Algorithm Design in Leaf Surface Separation by Degree in HSV Color Model and Estimation of Leaf Area by Linear Regression

Narumol Chumuang, Sattarpoom Thaiparnit, M. Ketcham
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引用次数: 20

Abstract

Plant leaves are very important for their respiration and photosynthesis. The two processes are significant factors for their growth. Measuring leave dimension is very important in studying and analyzing the photosynthesis of plants. Leaf dimension assessment with image evaluation is the most widely technique used for presenting. This paper proposed the algorithm of image segmentation to classify image elements and calculate leaf surface with a threshold segmentation technique by using the constant threshold in gray color model and calculating the degree of green color in the HSV models. Segmentation technique is used to separate good surface out of defective surface of leaf image. Moreover, this paper also proposed leaf area estimation with linear regression analysis with the pixel value on the leaf surface. Further to sixty experiments, they showed the accuracy to separate elements of good surface and defective surface are 98.72% and 96.47% respectively.
HSV颜色模型中叶面按度分离算法设计及线性回归估计叶面积
植物的叶子对它们的呼吸和光合作用非常重要。这两个过程是它们成长的重要因素。叶片维数的测定是研究和分析植物光合作用的重要手段。叶片尺寸评价与图像评价是目前应用最广泛的呈现技术。本文提出了一种基于阈值分割技术的图像分割算法,利用灰度模型中的恒定阈值和HSV模型中的绿色程度计算,对图像元素进行分类并计算叶片表面。采用分割技术将叶片图像的良好面和缺陷面分离出来。此外,本文还提出了利用叶片表面像素值进行线性回归分析的叶片面积估计方法。经过60次实验,对良好面和不良面元素的分离精度分别为98.72%和96.47%。
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