Tomato classification with YOLOv8: Enhancing automated sorting and quality assessment
This study presents the design and implementation of an automated system for sorting and measuring kidney tomatoes using a YOLOv8 model with a size estimation algorithm. The proposed system integrates computer vision and deep learning with a physical sorting mechanism to categorize tomatoes into thr...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525004526 |
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| author | Viviana Moya Michael Guerra Karina Pazmiño Faruk Abedrabbo Fernando A. Chicaiza David Pozo-Espín |
| author_facet | Viviana Moya Michael Guerra Karina Pazmiño Faruk Abedrabbo Fernando A. Chicaiza David Pozo-Espín |
| author_sort | Viviana Moya |
| collection | DOAJ |
| description | This study presents the design and implementation of an automated system for sorting and measuring kidney tomatoes using a YOLOv8 model with a size estimation algorithm. The proposed system integrates computer vision and deep learning with a physical sorting mechanism to categorize tomatoes into three classes: green, red, and damaged, while also determining their size. The classification model was trained on a dataset of 2,145 images of tomatoes taken from different sources and lighting conditions to enhance performance during training. The implemented prototype consists of a conveyor belt equipped with sensors and a high-resolution camera to capture and analyse tomato characteristics in real-time. A servo-driven sorting mechanism then directs the classified tomatoes into their respective bins. Experimental validation and testing show that the model achieves a classification accuracy of 99.6% and a size estimation accuracy of 97.1%, aiding in a reliable and efficient post-harvest sorting process. The proposed system not only reduces the probability of human error but also improves the precision of tomato classification. Future developments will focus on refining and adapting existing AI methodologies to improve their effectiveness in agricultural environments. This includes enhancing model robustness, improving classification accuracy under real-world conditions, and tailoring AI tools to better meet the demands of industrial tomato sorting. |
| format | Article |
| id | doaj-art-3a9353531e2642e1b465c4926136e710 |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-3a9353531e2642e1b465c4926136e7102025-08-20T02:56:32ZengElsevierSmart Agricultural Technology2772-37552025-12-011210122110.1016/j.atech.2025.101221Tomato classification with YOLOv8: Enhancing automated sorting and quality assessmentViviana Moya0Michael Guerra1Karina Pazmiño2Faruk Abedrabbo3Fernando A. Chicaiza4David Pozo-Espín5Universidad Internacional Del Ecuador UIDE, Facultad de Ciencias Técnicas, Av. Simón Bolivar, Quito, 170411, Quito, EcuadorUniversidad Internacional Del Ecuador UIDE, Facultad de Ciencias Técnicas, Av. Simón Bolivar, Quito, 170411, Quito, EcuadorUniversidad Internacional Del Ecuador UIDE, Facultad de Ciencias Técnicas, Av. Simón Bolivar, Quito, 170411, Quito, EcuadorUniversidad Internacional Del Ecuador UIDE, Facultad de Ciencias Técnicas, Av. Simón Bolivar, Quito, 170411, Quito, EcuadorUniversidad Tecnológica Indoamérica, Centro de Investigación MIST, Facultad de Ingenierías, Calle Agramonte y Av. Manuela Sáenz, Ambato, 180212, Ecuador; Corresponding author.Universidad de las Américas, Ingeniería en Electrónica y Automatización, Facultad de Ingeniería y Ciencias Aplicadas, Vía antigua a Nayón, Quito, 170513, EcuadorThis study presents the design and implementation of an automated system for sorting and measuring kidney tomatoes using a YOLOv8 model with a size estimation algorithm. The proposed system integrates computer vision and deep learning with a physical sorting mechanism to categorize tomatoes into three classes: green, red, and damaged, while also determining their size. The classification model was trained on a dataset of 2,145 images of tomatoes taken from different sources and lighting conditions to enhance performance during training. The implemented prototype consists of a conveyor belt equipped with sensors and a high-resolution camera to capture and analyse tomato characteristics in real-time. A servo-driven sorting mechanism then directs the classified tomatoes into their respective bins. Experimental validation and testing show that the model achieves a classification accuracy of 99.6% and a size estimation accuracy of 97.1%, aiding in a reliable and efficient post-harvest sorting process. The proposed system not only reduces the probability of human error but also improves the precision of tomato classification. Future developments will focus on refining and adapting existing AI methodologies to improve their effectiveness in agricultural environments. This includes enhancing model robustness, improving classification accuracy under real-world conditions, and tailoring AI tools to better meet the demands of industrial tomato sorting.http://www.sciencedirect.com/science/article/pii/S2772375525004526Tomato classificationAgricultural technologyYOLOv8Computer visionAutomated sorting |
| spellingShingle | Viviana Moya Michael Guerra Karina Pazmiño Faruk Abedrabbo Fernando A. Chicaiza David Pozo-Espín Tomato classification with YOLOv8: Enhancing automated sorting and quality assessment Smart Agricultural Technology Tomato classification Agricultural technology YOLOv8 Computer vision Automated sorting |
| title | Tomato classification with YOLOv8: Enhancing automated sorting and quality assessment |
| title_full | Tomato classification with YOLOv8: Enhancing automated sorting and quality assessment |
| title_fullStr | Tomato classification with YOLOv8: Enhancing automated sorting and quality assessment |
| title_full_unstemmed | Tomato classification with YOLOv8: Enhancing automated sorting and quality assessment |
| title_short | Tomato classification with YOLOv8: Enhancing automated sorting and quality assessment |
| title_sort | tomato classification with yolov8 enhancing automated sorting and quality assessment |
| topic | Tomato classification Agricultural technology YOLOv8 Computer vision Automated sorting |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525004526 |
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