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Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat

Rapid and effective acquisition of crop growth information is a crucial step of precision agriculture for making in-season management decisions. Active canopy sensor GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) is a portable device commonly used for non-destructively obtaining crop g...

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Detalles Bibliográficos
Autores principales: Zhang, Jiayi, Liu, Xia, Liang, Yan, Cao, Qiang, Tian, Yongchao, Zhu, Yan, Cao, Weixing, Liu, Xiaojun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427465/
https://www.ncbi.nlm.nih.gov/pubmed/30841552
http://dx.doi.org/10.3390/s19051108
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author Zhang, Jiayi
Liu, Xia
Liang, Yan
Cao, Qiang
Tian, Yongchao
Zhu, Yan
Cao, Weixing
Liu, Xiaojun
author_facet Zhang, Jiayi
Liu, Xia
Liang, Yan
Cao, Qiang
Tian, Yongchao
Zhu, Yan
Cao, Weixing
Liu, Xiaojun
author_sort Zhang, Jiayi
collection PubMed
description Rapid and effective acquisition of crop growth information is a crucial step of precision agriculture for making in-season management decisions. Active canopy sensor GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) is a portable device commonly used for non-destructively obtaining crop growth information. This study intended to expand the applicability of GreenSeeker in monitoring growth status and predicting grain yield of winter wheat (Triticum aestivum L.). Four field experiments with multiple wheat cultivars and N treatments were conducted during 2013–2015 for obtaining canopy normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) synchronized with four agronomic parameters: leaf area index (LAI), leaf dry matter (LDM), leaf nitrogen concentration (LNC), and leaf nitrogen accumulation (LNA). Duration models based on NDVI and RVI were developed to monitor these parameters, which indicated that NDVI and RVI explained 80%, 68–70%, 10–12%, and 67–73% of the variability in LAI, LDM, LNC and LNA, respectively. According to the validation results, the relative root mean square error (RRMSE) were all <0.24 and the relative error (RE) were all <23%. Considering the variation among different wheat cultivars, the newly normalized vegetation indices rNDVI (NDVI vs. the NDVI for the highest N rate) and rRVI (RVI vs. the RVI for the highest N rate) were calculated to predict the relative grain yield (RY, the yield vs. the yield for the highest N rate). rNDVI and rRVI explained 77–85% of the variability in RY, the RRMSEs were both <0.13 and the REs were both <6.3%. The result demonstrates the feasibility of monitoring growth parameters and predicting grain yield of winter wheat with portable GreenSeeker sensor.
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spelling pubmed-64274652019-04-15 Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat Zhang, Jiayi Liu, Xia Liang, Yan Cao, Qiang Tian, Yongchao Zhu, Yan Cao, Weixing Liu, Xiaojun Sensors (Basel) Article Rapid and effective acquisition of crop growth information is a crucial step of precision agriculture for making in-season management decisions. Active canopy sensor GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) is a portable device commonly used for non-destructively obtaining crop growth information. This study intended to expand the applicability of GreenSeeker in monitoring growth status and predicting grain yield of winter wheat (Triticum aestivum L.). Four field experiments with multiple wheat cultivars and N treatments were conducted during 2013–2015 for obtaining canopy normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) synchronized with four agronomic parameters: leaf area index (LAI), leaf dry matter (LDM), leaf nitrogen concentration (LNC), and leaf nitrogen accumulation (LNA). Duration models based on NDVI and RVI were developed to monitor these parameters, which indicated that NDVI and RVI explained 80%, 68–70%, 10–12%, and 67–73% of the variability in LAI, LDM, LNC and LNA, respectively. According to the validation results, the relative root mean square error (RRMSE) were all <0.24 and the relative error (RE) were all <23%. Considering the variation among different wheat cultivars, the newly normalized vegetation indices rNDVI (NDVI vs. the NDVI for the highest N rate) and rRVI (RVI vs. the RVI for the highest N rate) were calculated to predict the relative grain yield (RY, the yield vs. the yield for the highest N rate). rNDVI and rRVI explained 77–85% of the variability in RY, the RRMSEs were both <0.13 and the REs were both <6.3%. The result demonstrates the feasibility of monitoring growth parameters and predicting grain yield of winter wheat with portable GreenSeeker sensor. MDPI 2019-03-05 /pmc/articles/PMC6427465/ /pubmed/30841552 http://dx.doi.org/10.3390/s19051108 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Jiayi
Liu, Xia
Liang, Yan
Cao, Qiang
Tian, Yongchao
Zhu, Yan
Cao, Weixing
Liu, Xiaojun
Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat
title Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat
title_full Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat
title_fullStr Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat
title_full_unstemmed Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat
title_short Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat
title_sort using a portable active sensor to monitor growth parameters and predict grain yield of winter wheat
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427465/
https://www.ncbi.nlm.nih.gov/pubmed/30841552
http://dx.doi.org/10.3390/s19051108
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