图像处理技术在植物叶片表征中的应用

J. B. Cunha
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引用次数: 28

摘要

机器视觉和数字图像处理技术在农业领域的应用数量正在迅速增加。这些应用包括作物的陆地/空中遥感,病理胁迫条件的检测和识别,水果的形状和颜色表征,以及许多其他主题。事实上,园艺产品和植物的视觉特性的量化可以在改善和自动化农业管理任务中发挥重要作用。本文介绍了一种基于个人计算机的植物叶片特征识别系统。该系统采用数字扫描仪采集分辨率为150dpi的树叶图像。然后对这些图像进行处理,计算叶片的一些特征参数,如:叶片的面积和周长、孔的存在、宽度和长度。利用所实现的算法,测量值与实际值之间的误差通常分别小于/spl mnplus/3%和/spl mnplus/2.5%。这些测试和结果是通过使用所提出的系统和Delta-T设备的商业校准叶面积系统LiCor测量的已知尺寸图像和叶片图像集来实现的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of image processing techniques in the characterization of plant leafs
The number of applications using machine vision and digital image processing techniques in the agricultural sector is increasing rapidly. These applications include land/aerial remote sensing of crops, detection and recognition of pathological stress conditions, shape and color characterization of fruits, among many other topics. In fact, quantification of the visual properties of horticultural products and plants can play an important role to improve and automate agricultural management tasks. In this paper, is described a plant leaf characterization system based on a personal computer. This system uses a digital scanner to acquire leaf images with a resolution of 150 dpi. These images are afterwards processed in order to compute some leaf characteristic parameters, such as: leaf area and perimeter, existence of holes, width and length. With the implemented algorithms the errors between the measurements and the real values were typically less than /spl mnplus/3% and /spl mnplus/2.5% for the area and linear measurements, respectively. These tests and results were realized using sets of known size images and leaf images that were measured with the proposed system and with a commercial calibrated leaf area system LiCor from Delta-T devices.
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