| 基于改进Bayes抠图算法的麦穗小穗自动计数方法 |
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| 引用本文： | 刘哲,袁冬根,王恩.基于改进Bayes抠图算法的麦穗小穗自动计数方法[J].中国农业科技导报,2020,22(8):75-82. |
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| 作者姓名： | 刘哲 袁冬根 王恩 |
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| 作者单位： | 西北政法大学军民融合研究院, 西安 710122 |
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| 基金项目： | 国家自然科学基金项目（61473237）；陕西省科技厅重点研发项目（2017ZDXM-NY-088）。 |
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| 摘 要： | 小麦产量评估需人工获取田间单位面积的麦穗数和麦穗小穗数，往往耗时耗力。为了实现高效、自动地麦穗小穗计数，提出一种基于改进Bayes抠图算法的麦穗小穗自动计数方法。该方法首先利用改进Bayes抠图算法对获取地自然生长条件下的麦穗图像进行抠图，将麦穗从自然背景中分割出来。然后对该图像进行平滑滤波和二值化，运用迭代极限腐蚀运算对二值化图像进行腐蚀处理，去除麦穗图像中的麦芒，分离出麦穗上每个单独的麦穗小穗。再运用面积滤波滤除掉面积过小的区域，对剩余区域的黑洞进行填充，由此每个单独的麦穗小穗形成一个单独的连通区域，最后对连通区域进行标记和计数，完成麦穗小穗的自动计数。使用4个小麦品种的麦穗图像对麦穗上的小穗进行计数验证，结果表明，该方法在识别4个品种田间麦穗单幅图像中小穗数量的平均计数精度达到94.53%，平均相对误差为5.47%，对比已有麦穗小穗自动计数方法，计数精度显著提高，这对于小麦在线产量预估具有重要意义。
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| 关 键 词： | 麦穗计数 Bayes抠图算法 麦穗小穗计数 图像分割 产量预测 连通区域 |
| 收稿时间： | 2019-02-23 |
Automatic Counting Method of Wheat Grain Based on Improved Bayes Matting Algorithm |
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| LIU Zhe,YUAG Donggen,WANG EN.Automatic Counting Method of Wheat Grain Based on Improved Bayes Matting Algorithm[J].Journal of Agricultural Science and Technology,2020,22(8):75-82. |
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| Authors: | LIU Zhe YUAG Donggen WANG EN |
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| Institution: | Institute of Military and Civilian Integration, Northwest University of Political Science and Law, Xian 710122, China |
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| Abstract: | For yield evaluation, the number of ear and spikelet in the field unit area is acquired manually, which often takes time and energy. In order to realize efficient and automatic spikelet counting, this paper proposed an automatic spikelet counting method based on improved Bayes image matting algorithm. Firstly, the improved Bayes matting algorithm was used to mat the wheat ear image under the natural growth condition of the acquired land to separate wheat ear from the natural background. Then the image was smoothed and binarized, and the iterative limit corrosion operation was used to corrode the binarized image, remove the awn in the wheat ear image, and separate each individual wheat ear and spikelets on the wheat ear. The area filter was applied to remove the area that was too small, and the black hole of the remaining area was filled, so that each individual wheat ear spikelet formed a separate connected region. Finally, the connected region was marked and counted, and the automatic counting of wheat ear spikelet was completed. In this paper, the spikelets of four wheat varieties were counted and validated by using the image of spikelets. The experimental results showed that the average counting accuracy of spikelets in single image of four wheat varieties was 94.53% and the average relative error was 5.47%. Compared with the automatic counting method of spikelets in the literature, the counting accuracy of spikelets of four wheat varieties was significantly improved. |
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| Keywords: | wheat ear counting improved bayes matting method grain number counting image segmentation yield prediction |
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