全文总字数：9609字
题目：基于图像处理技术的植物表型特性参数研究
摘 要
随着计算机技术的不断发展，图像处理技术在有关植物监测研究上的应用已经颇为成熟。图像处理技术包括图像数字化、图像数据编码、图像分割和图像识别等。近年来数字图像处理技术已成为植物表型信息获取的手段。主要回顾了以往国内外专业人士通过数字图像处理技术对不同种类的植株进行建模与数据分析，在分析植物生长等方面的情况时选取确定合适的颜色模型和图像采集技术、植物生长试验设计、统计方法、计算机软件和硬件设备，了解分析图像采集方法对植物叶片表型信息获取的影响。利用工业相机获取叶片的形态学特征并分析颜色特征红绿蓝（Red Green Blue，RGB）值以及比值、差值、标准化值、归一化值。最后，分析了目前图像处理技术处理过程中存在的缺点，提出了以下展望：研究采取合理措施对植株进行无损的表型参数数据提取；对基于图像处理的植株生长状况、栽培管理情况进行动态检测；通过研究不同植株叶片特征参数间的相关性，加强学科之间的深度合作。
关键词：图像处理；植物表型；数据分析。
Topic: Study on plant phenotypic characteristic parameters based on image processing technology
Abstract
With the continuous development of computer technology, the application of image processing technology in plant monitoring research has been quite mature. Image processing technology includes image digitization, image data coding, image segmentation and image recognition. Image processing technology includes image digitization, image data coding, image segmentation and image recognition. In this paper, we mainly review the previous studies on Modeling and data analysis of different kinds of plants by professionals at home and abroad through digital image processing technology. In the analysis of plant growth and other aspects of the situation, select and determine the appropriate color model and image acquisition technology, test design of plant growing, statistical methods, computer software and hardware equipment, objective to understand the influence of image acquisition methods on obtaining phenotypic information of plant leaves. The morphological characteristics of leaves were obtained by industrial camera, and the red green blue (RGB) value, ratio, difference, standardized value and normalized value were analyzed. Finally, the shortcomings of the current image processing technology are analyzed; the following prospects are put forward: to take reasonable measures to extract the phenotypic parameters of plants; to dynamically detect the plant growth and cultivation management based on image processing; to strengthen the in-depth cooperation between disciplines by studying the correlation between leaf characteristic parameters of different plants.
Key words: image processing; plant phenotype; data analysis.
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