Use of image analysis to estimate anthocyanin and UV-excited fluorescent phenolic compound levels in strawberry fruit

Strawberry is rich in anthocyanins, which are responsible for the red color, and contains several colorless phenolic compounds. Among the colorless phenolic compounds, some, such as hydroxycinammic acid derivatives, emit blue-green fluorescence when excited with ultraviolet (UV) light. Here, we investigated the effectiveness of image analyses for estimating the levels of anthocyanins and UV-excited fluorescent phenolic compounds in fruit. The fruit skin and cut surface of 12 cultivars were photographed under visible and UV light conditions; colors were evaluated based on the color components of images. The levels of anthocyanins and UV-excited fluorescent compounds in each fruit were also evaluated by spectrophotometric and high performance liquid chromatography (HPLC) analyses, respectively and relationships between these levels and the image data were investigated. Red depth of the fruits differed greatly among the cultivars and anthocyanin content was well estimated based on the color values of the cut surface images. Strong UV-excited fluorescence was observed on the cut surfaces of several cultivars, and the grayscale values of the UV-excited fluorescence images were markedly correlated with the levels of those fluorescent compounds as evaluated by HPLC analysis. These results indicate that image analyses can select promising genotypes rich in anthocyanins and fluorescent phenolic compounds.     

植物叶面积系数法改进研究

植物叶面积计算是植物野外调查的重要关键内容,由于传统植物叶面积计算公式s=kab中,k值随物种的不同而存在很大差异,往往造成公式应用的困难。为了方便k值的正确选择,收集了具有代表性的9种形态的植物叶片,求出不同形态叶片的k值,为植物叶面积野外调查提供方便。k值计算方法为,利用计算机软件Image Pro Plus 6.0计算了植物叶片的面积、面积与外接矩形的面积比、最大直径、最小直径、宽度与长度共6个参数。借鉴传统植物叶片的分类方法,根据最大直径与最小直径的比值,将叶片分为披针形、长卵形、卵圆形、矩形或圆形4种类型,求出这4种类型叶片的k值分别为1/3,1/2,2/3和5/6。经过试验证明,公式在改进前,叶片面积k值的平均误差接近20%,改进后叶片面积的k值的平均误差仅为4%,叶片面积的估测误差明显降低。按叶形将叶片分类确定k值后,k值的选择方便快速,外业人员只需简单培训就可明显提升野外作业的效率和精度。     

面向植物生长智能监控的叶颜色特征提取

根据植物生长智能监控的需求,设计了叶颜色特征提取方法：首先提取叶整体颜色特征,根据特征数据能够判断植物叶是健康、存在整体颜色变异还是存在局部颜色变异;如果存在局部颜色变异,进行局部颜色变异部分分割、颜色特征提取和空间分布特征提取。将一组具有不同程度颜色变异的植物叶的阳面和阴面图像作为两个系列进行了对比测试。测试数据表明：特征参数能够区分健康的叶和存在变异的叶,能够区分不同类型和不同程度的颜色变异,能够为后续决策处理提供数据支持,特征提取方法适合植物生长智能监控。     

Segmentation algorithm for urinary sediment image combiningwavelet transform and 2D Maximum entropy threshold

In order to solve the problem that urine sediment visible components cannot be segmented effectively because of complex components, complicated defocusing in image and poor discrimination between object and background, a method based on combination algorithm wis designed to segment urine sediment. The wavelet transform wis used to erase the effect of defocusing. Then morphology wis utilized to get the subimages that include the particles. The segmentation method combining the wavelet transform based segmentation and the two dimensional entropy threshold based segmentation wis employed to segment urine sediment visible components. Experimental results show that the proposed method can segment urinary sediment images effectively and precisely.     

The Quantitative Method for Temperature Distribution of Cotton Leaves Based on Infrared Thermal Images

[Objective] The leaf temperature is an effective parameter reflecting crop physiological process and energy exchange. This study aimed to put forward a method to quantify the temperature distribution within cotton leaves. [Method] The infrared images of cotton leaves of potassium deficiency and normal cotton plants cultivated in nutrient solution were taken and studied. The irrelevant background in the infrared images was removed by using software of Stata and Surfer. The geostatistical spatial grid method was used to quantify the temperature distribution within cotton leaves. Finally, the temperature scales of different images were unified for better and faster comparison of leaf temperature differences among images. [Result] The processed image clearly reflected the differences among different treatments in leaf temperature. The mean leaf temperature, leaf vein temperature and leaf temperature outside leaf vein of potassium-deficient cotton were 0.52 ℃, 0.59 ℃ and 0.47 ℃higher than those of normal leaf, respectively. In addition, the temperature variation amplitude (0.76 ℃) and coefficient of variation (0.38%) of potassium deficient leaf were higher than those of normal leaf (0.54 ℃, 0.27%), and the temperature of the leaf vein increased the most (0.53 ℃). The analysis of the frequency distribution of temperature within the cotton leaf showed that the quartile deviation of temperature distribution of potassium-deficient leaf was 0.07 ℃ higher than that of normal leaf, indicating that the temperature distribution of normal leaf was relatively concentrated. [Conclusion] The proposed method is helpful for further monitoring of cotton growth and may guide cotton precise management.     

高分辨率影像服务在国土资源管理中的应用

分析了国土资源管理对高分辨率影像的迫切需求,探讨了高分辨率影像处理和分析及应用环节从数据到服务的发展趋势,设计并实现了高分辨率影像服务,并以泰州市国土资源管理为例,验证了影像服务的可行性,最后,展望了高分辨率遥感影像服务的应用前景。     

Clear-cut Detection in Boreal Forest Aided by Remote Sensing

The study compares the applicability of different remote sensing data and digital change detection methods in detecting clear-cut areas in boreal forest. The methods selected for comparisons are simple and straightforward and thus applicable in practical forestry. The data tested were from Landsat satellite imagery and high-altitude panchromatic aerial orthophotographs. The change detection was based on image differencing. Three different approaches were tested: (1) pixel-by-pixel differencing and segmentation; (2) pixel block-level differencing and thresholding; and (3) presegmentation and unsupervised classification. The study shows that the methods and data sources used are accurate enough for operational detection of clear-cut areas. The study suggests that predelineated segments or pixel blocks should be used for image differencing to decrease the number of misinterpreted small areas. For the same reason the use of a digital forest mask is crucial in operational applications.     

浅谈林业工作中常用的遥感影像及处理要点

在遥感影像定义的基础之上,讨论了遥感影像的四个基本特征.对林业工作中常用的中分辨率遥感影像Landsate TM5、中巴资源卫星以及常用的高分辨率遥感影像Spot5、Rapideye、Alos、QuickBird、WorldviewⅠ、WorldviewⅡ等进行了介绍并做了对比分析,总结各种遥感影像的优缺点;并讨论了遥感影像基础数据准备,DOM与DEM参考数据准备以及的遥感影像正射纠正,并以ALOS为例,对算法及图像增强等工作进行了探讨     

基于ETM＋影像融合的土地覆盖分类研究——以蓬安县为例

借助ERDAS软件,运用遥感数据融合技术对四川省蓬安县进行基于ETM＋融合影像的土地覆盖分类初步研究。研究中采用相同的训练样本区及最大似然法分类方法,对融合前后影像分别进行土地覆盖分类,通过对分类影像的Producers Accuracy,Users Accuracy,Kappa Accuracy三者的精度数据对比分析,上述的影像融合方法对提高土地覆盖分类的精度较为明显;就3种融合方法及重采样方式而言,乘积法融合法和立方卷积重采样法相对更为可取。     

遥感影像在占用征用林地检查中的应用探讨

利用遥感影像对内蒙古自治区南木林业局、林西县、杭锦旗占用征用林地情况进行了检查,分析了遥感影像变化与林地变化的关系,并对应用遥感技术提高林地检查精度进行了探讨。