Browsing by Author "Loggenberg, Kyle Devronne"
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- ItemA machine learning-remote sensing framework for modelling water stress in Shiraz vineyards(Stellenbosch : Stellenbosch University, 2018-12) Loggenberg, Kyle Devronne; Poona, Nitesh; Strever, Albert; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography & Environmental Studies.ENGLISH ABSTRACT: Water is a limited natural resource and a major environmental constraint for crop production in viticulture. The unpredictability of rainfall patterns, combined with the potentially catastrophic effects of climate change, further compound water scarcity, presenting dire future scenarios of undersupplied irrigation systems. Major water shortages could lead to devastating loses in grape production, which would negatively affect job security and national income. It is, therefore, imperative to develop management schemes and farming practices that optimise water usage and safeguard grape production. Hyperspectral remote sensing techniques provide a solution for the monitoring of vineyard water status. Hyperspectral data, combined with the quantitative analysis of machine learning ensembles, enables the detection of water-stressed vines, thereby facilitating precision irrigation practices and ensuring quality crop yields. To this end, the thesis set out to develop a machine learning–remote sensing framework for modelling water stress in a Shiraz vineyard. The thesis comprises two components. Component one assesses the utility of terrestrial hyperspectral imagery and machine learning ensembles to detect water-stressed Shiraz vines. The Random Forest (RF) and Extreme Gradient Boosting (XGBoost) ensembles were employed to discriminate between water-stressed and non-stressed Shiraz vines. Results showed that both ensemble learners could effectively discriminate between water-stressed and non-stressed vines. When using all wavebands (p = 176), RF yielded a test accuracy of 83.3% (KHAT = 0.67), with XGBoost producing a test accuracy of 80.0% (KHAT = 0.6). Component two explores semi-automated feature selection approaches and hyperparameter value optimisation to improve the developed framework. The utility of the Kruskal-Wallis (KW) filter, Sequential Floating Forward Selection (SFFS) wrapper, and a Filter-Wrapper (FW) approach, was evaluated. When using optimised hyperparameter values, an increase in test accuracy ranging from 0.8% to 5.0% was observed for both RF and XGBoost. In general, RF was found to outperform XGBoost. In terms of predictive competency and computational efficiency, the developed FW approach was the most successful feature selection method implemented. The developed machine learning–remote sensing framework warrants further investigation to confirm its efficacy. However, the thesis answered key research questions, with the developed framework providing a point of departure for future studies.