Dualex植物多酚-叶绿素仪：卫星和近端传感技术用于评估食用葡萄的产量和质量

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Dualex植物多酚-叶绿素仪：卫星和近端传感技术用于评估食用葡萄的产量和质量

发表时间：2022-05-05 10:26:08点击：1088

来源：北京博普特科技有限公司

Dualex是一款源自于法国国家科学院 (CNRS)及巴黎第十一大学技术，由奥地利PESSL公司生产（原法国Force-A公司）开发的新型多功能叶片测量仪。它可同时准确测量叶片的叶绿素含量、叶片表层的类黄酮和花青素含量，适用于植物生理学和农学（如水稻叶绿素浓度，玉米氮素状况，葡萄藤等）相关研究。

其测量对象可以是单子叶植物，双子叶植物或多年生植物。这款设备简单易用，可进a行实时和非破坏性测量。由于不需要校准标定和事先的样品制备，测量工作可在实验室或现场完成。此外，该设备在各种温度的和环境光照条件下均可正常使用。系统采用专利设计，光学传感器可进行简单、快速、无损测量叶片中的叶绿素、多酚以及花青素。

Dualex便携式智能气象-叶绿素-花青素-黄酮醇测量仪

摘要

食用葡萄是一种营养价值很高的作物，需要经常监测，以实现高产优质。非破坏性方法，如卫星和近距离遥感，被广泛用于估计作物产量和质量特征，光谱植被指数（SVI）通常用于呈现特定地点的信息。本研究的目的是评估从veraison到收获的食用葡萄在不同生长阶段从卫星和近端传感获得的SVI。这项研究在连续三年（2015年至2017年）在商业食用葡萄园（Vitis vinifera cv.Thompson Seedless）中进行。利用卫星图像（Landsat 8）和近距离遥感计算了标准化差异植被指数（NDVI）和绿色标准化差异植被指数（GNDVI），以评估鲜食葡萄的产量和质量特征。SVI与不同的测量日期和传感方法表现出不同程度的相关性。与NDVI相比，收获时基于卫星的GNDVI与作物质量特征的相关性更高（浆果直径r=0.522，pH r=0.537，浆果变形r=0.629）。与NDVI相比，veraison中部基于近端的GNDVI呈现出更高的相关性（r=−浆果直径为0.682，r=−0.565（对于berry变形）。与卫星遥感相比，近距离遥感在葡萄产量和品质特征方面更为准确。

关键词：光谱植被指数；精密葡萄栽培；遥感；餐桌上的葡萄；作物产量和质量评估

Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes

Abstract

Table grapes are a crop with high nutritional value that need to be monitored often to achieve high yield and quality. Non-destructive methods, such as satellite and proximal sensing, are widely used to estimate crop yield and quality characteristics, and spectral vegetation indices (SVIs) are commonly used to present site specific information. The aim of this study was the assessment of SVIs derived from satellite and proximal sensing at different growth stages of table grapes from veraison to harvest. The study took place in a commercial table grape vineyard (Vitis vinifera cv. Thompson Seedless) during three successive cultivation years (2015–2017). The Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were calculated by employing satellite imagery (Landsat 8) and proximal sensing (Crop Circle ACS 470) to assess the yield and quality characteristics of table grapes. The SVIs exhibited different degrees of correlations with different measurement dates and sensing methods. Satellite-based GNDVI at harvest presented higher correlations with crop quality characteristics (r = 0.522 for berry diameter, r = 0.537 for pH, r = 0.629 for berry deformation) compared with NDVI. Proximal-based GNDVI at the middle of veraison presented higher correlations compared with NDVI (r = −0.682 for berry diameter, r = −0.565 for berry deformation). Proximal sensing proved to be more accurate in terms of table grape yield and quality characteristics compared to satellite sensing.

Keywords:spectral vegetation index; precision viticulture; remote sensing; table grapes; crop yield and quality estimation