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Detection of citrus fruit and tree trunks in natural environments using a multi-elliptical boundary model

机译:使用多椭圆边界模型检测自然环境中的柑橘类水果和树干

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Intelligent detection is a key technology in precision agriculture. As items of different color cluster in different non-overlapping elliptical regions, this study proposed a method for constructing a multi elliptical boundary model in Cr-Cb co-ordinates to detect citrus fruit and tree trunks in natural light environments. Here, the detected citrus variety was spring sweet tangerine, and the parameters of the elliptical boundary models for detecting these fruit and tree trunks solved by color-space transformation and ellipse fitting. A series of image detection experiments were performed to evaluate the method's performance. The experimental results showed that the correct and false positive percentages in fruit identification from images were 90.8 and 11.2%, respectively. The number of correctly detected images in distinguishing tree trunks from background was 44 of 50 images. (C) 2018 Elsevier B.V. All rights reserved.
机译:智能检测是精密农业的关键技术。 作为不同非重叠椭圆区域中不同颜色簇的物品,本研究提出了一种在CR-CB中构建多椭圆边界模型的方法,该方法协调在自然光环境中检测柑橘类水果和树干。 在这里,检测到的柑橘品种是弹簧甜蜜桔,以及通过彩色空间变换和椭圆拟合来检测这些水果和树干的椭圆边界模型的参数。 进行一系列图像检测实验以评估方法的性能。 实验结果表明,图像果实鉴定的正确和假阳性百分比分别为90.8%和11.2%。 区分从背景的树干中正确检测到的图像的数量为44个图像。 (c)2018 Elsevier B.v.保留所有权利。

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