Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality

The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-des...

Full description

Saved in:

Bibliographic Details
Published in:	Remote sensing (Basel, Switzerland) Vol. 15; no. 22; p. 5412
Main Authors:	Lyu, Hongyi, Grafton, Miles, Ramilan, Thiagarajah, Irwin, Matthew, Wei, Hsiang-En, Sandoval, Eduardo
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.11.2023
Subjects:	Aircraft algorithms Artificial neural networks Berries Brix value Cameras Correlation coefficient Correlation coefficients Electrical conductivity Electrical resistivity Environmental aspects Fruits Grapes Learning algorithms Machine learning Methods multispectral imagery Neural networks Non-destructive testing normalized difference vegetation index Normalized difference vegetative index prediction Prediction models Quality management Regression analysis Regression models Remote sensing Root-mean-square errors Satellites Sensors Soil conductivity soil electrical conductivity Soils sugar content Support vector machines total soluble solids UAV multispectral imagery Unmanned aerial vehicles Vegetation vegetation indices vigor vines Vineyards wine grape wine grapes Wineries & vineyards Wines New Zealand
ISSN:	2072-4292, 2072-4292
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECa) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 °Brix, and coefficient of determination (R2) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R2 of 0.51 and RMSE of 1.19 °Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R2 of 0.54 and a RMSE of 1.16 °Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way.
AbstractList	The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (EC[sub.a] ) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 °Brix, and coefficient of determination (R[sup.2] ) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R[sup.2] of 0.51 and RMSE of 1.19 °Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R[sup.2] of 0.54 and a RMSE of 1.16 °Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way. The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECa) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 °Brix, and coefficient of determination (R2) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R2 of 0.51 and RMSE of 1.19 °Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R2 of 0.54 and a RMSE of 1.16 °Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way. The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECₐ) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 °Brix, and coefficient of determination (R²) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R² of 0.51 and RMSE of 1.19 °Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R² of 0.54 and a RMSE of 1.16 °Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way.
Audience	Academic
Author	Ramilan, Thiagarajah Wei, Hsiang-En Irwin, Matthew Sandoval, Eduardo Grafton, Miles Lyu, Hongyi
Author_xml	– sequence: 1 givenname: Hongyi orcidid: 0000-0003-2883-6259 surname: Lyu fullname: Lyu, Hongyi – sequence: 2 givenname: Miles orcidid: 0000-0002-4094-874X surname: Grafton fullname: Grafton, Miles – sequence: 3 givenname: Thiagarajah orcidid: 0000-0001-8476-3619 surname: Ramilan fullname: Ramilan, Thiagarajah – sequence: 4 givenname: Matthew surname: Irwin fullname: Irwin, Matthew – sequence: 5 givenname: Hsiang-En orcidid: 0000-0003-1919-4864 surname: Wei fullname: Wei, Hsiang-En – sequence: 6 givenname: Eduardo orcidid: 0000-0003-2695-7305 surname: Sandoval fullname: Sandoval, Eduardo
BookMark	eNptUl1rFDEUHaSCde2Lv2DAFxG2ZnIzH3ksba2ForVaX8OdzM2SZSZZk4xYf73ZHUUpJpCPc8854d7c58WR846K4mXFTgEkextiVXNei4o_KY45a_lacMmP_jk_K05i3LI8ACrJxHHx8z5atynvaPKJSnRDeRv8DzvhWH4md4hdYMJD5Kt1lJeND-UtBpwoUYilydcP3q0vKKYw62S_Lz53uLN7NxpsBr0rvSmvAu6o_DTjaNPDi-KpwTHSye99Vdy_u_xy_n598_Hq-vzsZq0FQFobIGKIYPoWajKsGyqUPRjQRmJHEqsBNa-aHpjhnekbCdxIkIjIO61rWBXXi-_gcat2IScXHpRHqw6ADxuFIVk9kqp5K1pBHKDuRNt0knUkeI-tqdtGZHhVvF68dsF_m3PGarJR0ziiIz9HBUww0UDTsEx99Yi69XNwOVPFOwkgRFPvWacLa4P5feuMTwF1ngNNVuf_NTbjZ20rgNddJbKALQIdfIyBjNI24b7AWWhHVTG1bwb1txmy5M0jyZ8i_If8C6bXtQI
CitedBy_id	crossref_primary_10_1016_j_compag_2025_110341 crossref_primary_10_1016_j_rsase_2025_101708 crossref_primary_10_7717_peerj_19264 crossref_primary_10_3390_rs16101655 crossref_primary_10_1038_s41598_025_16967_6 crossref_primary_10_1007_s11119_024_10177_2 crossref_primary_10_3390_rs16030584 crossref_primary_10_1002_ppj2_70024 crossref_primary_10_3390_horticulturae10030238 crossref_primary_10_3390_rs16244805
Cites_doi	10.1016/j.compag.2018.02.013 10.3390/s22093249 10.1371/journal.pone.0218132 10.1016/j.compag.2017.06.009 10.3390/agronomy11040655 10.5424/sjar/2009074-1092 10.1016/j.isprsjprs.2014.08.015 10.13031/ja.14126 10.3390/rs14184450 10.1007/s11119-015-9407-8 10.3389/fpls.2020.00790 10.1111/ajgw.12408 10.3390/rs11010023 10.3389/fpls.2021.683078 10.1111/j.1755-0238.2004.tb00007.x 10.1016/j.rse.2020.111679 10.1111/j.1755-0238.2010.00119.x 10.3390/agriculture11050457 10.3390/rs9040317 10.20870/oeno-one.2008.42.2.828 10.3390/rs15061497 10.1080/05704928.2014.966380 10.3390/rs70302971 10.1016/j.biosystemseng.2021.04.006 10.1111/j.1755-0238.2005.tb00277.x 10.1111/j.1755-0238.2010.00120.x 10.3390/s23031065 10.1007/s11119-012-9282-5 10.1111/j.1755-0238.2011.00136.x 10.3390/rs71114458 10.3390/rs14235918 10.1139/juvs-2016-0024 10.1080/01431161.2018.1471548 10.1016/S0098-3004(03)00082-7 10.3390/agriculture8070094 10.3390/rs11232869 10.3390/rs9040308 10.1016/j.measurement.2014.04.007
ContentType	Journal Article
Copyright	COPYRIGHT 2023 MDPI AG 2023 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 (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: COPYRIGHT 2023 MDPI AG – notice: 2023 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 (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD 8FE 8FG ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F28 FR3 H8D H8G HCIFZ JG9 JQ2 KR7 L6V L7M L~C L~D M7S P5Z P62 P64 PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7S9 L.6 DOA
DOI	10.3390/rs15225412
DatabaseName	CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Ecology Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection (subscription) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central - New (Subscription) Technology Collection Natural Science Collection Earth, Atmospheric & Aquatic Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Central Korea ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database (subscription) AAdvanced Technologies & Aerospace Database (subscription) ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection AGRICOLA AGRICOLA - Academic Directory of Open Access Journals (DOAJ)
DatabaseTitle	CrossRef Publicly Available Content Database Materials Research Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China Materials Business File Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences Engineered Materials Abstracts Natural Science Collection Chemoreception Abstracts ProQuest Central (New) Engineering Collection ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Engineering Database Aluminium Industry Abstracts ProQuest One Academic Eastern Edition Electronics & Communications Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection Ceramic Abstracts Ecology Abstracts Biotechnology and BioEngineering Abstracts ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Central (Alumni Edition) ProQuest One Community College Earth, Atmospheric & Aquatic Science Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Engineering Collection Biotechnology Research Abstracts ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Materials Science & Engineering Collection Corrosion Abstracts AGRICOLA AGRICOLA - Academic
DatabaseTitleList	Publicly Available Content Database CrossRef AGRICOLA
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography
EISSN	2072-4292
ExternalDocumentID	oai_doaj_org_article_527474e233584768908e42ba7f576423 A774325814 10_3390_rs15225412
GeographicLocations	New Zealand
GeographicLocations_xml	– name: New Zealand
GroupedDBID	29P 2WC 2XV 5VS 8FE 8FG 8FH AADQD AAHBH AAYXX ABDBF ABJCF ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ BHPHI BKSAR CCPQU CITATION E3Z ESX FRP GROUPED_DOAJ HCIFZ I-F IAO ITC KQ8 L6V LK5 M7R M7S MODMG M~E OK1 P62 PCBAR PHGZM PHGZT PIMPY PQGLB PROAC PTHSS TR2 TUS 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD ABUWG AZQEC C1K DWQXO F28 FR3 H8D H8G JG9 JQ2 KR7 L7M L~C L~D P64 PKEHL PQEST PQQKQ PQUKI PRINS 7S9 L.6 PUEGO
ID	FETCH-LOGICAL-c433t-f3ee0aa3fb735ef08d1a9b3f3cf9a8e9a1dac216b30f28fb6932f939aaa28cc53
IEDL.DBID	DOA
ISICitedReferencesCount	10
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001113800200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2072-4292
IngestDate	Fri Oct 03 12:43:51 EDT 2025 Thu Sep 04 16:57:25 EDT 2025 Fri Jul 25 11:57:31 EDT 2025 Tue Nov 04 18:11:23 EST 2025 Tue Nov 18 22:16:50 EST 2025 Sat Nov 29 07:19:34 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	22
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c433t-f3ee0aa3fb735ef08d1a9b3f3cf9a8e9a1dac216b30f28fb6932f939aaa28cc53
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-4094-874X 0000-0001-8476-3619 0000-0003-2695-7305 0000-0003-1919-4864 0000-0003-2883-6259
OpenAccessLink	https://doaj.org/article/527474e233584768908e42ba7f576423
PQID	2893344650
PQPubID	2032338
ParticipantIDs	doaj_primary_oai_doaj_org_article_527474e233584768908e42ba7f576423 proquest_miscellaneous_3040463660 proquest_journals_2893344650 gale_infotracacademiconefile_A774325814 crossref_citationtrail_10_3390_rs15225412 crossref_primary_10_3390_rs15225412
PublicationCentury	2000
PublicationDate	2023-11-01
PublicationDateYYYYMMDD	2023-11-01
PublicationDate_xml	– month: 11 year: 2023 text: 2023-11-01 day: 01
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	Remote sensing (Basel, Switzerland)
PublicationYear	2023
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Arab (ref_9) 2021; 22 Dambergs (ref_19) 2015; 50 Benelli (ref_17) 2021; 207 ref_14 ref_13 Marques (ref_31) 2018; 39 Hall (ref_45) 2003; 29 ref_12 Brook (ref_27) 2020; 240 ref_32 Carrillo (ref_10) 2016; 17 ref_30 Bramley (ref_3) 2011; 17 Bramley (ref_33) 2011; 17 Baluja (ref_2) 2013; 14 ref_15 ref_37 Bramley (ref_39) 2005; 11 Wei (ref_5) 2023; 3 Yu (ref_36) 2020; 11 Matese (ref_11) 2015; 7 Sanches (ref_41) 2014; 97 Lamb (ref_7) 2004; 10 Soubry (ref_29) 2017; 5 Kasimati (ref_18) 2021; 12 Jiang (ref_48) 2022; 65 Trought (ref_35) 2008; 42 Bramley (ref_16) 2003; 473a Trought (ref_38) 2011; 17 ref_47 SU (ref_34) 2014; 54 ref_46 ref_23 ref_44 ref_21 ref_20 ref_42 ref_1 Bramley (ref_43) 2019; 25 Gomes (ref_40) 2017; 140 Diago (ref_6) 2015; 7 ref_28 ref_26 (ref_25) 2010; 49 ref_8 ref_4 (ref_22) 2009; 7 Romero (ref_24) 2018; 147
References_xml	– volume: 147 start-page: 109 year: 2018 ident: ref_24 article-title: Vineyard Water Status Estimation Using Multispectral Imagery from an UAV Platform and Machine Learning Algorithms for Irrigation Scheduling Management publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.02.013 – ident: ref_20 doi: 10.3390/s22093249 – ident: ref_26 doi: 10.1371/journal.pone.0218132 – volume: 140 start-page: 244 year: 2017 ident: ref_40 article-title: Comparison of Different Approaches for the Prediction of Sugar Content in New Vintages of Whole Port Wine Grape Berries Using Hyperspectral Imaging publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2017.06.009 – ident: ref_12 doi: 10.3390/agronomy11040655 – volume: 7 start-page: 779 year: 2009 ident: ref_22 article-title: Precision Viticulture. Research Topics, Challenges and Opportunities in Site-Specific Vineyard Management publication-title: Span. J. Agric. Res. doi: 10.5424/sjar/2009074-1092 – volume: 97 start-page: 111 year: 2014 ident: ref_41 article-title: Spectroscopic Remote Sensing of Plant Stress at Leaf and Canopy Levels Using the Chlorophyll 680 Nm Absorption Feature with Continuum Removal publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2014.08.015 – volume: 65 start-page: 287 year: 2022 ident: ref_48 article-title: A Comparison of Supervised Machine Learning Algorithms for Predicting Subfield Yield Variability of Maize Grain publication-title: J. ASABE doi: 10.13031/ja.14126 – volume: 22 start-page: 100485 year: 2021 ident: ref_9 article-title: Prediction of Grape Yields from Time-Series Vegetation Indices Using Satellite Remote Sensing and a Machine-Learning Approach publication-title: Remote Sens. Appl. Soc. Environ. – ident: ref_8 doi: 10.3390/rs14184450 – volume: 17 start-page: 74 year: 2016 ident: ref_10 article-title: Use of Multi-Spectral Airborne Imagery to Improve Yield Sampling in Viticulture publication-title: Precis. Agric. doi: 10.1007/s11119-015-9407-8 – volume: 11 start-page: 790 year: 2020 ident: ref_36 article-title: Spatial Variability of Soil and Plant Water Status and Their Cascading Effects on Grapevine Physiology Are Linked to Berry and Wine Chemistry publication-title: Front. Plant Sci. doi: 10.3389/fpls.2020.00790 – volume: 25 start-page: 430 year: 2019 ident: ref_43 article-title: Spatio-temporal Variability in Vine Vigour and Yield in a Marlborough Sauvignon Blanc Vineyard publication-title: Aust. J. Grape Wine Res. doi: 10.1111/ajgw.12408 – ident: ref_32 doi: 10.3390/rs11010023 – volume: 12 start-page: 683078 year: 2021 ident: ref_18 article-title: Investigating a Selection of Methods for the Prediction of Total Soluble Solids among Wine Grape Quality Characteristics Using Normalized Difference Vegetation Index Data from Proximal and Remote Sensing publication-title: Front. Plant Sci. doi: 10.3389/fpls.2021.683078 – ident: ref_37 – ident: ref_1 – volume: 10 start-page: 46 year: 2004 ident: ref_7 article-title: Using Remote Sensing to Predict Grape Phenolics and Colour at Harvest in a Cabernet Sauvignon Vineyard: Timing Observations against Vine Phenology and Optimising Image Resolution publication-title: Aust. J. Grape Wine Res. doi: 10.1111/j.1755-0238.2004.tb00007.x – volume: 240 start-page: 111679 year: 2020 ident: ref_27 article-title: A Smart Multiple Spatial and Temporal Resolution System to Support Precision Agriculture from Satellite Images: Proof of Concept on Aglianico Vineyard publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.111679 – ident: ref_44 – volume: 17 start-page: 72 year: 2011 ident: ref_3 article-title: Vineyard Variability in Marlborough, New Zealand: Characterising Variation in Vineyard Performance and Options for the Implementation of Precision Viticulture publication-title: Aust. J. Grape Wine Res. doi: 10.1111/j.1755-0238.2010.00119.x – ident: ref_23 – volume: 49 start-page: 167 year: 2010 ident: ref_25 article-title: Remote Sensing Detection of Nutrient Uptake in Vineyards Using Narrow-Band Hyperspectral Imagery publication-title: Vitis – ident: ref_21 – ident: ref_42 doi: 10.3390/agriculture11050457 – volume: 3 start-page: 6 year: 2023 ident: ref_5 article-title: Evaluation of the Use of Two-Stage Calibrated PlanetScope Images and Environmental Variables for the Development of the Grapevine Water Status Prediction Model publication-title: Technol. Agron. – ident: ref_47 doi: 10.3390/rs9040317 – volume: 42 start-page: 67 year: 2008 ident: ref_35 article-title: The Impact of Differences in Soil Texture within a Vineyard on Vine Vigour, Vine Earliness and Juice Composition publication-title: OENO One doi: 10.20870/oeno-one.2008.42.2.828 – ident: ref_4 – ident: ref_14 doi: 10.3390/rs15061497 – volume: 50 start-page: 261 year: 2015 ident: ref_19 article-title: A Review of the State of the Art, Limitations, and Perspectives of Infrared Spectroscopy for the Analysis of Wine Grapes, Must, and Grapevine Tissue publication-title: Appl. Spectrosc. Rev. doi: 10.1080/05704928.2014.966380 – volume: 7 start-page: 2971 year: 2015 ident: ref_11 article-title: Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture publication-title: Remote Sens. doi: 10.3390/rs70302971 – volume: 207 start-page: 59 year: 2021 ident: ref_17 article-title: In-Field and Non-Destructive Monitoring of Grapes Maturity by Hyperspectral Imaging publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2021.04.006 – volume: 11 start-page: 33 year: 2005 ident: ref_39 article-title: Understanding Variability in Winegrape Production Systems 2. Within Vineyard Variation in Quality over Several Vintages publication-title: Aust. J. Grape Wine Res. doi: 10.1111/j.1755-0238.2005.tb00277.x – volume: 17 start-page: 79 year: 2011 ident: ref_38 article-title: Vineyard Variability in Marlborough, New Zealand: Characterising Spatial and Temporal Changes in Fruit Composition and Juice Quality in the Vineyard publication-title: Aust. J. Grape Wine Res. doi: 10.1111/j.1755-0238.2010.00120.x – ident: ref_15 doi: 10.3390/s23031065 – volume: 14 start-page: 40 year: 2013 ident: ref_2 article-title: Spatial Variability of Grape Composition in a Tempranillo (Vitis vinifera L.) Vineyard over a 3-Year Survey publication-title: Precis. Agric. doi: 10.1007/s11119-012-9282-5 – volume: 17 start-page: 217 year: 2011 ident: ref_33 article-title: Variation in Vine Vigour, Grape Yield and Vineyard Soils and Topography as Indicators of Variation in the Chemical Composition of Grapes, Wine and Wine Sensory Attributes publication-title: Aust. J. Grape Wine Res. doi: 10.1111/j.1755-0238.2011.00136.x – volume: 7 start-page: 14458 year: 2015 ident: ref_6 article-title: Using RPAS Multi-Spectral Imagery to Characterise Vigour, Leaf Development, Yield Components and Berry Composition Variability within a Vineyard publication-title: Remote Sens. doi: 10.3390/rs71114458 – ident: ref_13 doi: 10.3390/rs14235918 – volume: 5 start-page: 37 year: 2017 ident: ref_29 article-title: Monitoring Vineyards with UAV and Multi-Sensors for the Assessment of Water Stress and Grape Maturity publication-title: J. Unmanned Veh. Syst. doi: 10.1139/juvs-2016-0024 – volume: 39 start-page: 5377 year: 2018 ident: ref_31 article-title: Vineyard Properties Extraction Combining UAS-Based RGB Imagery with Elevation Data publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2018.1471548 – volume: 29 start-page: 813 year: 2003 ident: ref_45 article-title: Characterising and Mapping Vineyard Canopy Using High-Spatial-Resolution Aerial Multispectral Images publication-title: Comput. Geosci. doi: 10.1016/S0098-3004(03)00082-7 – ident: ref_46 doi: 10.3390/agriculture8070094 – volume: 473a start-page: 84 year: 2003 ident: ref_16 article-title: Being Profitable Precisely-A Case Study of Precision Viticulture from Margaret River publication-title: Aust. New Zealand Grapegrow. Winemak. [Annu. Tech. Issue] – ident: ref_30 doi: 10.3390/rs11232869 – ident: ref_28 doi: 10.3390/rs9040308 – volume: 54 start-page: 92 year: 2014 ident: ref_34 article-title: A Critical Review of Soil Moisture Measurement publication-title: Measurement doi: 10.1016/j.measurement.2014.04.007
SSID	ssj0000331904
Score	2.4122005
Snippet	The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this...
SourceID	doaj proquest gale crossref
SourceType	Open Website Aggregation Database Enrichment Source Index Database
StartPage	5412
SubjectTerms	Aircraft algorithms Artificial neural networks Berries Brix value Cameras Correlation coefficient Correlation coefficients Electrical conductivity Electrical resistivity Environmental aspects Fruits Grapes Learning algorithms Machine learning Methods multispectral imagery Neural networks Non-destructive testing normalized difference vegetation index Normalized difference vegetative index prediction Prediction models Quality management Regression analysis Regression models Remote sensing Root-mean-square errors Satellites Sensors Soil conductivity soil electrical conductivity Soils sugar content Support vector machines total soluble solids UAV multispectral imagery Unmanned aerial vehicles Vegetation vegetation indices vigor vines Vineyards wine grape wine grapes Wineries & vineyards Wines
SummonAdditionalLinks	– databaseName: Engineering Database (subscription) dbid: M7S link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELagIMGFNyJQkBFIiENUx84mzgmVR-G0WrWAerPGjt2uBMmSLBXl1zPjeLdCAi5cVtHasRx94_GMZ_wNY88LV4HWoc2VtyEv0UTPrQr4FNqmdLKo_MzGYhP1fK6Pj5tFOnAbU1rlRidGRd32js7I9yTVhSd2L_Fq9S2nqlEUXU0lNC6zK8SSUMTUvaPtGYtQKGCinFhJFXr3e8OI-xX6RIX8bR-KdP1_U8pxpzm4-b9zvMVuJBuT709CcZtd8t0ddi2VOz89v8t-xkQBfugRJ8-ha_li6H8sv-JLR5TQjm1vYQ2x5TOaofhz0g98AZTJRXScHE1dPu-7nNzWSEB7No1zCKsljUbhH4Kc94G_pwtefCLrOL_HPh28-_jmQ56KMOSuVGqdB-W9AFDB1mrmg9BtAQ3CqVxoQPsGihYIU6tEkDrYCg3C0KgGAKR2bqbus52u7_wDxm3hbKihQlSqslbSoikjlRfC2xraImTs5QYS4xJDORXK-GLQUyH4zAV8GXu27buaeDn-2Os1IbvtQVza8Y9-ODFpaZoZOeall0pRyLjSjdC-lBbqgL4YTjBjL0guDK14nI6DdHEBP4q4s8w-WtBKznRRZmx3IxcmqYLRXAhFxp5um3ERU2QGOt9_H41CVUrMbZV4-O8hHrHrVO9-ugy5y3YQYv-YXXVn6-U4PInS_wtk8w77 priority: 102 providerName: ProQuest
Title	Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality
URI	https://www.proquest.com/docview/2893344650 https://www.proquest.com/docview/3040463660 https://doaj.org/article/527474e233584768908e42ba7f576423
Volume	15
WOSCitedRecordID	wos001113800200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: DOA dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M~E dateStart: 20090101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: AAdvanced Technologies & Aerospace Database (subscription) customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: P5Z dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PCBAR dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database (subscription) customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M7S dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: BENPR dateStart: 20090301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PIMPY dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQQYILKi8RKCsjkBCHqE4mD-fYwhY4sIq2gAoXa-zYZSVIqt1tRTnw25lJ0qVIIC5crCjjWM7M2P5Gtr8R4mniCtQ6NDF4G-KMIHpsIdBTaKrMpUnhc9snmyhnM310VNWXUn3xmbCBHnhQ3G7OYVPmUwDe0Ct0pbTPUotlIKRMWIBnX1VWl4Kpfg4Gci2VDXykQHH97nJFKxVFQ0n62wrUE_X_bTru15iDbXFzBIdyb-jULXHFt7fF9TFP-efzO-J7v8Mv554U7CW2jayX3bfFV_rokE-ik-wlrrGXfCD8SMVxt5Q18hEs5tGUhFHlrGtjjjd75tizoZ05niy4Nd63YVvJLshXfDNLDiwb53fF-4Ppuxev4zF7QuwygHUcwHuFCMGWkPugdJNgRXYAFyrUvsKkQTaGBRVSHWxBSC5UUCFiqp3L4Z7YarvW3xfSJs6GEgtSapGVkFrCICl4pbwtsUlCJJ5faNS4kVqcM1x8MRRisPbNL-1H4smm7slAqPHHWvtsmE0NJsHuX5BrmNE1zL9cIxLP2KyGhyp1x-F444B-ikmvzB5BX0hznWSR2LmwvBnH8MpQKArAfHIqEo83Yhp9vKWCre9OVwZoDmTKtUI9-B89fihucDr74a7jjtgiR_CPxDV3tl6slhNxdX86q-eT3tUnfEr1kMsfUyrr_BPJ6zdv648_Ac9TBPc
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtQwFL2qpkhlwxsxpYARIMQiqmNn8lggVCilo7ajUSmorIzt2O1IkAyZoTB8FN_IvXlMhQTsumATRbFjOcnJffhxDsDj0MY6TX0eSGd8EGGIHhjp8cznWWRFGLuBqcUmktEoPT7Oxivws9sLQ8sqO5tYG-q8tDRGvilIF57YvfiL6ZeAVKNodrWT0GhgsecW3zBlmz0fbuP3fSLEzuujV7tBqyoQ2EjKeeClc1xr6U0iB87zNA91hv2T1mc6dZkOc02dNJJ7kXoTY4TjM5lprUVqLalEoMlfjQjsPVgdDw_GH5ajOlwipHnU8KBKmfHNaoYeErOwUPzm-WqBgL-5gdq37Vz9397KNbjSRtFsq4H9dVhxxQ1YawXdTxc34Ue9FIIdOkSiY7rI2bgqv08-401vack-lm3rua5L3mOgjYeTsmJjTWvViHCUYTDPRmURUGJeU-yeNe0c6umEWqMJLgI1Kz17Q1vYWENHsrgF7y7k0W9DrygLdweYCa3xiY4RBXGUSGEwWBPSce5MovPQ9-FZBwFlWw52kgL5pDAXI7ioc7j04dGy7rRhHvljrZeEpGUNYguvL5TViWqNjxrQ0EPkhJQ0KR6nGU9dJIxOPGab2ME-PCUcKrJp2B2r260Z-FDEDqa2MEeQYpCGUR82Ohyq1tjN1DkI-_BwWYxmiuaedOHKrzMl0VkQN13M1__dxANY2z062Ff7w9HeXbgsMKZstn5uQA8_t7sHl-zZfDKr7rf_HoOPFw3sXwI5cOw
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFLamgWAv3BGBAUaAEA9RHTtNnAeEBqUwDVXRuGjixdiOvVWCpCRlUH4av45zcumEBLztgZeqql3LSb6ci338fYQ8iGyipfRFKJzxYQwhemiEh2--yGLLo8SNTSs2kc5m8uAgyzfIz-EsDJZVDjaxNdRFZXGNfMRRFx7ZvdjI92UR-WT6dPElRAUp3Gkd5DQ6iOy51TdI35onuxN41g85n754-_xV2CsMhDYWYhl64RzTWniTirHzTBaRzmCuwvpMS5fpqNA4YSOY59KbBKIdn4lMa82ltagYAeb_TAo5JpYT5uMP6_UdJgDcLO4YUYXI2KhuwFdCPhbx33xgKxXwN4fQernpxf_5_lwiF_rYmu50L8NlsuHKK-R8L_N-tLpKfrQFEnTfAT4d1WVB87r6Pv8Mf3qDhfzQNtFL3ba8h_AbPg6rmuYaK9iQhpRCiE9nVRliut4S7x534-zrxRxHw20vhDqtPH2JB9toR1KyukbencqlXyebZVW6G4SayBqf6gQQkcSp4AZCOC4cY86kuoh8QB4PcFC2Z2ZHgZBPCjI0hI46gU5A7q_7Ljo-kj_2eoaoWvdADvH2h6o-VL1JUmNckIgdFwK3yhOZMelibnTqIQeFCQbkEWJSoaWD6VjdH9iAi0LOMLUDmYPgYxnFAdkeMKl6E9ioE0AG5N66GYwX7kjp0lVfGyXAhSBjXcJu_nuIu-QcoFm93p3t3SJbHALN7jzoNtmEp-1uk7P2eDlv6jvtS0jJx9NG9S-T8XhP
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Using+Remote+and+Proximal+Sensing+Data+and+Vine+Vigor+Parameters+for+Non-Destructive+and+Rapid+Prediction+of+Grape+Quality&rft.jtitle=Remote+sensing+%28Basel%2C+Switzerland%29&rft.au=Lyu%2C+Hongyi&rft.au=Grafton%2C+Miles&rft.au=Ramilan%2C+Thiagarajah&rft.au=Irwin%2C+Matthew&rft.date=2023-11-01&rft.issn=2072-4292&rft.eissn=2072-4292&rft.volume=15&rft.issue=22&rft_id=info:doi/10.3390%2Frs15225412&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2072-4292&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2072-4292&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2072-4292&client=summon