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Discrimination of tomato plants under different irrigation regimes: analysis of hyperspectral sensor data

机译:番茄在不同灌溉方式下的区别:高光谱传感器数据分析

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摘要

The development and implementation of both economically and environmentally sustainable precision crop management systems can be greatly enhanced through the use of hyperspectral sensing. In this study, the potential of narrow-waveband hyperspectral observations for the discrimination of water-stressed tomato plants (Solanum lycopersicum L.) was investigated in a field experiment conducted in southern Italy. The tomato crop was grown in a 1.8-ha test field that was split into two plots with different irrigation treatments: optimal and deficit water supplies, with the deficit supply using half of the water of the optimal supply in the second half of the crop growing cycle. Hyperspectral measurements were taken with a field spectroradiometer. To reduce the number of variables, principal component analysis was applied to each of six wavelength band sub-intervals across the whole wavelength interval from 400 to 1000nm. The retained principal components were then submitted to canonical discriminant analysis. Finally, the principal components and the canonical component were interpolated using multivariate and univariate geostatistical techniques, respectively, and then mapped. The two irrigation treatments produced different plant biomass and leaf area indices, which were higher under optimal than deficit water conditions, as was the plant water potential. These data show that the correlation between the individual bands varied during the crop cycle, so it was not feasible to choose a specific band to discriminate between the water treatments. However, we show that only a combination of all of the bands that use the full spectral information with differential weighting leads to clear discrimination of the two differently irrigated areas, with a mean accuracy of 75% to 77%. The processing of hyperspectral reflectance data using canonical discriminant analysis can thus provide valuable information for the agricultural producer for the identification of within-field areas of plant stress, so as to implement site-specific irrigation strategies. Copyright (c) 2014 John Wiley & Sons, Ltd.
机译:通过使用高光谱传感,可以极大地促进经济和环境可持续的精确作物管理系统的开发和实施。在这项研究中,通过在意大利南部进行的田间试验,研究了窄波高光谱观测对水胁迫番茄植株(Solanum lycopersicum L.)的识别潜力。番茄作物种植在一个1.8公顷的试验田中,该试验田被分成两个不同灌溉处理的地块:最佳和亏缺供水,亏缺供应使用了下半年作物中最佳供水的一半周期。高光谱测量是用场光谱仪进行的。为了减少变量的数量,将主成分分析应用于从400到1000nm的整个波长间隔中的六个波段子间隔中的每一个。然后将保留的主要成分提交规范判别分析。最后,分别使用多元和单变量地统计技术对主成分和规范成分进行插值,然后进行映射。两种灌溉处理产生不同的植物生物量和叶面积指数,在最佳条件下高于缺水条件,植物水势也更高。这些数据表明,各个波段之间的相关性在作物周期中会发生变化,因此选择特定的波段来区分水处理是不可行的。但是,我们显示,只有使用全光谱信息的所有频段与差分加权的组合才能清楚地区分两个灌溉区域,平均精度为75%至77%。因此,使用规范判别分析处理高光谱反射率数据可以为农业生产者提供有价值的信息,以识别植物胁迫的田间区域,从而实施特定地点的灌溉策略。版权所有(c)2014 John Wiley&Sons,Ltd.

著录项

  • 来源
    《Environmetrics》 |2015年第2期|77-88|共12页
  • 作者单位

    Consiglio Ric & Sperimentaz Agr, Ctr Ric Cerealicoltura CRA CER, I-71122 Foggia, Italy;

    Consiglio Ric & Sperimentaz Agr, Unita Ric & Sistemi Colturali Ambienti Caldoarid, Bari, Italy;

    Consiglio Ric & Sperimentaz Agr, Unita Ric & Sistemi Colturali Ambienti Caldoarid, Bari, Italy;

    Consiglio Ric & Sperimentaz Agr, Unita Ric & Sistemi Colturali Ambienti Caldoarid, Bari, Italy;

    Consiglio Ric & Sperimentaz Agr, Unita Ric & Sistemi Colturali Ambienti Caldoarid, Bari, Italy;

    Consiglio Ric & Sperimentaz Agr, Unita Ric & Sistemi Colturali Ambienti Caldoarid, Bari, Italy;

    CIHEAM MAIB Mediterranean Agron Inst Bari, Valenzano, BA, Italy;

    CIHEAM MAIB Mediterranean Agron Inst Bari, Valenzano, BA, Italy;

    CIHEAM MAIB Mediterranean Agron Inst Bari, Valenzano, BA, Italy;

    Consiglio Ric & Sperimentaz Agr, Unita Ric & Sistemi Colturali Ambienti Caldoarid, Bari, Italy;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    hyperspectral sensor; irrigation; principal component analysis; discriminant analysis; plant water potential;

    机译:高光谱传感器;灌溉;主要成分分析;判别分析;植物水势;

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