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Vineyard yield estimation based on the analysis of high resolution images obtained with artificial illumination at night

机译:Vineyard根据对夜间人工照明获得的高分辨率图像的分析得出估计值

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摘要

This paper presents a method for vineyard yield estimation based on the analysis of high-resolution images obtained with artificial illumination at night. First, this paper assesses different pixel-based segmentation methods in order to detect reddish grapes: threshold based, Mahalanobis distance, Bayesian classifier, linear color model segmentation and histogram segmentation, in order to obtain the best estimation of the area of the clusters of grapes in this illumination conditions. The color spaces tested were the original RGB and the Hue-Saturation-Value (HSV). The best segmentation method in the case of a non-occluded reddish table-grape variety was the threshold segmentation applied to the H layer, with an estimation error in the area of 13.55%, improved up to 10.01% by morphological filtering. Secondly, after segmentation, two procedures for yield estimation based on a previous calibration procedure have been proposed: (1) the number of pixels corresponding to a cluster of grapes is computed and converted directly into a yield estimate; and (2) the area of a cluster of grapes is converted into a volume by means of a solid of revolution, and this volume is converted into a yield estimate; the yield errors obtained were 16% and −17%, respectively.
机译:本文提出了一种基于对夜间人工照明获得的高分辨率图像进行分析的葡萄园产量估算方法。首先,本文评估了不同的基于像素的分割方法以检测带红色的葡萄:基于阈值,马氏距离,贝叶斯分类器,线性颜色模型分割和直方图分割,以获得对葡萄簇面积的最佳估计在这种照明条件下。测试的色彩空间是原始RGB和色相饱和度值(HSV)。在不封闭的带红色红色表纹葡萄品种中,最好的分割方法是将阈值分割应用于H层,估计误差在13.55%的范围内,通过形态滤波可以提高到10.01%。其次,在分割之后,提出了基于先前的校准程序的两种用于产量估计的程序:(1)计算与葡萄簇相对应的像素数量,并将其直接转换为产量估计; (2)通过旋转固体将一串葡萄的面积转换为体积,并将该体积转换为产量估算;获得的产率误差分别为16%和-17%。

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