Estimation of grain filling rate and thousand-grain weight of winter wheat (Triticum aestivum L.) using UAV-based multispectral images
Estimating grain filling rate (GFR) and thousand-grain weight (TGW) plays an important role in evaluating yield and guiding the selection of varieties and cultivation strategies of winter wheat (Triticum aestivum L.). However, the current GFR and TGW monitoring methods mainly rely on destructive sam...
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Veröffentlicht in: | European journal of agronomy 2024-09, Vol.159, p.127258, Article 127258 |
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creator | Zhang, Baoyuan Gu, Limin Dai, Menglei Bao, Xiaoyuan Sun, Qian Qu, Xuzhou Zhang, Mingzheng Liu, Xingyu Fan, Chengzhi Gu, Xiaohe Zhen, Wenchao |
description | Estimating grain filling rate (GFR) and thousand-grain weight (TGW) plays an important role in evaluating yield and guiding the selection of varieties and cultivation strategies of winter wheat (Triticum aestivum L.). However, the current GFR and TGW monitoring methods mainly rely on destructive sampling, which can not achieve rapid estimation in a large area of farmland. This study aims to establish a method for estimating GFR and TGW of winter wheat using multispectral UAV images. Initially, grey correlation analysis method was used to evaluate the contributions of Leaf Area Index (LAI), Chlorophyll Content (SPAD), Aboveground Biomass (AGB) to GFR. A new comprehensive indicator, called LAI-SPAD-AGB index (LSA), was proposed to characterize GFR by establishing a linear regression model between LSA and GFR. Subsequently, UAV-based multispectral images were used to estimate LAI, SPAD, AGB, employing the methods such as Partial Least Squares Regression (PLSR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Using the linear regression equation between LSA and GFR along with estimated LSA values, GFR was estimated and mapped. TGW was estimated based on GFR and grain-filling duration (GFD). Results showed the high GFR estimation accuracy (R2: 0.89, RMSE: 0.29 g/d, NRMSE: 10.0 %) and remarkable TGW estimation precision (R2: 0.92, RMSE: 4.20 g, NRMSE: 8.1 %). The parcel-scale distribution maps of estimated GFR and TGW were generated. The novel and non-destructive method of estimating GFR and TGW of winter wheat using UAV-based images can offer strong support for water and fertilizer management in the field.
•The grain filling rate of winter wheat had good correlation with LAI or SPAD or AGB.•An estimation model of grain filling rate was proposed based on LAI, SPAD and AGB.•The dynamic changes of LAI, SPAD and AGB were monitored using UAV images.•The grain filling rate of wheat at filling stage was mapped at the field scale. |
doi_str_mv | 10.1016/j.eja.2024.127258 |
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•The grain filling rate of winter wheat had good correlation with LAI or SPAD or AGB.•An estimation model of grain filling rate was proposed based on LAI, SPAD and AGB.•The dynamic changes of LAI, SPAD and AGB were monitored using UAV images.•The grain filling rate of wheat at filling stage was mapped at the field scale.</description><identifier>ISSN: 1161-0301</identifier><identifier>DOI: 10.1016/j.eja.2024.127258</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>aboveground biomass ; agricultural land ; agronomy ; chlorophyll ; equations ; fertilizers ; filling period ; Grain filling rate ; Grain weight ; leaf area index ; nondestructive methods ; regression analysis ; Triticum aestivum ; UAV ; unmanned aerial vehicles ; Vegetation index ; Winter wheat</subject><ispartof>European journal of agronomy, 2024-09, Vol.159, p.127258, Article 127258</ispartof><rights>2024 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c212t-2add3b8e9ad7cadb43bcc34885d38bbe912e6a7741dd71c7a80a0ea52286754d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1161030124001795$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Zhang, Baoyuan</creatorcontrib><creatorcontrib>Gu, Limin</creatorcontrib><creatorcontrib>Dai, Menglei</creatorcontrib><creatorcontrib>Bao, Xiaoyuan</creatorcontrib><creatorcontrib>Sun, Qian</creatorcontrib><creatorcontrib>Qu, Xuzhou</creatorcontrib><creatorcontrib>Zhang, Mingzheng</creatorcontrib><creatorcontrib>Liu, Xingyu</creatorcontrib><creatorcontrib>Fan, Chengzhi</creatorcontrib><creatorcontrib>Gu, Xiaohe</creatorcontrib><creatorcontrib>Zhen, Wenchao</creatorcontrib><title>Estimation of grain filling rate and thousand-grain weight of winter wheat (Triticum aestivum L.) using UAV-based multispectral images</title><title>European journal of agronomy</title><description>Estimating grain filling rate (GFR) and thousand-grain weight (TGW) plays an important role in evaluating yield and guiding the selection of varieties and cultivation strategies of winter wheat (Triticum aestivum L.). However, the current GFR and TGW monitoring methods mainly rely on destructive sampling, which can not achieve rapid estimation in a large area of farmland. This study aims to establish a method for estimating GFR and TGW of winter wheat using multispectral UAV images. Initially, grey correlation analysis method was used to evaluate the contributions of Leaf Area Index (LAI), Chlorophyll Content (SPAD), Aboveground Biomass (AGB) to GFR. A new comprehensive indicator, called LAI-SPAD-AGB index (LSA), was proposed to characterize GFR by establishing a linear regression model between LSA and GFR. Subsequently, UAV-based multispectral images were used to estimate LAI, SPAD, AGB, employing the methods such as Partial Least Squares Regression (PLSR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Using the linear regression equation between LSA and GFR along with estimated LSA values, GFR was estimated and mapped. TGW was estimated based on GFR and grain-filling duration (GFD). Results showed the high GFR estimation accuracy (R2: 0.89, RMSE: 0.29 g/d, NRMSE: 10.0 %) and remarkable TGW estimation precision (R2: 0.92, RMSE: 4.20 g, NRMSE: 8.1 %). The parcel-scale distribution maps of estimated GFR and TGW were generated. The novel and non-destructive method of estimating GFR and TGW of winter wheat using UAV-based images can offer strong support for water and fertilizer management in the field.
•The grain filling rate of winter wheat had good correlation with LAI or SPAD or AGB.•An estimation model of grain filling rate was proposed based on LAI, SPAD and AGB.•The dynamic changes of LAI, SPAD and AGB were monitored using UAV images.•The grain filling rate of wheat at filling stage was mapped at the field scale.</description><subject>aboveground biomass</subject><subject>agricultural land</subject><subject>agronomy</subject><subject>chlorophyll</subject><subject>equations</subject><subject>fertilizers</subject><subject>filling period</subject><subject>Grain filling rate</subject><subject>Grain weight</subject><subject>leaf area index</subject><subject>nondestructive methods</subject><subject>regression analysis</subject><subject>Triticum aestivum</subject><subject>UAV</subject><subject>unmanned aerial vehicles</subject><subject>Vegetation index</subject><subject>Winter wheat</subject><issn>1161-0301</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9UMtOwzAQzAEkSuEDuPlYDgm286w4VVV5SJW4tFytjb1JHaVJsZ1W_ADfjaNw5rQr7czszATBA6MRoyx7aiJsIOKUJxHjOU-Lq2DGWMZCGlN2E9xa21BKC54ms-BnY50-gtN9R_qK1AZ0RyrdtrqriQGHBDpF3KEfrF_C6X5BXR_ciL_ozqEhlwOCI4ud0U7L4UgAverZL9vokQx21NqvPsMSLCpyHFqn7QmlM9AS_7xGexdcV9BavP-b82D_stmt38Ltx-v7erUNJWfchRyUissCl6ByCapM4lLKOCmKVMVFWeKSccwgzxOmVM5kDgUFipByXmR5mqh4Hiwm3ZPpvwbvUhy1ldi20KGPKGKWxlm-THjioWyCStNba7ASJ-PNmm_BqBh7Fo3wPYuxZzH17DnPEwd9hrNGI6zU2ElU2vi8QvX6H_Yv64aJ7A</recordid><startdate>202409</startdate><enddate>202409</enddate><creator>Zhang, Baoyuan</creator><creator>Gu, Limin</creator><creator>Dai, Menglei</creator><creator>Bao, Xiaoyuan</creator><creator>Sun, Qian</creator><creator>Qu, Xuzhou</creator><creator>Zhang, Mingzheng</creator><creator>Liu, Xingyu</creator><creator>Fan, Chengzhi</creator><creator>Gu, Xiaohe</creator><creator>Zhen, Wenchao</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>202409</creationdate><title>Estimation of grain filling rate and thousand-grain weight of winter wheat (Triticum aestivum L.) using UAV-based multispectral images</title><author>Zhang, Baoyuan ; Gu, Limin ; Dai, Menglei ; Bao, Xiaoyuan ; Sun, Qian ; Qu, Xuzhou ; Zhang, Mingzheng ; Liu, Xingyu ; Fan, Chengzhi ; Gu, Xiaohe ; Zhen, Wenchao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c212t-2add3b8e9ad7cadb43bcc34885d38bbe912e6a7741dd71c7a80a0ea52286754d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>aboveground biomass</topic><topic>agricultural land</topic><topic>agronomy</topic><topic>chlorophyll</topic><topic>equations</topic><topic>fertilizers</topic><topic>filling period</topic><topic>Grain filling rate</topic><topic>Grain weight</topic><topic>leaf area index</topic><topic>nondestructive methods</topic><topic>regression analysis</topic><topic>Triticum aestivum</topic><topic>UAV</topic><topic>unmanned aerial vehicles</topic><topic>Vegetation index</topic><topic>Winter wheat</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Baoyuan</creatorcontrib><creatorcontrib>Gu, Limin</creatorcontrib><creatorcontrib>Dai, Menglei</creatorcontrib><creatorcontrib>Bao, Xiaoyuan</creatorcontrib><creatorcontrib>Sun, Qian</creatorcontrib><creatorcontrib>Qu, Xuzhou</creatorcontrib><creatorcontrib>Zhang, Mingzheng</creatorcontrib><creatorcontrib>Liu, Xingyu</creatorcontrib><creatorcontrib>Fan, Chengzhi</creatorcontrib><creatorcontrib>Gu, Xiaohe</creatorcontrib><creatorcontrib>Zhen, Wenchao</creatorcontrib><collection>CrossRef</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>European journal of agronomy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Baoyuan</au><au>Gu, Limin</au><au>Dai, Menglei</au><au>Bao, Xiaoyuan</au><au>Sun, Qian</au><au>Qu, Xuzhou</au><au>Zhang, Mingzheng</au><au>Liu, Xingyu</au><au>Fan, Chengzhi</au><au>Gu, Xiaohe</au><au>Zhen, Wenchao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation of grain filling rate and thousand-grain weight of winter wheat (Triticum aestivum L.) using UAV-based multispectral images</atitle><jtitle>European journal of agronomy</jtitle><date>2024-09</date><risdate>2024</risdate><volume>159</volume><spage>127258</spage><pages>127258-</pages><artnum>127258</artnum><issn>1161-0301</issn><abstract>Estimating grain filling rate (GFR) and thousand-grain weight (TGW) plays an important role in evaluating yield and guiding the selection of varieties and cultivation strategies of winter wheat (Triticum aestivum L.). However, the current GFR and TGW monitoring methods mainly rely on destructive sampling, which can not achieve rapid estimation in a large area of farmland. This study aims to establish a method for estimating GFR and TGW of winter wheat using multispectral UAV images. Initially, grey correlation analysis method was used to evaluate the contributions of Leaf Area Index (LAI), Chlorophyll Content (SPAD), Aboveground Biomass (AGB) to GFR. A new comprehensive indicator, called LAI-SPAD-AGB index (LSA), was proposed to characterize GFR by establishing a linear regression model between LSA and GFR. Subsequently, UAV-based multispectral images were used to estimate LAI, SPAD, AGB, employing the methods such as Partial Least Squares Regression (PLSR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Using the linear regression equation between LSA and GFR along with estimated LSA values, GFR was estimated and mapped. TGW was estimated based on GFR and grain-filling duration (GFD). Results showed the high GFR estimation accuracy (R2: 0.89, RMSE: 0.29 g/d, NRMSE: 10.0 %) and remarkable TGW estimation precision (R2: 0.92, RMSE: 4.20 g, NRMSE: 8.1 %). The parcel-scale distribution maps of estimated GFR and TGW were generated. The novel and non-destructive method of estimating GFR and TGW of winter wheat using UAV-based images can offer strong support for water and fertilizer management in the field.
•The grain filling rate of winter wheat had good correlation with LAI or SPAD or AGB.•An estimation model of grain filling rate was proposed based on LAI, SPAD and AGB.•The dynamic changes of LAI, SPAD and AGB were monitored using UAV images.•The grain filling rate of wheat at filling stage was mapped at the field scale.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.eja.2024.127258</doi></addata></record> |
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subjects | aboveground biomass agricultural land agronomy chlorophyll equations fertilizers filling period Grain filling rate Grain weight leaf area index nondestructive methods regression analysis Triticum aestivum UAV unmanned aerial vehicles Vegetation index Winter wheat |
title | Estimation of grain filling rate and thousand-grain weight of winter wheat (Triticum aestivum L.) using UAV-based multispectral images |
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