Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning
| Autor: | Berno Greyling, Nitesh K. Poona, Kyle Loggenberg, Albert Strever |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
Science 0211 other engineering and technologies 02 engineering and technology vineyard Machine learning computer.software_genre 01 natural sciences Vineyard water stress Extreme gradient boosting 021101 geological & geomatics engineering 0105 earth and related environmental sciences Mathematics business.industry terrestrial hyperspectral imaging Water stress Hyperspectral imaging Ranging Filter (signal processing) Random forest machine learning General Earth and Planetary Sciences Artificial intelligence business computer Smoothing tree-based ensemble |
| Zdroj: | Remote Sensing, Vol 10, Iss 2, p 202 (2018) Remote Sensing Volume 10 Issue 2 Pages: 202 |
| ISSN: | 2072-4292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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