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中国生物防治学报 ›› 2017, Vol. 33 ›› Issue (6): 833-841.DOI: 10.16409/j.cnki.2095-039x.2017.06.017

• 研究论文 • 上一篇    下一篇

多生育期小麦条锈病光谱波段优选及监测研究

刘鹏1, 张竞成1, 杨娉婷1, 王保通2, 吴开华1   

  1. 1. 杭州电子科技大学生命信息与仪器工程学院, 杭州 310018;
    2. 西北农林科技大学植物保护学院/旱区作物逆境生物学国家重点实验室, 杨凌 712100
  • 收稿日期:2017-07-17 出版日期:2017-12-08 发布日期:2017-12-16
  • 通讯作者: 张竞成,博士,副研究员,E-mail:zhangjc_rs@163.com
  • 作者简介:刘鹏,硕士研究生,E-mail:1016561228@qq.com
  • 基金资助:
    国家自然科学基金(61661136004,41671415);浙江省科技计划项目(2016C32087)

Analysis on Monitoring of Wheat Stripe Rust at Multiple Stages and Optimization of Bands for Disease Detection

LIU Peng1, ZHANG Jingcheng1, YANG Pingting1, WANG Baotong2, WU Kaihua1   

  1. 1. Hangzhou Dianzi University, School of Life Information and Instrument Engineering, Hanzghou 31000, China;
    2. State Key Laboratory of Crop Stress Biology for Arid Areas/College of Plant Protection, Northwest A & amp;F University, Yangling 712100, China
  • Received:2017-07-17 Online:2017-12-08 Published:2017-12-16

摘要: 针对不同时期对小麦条锈病高光谱监测的敏感波段进行优选,有利于从根本上提高病害监测的精度。本研究以小麦条锈病这种小麦中主要的病害为例,基于3年份多个生育期的小麦条锈病大田控制试验,结合t检验、相关性分析等统计检验和偏最小二乘判别分析法(PLS-DA)、偏最小二乘法回归(PLSR)成分系数对信息重要性的指示意义,建立一套病害诊断特征波段筛选方法,并针对病害发展在不同阶段的特点,分别优选出各阶段最适于病害监测的波段。经过分析,将病害侵染分为3个阶段,并在能够进行防治的前期和中期分别采用不同策略进行波段优选,得到适于早期监测的4个波段:576、705、712、1416 nm;适于中期监测的5个波段:558、632、675、696、712 nm。采用上述波段在前期和中期进行病情监测,前期分类精度达到了0.78,中期反演精度预测值与真实值的标准误差(RMSE)值0.12,这一结果表明这些波段能够为病害发展过程提供高光谱监测,为进一步的特征构建提供依据。

关键词: 小麦, 条锈病, 高光谱, 波段优选, 多生育期

Abstract: Selecting the sensitive hyperspectral bands at different stages for monitoring of wheat stripe rust is beneficial to substantially improve the accuracy of disease monitoring. With one of the most important wheat diseases, the stripe rust, as an example, the present study proposed a framework of bands selection and optimization for disease detection at multiple stages. The study was conducted in a field experiment over three years. Based on the combination of independent t-test, correlation analysis, and the significance of component coefficients of Partial Least Square Discriminant Analysis (PLS-DA) and Partial Least Squares Regression (PLSR), the relative importance of bands for disease detection at specific stages were derived. For the efficient control of the stripe rust, the bands selection and optimization analysis was performed at two stages (i.e., early stage and middle stages) from the total three stages. Based on the identified bands at early and middle stages, a relatively satisfactory accuracy of 0.78 was achieved, with a root mean square error (RMSE) of 0.12. The results suggest that the hyperspectral technique has great potential in disease detection. The identified bands provide a basis for development of spectral index for disease detection in future.

Key words: wheat, stripe rust, hyperspectral, bands selection, multiple stages

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