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Corn Classification System based on Computer Vision

Department of Computer Science and Technology, Northeast Agricultural University, Harbin 150030, China
Author to whom correspondence should be addressed.
Symmetry 2019, 11(4), 591;
Received: 5 March 2019 / Revised: 19 April 2019 / Accepted: 22 April 2019 / Published: 24 April 2019
PDF [3899 KB, uploaded 24 April 2019]


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. View Full-Text
Keywords: corn classification; computer vision; image collection system; line profile segmentation corn classification; computer vision; image collection system; line profile segmentation

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Li, X.; Dai, B.; Sun, H.; Li, W. Corn Classification System based on Computer Vision. Symmetry 2019, 11, 591.

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