田间光照条件下应用半球图像解析玉米冠层结构参数

半球图像法是一种重要的作物冠层结构参数间接获取方法,该文针对半球图像法易受田间变化光照影响,提出一种基于多曝光图像融合映射的变化光照去除方法。首先,在田间条件下获取一组不同曝光值的冠层半球图像序列,然后通过图像亮度和曝光时间的线性方程组计算冠层的光照辐射强度图,而后采用直方图均衡化思想将不同时刻光照辐射强度图向RGB空间映射,映射后半球图像亮度均衡分布,消除光线变化对图像亮度的影响。在冠层参数解析阶段,利用Beer-Lambert定律反演玉米半球图像冠层叶面积指数(leaf area index,LAI)和平均叶倾角(mean leaf angle,MLA)的计算方法。试验结果表明,变化光照条件下半球图像冠层参数解析方法与直接测量法具有较高的相关性,LAI测量相关系数为0.970。该方法扩展了半球图像法的适用范围,为田间环境下冠层参数的自动连续监测提供了解决方案。

Application of hemispherical photography on analysis of maize canopy structural parameters under natural light

Abstract: Hemispherical photography (HP) has already proven to be a powerful indirect method for measuring various components of canopy structure. One of the main problems using HP for the determination of canopy structure such as leaf area index (LAI) and mean leaf angle (MLA), is the selection of the optimal brightness threshold to distinguish leaf area from sky area and thus produce a binary image. In this process, one of the challenges is how to overcome various natural light conditions which sometimes strongly affect the profile of the crop images taken outdoors. In this paper, a fusion and mapping method of illumination invariant multiple exposure images was proposed in order to eliminate negative impact of variant illumination. Firstly, a series of multiple-exposure maize canopy hemispherical images were captured under natural light condition. The camera (Canon EOS 5D Mark III, sigma 8mm f 3.5 ex DG FISHEYE) was placed in the bottom of canopy towards the sky, and it provided vertical 180° and horizontal 360° canopy images. The images were captured at different exposure time such as 1/800, 1/400, 1/200, 1/125, 1/30 and 1 s. Secondly, the multiple photographs were fused into a single radiance map, so shadow and highlights parts of original images were extended to a large range. We were able to determine the irradiance value for each pixel, and plot it against the measured pixel value discretized according to the 256 pixel values commonly observed in 8-bit images for each exposure time. The pixel values were proportional to the true irradiance values in the scene. The pixel values, exposure time, and irradiance values formed a problem of least square. Finally, we also employed a histogram equalization method to map irradiance values to RGB color space. After mapping processing, the brightness of image had a more proper distribution, the dark regions were lit more brightly and the saturated regions were depressed to normal brightness condition; moreover, the histograms of images shared the similar distribution, that meant the pixels of variant illumination images after mapping processing had a high similarity. The comparison results showed that plant pixel of HP acquired at 14:00 and 17:00 with the threshold value of 180 had a difference of 15.4%, and our method reduced the difference, which was only 2.8%. In the analysis phase of canopy structure parameters, plant pixels were extracted from those photos, and then LAI and MLA could be inversed by Beer-Lambert theory based on the quantitative relationship between radiation condition and canopy structure. The experiment was conducted in 2013, and the planting density was 60 000 plants/hm2 with normal water and fertilizer management. The hemispherical images were obtained on August 6th, 13th, 19th, 22nd and 26th and September 12th, and the distribution of LAI and MLA was consistent with the rules of growth and development of maize canopy. Moreover, a performance comparison of direct surveying method and our method was carried out, and the LAI and MLA values of 13 samples were collected with the 2 methods. The results of regression analysis showed that our method had a high consistency with direct surveying method of canopy structure parameter, and correlation coefficient between the values from the 2 methods hit 0.94. The line slope was 1.463, which indicated the values from our method were lower than the direct surveying method. The method proposed in this paper expands the application range of HP, and provides a solution for automatic monitoring of canopy structure parameters.