{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T15:18:54Z","timestamp":1791559134636,"version":"4.3.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Real-time detecting and identifying impurities in wheat grain mass is crucial for wheat storage silos, flour mills and modern combines. Depending on the detection objectives, accuracy is typically prioritized in laboratory-based applications, whereas real-time detection scenarios require a trade-off between accuracy and speed. Consequently, detection algorithms should be developed in alignment with the specific performance demands of each application. Given the strong object detection capabilities of YOLO models, four updated algorithms\u2014YOLOv5n, YOLOv5x, YOLOv8n, and YOLOv8x\u2014were employed to achieve the research objectives. In this study, a total of 700 labeled images of three different resolutions, encompassing 11 distinct classes, were used to train the algorithms. The results of this study demonstrated that increasing the model size had no significant effect on mAP but substantially reduced processing speed. For laboratory applications, YOLOv5x and YOLOv8x exhibited nearly identical performance, making them suitable candidates. Among the tested models and image sizes, YOLOv5n with an image resolution of 320\u2009\u00d7\u2009320 maintained accuracies while improving detection speed by 4%, making it a suitable choice for real-time applications. Overall, the mAP@50 for impurities with similar visual characteristics, such as wheat grains, sun pest-damaged grains, and shriveled grains, was 88%, 86%, and 85%, respectively, while for other impurities, it exceeded 95%. These findings underscore the potential of YOLO models for impurity detection in wheat, providing a non-destructive testing method that could be extended to impurity recognition in other grains.<\/jats:p>","DOI":"10.1038\/s41598-025-23032-9","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T13:23:35Z","timestamp":1763472215000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhanced YOLO-based framework for accurate detection and identification of common wheat impurities with distinct objects"],"prefix":"10.1038","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6877-6281","authenticated-orcid":false,"given":"Hossein","family":"Bagherpour","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Negar Fattahi","family":"Peyruo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"key":"23032_CR1","unstructured":"FAO, 2023. FAOSTAT. https:\/\/www.fao.org\/faostat\/en\/#data (2021)."},{"key":"23032_CR2","doi-asserted-by":"publisher","first-page":"147","DOI":"10.15316\/SJAFS.2021.242","volume":"35","author":"N Bozkan","year":"2021","unstructured":"Bozkan, N. & Karaca, M. The Species and Intensities of Weed Seeds Obtained from Wheat Flour Mill Plants in Turkey. Selcuk J. Agric. Food Sci. 35, 147\u2013154 (2021).","journal-title":"Selcuk J. Agric. Food Sci."},{"key":"23032_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105931","volume":"181","author":"Y Shen","year":"2021","unstructured":"Shen, Y., Yin, Y., Li, B., Zhao, C. & Li, G. Detection of impurities in wheat using terahertz spectral imaging and convolutional neural networks. Comput. Electron. Agric. 181, 105931 (2021).","journal-title":"Comput. Electron. Agric."},{"key":"23032_CR4","doi-asserted-by":"publisher","first-page":"7627","DOI":"10.3390\/s22197627","volume":"22","author":"M Chen","year":"2022","unstructured":"Chen, M., Jin, C., Ni, Y., Xu, J. & Yang, T. Online detection system for wheat machine harvesting impurity rate based on DeepLabV3+. Sensors 22, 7627 (2022).","journal-title":"Sensors"},{"key":"23032_CR5","unstructured":"USDA. Inspecting Grain: Practical Procedures for Grain Handlers. (USDA, Marketing and Regulatory Programs; Grain Inspection (2016)."},{"key":"23032_CR6","doi-asserted-by":"publisher","first-page":"43","DOI":"10.15586\/qas.v15i3.1220","volume":"15","author":"J Zhu","year":"2023","unstructured":"Zhu, J. et al. Inspection and classification of wheat quality using image processing. Qual. Assur Saf Crops Foods 15, 43\u201354 (2023).","journal-title":"Qual. Assur Saf Crops Foods"},{"key":"23032_CR7","first-page":"105","volume":"18","author":"OAA Gadalla","year":"2022","unstructured":"Gadalla, O. A. A., \u00d6ztekin, Y. B. & Baitu, G. P. Application of computer vision and image processing technology in agro-product quality control-review. Tar\u0131m Makinalar\u0131 Bilimi Dergisi 18, 105\u2013113 (2022).","journal-title":"Tar\u0131m Makinalar\u0131 Bilimi Dergisi"},{"key":"23032_CR8","doi-asserted-by":"publisher","unstructured":"Ofir, N. & Nebel, J.-C. Classic versus deep learning approaches to address computer vision challenges. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2101.09744 (2021).","DOI":"10.48550\/ARXIV.2101.09744"},{"key":"23032_CR9","doi-asserted-by":"publisher","unstructured":"Radovanovic, D. & Dukanovic, S. Image-based plant disease detection: A comparison of deep learning and classical machine learning algorithms. In 2020 24th International Conference on Information Technology (IT) 1\u20134 (IEEE, 2020). https:\/\/doi.org\/10.1109\/IT48810.2020.9070664.","DOI":"10.1109\/IT48810.2020.9070664"},{"key":"23032_CR10","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1080\/10942912.2020.1778724","volume":"23","author":"A Ali","year":"2020","unstructured":"Ali, A. et al. Machine learning approach for the classification of corn seed using hybrid features. Int. J. Food Prop. 23, 1110\u20131124 (2020).","journal-title":"Int. J. Food Prop."},{"key":"23032_CR11","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.biosystemseng.2008.09.028","volume":"102","author":"R Choudhary","year":"2009","unstructured":"Choudhary, R., Mahesh, S., Paliwal, J. & Jayas, D. S. Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosys. Eng. 102, 115\u2013127 (2009).","journal-title":"Biosys. Eng."},{"key":"23032_CR12","doi-asserted-by":"publisher","first-page":"4133","DOI":"10.3390\/app12094133","volume":"12","author":"Z Fazel-Niari","year":"2022","unstructured":"Fazel-Niari, Z. et al. Quality assessment of components of wheat seed using different classifications models. Appl. Sci. 12, 4133 (2022).","journal-title":"Appl. Sci."},{"key":"23032_CR13","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.compag.2012.02.005","volume":"83","author":"A Pourreza","year":"2012","unstructured":"Pourreza, A., Pourreza, H., Abbaspour-Fard, M.-H. & Sadrnia, H. Identification of nine Iranian wheat seed varieties by textural analysis with image processing. Comput. Electron. Agric. 83, 102\u2013108 (2012).","journal-title":"Comput. Electron. Agric."},{"key":"23032_CR14","doi-asserted-by":"publisher","first-page":"100136","DOI":"10.1016\/j.atech.2022.100136","volume":"3","author":"D Agarwal","year":"2023","unstructured":"Agarwal, D., Sweta, & Bachan, P. Machine learning approach for the classification of wheat grains. Smart Agric. Technol. 3, 100136 (2023).","journal-title":"Smart Agric. Technol."},{"key":"23032_CR15","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.biosystemseng.2006.06.001","volume":"95","author":"BP Dubey","year":"2006","unstructured":"Dubey, B. P., Bhagwat, S. G., Shouche, S. P. & Sainis, J. K. Potential of artificial neural networks in varietal identification using morphometry of wheat grains. Biosys. Eng. 95, 61\u201367 (2006).","journal-title":"Biosys. Eng."},{"key":"23032_CR16","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.compag.2016.01.033","volume":"122","author":"M Olgun","year":"2016","unstructured":"Olgun, M. et al. Wheat grain classification by using dense SIFT features with SVM classifier. Comput. Electron. Agric. 122, 185\u2013190 (2016).","journal-title":"Comput. Electron. Agric."},{"key":"23032_CR17","doi-asserted-by":"publisher","first-page":"192","DOI":"10.47000\/tjmcs.897631","volume":"13","author":"Y Ba\u015fol","year":"2021","unstructured":"Ba\u015fol, Y. & Toklu, S. A deep learning-based seed classification with mobile application. Turk. J. Math. Comput. Sci. 13, 192\u2013203 (2021).","journal-title":"Turk. J. Math. Comput. Sci."},{"key":"23032_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/7041310","volume":"2020","author":"K Kiratiratanapruk","year":"2020","unstructured":"Kiratiratanapruk, K. et al. Development of paddy rice seed classification process using machine learning techniques for automatic grading machine. J. Sens. 2020, 1\u201314 (2020).","journal-title":"J. Sens."},{"key":"23032_CR19","doi-asserted-by":"publisher","unstructured":"Kundu, N., Rani, G. & Dhaka, V. S. Seeds classification and quality testing using deep learning and YOLO v5. In Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence 153\u2013160 (2021). https:\/\/doi.org\/10.1145\/3484824.3484913.","DOI":"10.1145\/3484824.3484913"},{"key":"23032_CR20","doi-asserted-by":"publisher","unstructured":"Wafy, M., Ibrahim, H. & Kamel, E. Identification of weed seeds species in mixed sample with wheat grains using SIFT algorithm. In 2013 9th International Computer Engineering Conference (ICENCO) 11\u201314 (IEEE, 2013). https:\/\/doi.org\/10.1109\/ICENCO.2013.6736468.","DOI":"10.1109\/ICENCO.2013.6736468"},{"key":"23032_CR21","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/S0168-1699(02)00004-2","volume":"33","author":"PM Granitto","year":"2002","unstructured":"Granitto, P. M., Navone, H. D., Verdes, P. F. & Ceccatto, H. A. Weed seeds identification by machine vision. Comput. Electron. Agric. 33, 91\u2013103 (2002).","journal-title":"Comput. Electron. Agric."},{"key":"23032_CR22","doi-asserted-by":"publisher","first-page":"35441","DOI":"10.1007\/s11042-020-10174-3","volume":"80","author":"S Lingwal","year":"2021","unstructured":"Lingwal, S., Bhatia, K. K. & Tomer, M. S. Image-based wheat grain classification using convolutional neural network. Multimed. Tools Appl. 80, 35441\u201335465 (2021).","journal-title":"Multimed. Tools Appl."},{"key":"23032_CR23","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.3390\/s19051090","volume":"19","author":"G ElMasry","year":"2019","unstructured":"ElMasry, G., Mandour, N., Al-Rejaie, S., Belin, E. & Rousseau, D. Recent applications of multispectral imaging in seed phenotyping and quality monitoring\u2014An overview. Sensors 19, 1090 (2019).","journal-title":"Sensors"},{"key":"23032_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2024.100543","volume":"9","author":"S Gawdiya","year":"2024","unstructured":"Gawdiya, S. et al. Field scale wheat yield prediction using ensemble machine learning techniques. Smart Agric. Technol. 9, 100543 (2024).","journal-title":"Smart Agric. Technol."},{"key":"23032_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117014","volume":"199","author":"M Khojastehnazhand","year":"2022","unstructured":"Khojastehnazhand, M. & Roostaei, M. Classification of seven Iranian wheat varieties using texture features. Expert Syst. Appl. 199, 117014 (2022).","journal-title":"Expert Syst. Appl."},{"key":"23032_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.105016","volume":"166","author":"E Kaya","year":"2019","unstructured":"Kaya, E. & Saritas, \u0130. Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and Gaborlet features. Comput. Electron. Agric. 166, 105016 (2019).","journal-title":"Comput. Electron. Agric."},{"key":"23032_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109375","volume":"226","author":"Z Qi","year":"2024","unstructured":"Qi, Z. et al. Research on wheat broken rate and impurity rate detection method based on DeepLab-EDA model and system construction. Comput. Electron. Agric. 226, 109375 (2024).","journal-title":"Comput. Electron. Agric."},{"key":"23032_CR28","doi-asserted-by":"publisher","first-page":"2684","DOI":"10.3390\/pr12122684","volume":"12","author":"X Chen","year":"2024","unstructured":"Chen, X., Guan, Z., Li, H. & Zhang, M. A machine vision-based method of impurity detection for rapeseed harvesters. Processes 12, 2684 (2024).","journal-title":"Processes"},{"key":"23032_CR29","doi-asserted-by":"publisher","first-page":"9550","DOI":"10.3390\/s22239550","volume":"22","author":"Z Guan","year":"2022","unstructured":"Guan, Z. et al. Development of impurity-detection system for tracked rice combine harvester based on DEM and mask R-CNN. Sensors 22, 9550 (2022).","journal-title":"Sensors"},{"key":"23032_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106135","volume":"185","author":"X Hu","year":"2021","unstructured":"Hu, X. et al. Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network. Comput. Electron. Agric. 185, 106135 (2021).","journal-title":"Comput. Electron. Agric."},{"key":"23032_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2025.100794","volume":"10","author":"F Fatehi","year":"2025","unstructured":"Fatehi, F., Bagherpour, H. & Amiri Parian, J. Enhancing the performance of YOLOv9t through a knowledge distillation approach for real-time detection of bloomed damask roses in the field. Smart Agric. Technol. 10, 100794 (2025).","journal-title":"Smart Agric. Technol."},{"key":"23032_CR32","doi-asserted-by":"publisher","first-page":"3873","DOI":"10.3390\/s25133873","volume":"25","author":"J Zhang","year":"2025","unstructured":"Zhang, J., Beliaeva, Z. V. & Huang, Y. Accuracy-efficiency trade-off: Optimizing YOLOv8 for structural crack detection. Sensors 25, 3873 (2025).","journal-title":"Sensors"},{"key":"23032_CR33","doi-asserted-by":"publisher","unstructured":"Ilyas, A., Rahmani, I., Imran, S., Hashmi, T. S. S. & Yousaf, M. N. Deep Learning Application for Vehicle Detection through Surveillance Drones. In 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET) 1\u20136 (IEEE, 2024). https:\/\/doi.org\/10.1109\/ICECET61485.2024.10698500.","DOI":"10.1109\/ICECET61485.2024.10698500"},{"key":"23032_CR34","doi-asserted-by":"publisher","unstructured":"Kim, J., Sung, J.-Y. & Park, S. Comparison of faster-RCNN, YOLO, and SSD for real-time vehicle type recognition. In 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) 1\u20134 (IEEE, 2020). https:\/\/doi.org\/10.1109\/ICCE-Asia49877.2020.9277040.","DOI":"10.1109\/ICCE-Asia49877.2020.9277040"},{"key":"23032_CR35","doi-asserted-by":"publisher","first-page":"127","DOI":"10.18178\/joig.12.2.127-136","volume":"12","author":"E Casas","year":"2024","unstructured":"Casas, E., Ramos, L., Bendek, E. & Rivas-Echeverria, F. Yolov5 vs. yolov8: Performance benchmarking in wildfire and smoke detection scenarios. J. Image Graph. 12, 127\u2013136 (2024).","journal-title":"J. Image Graph."},{"key":"23032_CR36","doi-asserted-by":"publisher","unstructured":"Hao, Y. et al. Understanding the impact of image quality and distance of objects to object detection performance. In 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) 11436\u201311442 (IEEE, 2023). https:\/\/doi.org\/10.1109\/IROS55552.2023.10342139.","DOI":"10.1109\/IROS55552.2023.10342139"},{"key":"23032_CR37","doi-asserted-by":"publisher","first-page":"985","DOI":"10.3390\/electronics12040985","volume":"12","author":"O Rukundo","year":"2023","unstructured":"Rukundo, O. Effects of image size on deep learning. Electronics 12, 985 (2023).","journal-title":"Electronics"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-23032-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-23032-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-23032-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T02:04:29Z","timestamp":1763863469000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-23032-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,18]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["23032"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-23032-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,18]]},"assertion":[{"value":"6 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"40436"}}