基于图像处理技术的果树树叶稀密程度的检测

为了解决现有果树树叶稀密程度检测方法要求采集图像时采用标准白板标定或固定成像距离的问题，本文提出一种新的基于图像处理技术的检测果树树叶稀密程度的方法——最大轮廓矩形法。该方法采用超绿色法、Ostu、中值滤波去噪、腐蚀和膨胀等图像处理技术将果树图像有效分割出来，通过检测经图像处理后的二值图像中整棵果树最大轮廓所占的面积，再检测整幅图像中树叶与树干所占的面积，根据果树树叶稀密程度的定义即可计算树叶稀密程度。结果表明，该方法不需要固定成像距离和使用白板标定，最大轮廓矩形法对果树图像面积的检测是因果树实际图像而异的，不存在现有方法统一采用相机所设定的图像大小作为最大轮廓而导致所检测到的树叶稀密程度偏小的问题；20张果树样本图像采用两种方法检测的果树树叶稀密程度的最大差值为0．2950，最小差值为0．0027。

Detection for theFruiter Leaf Density Based on Image Processing Technology

In order to solve the problem that the existing method for detecting the leaf density of fruit trees is advisable to consider the adoption of standard whiteboard or fixed imaging distance when grab images. This paper presents a new method for detecting the leaf density which based on image processing - maximum profile rectangle technology. Images effectively segmented by using ultra - green method, Ostu, median filter algorithm, erosion and dilation and other image processing technologies; Detecting the largest share of fruit trees area of binary image after processed, then test the entire image area occupied by the leaves and tree trunks, the leaf density can be calculated according to its definition. Experimental results show that it doesn＇ t need to use the fixed imaging distance and the white calibration image, the detection of fruit trees image area due to the actual image varies in the maximum profile rectangle technology, there is no uniform application of the conventional method which set the image size in the camera as a result of the maximum outline of the all images, it avoid the problem that results is too small. Among the 20 tested images, the maximum difference is 0. 2950 and minimum difference is 0. 0027 compared with the existing method.