| 基于机器视觉的大田环境小麦麦穗计数方法 |
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| 引用本文： | 范梦扬,马钦,刘峻明,王庆,王越,段熊春.基于机器视觉的大田环境小麦麦穗计数方法[J].农业机械学报,2015,46(S1):234-239. |
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| 作者姓名： | 范梦扬 马钦 刘峻明 王庆 王越 段熊春 |
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| 作者单位： | 中国农业大学;农业部农业信息获取技术重点实验室,中国农业大学;农业部农业信息获取技术重点实验室,中国农业大学;农业部农业信息获取技术重点实验室,中国农业大学;农业部农业信息获取技术重点实验室,中国农业大学;农业部农业信息获取技术重点实验室,中国农业大学;农业部农业信息获取技术重点实验室 |
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| 基金项目： | “十二五”国家科技支撑计划资助项目（2012BAD20B0103） |
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| 摘 要： | 基于机器视觉技术研究了一种低成本、针对局部小范围的小麦麦穗计数方法。通过部署的田间摄像头采集大田环境下小麦麦穗低分辨率群体图像，实现了复杂大田环境下小麦麦穗图像的降噪增强处理；提取麦穗的颜色、纹理特征，采用SVM学习的方法，精确提取小麦麦穗轮廓，同时构建麦穗特征数据库，对麦穗的二值图像细化得到麦穗骨架；最后通过计算麦穗骨架的数量以及麦穗骨架有效交点的数量，即可得到图像中麦穗的数量。经过2014年5月和2015年5月在方城县赵河镇示范区的试验测试，以小麦麦穗图像640像素×480像素（约250穗）为例，小麦麦穗计数平均耗时1.7 s，准确率达到93.1%，满足大田环境下小麦麦穗计数要求，可以为小麦估产提供可靠的参考数据。
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| 关 键 词： | 小麦麦穗 机器视觉 图像特征 支持向量机 骨架 |
| 收稿时间： | 2015/10/28 0:00:00 |
Counting Method of Wheatear in Field Based on Machine Vision Technology |
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| Fan Mengyang,Ma Qin,Liu Junming,Wang Qing,Wang Yue and Duan Xiongchun.Counting Method of Wheatear in Field Based on Machine Vision Technology[J].Transactions of the Chinese Society of Agricultural Machinery,2015,46(S1):234-239. |
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| Authors: | Fan Mengyang Ma Qin Liu Junming Wang Qing Wang Yue and Duan Xiongchun |
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| Institution: | China Agricultural University;Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture,China Agricultural University;Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture,China Agricultural University;Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture,China Agricultural University;Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture,China Agricultural University;Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and China Agricultural University;Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture |
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| Abstract: | Wheat is a main crop in China and the timely and accuracy estimation of wheat yield is significant. The number of wheater in certain area is an important element in wheat yield estimation. The counting method of wheatear based on machine vision technology was studied, which was cheap and suitable for local area. The method was very significant for wheat growth monitoring and yield estimation. Firstly, the counting method for wheatear in field based on machine vision technology was studied by collecting images of wheatear colony with cameras deployed in the field. The analysis method for wheatear image feature, the thinning method for wheat ear outline and wheatear counting method based on skeleton were realized. The low resolution images of wheat plant were collected with cameras deployed in field. Then the color features and texture features of images were extracted. The outline of wheatear was extracted to get binary image of wheatear by using learning method of SVM. The database of wheatear feature was constructed at the same time and wheatear skeletons were generated by thinning the wheatear binary image. Finally, the number of wheatears was calculated by calculating the number of skeletons and skeleton intersection points. The method was tested in Zhaohe Demonstration Area, Fangcheng County, in May of 2014 and 2015. As a result, it took averagely only 1.7 s to calculate the number of wheatears and the accuracy was 93.1%, which means the wheatear counting method presented meets the requirement of both speed and accuracy, and it can provide reliable data for wheat yield estimation. |
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| Keywords: | Wheatear Machine vision Image feature Support vector machine Skeleton |
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