{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T14:39:54Z","timestamp":1768487994329,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T00:00:00Z","timestamp":1658361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Quality assessment is one of the most common processes in the agri-food industry. Typically, this task involves the analysis of multiple views of the fruit. Generally speaking, analyzing these single views is a highly time-consuming operation. Moreover, there is usually significant overlap between consecutive views, so it might be necessary to provide a mechanism to cope with the redundancy and prevent the multiple counting of defect points. This paper presents a method to create surface maps of fruit from collections of views obtained when the piece is rotating. This single image map combines the information contained in the views, thus reducing the number of analysis operations and avoiding possible miscounts in the number of defects. After assigning each piece with a simple geometrical model, 3D rotation between consecutive views is estimated only from the captured images, without any further need for sensors or information about the conveyor. The fact that rotation is estimated directly from the views makes this novel methodology readily usable in high-throughput industrial inspection machines without any special hardware modification. As proof of this technique\u2019s usefulness, an application is shown where maps have been used as input to a CNN to classify oranges into different categories.<\/jats:p>","DOI":"10.3390\/s22145452","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T22:38:50Z","timestamp":1658443130000},"page":"5452","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Single Fusion Image from Collections of Fruit Views for Defect Detection and Classification"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0679-912X","authenticated-orcid":false,"given":"Antonio","family":"Albiol","sequence":"first","affiliation":[{"name":"Departamento de Comunicaciones, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3878-4379","authenticated-orcid":false,"given":"Carlos","family":"S\u00e1nchez de Mer\u00e1s","sequence":"additional","affiliation":[{"name":"Departamento de Comunicaciones, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1970-3289","authenticated-orcid":false,"given":"Alberto","family":"Albiol","sequence":"additional","affiliation":[{"name":"ITEAM Research Institute, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8166-8138","authenticated-orcid":false,"given":"Sara","family":"Hinojosa","sequence":"additional","affiliation":[{"name":"Multiscan Technologies SL, 03820 Cocentaina, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,21]]},"reference":[{"key":"ref_1","unstructured":"Galanakis, C.M. (2019). Assessment of fresh fruit and vegetable quality with non-destructive methods. Food Quality and Shelf Life, Academic Press. Chapter 10."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103","DOI":"10.14429\/dlsj.2.11379","article-title":"Non-destructive Quality Monitoring of Fresh Fruits and Vegetables","volume":"2","author":"Chauhan","year":"2017","journal-title":"Def. Life Sci. J."},{"key":"ref_3","first-page":"85","article-title":"Optical non-destructive techniques for small berry fruits: A review","volume":"2","author":"Li","year":"2019","journal-title":"Artif. Intell. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s11947-010-0411-8","article-title":"Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables","volume":"4","author":"Cubero","year":"2011","journal-title":"Food Bioprocess Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2008.11.006","article-title":"Automatic sorting of satsuma (Citrus unshiu) segments using computer vision and morphological features","volume":"66","author":"Blasco","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shiraishi, Y., and Takeda, F. (2011, January 19\u201321). Proposal of whole surface inspection system by simultaneous six-image capture of prolate spheroid-shaped fruit and vegetables. Proceedings of the 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICMSAO.2011.5775528"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jfoodeng.2017.02.008","article-title":"Automatic detection of defective apples using NIR coded structured light and fast lightness correction","volume":"203","author":"Zhang","year":"2017","journal-title":"J. Food Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.compag.2009.09.014","article-title":"In-line detection of apple defects using three color cameras system","volume":"70","author":"Zou","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/S0168-1699(02)00093-5","article-title":"Computer vision based system for apple surface defect detection","volume":"36","author":"Li","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"449","DOI":"10.13031\/2013.20394","article-title":"Three-dimensional Shape Measurement of Strawberries by Volume Intersection Method","volume":"49","author":"Imou","year":"2006","journal-title":"Trans. ASABE"},{"key":"ref_11","first-page":"140","article-title":"Whole surface image reconstruction for machine vision inspection of fruit","volume":"Volume 6761","author":"Chen","year":"2007","journal-title":"Proceedings of the Optics for Natural Resources, Agriculture, and Foods II"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4499","DOI":"10.1016\/j.biortech.2008.11.059","article-title":"Using parabolic mirrors for complete imaging of apple surfaces","volume":"100","author":"Reese","year":"2009","journal-title":"Bioresour. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pham, Q.T., and Liou, N.S. (2020). Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects. Appl. Sci., 10.","DOI":"10.3390\/app10082851"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1007\/s11694-018-9728-1","article-title":"Mechatronic components in apple sorting machines with computer vision","volume":"12","author":"Navid","year":"2018","journal-title":"J. Food Meas. Charact."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Albiol, A., Albiol, A., and S\u00e1nchez de Mer\u00e1s, C. (2021). Fast 3D Rotation Estimation of Fruits Using Spheroid Models. Sensors, 21.","DOI":"10.3390\/s21062232"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.lwt.2017.08.086","article-title":"Detecting decayed peach using a rotating hyperspectral imaging testbed","volume":"87","author":"Sun","year":"2018","journal-title":"LWT"},{"key":"ref_17","unstructured":"Wijewickrema, S.N.R., Papli\u0144ski, A.P., and Esson, C.E. (2006, January 20\u201322). Texture unwrapping for spherical objects on conveyors. Proceedings of the 6th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision, World Scientific and Engineering Academy and Society (WSEAS), Venice, Italy."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/S0168-1699(02)00002-9","article-title":"Multispectral inspection of citrus in real-time using machine vision and digital signal processors","volume":"33","author":"Aleixos","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s11263-006-0002-3","article-title":"Automatic Panoramic Image Stitching using Invariant Features","volume":"74","author":"Brown","year":"2007","journal-title":"Int. J. Comput. Vis."},{"key":"ref_20","unstructured":"(2022, February 01). Animation Showing the Map Creation. Available online: https:\/\/github.com\/csdemeras\/FSMap_complement\/raw\/main\/mapCreation.mp4."},{"key":"ref_21","unstructured":"(2022, February 01). Supplementary Map Examples. Available online: https:\/\/github.com\/csdemeras\/FSMap_complement\/raw\/main\/supplement.pdf."},{"key":"ref_22","unstructured":"OECD, Organisation for Economic Co-operation and Development (2010). Citrus Fruits, OECD Publishing."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_24","unstructured":"Kolen, J.F., and Kremer, S.C. (2001). Gradient Flow in Recurrent Nets: The Difficulty of Learning LongTerm Dependencies. A Field Guide to Dynamical Recurrent Networks, IEEE."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5452\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:55:39Z","timestamp":1760140539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5452"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,21]]},"references-count":27,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145452"],"URL":"https:\/\/doi.org\/10.3390\/s22145452","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,21]]}}}