Browsing by Author "Hacking, Christopher James"

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    2-D and 3-D proximal remote sensing for yield estimation in a Shiraz vineyard
    (Stellenbosch : Stellenbosch University, 2020-03) Hacking, Christopher James; Poblete-Echeverria, Carlos; Poona, Nitesh; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.
    ENGLISH 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.