2-D and 3-D proximal remote sensing for yield estimation in a Shiraz vineyard

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dc.contributor.advisorPoblete-Echeverria, Carlosen_ZA
dc.contributor.advisorPoona, Niteshen_ZA
dc.contributor.authorHacking, Christopher Jamesen_ZA
dc.contributor.otherStellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.en_ZA
dc.date.accessioned2020-02-19T05:22:43Z
dc.date.accessioned2020-04-28T15:11:00Z
dc.date.available2020-02-19T05:22:43Z
dc.date.available2020-04-28T15:11:00Z
dc.date.issued2020-03
dc.descriptionThesis (MSc)--Stellenbosch University, 2020en_ZA
dc.description.abstractENGLISH ABSTRACT: Precision viticulture aims to minimise production input expenses through the efficient management of vineyards, yielding the desired quantity and quality, while reducing the environmental footprint associated with modern farming. Precision viticulture practices aim to manage the inherent spatial variability in vineyards. Estimating vineyard yield provides insight into this process, enabling informed managerial decisions regarding production inputs. At the same time, yield information is important to the winery, as it facilitates logistical planning for harvest. Traditional yield estimation methods are destructive by nature and require in-situ sampling, which is labour-intensive and time-consuming. Proximal remote sensing (PRS) presents a suitable alternative for estimating yielding a non-destructive manner. PRS employs terrestrial proximal sensors for data acquisition that can be combined with computer vision (CV) techniques to process and analyse the data, generating the estimated yield for the vineyard. This research intends to investigate 2-dimensional (2-D) and 3-dimensional (3-D) PRS and related CV techniques for estimating yield in a vertically shoot position (VSP) trellised Shiraz vineyard. This research is presented as two components. The first component evaluates 2-D and 3-D methodologies for estimating yielding a vineyard. Three experiments are presented at bunch-and plant-level, incorporating both laboratory and in-situ experimental conditions. Under laboratory conditions (bunch-level only), the 2-D methodology achieved an r2 of 0.889, while the 3-D methodology achieved a higher r2 of 0.950. Both methodologies demonstrate the potential of PRS and associated CV techniques for estimating yield. The in-situ plant-level results favoured the 2-D methodology (fullcanopy (FC): r2= 0.779; leaf removal (LR): r2= 0.877) over the 3-D methodology (FC: r2= 0.487; LR: r2= 0.623). The general performance of the 2-D methodology was superior, and thus implemented in the subsequent component. The two set out to determine the ideal phenological stage for estimating yield. The 2-D methodology was employed with slight improvements and multitemporal digital imagery were acquired on a weekly basis for 12 weeks; culminating in a final acquisition two days prior to harvest. This component also successfully implemented image segmentation using an unsupervised k-means clustering (KMC) technique, an improvement to the colour thresholding (CT) technique implemented in component one. The ideal phenological stage was approximately two weeks prior to harvest (final stages of berry ripening), which achieved a global (bunch-level: 50 bunches) r2of 0.790 for estimating yield. This research successfully implements 2-D and 3-D PRS and CV techniques for estimating yield in a Shiraz vineyard, and thereby accomplishes the aim of this research. The research demonstrates the suitability of the methodologies–specifically the 2-D methodology, which demonstrated superior performance (simple data acquisition and analysis with competitive results). Future research could refine the presented methodologies for operational use.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Presisie-wingerdbou het ten doel om die produksie insetkoste te verminder deur doeltreffende bestuur van wingerde, die gewenste opbrengsen kwaliteit te lewer, en terselfdertyd die omgewingsvoetspoor van moderne boerdery te verminder. Presisie wingerdboukundige praktyke is daarop gemik om die inherente ruimtelike variasie in wingerde te bestuur. Opbrengsberaming in die wingerd gee insig in hierdie proses, wat ingeligte bestuursbesluite rakende produksie-insette moontlik maak. Terselfdertyd is opbrengs inligting belangrik vir die kelder, aangesien dit die logistieke beplanning tydens oestyd vergemaklik. Tradisionele opbrengsberamingsmetodes is destruktief van aard en benodig in-situ monsterneming, wat arbeidsintensief en tydrowend is. Kort-afstand waarneming (KAW) is 'n geskikte alternatief om die opbrengs op nie-destruktiewe wyse te skat. KAW gebruik land-gebasseerde kort-afstand sensors vir die insamel van data wat gekombineer kan word met rekenaarvisie-tegnieke om die data te verwerk en te ontleed, wat die geskatte opbrengs vir die wingerd lewer. Hierdie navorsing het ten doel om twee dimensionele (2-D) en drie dimensionele (3-D) KAW en verwante rekenaarvisietegnieke te ondersoek om die opbrengs in 'n vertikale loot geposisioneerde (VLP) opgeleide Shiraz-wingerd te skat. Hierdie navorsing word as twee komponente aangebied. Die eerste komponent evalueer 2-D en 3-D metodologieë vir die beraming van die opbrengs in 'n wingerd. Drie eksperimente word op tros-asook plantvlak aangebied, sowel as laboratorium-en in-situ. Onder laboratoriumtoestande (slegs op trosvlak) het die 2-D-metodologie 'n r2 van 0,889 behaal, terwyl die 3-D-metodologie 'n hoër r2 van 0,950 behaal het. Albei metodologieë demonstreer die moontlikheid van KAW en gepaardgaande rekenaarvisie-tegnieke om die opbrengs te skat. Die plant-vlak in-situ resultate het die 2-D-metodologie (vol-lower 2 = 0.779; blaarverwydering r2 = 0.877) bevoordeel bo die 3-D-metodologie (vol-lower 2 = 0.487; blaarverwydering r2 = 0.623). Die algehele prestasie van die 2-D-metodologie was beter en is gevolglik in die daaropvolgende komponent gebruik.Komponent twee het ten doel gehad om die ideale fenologiese stadium vir die beraming van opbrengs te bepaal. Die 2-D-metodologie is met geringe verbeterings gebruik en multitemporale digitale beelde is weekliks ingesamel oor 12 weke, met die laaste beelde verkry twee dae voor oes. Hierdie komponent het ook beeldsegmentering suksesvol geïmplementeer met behulp van 'n onbewaakte k-gemiddeld groeperingstegniek, 'n verbetering in die kleurdrempelwaarde-tegniek wat in komponent een geïmplementeer is. Die ideale fenologiese stadium was ongeveer twee weke voor oes (finale stadiums van korrelrypwording), wat 'n algehele (trosvlak: 50 trosse) r2 van 0,790 behaal het om die opbrengs te skat. Hierdie navorsing implementeer suksesvol 2-D en 3-D KAW en rekenaarvisietegnieke om opbrengs in 'n Shiraz-wingerd te skat,en hierdeur is die doel van die navorsing wel bereik. Die navorsing toon die geskiktheid van die metodologieë, spesifiek die 2-D-metodologie wat uitstekende prestasie getoon het (eenvoudige data-verkryging en -ontleding met mededingende resultate). Toekomstige navorsing kan die voorgestelde metodes vir operasionele gebruik verder verfyn.af_ZA
dc.description.versionMasters
dc.format.extentxvii, 92 pages : illustrationsen_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/108365
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subjectShiraz Vineyarden_ZA
dc.subjectRemote sensing imagesen_ZA
dc.subjectImages, Remote sensingen_ZA
dc.subjectRemote sensingen_ZA
dc.subjectPrecision viticulture -- Yieldsen_ZA
dc.subjectKinect (Programmable controller)
dc.subjectImage segmentationen_ZA
dc.subjectSegmentation imageen_ZA
dc.subjectComputer visionen_ZA
dc.subjectMachine visionen_ZA
dc.subjectViticulture -- Remote sensingen_ZA
dc.subjectUCTD
dc.title2-D and 3-D proximal remote sensing for yield estimation in a Shiraz vineyarden_ZA
dc.typeThesisen_ZA
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