小波变换与分水岭算法融合的番茄冠层叶片图像分割  

Segmentation of Tomato Leaves from Canopy Images by Combination of Wavelet Transform and Watershed Algorithm

作　　者：丁永军 张晶晶 LEE Won Suk 李民赞[3] 

机构地区：[1]兰州城市学院电子与信息工程学院,兰州730070 [2]佛罗里达大学农业与生物工程系,盖恩斯维尔326110570 [3]中国农业大学现代精细农业系统集成研究教育部重点实验室,北京100083

出　　处：《农业机械学报》2017年第9期32-37,共6页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金项目(31360291;31271619);国家留学基金委西部地区人才培养特别项目(201408625069);兰州城市学院博士科研启动基金项目(LZCU-BS2013-07)

摘　　要：在基于机器视觉的作物营养诊断研究中,通常需要采集叶片样本并在实验室条件下定量测定其营养素含量,但由于叶片间相互重叠,往往使得叶片样本不能清晰地反映在群体番茄冠层图像中。为了解决这一问题,需要利用图像分析技术有效提取作物冠层图像中的叶片,并根据处理结果采集实验室测定样本。本文从复杂背景剔除、梯度图计算、小波变换、标记选取、分水岭分割等环节出发,实现了基于小波变换与分水岭算法融合的番茄冠层多光谱图像叶片分割。首先对比了4种复杂背景剔除算法,发现当增强因子a=1.3时,基于归一化植被指数(Normalized difference vegetation index,NDVI)的阈值分割目标提取准确,适合各种光照条件,时空复杂度低。其次在梯度图计算方面,近红外(Near infrared,NIR)波段图像形态学梯度在保持目标边缘的同时,能消除大量由叶脉、光照等引起的叶片内纹理细节。然后以小波分析为基础进行标记选取,发现当选取db4小波函数、4层小波分解低频系数、阈值为18的H-maxima变换能得到最优的目标标记结果。最后对多光谱番茄冠层图像的小波变换分水岭分割和数学形态学分水岭分割结果进行叠加,发现对复杂背景及不同光照强度下的番茄冠层叶片平均误分率为21%,为基于多光谱图像分析的番茄叶片营养素含量检测提供了一定的技术支持。In the study of crop nutrition diagnosis based on machine vision, it is usually necessary to collect leaf samples and quantitatively determine their nutrient content under laboratory conditions. However, due to the overlapping of leaves, the leaf samples cannot be clearly reflected in the canopy image. In order to solve this problem, it is needed to use image analysis technology to effectively extract the leaves in the crop canopy image and according to the processing results to collect laboratory test samples. Based on the complex background extraction, gradient graph calculation, wavelet transform, marker selection and watershed segmentation, the leaf segmentation of tomato canopy multispectral image was realized. Firstly, four kinds of complex background elimination algorithms were compared. It was found that the threshold segmentation based on normalized difference vegetation index （NDVI） was accurate when the enhancement factor was 1.3, which was suitable under various lighting conditions, and the space-time complexity was low. Secondly, in the aspect of gradient graph calculation, themorphological gradient of near-infrared （NIR） band image can eliminate the texture of the leaves caused by veins, light and so on while keeping the target edge. Then, markers of leaves were selected according to wavelet transform that used the low-frequency coefficient of 4-level db4 wavelet decomposition and H- maxima transform with threshold of 18. Finally, the results of wavelet transform watershed segmentation and mathematical morphology watershed segmentation were superimposed, and it was found that the average segmentation error rate of tomato canopy leaves was 21% for complex background and different light intensities, which provided some technical support for the analysis of tomato leaf nutrient content detection.

关 键 词：图像分割 番茄叶片 小波变换 标记分水岭 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] S24[自动化与计算机技术—控制科学与工程]