文献类型： 外文期刊

作者： Li, Han ¹ ; Lee, Won Suk ² ; Wang, Ku ³ ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

2.Univ Florida, Dept Agr & Biol Engn, Gainesville, FL 32611 USA

3.China Agr Univ, Beijing 100083, Peoples R China

关键词： Circular Hough transform;Colour component;Normalized cross correlation;Texture feature;Yield mapping

期刊名称：PRECISION AGRICULTURE （ 影响因子：5.385； 五年影响因子：5.004 ）

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收录情况： SCI

摘要： A fast normalized cross correlation (FNCC) based machine vision algorithm was proposed in this study to develop a method for detecting and counting immature green citrus fruit using outdoor colour images toward the development of an early yield mapping system. As a template matching method, FNCC was used to detect potential fruit areas in the image, which was the very basis for subsequent false positive removal. Multiple features, including colour, shape and texture features, were combined in this algorithm to remove false positives. Circular Hough transform (CHT) was used to detect circles from images after background removal based on colour components. After building disks centred in centroids resulted from both FNCC and CHT, the detection results were merged based on the size and Euclidian distance of the intersection areas of the disks from these two methods. Finally, the number of fruit was determined after false positive removal using texture features. For a validation dataset of 59 images, 84.4 % of the fruits were successfully detected, which indicated the potential of the proposed method toward the development of an early yield mapping system.