{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T04:57:13Z","timestamp":1774241833716,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,4,24]],"date-time":"2019-04-24T00:00:00Z","timestamp":1556064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Heilongjiang Province of China","award":["F2017002"],"award-info":[{"award-number":["F2017002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Automated classification of corn is important for corn sorting in intelligent agriculture. This paper presents a reliable corn classification method based on techniques of computer vision and machine learning. To discriminate different damaged types of corns, a line profile segmentation method is firstly used to segment and separate a group of touching corns. Then, twelve color features and five shape features are extracted for each individual corn object. Finally, a maximum likelihood estimator is trained to classify normal and damaged corns. To evaluate the performance of the proposed method, a private dataset consisting of images of normal corn and six kinds of damage corns, including heat-damaged, germ-damaged, cob-rot-damaged, blue eye mold-damaged, insect-damaged, and surface mold-damaged, were collected in this work. The proposed method achieved an accuracy of 96.67% for the classification between normal corns and the first four common damaged corns, and an accuracy of 74.76% was achieved for the classification between normal corns and six kinds of damaged corns. The experimental results demonstrated the effectiveness of the proposed corn classification system.<\/jats:p>","DOI":"10.3390\/sym11040591","type":"journal-article","created":{"date-parts":[[2019,4,25]],"date-time":"2019-04-25T03:02:59Z","timestamp":1556161379000},"page":"591","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Corn Classification System based on Computer Vision"],"prefix":"10.3390","volume":"11","author":[{"given":"Xiaoming","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Northeast Agricultural University, Harbin 150030, China"}]},{"given":"Baisheng","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Northeast Agricultural University, Harbin 150030, China"}]},{"given":"Hongmin","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Northeast Agricultural University, Harbin 150030, China"}]},{"given":"Weina","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Northeast Agricultural University, Harbin 150030, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,24]]},"reference":[{"key":"ref_1","unstructured":"USDA (2019, March 01). World Agricultural Supply and Demand Estimates, Available online: https:\/\/www.usda.gov\/oce\/commodity\/wasde\/."},{"key":"ref_2","unstructured":"Hill, L.D. (1990). Grain Grades and Standards: Historical Issues Shaping the Future, University of Illinois Press."},{"key":"ref_3","unstructured":"Rendleman, M., and Legacy, J. (2019, March 01). Grain Grading and Handling, Available online: https:\/\/eric.ed.gov\/?id=ED208230."},{"key":"ref_4","unstructured":"U.S. Department of Agriculture, Grain Inspection, Packers and Stockyards Administration, and Federal Grain Inspection Service (2007). Grain Inspection Handbook, Book II, Chapter 4: Corn."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.13031\/2013.31521","article-title":"Discrimination of Whole from Broken Corn Kernels with Image Analysis","volume":"33","author":"Zayas","year":"1990","journal-title":"Transac. ASAE"},{"key":"ref_6","first-page":"491","article-title":"Design of an automated corn kernel inspection system for machine vision","volume":"40","author":"Ni","year":"1997","journal-title":"Trans. ASAE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"833","DOI":"10.13031\/2013.21293","article-title":"Corn kernel crown shape identification using image processing","volume":"40","author":"Ni","year":"1997","journal-title":"Trans. ASAE"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"567","DOI":"10.13031\/2013.19408","article-title":"Size Grading of Corn Kernels with Machine Vision","volume":"17","author":"Ni","year":"1998","journal-title":"Am. Soc. Agric. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1006\/jcrs.1998.0240","article-title":"Identification of Damaged Kernels in Wheat using a Colour Machine Vision System","volume":"30","author":"Luo","year":"1998","journal-title":"J. Cereal Sci."},{"key":"ref_10","first-page":"235","article-title":"Implementing a Computer Vision System for Corn Kernel Damage Evaluation","volume":"17","author":"Steenhoek","year":"2001","journal-title":"Am. Soc. Agric. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.compag.2007.10.001","article-title":"Computer image analysis of seed shape and seed color for flax cultivar description","volume":"61","author":"Dana","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.compag.2009.09.003","article-title":"Combining discriminant analysis and neural networks for corn variety identification","volume":"71","author":"Chen","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.compag.2011.05.007","article-title":"Leaf classification in sunflower crops by computer vision and neural networks","volume":"78","author":"Arribas","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","first-page":"315","article-title":"A rapid corn sorting algorithm based on machine vision","volume":"45","author":"Gao","year":"2012","journal-title":"J. Theor. Appli. Inf. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.biosystemseng.2013.09.003","article-title":"Automatic corn (Zea mays) kernel inspection system using novelty detection based on principal component analysis","volume":"117","year":"2014","journal-title":"Biosys. Eng."},{"key":"ref_16","first-page":"131","article-title":"Method of image detection for ear of corn based on computer vision","volume":"30","author":"Liu","year":"2014","journal-title":"Transac. Chin. Soc. Agric. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2361","DOI":"10.1016\/j.eswa.2014.11.011","article-title":"Feature decision-making ant colony optimization system for an automated recognition of plant species","volume":"42","author":"Mohammad","year":"2015","journal-title":"Exp. Syst. Appl."},{"key":"ref_18","first-page":"298","article-title":"Design and experiment of fresh corn quality detection classifier based on machine vision","volume":"32","author":"Gao","year":"2016","journal-title":"Transac. Chin. Soc. Agric. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.compag.2018.04.008","article-title":"Identification and classification of damaged corn kernels with impact acoustics multi-domain patterns","volume":"150","author":"Sun","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, J., Dai, L.M., and Cheng, F. (2019). Classification of Frozen Corn Seeds Using Hyperspectral VIS\/NIR Reflectence Imaging. Molecules, 24.","DOI":"10.3390\/molecules24010149"},{"key":"ref_21","first-page":"1","article-title":"Applications of Computer Vision in Plant Pathology: A Survey","volume":"2","author":"Chouhan","year":"2019","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sabzi, S., Abbaspour-Gilandeh, Y., Garc\u00eda-Mateos, G., Ruiz-Canales, A., Molina-Mart\u00ednez, J.M., and Arribas, J.I. (2019). An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video. Agronomy, 9.","DOI":"10.3390\/agronomy9020084"},{"key":"ref_23","unstructured":"Mahdi, A., and Qin, J. (2019, March 01). Line Profile Based Segmentation Algorithm for Touching Corn Kernels. Available online: https:\/\/arxiv.org\/pdf\/1706.00396."},{"key":"ref_24","unstructured":"Gonzalez, R.C., and Woods, R.E. (2007). Digital Image Processing, Pearson. [3rd ed.]."},{"key":"ref_25","unstructured":"White, P.J., and Johnson, L.A. (2003). Corn: Chemistry and Technology, American Association of Cereal Chemists. [2nd ed.]."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"757","DOI":"10.13031\/2013.2759","article-title":"Corn Whiteness Measurement and Classification Using Machine Vision","volume":"43","author":"Liu","year":"2000","journal-title":"Trans. ASAE"},{"key":"ref_27","first-page":"1","article-title":"The image of maize seeds\u2019 modal characteristics extraction","volume":"3","author":"Zheng","year":"2008","journal-title":"Sci. Pap. Online"},{"key":"ref_28","unstructured":"Duda, R.O., Hart, P.E., and Stork, D.G. (2012). Pattern Classification, Wiley. [2nd ed.]."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1081\/STA-200056853","article-title":"A Bayes Empirical Bayes Decision Rule for Classification","volume":"34","author":"Li","year":"2005","journal-title":"Commun. Stat."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/4\/591\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:46:46Z","timestamp":1760186806000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/4\/591"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,24]]},"references-count":29,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["sym11040591"],"URL":"https:\/\/doi.org\/10.3390\/sym11040591","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,24]]}}}