Precision viticulture: a review
DOI:
https://doi.org/10.48165/Keywords:
Remote sensing, Proximal sensing, Variable-rate technology, Robots, Decision support system, VineyardAbstract
The precision viticulture aims to optimize grape (Vitis spp. L.) vineyard management; reducing use of resources and environmental impacts; and maximizing quality yield. New technologies as UAVs, satellites, proximal sensors, variable rate machines (VRT) and robots are being developed and used more frequently in some parts of the world in recent years. Developments and abilities of computers, software and informatic systems to read, analyze, process and transfer a huge amount of data are major milestones in precision viticulture. In addition, different decision support systems (DSSs) for making better crop management decisions at the right time also assist vine growers. In the fragmented small vineyards in India, relatively cheaper technologies like UAV, proximal monitoring through various tools, and DSSs developed by the ICAR-NRC for Grapes, Pune, Maharashtra, India can be used by individual grape grower or through farmers’ cooperatives/groups to make grape cultivation technologically-, economically- and environmentally- viable. Therefore, current status of precision viticulture technologies and their potential applications in viticulture, have been discussed.
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Ammoniaci M, Kartsiotis SP, Perria R, and Storchi P. 2021. State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture. Agriculture 11:201. https://doi.org/10.3390/agriculture 11030201
Anastasiou E, Balafoutis A, Darra N, Psiroukis V, Biniari A, Xanthopoulos G and Fountas S. 2018. Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes. Agriculture 8: 94. https://doi:10.3390/
agriculture8070094
Aquino A, Barrio I, Diago MP, Millan B ad TardaguilaJ. 2018. VitisBerry®: an Android-smartphone application to early evaluate the number of grapevine berries by means of image analysis. Computers and Electronics in Agriculture
:19–28.
Aquino A, Millan B, Gaston D, Diago MP and Tardaguila J. 2015. vitisFlower® development and testing of a novel android-smartphone application for assessing the number of grapevine flowers per inflorescence using artificial vision techniques. Sensors 15: 21204–21218.
Arno J, Escola A, Valles JM, Liorens J, Saniz R, Masip J, Palacin J, and Rossel-Pola JR. 2013. Leaf area index estimation in vineyards using a ground-based LiDAR scanner. Precision Agric 14, 290–306.
Ballard DH and Brown CM. 1982. Computer Vision. Prentice Hall, Englewood Cliffs, New Jersey. 539 P
Barnaba FE, Bellincontro A and Mencarelli F. 2013. Portable NIR-AOTF spectroscopy combined with winery FTIR
spectroscopy for an easy, rapid, in-field monitoring of Sangiovese grape quality. Journal of the Science of Food and Agriculture 94: 1071–1077.
Bellvert J, Zarco-Tejada PJ, Girona J and Fereres E. 2014. Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precision Agriculture 15:361–376.
Ben Ghozlen N, Cerovic ZG, Germain C, Toutain S and Latouche G. 2010. Non-destructive optical monitoring of grape maturation by proximal sensing. Sensors 10:10040–10068.
Benelli A, Cevoli C, Ragni L and Fabbri A. 2021. In-field and non destructive monitoring of grapes maturity by hyperspectral imaging. Biosystems Engineering 207:59-67.
Birchmore W, Scott E, Zanker T, Emmett B and Perry W. 2015. Smart-phone app field assessment of powdery mildew. Australian and New Zealand Grapegrower and Winemaker 622: 46-47.
Blackmore S. 2014. A vision of farming with robots in 2050. Address to the Oxford Farming Conference. January 8, 2014. Oxford, UK. Book Gallery, Greenwood Publishing Group. 251 P.
Bramley R. 2003. Smarter thinking on soil survey. Australian and New Zealand Wine Industry Journal 18:88–94.
Bramley RGV and Lamb DW. 2003. Making sense of vineyard variability in Australia. In: Precision Viticulture. Proceedings of an International Symposium Held as Part of the IX Congreso Latinoamericano de Viticultura y Enologia, Chile; Centro de Agricultura de Precision, Pontificia Universidad Catolica de Chile, Facultad de Agronomia e Ingeneria Forestal: Santiago, Chile. pp. 35–54.
Bramley RGV, Le Moigne M, Evans S, Ouzman J, Florin L, Fadaili EM, Hinze CJ and Cerovic ZG. 2011. On-the-go sensing of grape berry anthocyanins during commercial harvest: development and prospects. Australian Journal of Grape and Wine Research 17: 316–326.
Bramley RGV. 2010. Precision Viticulture: managing vineyard variability for improved quality outcomes. In: Managing wine quality Vol-I [Reynold AG (Ed)]. Woodhead Publishing, UK. Pp 445-480.
Brischetto C, Bove F, Fedele G and Rossi V. 2021. A Weather Driven Model for Predicting Infections of Grapevines by Sporangia of Plasmopara viticola. Front. Plant Sci. 12:636607.
Burrell J, Brooke T, and Beckwith R. 2004. Vineyard computing: sensor networks in agricultural production. IEEE Pervasive Comput. 3:38–45.
Byju G, Nedunchezhiyan M, George J, Sunitha S, Soman S and Hridya AC. 2020. Evaluation of fertilizer best management practices by SSNM and customized fertilizers for cassava (Manihot esculenta) cultivation in India. Current Horticulture 8(1): 56–64.
Casa R. 2017. Agricoltura di precisione. Metodi e tecnologie per migliorare l’efficienza e la sostenibilita dei sistemi colturali. Edagricole Calderini. 450 P.
Cerovic ZC, Goutouly JP, Hilbert G, and Moise N. 2008. Mapping wine grape quality attributes using portable fluorescence-based sensors. Frutic 9: 301–310.
Chen P, Xiao Q, Zhang J, Xie C and Wang B. 2020. Occurrence prediction of cotton pests and diseases by bidirectional long short-term memory networks with climate and atmosphere circulation. Computers and Electronics in Agriculture 176: 105612. https://doi:10.1016/j.compag.2020.105612
Chougule Archana and Jha VK. 2019. Decision support for grape crop protection using ontology. Int. J. Reasoning based Intelligent Systems 11:24-37.
Comba L, Gay P, Primicerio J, and Aimonino DR. 2015. Vineyard detection from unmanned aerial systems images. Comput. Electron. Agric. 114: 78–87.
DA&FW. 2022. Department of Agriculture and Farmer’s Welfare, Govt. of India. https://www.agricoop.gov.in/en/ statistics/horticulture.
Darra N, Psomiadis E, Kasimati A, Anastasiou A, Anastasiou E, and Fountas S. 2021. Remote and Proximal Sensing Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards. Agronomy 11: 741. https://doi:10.3390/agronomy11040741
De Bei R, Fuentes S, Gilliham M, Tyerman S, Edwards E, Bianchini N, Smith J, and Collins C. 2016. VitiCanopy: A Free Computer App to Estimate Canopy Vigor and Porosity for Grapevine. Sensors 16: 585. https://doi.
org/10.3390/s16040585
Di Gennaro SF and Matese A. 2020. Evaluation of novel precision viticulture tool for canopy biomass estimation and missing plant detection based on 2.5D and 3D approaches using RGB images acquired by UAV platform. Plant Methods 16:91. https://doi:10.1186/s13007-020-
-2
Di Gennaro SF, Toscano P, Cinat P, Berton A, and Matese A. 2019. A Low-Cost and Unsupervised Image Recognition Methodology for Yield Estimation in a Vineyard. Front. Plant Sci.10. https://doi:10.3389/fpls.2019.00559
Diago M P, Rey-Carames C, Le Moigne M, Fadaili EM, Tardaguila J, and Cerovic Z G. 2016a. Calibration of non invasive fluorescence-based sensors for the manual and on-the-go assessment of grapevine vegetative status in the field. Australian Journal of Grape and Wine Research 22: 438–449.
Diago MP, Aquino A, Millan B, Palacios F, and Tardaguila J. 2019. On-the-go assessment of vineyard canopy porosity, bunch and leaf exposure by image analysis, Aust. J. Grape Wine Res. 25: 363–374.
Diago MP, Bellincontro A, Scheidweiler M, Tardaguila J, Tittmann S and Stoll M. 2017. Future opportunities of proximal near infrared spectroscopy approaches to determine the variability of vineyard water status. Aust. J. Grape Wine Res. 23: 409–414.
Diago MP, Fernández-Novales J, Fernandes AM, Melo-Pinto P and Tardaguila J. 2016b. Use of Visible and Short Wave Near-Infrared Hyperspectral Imaging to Fingerprint Anthocyanins in Intact Grape Berries. Journal of Agricultural and Food Chemistry 64: 7658–7666.
Diago MP, Krasnow M, Bubola M, Millan B, and Tardaguila J. 2016c. Assessment of vineyard canopy porosity using machine vision. Am. J. Enol. Vitic. 67:229–238.
Donna P, Tonni M, Divittini A, and Valenti L. Concimare. 2013. il vigneto guidati dal satellite. L’Informatore Agrar. 22: 42–46.
FAOSTAT. 2022. FAO Web Page. Available online: http://www. fao.org/faostat.
Fernandez-Novales J, Tardaguila J, Gutierrez S and Diago MP. 2019. On-The-Go VIS + SW − NIR Spectroscopy as a Reliable Monitoring Tool for Grape Composition within the Vineyard. Molecules 24:2795. https://doi.org/10.3390/
molecules
Gebbers R and Adamchuk V. 2010. Precision Agriculture and Food Security. Science 327: 828–831.
Ghule S, Sawant IS, Shetty DS and Sawant SD. 2015. Epidemiology and weather-based forecasting model for anthracnose of grape under the semi-arid tropical region of Maharashtra. J. Agrometeorology 17: 265-267.
Grant O, Baluja J, Ochagavía H, Diago MP and Tardaguila J. 2016. Thermal imaging to detect spatial and temporal variation in the water status of grapevine (Vitis vinifera L.). The Journal of Horticultural Science and Biotechnology 91:44–55.
Gutierrez S, Fernandez-Novales J, Diago MP, Iniguez R and Tardaguila J. 2021a. Assessing and mapping vineyard water status using a ground mobile thermal imaging platform. Irrigation Science 39:457–468.
Gutierrez S, Hernandez I, Ceballos S, Barrio I, Diez Navajas AM and Tardaguila J. 2021b. Deep learning for the differentiation of downy mildew and spider mite in grapevine under field conditions, Computers and Electronics in Agriculture 182:105991. https:// doi:10.1016/j.compag.2021.105991
Hill GN, Evans KJ, Beresford RM and Dambergs RG. 2014. Comparison of methods for the quantification of botrytis bunch rot in white wine grapes. Aust. J. Grape Wine Res. 20: 432–441.
Hubbard SS, Schmutz M, Balde A, Falco N, Peruzzo L, Dafflon B, Leger E, and Wu Y. 2021. Estimation of soil classes and their relationship to grapevine vigour in a Bordeaux vineyard: advancing the practical joint use of electromagnetic induction (EMI) and NDVI datasets for precision viticulture. Precision Agric 22: 1353–1376.
Jaslam PKM, Kumar AP and Kumar A. 2022. Time series modelling and forecasting of vegetable production in United Arab Emirates. Current Horticulture 11(1): 8–15
Joseph V, Sinfield DF, and Oliver C. 2010. Evaluation of sensing technologies for on-the-go detection of macro nutrients in cultivated soils. Computers and Electronics in Agriculture 70:1-18.
Karakizi C, Oikonomou M, and Karantzalos K. 2016. Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data. Remote Sens. 8: 235. https:// doi.org/10.3390/rs8030235
Khaliq A, Comba L, Biglia A, Aimonino DR, Chiaberge M, and Gay P. 2019. Comparison of Satellite and UAV-Based
Multispectral Imagery for Vineyard Variability Assessment. Remote Sens. 11: 436. https://doi.org/10.3390/rs11040436
Latouche G, Debord C, Raynal M, Milhade C and Cerovic Z. 2015. First detection of the presence of naturally occurring grapevine downy mildew in the field by a fluorescence based method. Photochemical and Photobiological Sciences 14: 807–1813.
Lee WS and Tardaguila J. 2021. Pests and diseases management, Advanced Automation for Tree Fruit Orchards and Vineyards. Springer, New York.
Lessio F and Alma A. 2021. Models Applied to Grapevine Pests: A Review. Insects 12: 169-181.
Liu S, Zeng X and Whitty M. 2020a. 3DBunch: a Novel iOS Smartphone application to evaluate the number of grape berries per bunch using image analysis techniques, IEEE Access 8:114663–114674.
Liu S, Zeng X and Whitty M. 2020b. A vision-based robust grape berry counting algorithm for fast calibration free bunch weight estimation in the field. Computers and Electronics in Agriculture 173:105360. https:// doi:10.1016/j.compag.2020.105360
Losciale P, Manfrini L, Morandi B, Pierpaoli E, Zibordi M, Stellacci AM, Salvati L, and Corelli Grappadelli L. 2015. A multivariate approach for assessing leaf photo assimilation performance using the IPL index. Physiol. Plant. 154: 609–620.
Matese A, and Di Gennaro S. 2018. Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture. Agriculture 8: 116. https://doi:10.3390/agriculture8070116
Matese A, and Di Gennaro SF. 2015. Technology in precision viticulture: A state of the art review. Int. J. Wine Res.7: 69–81.
Matese A, Vaccari FP, Tomasi D, Di Gennaro SF, Primicerio J, Sabatini F, and Guidoni S. 2013. CrossVit: enhancing canopy monitoring management practices in viticulture. Sensors (Basel) 13:7652-67.
Mondino EB, and Gajetti M. 2017. Preliminary considerations about costs and potential market of remote sensing from UAV in the Italian viticulture context. Eur. J. Remote Sens. 50: 310–319.
Mundankar KY, Sawant SD, Sawant IS, Sharma J and Adsule PG. 2007. Knowledge based decision support system for management of powdery mildew disease in grapes. In: Proceedings of 3rd Indian International Conference on Artificial Intelligence (IICAI-07). Pp 1563–1571.
Ouyang J, De Bei R, and Collins C. 2021. Assessment of canopy size using UAV-based point cloud analysis to detect the severity and spatial distribution of canopy decline. OENO One 55: 253–266.
Palacios F, Bueno G, Salido J, Diago MP, Hernandez I and Tardaguila J. 2020. Automated grapevine flower detection and quantification method based on computer vision and deep learning from on-the-go imaging using a mobile sensing platform under field conditions. Computers and Electronics in Agriculture 178: 105796. https://doi.
org/10.1016/j.compag.2020.105796
Palacios F, Diago MP and Tardaguila J. 2019. A non-invasive method based on computer vision for grapevine cluster compactness assessment using a mobile sensing platform under field conditions. Sensors (Basel) 19: 3799. https://
doi:10.3390/s19173799
Pertot TC, Rossi V, Mugnai L, Hoffmann C, Grando MS, Gary C, Lafond D, Duso C, Thiery D, Mazzoni V and Anfora G. 2017. A critical review of plant protection tools for reducing pesticide use on grapevine and new perspectives for the implementation of IPM in viticulture. Crop Protection
:70-84.
Petrie PR, Wang Y, Liu S, Lam S, Whitty MA and Skewes MA. 2019. The accuracy and utility of a low-cost thermal camera and smartphone-based system to assess grapevine water status. Biosystems Engineering 179:126–139.
Pichon L, Brunel G, Payan JC, Taylor J, Bellon-Maurel V and Tisseyre B. 2021. ApeX-Vigne: experiences in monitoring vine water status from within-field to regional scales using crowdsourcing data from a free mobile phone application. Precision Agriculture 22: 608–662.
Power DJ. 2002. Decision support systems: concepts and resources for managers. Book Gallery. 67. https:// scholarworks.uni.edu/facbook/67 .
Priori S, Pellegrini S, Perria R, Puccioni S, Storchi P, Valboa G, and Costantini EAC. 2019. Scale effect of terroir under three contrasting vintages in the Chianti Classico area (Tuscany, Italy). Geoderma 334: 99–112.
Proffit T, Bramley R, Lamb D and Winter E. 2006. Precision Viticulture – A New Era in Vineyard Management and Wine Production. Published by Winetitles Pty Ltd., Ashford, South Australia. Pp 1–90.
Ridwan MA, Radzi NAM, Ahmad WSHMW, Mustafa IS, Din NM, Jalil YE, Isa AM, Othman NS, and Zaki WMDW. 2018. Applications of Landsat-8 Data: A Survey. Int. J. Eng. Technol. 7: 436–441.
Roman C, Peris M, Esteve J, Tejerina M, Cambray J, Vilardell P and Planas S. 2022. Pesticide dose adjustment in fruit and grapevine orchards by DOSA3D: Fundamentals of the system and on-farm validation. Science Total Environment
:152-158.
Rosa M, Genesio R, Gozzini B, Maracchi G and Orlandini S. 1993. PLASMO: a computer program for grapevine downy mildew development forecasting. Computers and Electronics in Agriculture 9: 205-215.
Roscher R, Herzog K, Kunkel A, Kicherer A, Topfer R and Forstner W. 2014. Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields. Computers and Electronics in Agriculture 100:148–158.
Rose JC, Kicherer A, Wieland M, Klingbeil L, Topfer R and Kuhlmann H. 2016. Towards automated large-scale 3D phenotyping of vineyards under field conditions. Sensors 16: 2136–2161.
Santangelo T, Di Lorenzo R, La Loggia G, and Maltese A. 2013. On the relationship between some production parameters and a vegetation index in viticulture. Remote Sensing
for Agriculture, Ecosystems, and Hydrology XV. https:// doi:10.1117/12.2029805
Schirrmann M, Gebbers R, and Kramer E. 2013. Performance of automated near–infrared reflectance spectrometry for continuous in situ mapping of soil fertility at field scale. Vadose Zone Journal 12:1-14.
Simone P, Bianconi N, and Costantini EAC. 2014. Can γ-radiometrics predict soil textural data and stoniness in different parent materials? A comparison of two machine learning methods. Geoderma 226–227:354-364.
Somkuwar RG and More TA. 1996. Inheritance of downy mildew resistance in muskmelon (Cucumis melo L.). Indian J. Hort. 53 (1): 50-52.
Somkuwar RG, Bahetwar Anita, Khan Ishrat, Satisha J, Ramteke SD, Itroutwar Prerna, Bhongale Aarti and Oulkar Dashrath. 2014a. Changes in growth, photosynthetic activities, biochemical parameters and amino acid profile of Thompson Seedless grapes (Vitis vinifera L.). Journal of Environmental Biology 36(6): 1157-1163.
Somkuwar RG, Bhange MA, Sharma AK, Oulkar DP and Bhongale AK. 2018. Cluster thinning influences photosynthetic activity, fruit composition and wine quality of grapes under tropical environment. Indian J. Hort. 75(4): 574-582.
Somkuwar RG, Samarth RR, Itroutwar Prerna and Navale Supriya. 2014b. Effect of cluster thinning on bunch yield, berry quality and biochemical changes in local clone of table grape cv. Jumbo Seedless (Nana Purple). Indian J. Hort. 71(2): 184-189.
Somkuwar RG. 2018. Constrains in dry land fruit a production measure to overcome: A Review In: Recent advances in improvement and production technology of Arid zone crops. Mahatma Phule Krishi Vidhyapeeth, Rahuri. Pp 329-335.
Somkuwar, RG and Ghule VS. 2020. Canopy management practices for production of quality grapes. In: Advances in Production Technology and Postharvest management in fruit crops (Ranpise SA (Ed.). Mahatma Phule Krishi Vidhyapeeth, Rahuri. Pp-91-110.
Soubry I, Patias P, and Tsioukas V. 2017. Monitoring vineyards with UAV and multi-sensors for the assessment of water stress and grape maturity. J. Unmanned Veh. Syst. 5:37–50.
Taylor JA, Tisseyre B and Leroux C. 2019. A simple index to determine if within-field spatial production variation exhibits potential management effects: application in vineyards using yield monitor data. Precision Agriculture
: 880–895.
Vanino S, Pulighe G, Nino P, De Michele C, Falanga Bolognesi S, and D’Urso G. 2015. Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment. Remote Sens. 7: 14708–14730.
Vidal A, Talens P, Prats-Montalban JM, Cubero S, Albert F and Blasco J. 2013. In-line estimation of the standard colour index of citrus fruits using a computer vision system developed for a mobile platform. Food Bioproc. Tech 6: 3412–3419.
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