Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning

Gold, Kaitlin M.; Townsend, Philip A.; Herrmann, Ittai; Gevens, A. J.

doi:10.1016/j.plantsci.2019.110316

Cited by 83 publications

(52 citation statements)

References 43 publications

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“…To move towards the goal of in-field disease differentiation, a greater number of isolates and pathovars with independent validation must be tested to confirm these initial findings. Importantly, these findings must be explored in a variety of cultivars, which has been shown to have a strong impact on early infection reflectance [22]. Despite the need for further validation, this work supports the future use of in-situ spectroscopy as an effective tool for plant disease detection and differentiation, and highlights the importance of careful consideration of underlying pathosystem biology and comparative disease physiology when seeking to detect and differentiate plant diseases.…”

Section: Discussionmentioning

confidence: 59%

“…This gives evidence to the hypothesis that differences in phenolic concentrations may discriminate symptomatic late and early blight infections. Gold et al found that phenolic concentration varied across cultivars and between healthy and diseased plants undergoing the early stages of late blight infection [22]. Future work addressing and quantifying specific phenolic and secondary metabolites associated with early infection could yield even greater detection accuracy across a wider variety of biotic and abiotic potato stressors.…”

Section: Discussionmentioning

confidence: 99%

“…Plant pathogens damage, impair, and/or alter foliar function [16], thus changing the chemical composition of the foliage, such as through production of systemic effectors or secondary metabolites, or by physical presence of pathogen structures, such as hyphae and spores, which can be detected using spectral data [2,17]. The use of hyperspectral VSWIR measurements allows us to non-destructively assess both the broad and specific impacts of plant pathogens on plant chemistry, physiology, and morphology [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato

et al. 2020

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In-vivo foliar spectroscopy, also known as contact hyperspectral reflectance, enables rapid and non-destructive characterization of plant physiological status. This can be used to assess pathogen impact on plant condition both prior to and after visual symptoms appear. Challenging this capacity is the fact that dead tissue yields relatively consistent changes in leaf optical properties, negatively impacting our ability to distinguish causal pathogen identity. Here, we used in-situ spectroscopy to detect and differentiate Phytophthora infestans (late blight) and Alternaria solani (early blight) on potato foliage over the course of disease development and explored non-destructive characterization of contrasting disease physiology. Phytophthora infestans, a hemibiotrophic pathogen, undergoes an obligate latent period of two–seven days before disease symptoms appear. In contrast, A. solani, a necrotrophic pathogen, causes symptoms to appear almost immediately when environmental conditions are conducive. We found that respective patterns of spectral change can be related to these differences in underlying disease physiology and their contrasting pathogen lifestyles. Hyperspectral measurements could distinguish both P. infestans-infected and A. solani-infected plants with greater than 80% accuracy two–four days before visible symptoms appeared. Individual disease development stages for each pathogen could be differentiated from respective controls with 89–95% accuracy. Notably, we could distinguish latent P. infestans infection from both latent and symptomatic A. solani infection with greater than 75% accuracy. Spectral features important for late blight detection shifted over the course of infection, whereas spectral features important for early blight detection remained consistent, reflecting their different respective pathogen biologies. Shortwave infrared wavelengths were important for differentiation between healthy and diseased, and between pathogen infections, both pre- and post-symptomatically. This proof-of-concept work supports the use of spectroscopic systems as precision agriculture tools for rapid and early disease detection and differentiation tools, and highlights the importance of careful consideration of underlying pathogen biology and disease physiology for crop disease remote sensing.

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Section: Discussionmentioning

confidence: 59%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato

et al. 2020

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“…This accuracy was on the same order of magnitude as the one (93.0%) obtained at 4 DPI by Römer et al [40] who applied an SVM classifier to ultraviolet-induced fluorescence data acquired between 370 to 800 nm for classifying healthy leaves and leaves infected with Puccinia triticia in the case of winter wheat (Triticum aestivum). It was higher than the one obtained with a Partial Least Square Discriminant Analysis (PLS-DA) method for sorting leaves infected with potato late blight (89.77% [21] and 65-72.73% [41]). At the canopy level, the highest classification accuracy was obtained at 5 DPI (89.06%,).…”

Section: Discussionmentioning

confidence: 62%

“…Fernández et al [21] Potato late blight NDSI between 400 and 2400 nm. Gold et al [22,41] Pear fire blight…”

Section: Disease Vegetation Index (*) Authorsmentioning

confidence: 99%

Potato Late Blight Detection at the Leaf and Canopy Levels Based in the Red and Red-Edge Spectral Regions

et al. 2020

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Potato late blight, caused by Phytophthora infestans, is a major disease worldwide that has a significant economic impact on potato crops, and remote sensing might help to detect the disease in early stages. This study aims to determine changes induced by potato late blight in two parameters of the red and red-edge spectral regions: the red-well point (RWP) and the red-edge point (REP) as a function of the number of days post-inoculation (DPI) at the leaf and canopy levels. The RWP or REP variations were modelled using linear or exponential regression models as a function of the DPI. A Support Vector Machine (SVM) algorithm was used to classify healthy and infected leaves or plants using either the RWP or REP wavelength as well as the reflectances at 668, 705, 717 and 740 nm. Higher variations in the RWP and REP wavelengths were observed for the infected leaves compared to healthy leaves. The linear and exponential models resulted in higher adjusted R2 for the infected case than for the healthy case. The SVM classifier applied to the reflectance of the red and red-edge bands of the Micasense® Dual-X camera was able to sort healthy and infected cases with both the leaf and canopy measurements, reaching an overall classification accuracy of 89.33% at 3 DPI when symptoms were visible for the first time with the leaf measurements and of 89.06% at 5 DPI, i.e., two days after the symptoms became apparent, with the canopy measurements. The study shows that RWP and REP at leaf and canopy levels allow detecting potato late blight, but these parameters are less efficient to sort healthy and infected leaves or plants than the reflectance at 668, 705, 717 and 740 nm. Future research should consider larger samples, other cultivars and the test of unmanned aerial vehicle (UAV) imagery for field-based detection.

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Exploring Machine Learning Algorithms for Gene Function Prediction in Crops

Jakhmola‐Mani,

Sonali,

Pandey

et al. 2024

Bioinformatics for Plant Research and Crop Breeding

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Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning

Cited by 83 publications

References 43 publications

Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato

Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato

Potato Late Blight Detection at the Leaf and Canopy Levels Based in the Red and Red-Edge Spectral Regions

Exploring Machine Learning Algorithms for Gene Function Prediction in Crops

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