| 基于图像处理技术的大田麦穗计数 |
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| 引用本文： | 刘 涛,孙成明,王力坚,仲晓春,朱新开,郭文善.基于图像处理技术的大田麦穗计数[J].农业机械学报,2014,45(2):282-290. |
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| 作者姓名： | 刘 涛 孙成明 王力坚 仲晓春 朱新开 郭文善 |
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| 作者单位： | 扬州大学;扬州大学;扬州大学;中国农业科学院;扬州大学;扬州大学 |
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| 基金项目： | 国家自然科学基金资助项目(31171480)、“十二五”国家科技支撑计划资助项目(2012BAD04B08)和江苏高校优势学科建设工程资助项目(2011-05) |
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| 摘 要： | 为了实现不同播种方式下单位面积小麦穗数的智能计算,设计了一种利用图像分析技术实现大田麦穗快速计数的方法,分析了利用颜色特征和纹理特征分割麦穗的优缺点和粘连区域麦穗个数的计算方法。通过对撒播和条播各35幅样本图像进行计数实验,准确率分别为95.77%和96.89%。结果表明,利用颜色特征和纹理特征均可提取大田环境下麦穗图像,其中利用颜色特征提取速度快。麦穗骨架角点个数能够反映粘连区域麦穗个数,在条播和撒播小麦田中计数准确率均较高。
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| 关 键 词： | 小麦 穗数 图像处理 颜色特征 纹理特征 |
| 收稿时间： | 2013/9/28 0:00:00 |
In-field Wheatear Counting Based on Image Processing Technology |
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| Liu Tao,Sun Chengming,Wang Lijian,Zhong Xiaochun,Zhu Xinkai and Guo Wenshan.In-field Wheatear Counting Based on Image Processing Technology[J].Transactions of the Chinese Society of Agricultural Machinery,2014,45(2):282-290. |
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| Authors: | Liu Tao Sun Chengming Wang Lijian Zhong Xiaochun Zhu Xinkai and Guo Wenshan |
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| Institution: | Yangzhou University;Yangzhou University;Yangzhou University;Chinese Academy of Agriculture Sciences;Yangzhou University;Yangzhou University |
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| Abstract: | The number of wheatears in each square meter is a main parameter of grain production estimate. In order to intelligently calculate the number of wheatears in certain parts, a in-field wheatear counting method based on image analysis technique was designed. Firstly, several color features such as normalized difference index were analyzed to get suitable features, which were used to extract wheatear from original image. Secondly, a comparison of the five texture features (energy, contrast, homogeneity, entropy and relation) was performed and the appropriate features were selected to segment wheat images. Finally, the number of ears was calculated. In this step, erosion and dilation operations in binary mathematical morphology were performed so as to clear impurities and awns. Hole filling algorithm and thinning algorithm were used to get unbroken wheatear and its skeleton. Corner detection algorithm was selected to get the corners of skeleton with the purpose of estimating the wheatear number of connected region. The advantages and disadvantages of the color segmentation and texture segmentation were deeply analyzed. Twenty images with 71×92 pixels were used to evaluate the run-time of color segmentation and texture segmentation. The former took 16.97 ms and the latter took 17.76 s. To validate the effectiveness of the designed method, 35 drilling wheat images and 35 broadcasting wheat images were tested, and the average counting accuracy data for drilling wheat and broadcasting wheat were 95.77% and 96.89%, respectively.The experimental results showed that the color feature and the texture feature could be used to extract wheatear from original wheat image, and the color segmentation was faster than texture segmentation but less environmental adaptability. The corners of skeleton had close relationship with the number of wheatears in connected region. |
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| Keywords: | Wheat Ear number Image processing Color feature Texture feature |
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