Potato Late Blight Detection at the Leaf and Canopy Level Using Hyperspectral Data

Fernández, Claudio Ignacio; Leblon, Brigitte; Haddadi, Ata; Wang, Jinfei; Wang, Keri

doi:10.1080/07038992.2020.1769471

Cited by 18 publications

(28 citation statements)

References 58 publications

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“…The study used data that were acquired as part of the experiment, which is detailed in Fernández et al [21]. We will here summarize the main steps of the experiment.…”

Section: Methodsmentioning

confidence: 99%

“…To follow the spectral changes of each leaflet by acquiring spectra over the same leaflets, we marked the healthy and inoculated leaflets at the base of the petiole with a small piece of white tape which was numbered. Control (healthy) and infected leaflets were chosen from the middle and the top of the canopy so that they were larger than the leaf clip measurement area to avoid low reflectance measurements when the leaves were too small [21]. Healthy plants were placed in a separate chamber to avoid cross-contamination.…”

Section: Methodsmentioning

confidence: 99%

“…This allowed obtaining a total of 32 healthy and 32 infected canopy reflectance spectra at each day of the experiment. The measured plant area was 82.83 cm 2 according to Equation (1) as reported by Fernández et al [21]:…”

Section: Methodsmentioning

confidence: 99%

“…At the canopy level, the t-test was computed using for RWP and REP data from 32 healthy and 32 infected spectral measurements. The variations of RWP and REP were also described by the ratio variations (ΔRV), which were computed with the mean daily RWP and REP wavelengths, following Fernández et al [21], as follows:…”

Section: Methodsmentioning

confidence: 99%

“…In the literature, there are several studies that related band reflectances or vegetation indices to late blight occurrence in potato or tomato crops [16][17][18][19][20][21][22], but none of them tested the use of RWP and REP to detect the disease. All of them but Fernández et al [21] and Gold et al [22] were based on one or two dates of experimentations without reporting the moment of inoculation, and thus they were not able to determine the time after inoculation when the disease could be detected with spectral information. This study aims to improve the understanding of the spectral changes of RWP and REP at the leaf and canopy levels due to late blight disease on potato plants from the moment of inoculation.…”

Section: Introductionmentioning

confidence: 99%

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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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“…The study used data that were acquired as part of the experiment, which is detailed in Fernández et al [21]. We will here summarize the main steps of the experiment.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Potential of laboratory hyperspectral data for in-field detection of Phytophthora infestans on potato

2021

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Characterizing Diploid and Tetraploid Potato Cultivars with Reflectance Spectroscopy

Marín-Ortiz,

Hoyos-Carvajal,

Botero-Fernández

et al. 2024

Potato Res.

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Characterizing potato cultivars is essential for diagnosing the specific production and management requirements. Employing techniques that facilitate rapid, objective, and cost-effective identification of various aspects of plant physiology is crucial for generating this valuable information. In this study, our goal was to identify different potato cultivars across three distinct phenological phases utilizing reflectance spectroscopy within the visible and near-infrared ranges. Reflectance spectra were measured using a portable spectrometer on leaves obtained from five different potato cultivars. We constructed classification models to determine the accurate classification percentages for each cultivar within specific phenological stages. General trends in the reflectance spectra were observed, which were consistent across all five cultivars within the measured range. During the vegetative phase, there was a uniform reflectance of around 5% in the green region. This value experienced a slight decrease during the flowering phase and subsequently rose to approximately 12% during the senescence phase. In the near-infrared (NIR) range, a reduction in reflectance from 50 to 30% was noted during the flowering phase, followed by a cultivar-dependent increase towards senescence. The specific wavelengths identified in the spectra enabled the accurate classification of plants from each cultivar across the vegetative, flowering, and senescence phases. The classification rates were notably high, with a success rate of 87% for Var. Paola, 96% for Var. Paysandú, 93% for Var. Violeta, and 100% for cv. 448 and 440. The Var. Paola exhibited superior correct classification rates, ranging from 85% for Paysandú and Violeta, 90% for cv. 440, to 100% for cv. 448. The characterization of potato cultivars using spectroscopy techniques yields valuable information that serves as fundamental input for precision agriculture systems.

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Potato Late Blight Detection at the Leaf and Canopy Level Using Hyperspectral Data

Cited by 18 publications

References 58 publications

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

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

Potential of laboratory hyperspectral data for in-field detection of Phytophthora infestans on potato

Characterizing Diploid and Tetraploid Potato Cultivars with Reflectance Spectroscopy

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