Deep learning model for recognizing fresh and rotten fruits in industrial processes
| dc.contributor.author | Carlos Arias | eng |
| dc.contributor.author | Camilo Baldovino | eng |
| dc.contributor.author | José Gómez | eng |
| dc.contributor.author | Brian Restrepo | eng |
| dc.contributor.author | Sánchez, Sergio | eng |
| dc.date.accessioned | 2025-02-06 00:00:00 | |
| dc.date.accessioned | 2025-08-16T14:15:16Z | |
| dc.date.available | 2025-02-06 00:00:00 | |
| dc.date.issued | 2025-02-06 | |
| dc.description.abstract | The detection of fruit condition is essential to ensure quality control in industrial processes. Currently, this task is often performed manually, which is inefficient and time-consuming for operators. Therefore, it is crucial to implement emerging technologies that reduce human effort, costs, and production time while enabling more effective defect detection in fruits. In this context, this work presents the implementation of an artificial intelligence model based on computer vision to identify the condition of fruits. Various models were compared, including YOLOv8, YOLOv11, Detectron2, and Fast R-CNN, trained on a dataset that classifies fruits into two categories: ripe and rotten. The models were evaluated in terms of accuracy, speed, and robustness under different lighting and background conditions to select the most suitable for real-time applications. The results showed that YOLOv8 achieved the best generalization, reaching a mAP@50 of 83.8% and an accuracy of 77.3%. | eng |
| dc.format.mimetype | application/pdf | eng |
| dc.identifier.doi | 10.32397/tesea.vol6.n1.811 | |
| dc.identifier.eissn | 2745-0120 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12585/14168 | |
| dc.identifier.url | https://doi.org/10.32397/tesea.vol6.n1.811 | |
| dc.language.iso | eng | eng |
| dc.publisher | Universidad Tecnológica de Bolívar | eng |
| dc.relation.bitstream | https://revistas.utb.edu.co/tesea/article/download/811/453 | |
| dc.relation.citationedition | Núm. 1 , Año 2025 : Transactions on Energy Systems and Engineering Applications | eng |
| dc.relation.citationendpage | 14 | |
| dc.relation.citationissue | 1 | eng |
| dc.relation.citationstartpage | 1 | |
| dc.relation.citationvolume | 6 | eng |
| dc.relation.ispartofjournal | Transactions on Energy Systems and Engineering Applications | eng |
| dc.relation.references | Guillermo Aznarán Castillo. La competitividad global agrícola. UNMSM, 2003. [2] Andres Vergara Narvaez and Marcos Alvarez Herrera. Análisis de la competitividad agrícola del departamento de sucre. Tesis de pregrado, Universidad Tecnológica de Bolívar, 2023. [3] Francisco Javier Maza Ávila, Juan Carlos Vergara Schmalbach, Gustavo Adolfo Herrera Seba, Anny del Mar Agámez Arias, and Walter José Mejía Valeta. Potencialidad de la capacidad agrícola de la zona de desarrollo económico y social-zodes montes de maría del departamento de bolívar-colombia. Desarrollo Regional y Competitividad, 13, 2012. [4] Ivan D Mardini G, Christian G Quintero M, César A Viloria N, Winston S Percybrooks B, Heydy S Robles N, and Karen Villalba R. A deep-learning-based grading system (asag) for reading comprehension assessment by using aphorisms as open-answer-questions. Education and Information Technologies, 29(4):4565–4590, 2024. [5] María Valle, Jairo A Cardona, Cesar Viloria-Nunez, et al. Predicting atmospheric dispersion of industrial chemicals using machine learning approaches. IEEE Access, 2025. [6] Bethsy Guerrero, César Viloria-Núñez, Miguel Ángel Jimeno Paba, et al. Enhancing disaster management through multi-objective water wave optimization for medical supplies storage and distribution. Transactions on Energy Systems and Engineering Applications, 5(2):1–12, 2024. [7] Mukhriddin Mukhiddinov, Azamjon Muminov, and Jinsoo Cho. Improved classification approach for fruits and vegetables freshness based on deep learning. Sensors, 22(21):8192, 2022. [8] Aafreen Kazi and Siba Prasada Panda. Determining the freshness of fruits in the food industry by image classification using transfer learning. Multimedia Tools and Applications, 81(6):7611–7624, 2022. [9] Archana Chougule, Apurva Pawar, Rahul Kamble, Juned Mujawar, and Akshay Bhide. Recognizing fresh and rotten fruits using deep learning techniques. In Data Engineering and Intelligent Computing: Proceedings of ICICC 2020, pages 205–212. Springer, 2021. [10] Sovon Chakraborty, FM Javed Mehedi Shamrat, Md Masum Billah, Md Al Jubair, Md Alauddin, and Rumesh Ranjan. Implementation of deep learning methods to identify rotten fruits. In 2021 5th international conference on trends in electronics and informatics (ICOEI), pages 1207–1212. IEEE, 2021. [11] Abha Singh, Gayatri Vaidya, Vishal Jagota, Daniel Amoako Darko, Ravindra Kumar Agarwal, Sandip Debnath, and Erich Potrich. Recent advancement in postharvest loss mitigation and quality management of fruits and vegetables using machine learning frameworks. Journal of Food Quality, 2022(1):6447282, 2022. [12] Google Cloud. Ai and machine learning products. https://cloud.google.com/products/ai, 2023. Accessed: 2024-12-01. [13] Google Cloud. Introduction to vertex ai. https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform?hl=es- 419, 2023. Accessed: 2024-12-01. [14] Amazon Web Services. ¿qué es amazon sagemaker? https://docs.aws.amazon.com/es_es/sagemaker/latest/dg/whatis.html, 2023. Accessed: 2024-12-01. [15] Amazon Web Services. Amazon sagemaker. https://aws.amazon.com/es/sagemaker/, 2023. Accessed: 2024-12-01. [16] Yuhang Fu, Minh Nguyen, and Wei Qi Yan. Grading methods for fruit freshness based on deep learning. SN Computer Science, 3(4):264, 2022. [17] Feng Xiao, Haibin Wang, Yaoxiang Li, Ying Cao, Xiaomeng Lv, and Guangfei Xu. Object detection and recognition techniques based on digital image processing and traditional machine learning for fruit and vegetable harvesting robots: an overview and review. Agronomy, 13(3):639, 2023. [18] Youness Bahaddou, Lahcen Tamym, and Lyes Benyoucef. Ensuring fruits and vegetables freshness in sustainable agricultural supply chain networks: A deep learning approach. In IFIP International Conference on Advances in Production Management Systems, pages 364–378. Springer, 2024. [19] Hanwen Kang and Chao Chen. Fruit detection, segmentation and 3d visualisation of environments in apple orchards. Computers and Electronics in Agriculture, 171:105302, 2020. [20] Diwakar Agarwal and Anuja Bhargava. On-tree fruit detection system using darknet-19 based ssd network. Journal of Food Measurement and Characterization, 18(8):7067–7076, 2024. [21] Xiuli Li, Yi Qin, Fujie Wang, Feng Guo, and John TW Yeow. Pitaya detection in orchards using the mobilenet-yolo model. In 2020 39th Chinese Control Conference (CCC), pages 6274–6278. IEEE, 2020. [22] Gang Wu, Bin Li, Qibing Zhu, Min Huang, and Ya Guo. Using color and 3d geometry features to segment fruit point cloud and improve fruit recognition accuracy. Computers and electronics in agriculture, 174:105475, 2020. [23] Connor C Mullins, Travis J Esau, Qamar U Zaman, Chloe L Toombs, and Patrick J Hennessy. Leveraging zero-shot detection mechanisms to accelerate image annotation for machine learning in wild blueberry (vaccinium angustifolium ait.). Agronomy, 14(12):2830, 2024. [24] Hamzeh Mirhaji, Mohsen Soleymani, Abbas Asakereh, and Saman Abdanan Mehdizadeh. Fruit detection and load estimation of an orange orchard using the yolo models through simple approaches in different imaging and illumination conditions. Computers and Electronics in Agriculture, 191:106533, 2021. [25] Unseok Lee, Md Parvez Islam, Nobuo Kochi, Kenichi Tokuda, Yuka Nakano, Hiroki Naito, Yasushi Kawasaki, Tomohiko Ota, Tomomi Sugiyama, and Dong-Hyuk Ahn. An automated, clip-type, small internet of things camera-based tomato flower and fruit monitoring and harvest prediction system. Sensors, 22(7):2456, 2022. [26] Hongyan Zhu, Shuai Qin, Min Su, Chengzhi Lin, Anjie Li, and Junfeng Gao. Harnessing large vision and language models in agriculture: A review. arXiv preprint arXiv:2407.19679, 2024. [27] Santi Sukkasem, Watchareewan Jitsakul, and Phayung Meesad. Fruit classification with deep transfer learning using image processing. In 2023 7th International Conference on Information Technology (InCIT), pages 464–469. IEEE, 2023. [28] Yonis Gulzar. Fruit image classification model based on mobilenetv2 with deep transfer learning technique. Sustainability, 15(3):1906, 2023. [29] Xiaohong Kou, Yuan Feng, Shuai Yuan, Xiaoyang Zhao, Caie Wu, Chao Wang, and Zhaohui Xue. Different regulatory mechanisms of plant hormones in the ripening of climacteric and non-climacteric fruits: a review. Plant Molecular Biology, pages 1–21, 2021. [30] Andrés Alejandro Garcés Cadena, Oswaldo Aníbal Menéndez Granizo, Edgar Patricio Córdova, and Alvaro Javier Prado Romo. Clasificación de calidad de manzana para monitoreo de cosechabilidad utilizando visión por computador y algoritmos de aprendizaje profundo. Ingeniare. Revista chilena de ingeniería, 31:0–0, 2023. [31] et al. Zhang. Automatic plant phenotyping analysis of melon (cucumis melo l.) germplasm resources using deep learning methods and computer vision. Plant Methods, 20(1):1–15, 2024. [32] VK Venkatkumar. Yolov8 architecture & cow counter with region based dragging using yolov8. https://medium.com/@VK_ Venkatkumar/yolov8-architecture-cow-counter-with-region-based-dragging-using-yolov8-e75b3ac71ed8, 2023. Accessed: 2024-12-01. [33] Nikhil Rao. Yolov11 explained: Next-level object detection with enhanced speed and accuracy. https://medium.com/ @nikhil-rao-20/yolov11-explained-next-level-object-detection-with-enhanced-speed-and-accuracy-2dbe2d376f71, 2023. Accessed: 2024-12-01. [34] Hiroto Schwert. Digging into detectron 2. https://medium.com/@hirotoschwert/digging-into-detectron-2-47b2e794fabd, 2020. Accessed: 2024-12-01. [35] Towards Data Science. Fast r-cnn for object detection: A technical summary. https://towardsdatascience.com/fast-r-cnn-forobject- detection-a-technical-summary-a0ff94faa022, 2020. Accessed: 2024-12-01. | eng |
| dc.rights | Carlos Arias, Camilo Baldovino, José Gómez, Brian Restrepo, Sergio Sánchez - 2025 | eng |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | eng |
| dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | eng |
| dc.rights.creativecommons | This work is licensed under a Creative Commons Attribution 4.0 International License. | eng |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | eng |
| dc.source | https://revistas.utb.edu.co/tesea/article/view/811 | eng |
| dc.subject | Artificial intelligence | eng |
| dc.subject | Machine learning | eng |
| dc.subject | Deep learning | eng |
| dc.subject | detection | eng |
| dc.title | Deep learning model for recognizing fresh and rotten fruits in industrial processes | spa |
| dc.title.translated | Deep learning model for recognizing fresh and rotten fruits in industrial processes | spa |
| dc.type | Artículo de revista | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_6501 | eng |
| dc.type.coarversion | http://purl.org/coar/version/c_970fb48d4fbd8a85 | eng |
| dc.type.content | Text | eng |
| dc.type.driver | info:eu-repo/semantics/article | eng |
| dc.type.local | Journal article | eng |
| dc.type.version | info:eu-repo/semantics/publishedVersion | eng |