Estimation of Leaf Area Index of Table Grapes Using a Digital Camera Equipped with Fisheye Lens and the Image Processing Software “Fiji-ImageJ”

Michio Hamada, M. Shiraishi
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引用次数: 2

Abstract

Using a digital camera equipped with a fisheye lens and the image processing free-software “Fiji-Image J”, we developed a new protocol for determination of the leaf area index (LAI) of table grapes with the same level of measurement accuracy as the plant canopy analyzer (PCA). A regression analysis between the leaf width (X) and single leaf area (Y) indicated highly signifi cant values (Y = 0.5716X 2.0425 , R 2 = 0.99**). A highly significant correlation was obtained between the actual LAI and the LAI measured by PCA (r = 0.964**). Regarding “Fiji-Image J”, we performed the following three-step image processing: (1) R-, G-, and B-separation of the original image taken, (2) correction of the three separated images by “Subtract background algorithm” and “Minimum” threshold treatment mode, (3) calculation of cumulative vegetation rate in the three images. As the range of LAI values was 1 to 4, a highly significant linear correlation was noted between the cumulative vegetation rate (X) and actual LAI (Y): Y = 0.0769X − 18.325, R 2 = 0.82**.
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利用鱼眼镜头数码相机和图像处理软件“Fiji-ImageJ”估算鲜食葡萄叶面积指数
利用配备鱼眼镜头的数码相机和图像处理免费软件“Fiji-Image J”,我们开发了一种新的测定鲜食葡萄叶面积指数(LAI)的方法,其测量精度与植物冠层分析仪(PCA)相同。叶片宽度(X)与单叶面积(Y)之间的回归分析结果显示,Y = 0.5716X 2.0425, r2 = 0.99**。实际LAI与PCA测得的LAI呈极显著相关(r = 0.964**)。对于“Fiji-Image J”,我们进行了以下三步图像处理:(1)对拍摄的原始图像进行R-、G-、b分离,(2)通过“减去背景算法”和“最小”阈值处理模式对分离后的三幅图像进行校正,(3)计算三幅图像的累积植被率。在LAI值为1 ~ 4的范围内,累积植被率(X)与实际LAI (Y)呈极显著的线性相关:Y = 0.0769X−18.325,r2 = 0.82**。
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