基于衍射重构技术的作物真菌病害孢子微型检测装置
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国家自然科学基金项目(31701324、61673195)、江苏省农业科技自主创新资金项目(CX(18)3043)、中国博士后科学基金项目(2018M642182)、江苏省高校优势学科建设工程项目(PAPD)和江苏省优秀青年科学基金项目(BK20180099)


Micro Detection Device for Fungal Spores of Crops Based on Diffraction Reconstruction
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    摘要:

    为了解决现有孢子检测系统体积大、成本高等问题,提出了一种基于衍射重构技术的作物真菌病害孢子检测方法。根据惠更斯-菲涅尔原理、角谱理论,利用衍射成像复合重构计算设计了一种包含富集、进样机构的真菌病害孢子检测系统。该系统可以定时完成富集、进样、拍摄、重构和检测等操作,并通过重构算法实现对真菌病害孢子原像的重建,根据重构后的图像提取面积(Area)、细度(Thinness ratio)两个重要形态学特征,对稻瘟病孢子进行检测识别。实验结果表明,所设计装置对稻瘟病孢子的检测结果与人工显微镜识别结果高度线性相关,决定系数为0.99,平均检测误差为5.91%,具有较好的准确性。

    Abstract:

    Collecting airborne spores of rice blast in rice fields using spore trap devices has currently become an important approach for devising strategies early and effectively controlling rice blast. In order to solve the problems of large volume and high cost of existing spore detection system, a spore detection method for crop fungal diseases was proposed based on diffraction reconstruction technology. Based on Huygens-Fresnel principle and angular spectrum theory, a spore detection system for fungal diseases, including enrichment and sampling mechanism, was designed by using diffraction imaging complex reconstruction calculation. The system can complete a series of operations such as enrichment, sampling, shooting, reconstructing and detection, and reconstruct the original spore image of fungal diseases by reconstruction algorithm. According to the morphological characteristics of reconstructed images, two important parameters, area and thinness ratio, were extracted to detect and identify spores. Rice blast spores were selected as the research object to carry out detection and verification experiments. The experimental results showed that the system could capture the micro-images of diffractions of rice blast spores, with 2592 pixels×1944 pixels resolution. The experiments validated that the correlation coefficient between the detection results of rice blast spores and the identification results of artificial microscope can reach 0.99, while the average detection error rate was 5.91%, which had good accuracy. The research provided a design scheme for the research and development of low-cost early warning equipment for crop fungal diseases. 

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杨宁,陈驰原,李国晓,王爱英,张荣标,唐健.基于衍射重构技术的作物真菌病害孢子微型检测装置[J].农业机械学报,2019,50(4):42-48.

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  • 收稿日期:2018-10-17
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  • 在线发布日期: 2019-04-10
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