Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image

文献类型： 外文期刊

第一作者： Fu, Jun

作者： Fu, Jun;Yuan, Haikuo;Zhao, Rongqiang;Ren, Luquan;Fu, Jun;Yuan, Haikuo;Zhao, Rongqiang;Chen, Zhi;Ren, Luquan;Fu, Jun;Chen, Zhi

作者机构：

关键词： corn damage; peeling damage; recognition method; RGB image; corn ear harvest

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.679； 五年影响因子：2.736 ）

ISSN：

年卷期： 2020 年 10 卷 10 期

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收录情况： SCI

摘要： Corn ear damage caused by peeling significantly influence the output and quality of corn harvest. Ear damage recognition is the basis to adjust working parameters and to reduce damage. Image processing is attracting increasing attentions in the field of agriculture. Conventional image processing methods are difficult to be used for recognizing corn ear damage caused by peeling in field harvesting. To address the this problem, in this paper, we propose a peeling damage recognition method based on RGB image. For our method, we develop a dictionary-learning-based method to recognize corn kernels and a thresholding method to recognize ear damage regions. To obtain better performance, we also develop the corroding algorithm and the expanding algorithm for the post-processing of recognized results. The experimental results demonstrate the practicality and accuracy of the proposed method. This study could provide the theoretical basis to develop online peeling damage detection system for corn ear harvesters.

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