Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy

Maimaitiyiming, Matthew; Ghulam, Abduwasit; Bozzolo, Arianna; Wilkins, Joseph L.; Kwasniewski, Misha T.

doi:10.3390/rs9070745

Cited by 106 publications

(94 citation statements)

References 101 publications

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“…In this study, only three red-edge wavebands (Table 2) were selected by XGBoost with none selected by RF. These results contradict those reported by [4], which found that wavebands in the red-edge region (695-730 nm) were ideal for early water stress detection in vineyards. However, given the overlapping wavebands that occur in the blue and green regions, and the results of our study, we can conclude that the red-edge wavebands may not be important for discriminating between stressed and non-stressed Shiraz vines.…”

Section: Classification Using Subset Of Important Wavebandscontrasting

confidence: 56%

“…Remote sensing provides a fast and cost-effective method for detecting vineyard water stress [4], and can thereby help alleviate devastating losses in crop production [7] and safeguard high-quality grape yields [8]. Several studies, for example [7,9], have modelled water stress in vineyards using spectral remote sensing techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Water stress promotes stomatal closure [2], which inhibits photosynthesis and transpiration, leading to an increase in vine leaf temperature [3,4]. Reduced water availability impacts on vine health and productivity, and ultimately on grape quality [5].…”

Section: Introductionmentioning

confidence: 99%

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Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

et al. 2018

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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.

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Section: Classification Using Subset Of Important Wavebandscontrasting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

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Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

et al. 2018

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“…The three irrigation regimes are: full replacement of evapotranspiration losses (ET), 50% replacement of ET, and non-irrigated, each replicated three times (Figure S1). Further description of the vineyard design is available in Maimaitiyiming et al, 2017 49 . Irrigation treatments began in 2013, with all vines receiving full irrigation during establishment.…”

Section: Methodsmentioning

confidence: 99%

Rootstock effects on scion phenotypes in a ‘Chambourcin’ experimental vineyard

Migicovsky

Harris

Klein

et al. 2018

Preprint

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30Understanding how root systems modulate shoot system phenotypes is a fundamental question in 31 plant biology and will be useful in developing resilient agricultural crops. Grafting is a common 32 horticultural practice that joins the roots (rootstock) of one plant to the shoot (scion) of another, 33 providing an excellent method for investigating how these two organ systems affect each other. 34 65 66Grapevine (Vitis L. spp.) is an excellent model for examining rootstock-scion interactions due to 67 the ease of cloning, available genomic resources, ability to grow across diverse environments, 68 3 and high economic value. Widespread grafting of grapevine began in the late 19 th century after 69 the European wine industry was devastated by the spread of phylloxera (Daktulosphaira 70 vitifoliae Fitch), an aphid-like insect introduced from North America. While many North 71 American Vitis species can withstand phylloxera infestations, roots of the European wine grape 72 Vitis vinifera L. cannot tolerate phylloxera attacks, which lead to a rapid decline in vigour and 73 often death 3 . However, V. vinifera vines with susceptible roots can be grafted to phylloxera-74 tolerant North American Vitis rootstocks, thus circumventing phylloxera sensitivity. Worldwide 75 more than 80% of all vineyards grow vines grafted onto rootstocks composed of American Vitis 76 species or hybrids 3 . 78Although initial grapevine grafting was driven by the need for phylloxera tolerance, additional 79 benefits exist. For example, certain Vitis rootstocks provide resistance to additional pests and 80 pathogens such as nematodes 4 . Rootstocks can also be used to increase tolerance to abiotic 81 stresses including drought 5,6 , salinity 7 , and calcareous soils 8 . Lastly, grafting can modify mineral 82 nutrition 9 , scion vigor 10 , rate of ripening 11 , and fruit phenolic compounds 12 . Thus, grafting is a 83 valuable tool for improving grapevine fruit quality and response to stress. 85Grapevine producers rely on experimental trials to identify elite rootstocks that will best fit their 86 specific growing conditions. Most commonly used grapevine rootstocks are hybrid derivatives of 87 two or three phylloxera-tolerant native North American species, Vitis riparia and Vitis rupestris, 88 which root easily from dormant cuttings, and Vitis cinerea var. helleri (Vitis berlandieri), which 89 is adapted to chalky soils 13 . Interestingly, despite the global diversity of soils, climates and grape 90 varieties, only a handful of rootstock cultivars derived from these three species are in widespread 91 use 3 . 93The result of over a century of grafting grapevines is a wealth of information characterizing 94 graft-transmissible traits. In some cases, the biological mechanisms underlying beneficial effects 95 are now understood. For example, salt (NaCl) tolerant rootstocks can exclude sodium (Na + ) from 96 the shoot, due to VisHKT1;1, a gene which can could serve as a valuable genetic marker for 97 rootstock breeding 14 . However, for many oth...

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“…This resulted in 2151 individual spectral bands. For further analysis, we only focus on the visible and near-infrared (400-1100 nm, VNIR) region due to its high signal-to-noise ratio and easy accessibility from commonly available handheld spectroradiometers, as well as from satellite sensors and unmanned aerial vehicles (UAVs) [30,42,45].…”

Section: Field Spectroscopy Measurementsmentioning

confidence: 99%

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

Maimaitiyiming

Sagan

Kwasniewski³

2019

Remote Sensing

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Reliable assessment of grapevine productivity is a destructive and time-consuming process. In addition, the mixed effects of grapevine water status and scion-rootstock interactions on grapevine productivity are not always linear. Despite the potential opportunity of applying remote sensing and machine learning techniques to predict plant traits, there are still limitations to previously studied techniques for vine productivity due to the complexity of the system not being adequately modeled. During the 2014 and 2015 growing seasons, hyperspectral reflectance spectra were collected using a handheld spectroradiometer in a vineyard designed to investigate the effects of irrigation level (0%, 50%, and 100%) and rootstocks (1103 Paulsen, 3309 Couderc, SO4 and Chambourcin) on vine productivity. To assess vine productivity, it is necessary to measure factors related to fruit ripeness and not just yield, as an over cropped vine may produce high-yield but poor-quality fruit. Therefore, yield, Total Soluble Solids (TSS), Titratable Acidity (TA) and the ratio TSS/TA (maturation index, IMAD) were measured. A total of 20 vegetation indices were calculated from hyperspectral data and used as input for predictive model calibration. Prediction performance of linear/nonlinear multiple regression methods and Weighted Regularized Extreme Learning Machine (WRELM) were compared with our newly developed WRELM-TanhRe. The developed method is based on two activation functions: hyperbolic tangent (Tanh) and rectified linear unit (ReLU). The results revealed that WRELM and WRELM-TanhRe outperformed the widely used multiple regression methods when model performance was tested with an independent validation dataset. WRELM-TanhRe produced the highest prediction accuracy for all the berry yield and quality parameters (R2 of 0.522–0.682 and RMSE of 2–15%), except for TA, which was predicted best with WRELM (R2 of 0.545 and RMSE of 6%). The results demonstrate the value of combining hyperspectral remote sensing and machine learning methods for improving of berry yield and quality prediction.

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Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy

Cited by 106 publications

References 101 publications

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

Rootstock effects on scion phenotypes in a ‘Chambourcin’ experimental vineyard

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

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