| 基于图像处理的小麦条锈病菌夏孢子模拟捕捉的自动计数 |
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| 引用本文： | 李小龙,马占鸿,孙振宇,王海光.基于图像处理的小麦条锈病菌夏孢子模拟捕捉的自动计数[J].农业工程学报,2013,29(2):199-206. |
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| 作者姓名： | 李小龙 马占鸿 孙振宇 王海光 |
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| 作者单位： | 中国农业大学农学与生物技术学院，北京 100193;中国农业大学农学与生物技术学院，北京 100193;中国农业大学农学与生物技术学院，北京 100193;中国农业大学农学与生物技术学院，北京 100193 |
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| 基金项目： | 公益性行业（农业）科研专项经费项目（200903035） |
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| 摘 要： | 利用孢子捕捉器进行气传植物病原真菌孢子捕捉,实现田间病原真菌孢子数量的监测,对于气传植物真菌病害的预测预报和防治决策具有重要意义。目前对捕捉到的孢子多采用传统显微镜孢子计数方法,由于孢子个体小、数量大,利用这种计数方法费时费力,易造成较大计数误差。为了获得一种孢子捕捉器捕捉孢子的自动计数方法,提高计数的准确性和工作效率,本研究利用透明胶带、凡士林玻片和Eppendorf离心管3种方法模拟捕捉小麦条锈病菌夏孢子,利用显微镜照相技术获得孢子图像,在MATLAB软件环境下,对图像进行基于最近邻插值法的缩放处理、基于K-means聚类算法的分割处理、形态学操作修饰和分水岭分割等一系列的处理,实现夏孢子的自动计数和标记。结果表明,3种模拟方法获得的孢子图像经过处理后,均可获得较好的孢子计数结果。透明胶带、凡士林玻片、Eppendorf离心管模拟捕捉条锈病菌夏孢子的平均计数准确率最低分别为98.5%、98.7%、99.9%,Eppendorf离心管模拟捕捉条锈病菌夏孢子和小麦白粉病菌分生孢子的平均计数准确率为99.8%。本研究为实现田间利用孢子捕捉器捕捉孢子的自动计数提供了一种简便、快捷、准确、高效的方法。
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| 关 键 词： | 农作物 灾害 图像处理 小麦条锈病 自动计数 模拟 |
| 收稿时间： | 6/3/2012 12:00:00 AM |
| 修稿时间： | 2012/11/12 0:00:00 |
Automatic counting for trapped urediospores of Puccinia striiformis f. sp. tritici based on image processing |
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| Li Xiaolong,Ma Zhanhong,Sun Zhenyu and Wang Haiguang.Automatic counting for trapped urediospores of Puccinia striiformis f. sp. tritici based on image processing[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(2):199-206. |
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| Authors: | Li Xiaolong Ma Zhanhong Sun Zhenyu and Wang Haiguang |
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| Institution: | (College of Agriculture and Biotechnology,China Agricultural University,Beijing 100193,China) |
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| Abstract: | Abstract: Using spore traps to capture the airborne plant pathogen spores in the fields is a key means to monitor the pathogen amount. It is of great significance for forecasting and management decision-making of airborne plant diseases. Currently, the traditional microscopic spore counting method is usually used to count the trapped spores. Due to the great number of the trapped spores, this method is time-consuming and labor consumptive, and often leads to a great error. To find out a method for automatic counting of in-field trapped pathogen spores and improve accuracy and efficiency of spore counting, urediospores of Puccinia striiformis f. sp. tritic, the causal agent of wheat stripe rust, was trapped (via indoor simulation) using transparent tapes, glass slides with vaseline and Eppendorf centrifuge tubes in this study. And then the images of the trapped spores were acquired using a microscope camera. Finally, using MATLAB software, the urediospores were automatically counted and marked through a series of image processing including image zooming using the nearest neighbor interpolation method, image segmentation using K_means clustering algorithm, morphological image modification and watershed image segmentation. The satisfactory results for counting the trapped spores were obtained after processing the spore images acquired by using the three kinds of simulation methods. The average counting accuracy for the urediospores of P. striiformis f. sp. tritici trapped on transparent tapes, glass slides with vaseline and in Eppendorf centrifuge tubes was 98.5%, 98.7% and 99.9%, respectively. Average counting accuracy for the urediospores of P. striiformis f. sp. tritici mixed with the conidia of Blumeria graminis f. sp. tritici, which could cause wheat powdery mildew, was 99.8%. The research provided a simple, fast, accurate and efficient method for automatic counting of in-field trapped pathogen spores. |
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| Keywords: | crops disasters image processing wheat stripe rust automatic counting simulation |
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