.F.M. and Nizam, Nurul Zarirah and Muhammad, Sufri (2024) Botanical vegetables recognition on Raspberry Pi using single shot detector (SSD). Journal of Science and Technology, 16 (1). pp. 56-64. ISSN 2229-8460; eISSN: 2600-7924 https://publisher.uthm.edu.my/ojs/index.php/JST/article/view/16367/6536 10.30880/jst.2024.16.01.006 |
| spellingShingle | Iqbal Mortadza, M. Shah Zainudin, M.N. Idris, M.I. Mohd Saad, W.H. Kamarudin, M.R. Napiah, Z.A.F.M. Nizam, Nurul Zarirah Muhammad, Sufri Botanical vegetables recognition on Raspberry Pi using single shot detector (SSD) |
| title | Botanical vegetables recognition on Raspberry Pi using single shot detector (SSD) |
| title_full | Botanical vegetables recognition on Raspberry Pi using single shot detector (SSD) |
| title_fullStr | Botanical vegetables recognition on Raspberry Pi using single shot detector (SSD) |
| title_full_unstemmed | Botanical vegetables recognition on Raspberry Pi using single shot detector (SSD) |
| title_short | Botanical vegetables recognition on Raspberry Pi using single shot detector (SSD) |
| title_sort | botanical vegetables recognition on raspberry pi using single shot detector (ssd) |
| url | http://psasir.upm.edu.my/id/eprint/115454/ http://psasir.upm.edu.my/id/eprint/115454/ http://psasir.upm.edu.my/id/eprint/115454/ http://psasir.upm.edu.my/id/eprint/115454/1/115454.pdf |