Abstract:The detection of water stress in vineyards plays an integral role in the sustainability of high-quality grapes and prevention of devastating crop loses. Hyperspectral remote sensing technologies combined with machine learning provides a practical means for modelling vineyard water stress. In this study, we applied two ensemble learners, i.e., random forest (RF) and extreme gradient boosting (XGBoost), for discriminating stressed and non-stressed Shiraz vines using terrestrial hyperspectral imaging. Additionally, we evaluated the utility of a spectral subset of wavebands, derived using RF mean decrease accuracy (MDA) and XGBoost gain. Our results show that both ensemble learners can effectively analyse the hyperspectral data. When using all wavebands (p = 176), RF produced a test accuracy of 83.3% (KHAT (kappa analysis) = 0.67), and XGBoost a test accuracy of 80.0% (KHAT = 0.6). Using the subset of wavebands (p = 18) produced slight increases in accuracy ranging from 1.7% to 5.5% for both RF and XGBoost. We further investigated the effect of smoothing the spectral data using the Savitzky-Golay filter. The results indicated that the Savitzky-Golay filter reduced model accuracies (ranging from 0.7% to 3.3%). The results demonstrate the feasibility of terrestrial hyperspectral imagery and machine learning to create a semi-automated framework for vineyard water stress modelling.

Business process planning and control is important for effectively managing and improving processes relating to the management of physical assets. This is especially true when processes affect the uptime and value creation by physical assets. This article presents a case study where an asset management process is analysed using a technique called 'process mining', with which it is possible to investigate the process as it is being performed in the real world. By applying process mining instead of a traditional mathematical approach, real-world issues can be identified and corrected to improve the effectiveness of the given process. A process model is first constructed to investigate process execution patterns, after which dotted charts are used to identify problem areas within the process and to propose possible areas for improvement. OPSOMMINGBesigheidsprosesbeplanning en -beheer is belangrik vir die doeltreffende en die verbetering van prosesse wat verband hou met die bestuur van fisiese bates. Dit is veral waar wanneer prosesse beïnvloed word deur die operasionele tyd en waardeskepping van die fisiese bates. Hierdie artikel bied 'n gevallestudie aan waar 'n batebestuur proses ontleed word met behulp van 'n tegniek genaamd prosesmyn. Met prosesmyn, is dit moontlik om die proses te ondersoek soos dit uitgevoer word in die werklikheid. Deur die toepassing van prosesmyn in plaas van 'n tradisionele wiskundige benadering te volg, kan regte wêreld kwessies geïdentifiseer en reggestel word om die doeltreffendheid van die gegewe prosesse te verbeter. 'n Proses model word eers gebou om die uitvoering van die proses te ondersoek, en daarna word kolkaarte gebruik om probleemareas te identifiseer binne die proses vir moontlike verbetering.