Detection of Stress in Cotton (Gossypium hirsutum L.) Caused by Aphids Using Leaf Level Hyperspectral Measurements

Chen, Tingting; Zeng, Ruier; Guo, Wenxuan; Hou, Xueying; Lan, Yubin; Zhang, Lei

doi:10.3390/s18092798

Cited by 32 publications

(31 citation statements)

References 35 publications

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“…However, the lack of an effective means for analyzing hyperspectral information makes it difficult to conduct systematic quantitative analysis of hyperspectral indices on all possible two-band combinations (from 350 nm to 2500 nm), precise feature bands indicating biochemical components in plants might therefore not be fully explored and utilized [26]. Therefore, the development and optimization of techniques such as a spectral vegetation index that requires only limited data is of great significance and practical value in monitoring the physiological and ecological parameters of plants [27]. Studies have shown that the paired spectral vegetation index, a method that minimizes interference from LAI and background reflectance, can be used to estimate leaf chlorophyll concentrations [28].…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves

et al. 2020

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The purpose of this study is to determine a method for quickly and accurately estimating the chlorophyll content of peanut plants at different plant densities. This was explored using leaf spectral reflectance to monitor peanut chlorophyll content to detect sensitive spectral bands and the optimum spectral indicators to establish a quantitative model. Peanut plants under different plant density conditions were monitored during three consecutive growth periods; single-photon avalanche diode (SPAD) and hyperspectral data derived from the leaves under the different plant density conditions were recorded. By combining arbitrary bands, indices were constructed across the full spectral range (350–2500 nm) based on blade spectra: the normalized difference spectral index (NDSI), ratio spectral index (RSI), difference spectral index (DSI) and soil-adjusted spectral index (SASI). This enabled the best vegetation index reflecting peanut-leaf SPAD values to be screened out by quantifying correlations with chlorophyll content, and the peanut leaf SPAD estimation models established by regression analysis to be compared and analyzed. The results showed that the chlorophyll content of peanut leaves decreased when plant density was either too high or too low, and that it reached its maximum at the appropriate plant density. In addition, differences in the spectral reflectance of peanut leaves under different chlorophyll content levels were highly obvious. Without considering the influence of cell structure as chlorophyll content increased, leaf spectral reflectance in the visible (350–700 nm): near-infrared (700–1300 nm) ranges also increased. The spectral bands sensitive to chlorophyll content were mainly observed in the visible and near-infrared ranges. The study results showed that the best spectral indicators for determining peanut chlorophyll content were NDSI (R520, R528), RSI (R748, R561), DSI (R758, R602) and SASI (R753, R624). Testing of these regression models showed that coefficient of determination values based on the NDSI, RSI, DSI and SASI estimation models were all greater than 0.65, while root mean square error values were all lower than 2.04. Therefore, the regression model established according to the above spectral indicators was a valid predictor of the chlorophyll content of peanut leaves.

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Section: Discussionmentioning

confidence: 99%

Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves

et al. 2020

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“…In parallel with the chlorophyll fluorescence measurements, canopy reflectance spectra were measured according to the methods of Chen et al (2018) [24] by using a FieldSpec UV/VNIR spectraradiometer (ASD Inc., Boulder, Colorado, USA) over the 325-1075 nm wavelength range at 3 nm intervals. The 3 nm intervals are automatically interpolated to 1nm intervals by this instrument.…”

Section: Canopy Reflectance Measurementsmentioning

confidence: 99%

Monitoring of water stress in peanut using multispectral indices derived from canopy hyperspectral

Chen

Wang

Huang

et al. 2018

International Journal of Precision Agricultural Aviation

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Drought stress was a severe environmental constraint to peanut growth all over the world, and became more and more serious with the global warming context. So, timely and accurate monitoring of water status in peanut is important for farmer to irrigate promptly and acquire higher yield. Our study was conducted to select the most appropriate multispectral indices for water stress monitoring of two peanut cultivars based on canopy spectral reflectance in visible-infrared (VIS) and near infrared (NIR) region. The physiological parameters chlorophyll fluorescence (Fv/Fm), chlorophyll content (SPAD) and leaf relative water content (LRWC) decreased as the drought stress level increased and showed significant relationships between each other. Decreases on the canopy spectral reflectance were observed in both cultivars, especially in NIR region (720-900 nm) as the leaf water loss was intensified. Six indices (RDVI, TCARI, OSAVI, TCARI/OSAVI, MTVI, and EVI-2) showed higher polynomial relationship (R 2 >R 2 0.05 , n=93) with the physiology parameters (Fv/Fm, SPAD and LRWC, respectively) based on the pooled data, which included the two cultivars, three drought stress treatments and the replications. After testing the above six sensitive indices under different drought stress, MTVI was the only multispectral indices, which showed significant curvilinear relationships with the three parameters under different drought stress conditions and might be a useful tool in the development of automatic systems. Our results may provide a non-destructive, simple and real-time method for water status monitoring in peanut production that can assist farmers in timely irrigation.

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“…Remote sensing has been used to detect stress caused by arthropod herbivory in a variety of plant species, such as maize [17], soybean [4], rice [18,19], wheat [20], peach trees [12,21], cotton [22] and potato [23].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae) Infestation in Soybean Using Hyperspectral Remote Sensing

Barros¹,

Schutze²,

Filho³

et al. 2020

Preprint

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Although monitoring and observing insect pest populations in the fields is essential in crop management, it is still a laborious and sometimes ineffective process. High infestation levels may diminish the photosynthetic activity of soybean plants, affecting their development and reducing the yield. An imprecise decision making in integrated pest management program may lead to an ineffective control in infested areas or the excessive use of insecticides. In order to reach a more efficient control of arthropods population it is important to evaluate the infestation in time to mitigate its negative effects on the crop and remote sensing is an important tool for monitoring. It was proposed that infested soybean areas could be identified, and the arthropods quantified from non-infested areas in a field by hyperspectral remote sensing. Thus, the goals of this study were to investigate and discriminate the reflectance characteristics of soybean non-infested and infested with Bemisia tabaci using hyperspectral remote sensing data. Therefore, samples of infested and non-infested soybean leaves were collected and transported to the laboratory to obtain the hyperspectral curves. The results obtained allowed to discriminate the different levels of infestation and to separate healthy from whitefly infested soybean leaves based on their reflectance.

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Detection of Stress in Cotton (Gossypium hirsutum L.) Caused by Aphids Using Leaf Level Hyperspectral Measurements

Cited by 32 publications

References 35 publications

Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves

Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves

Monitoring of water stress in peanut using multispectral indices derived from canopy hyperspectral

Monitoring Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae) Infestation in Soybean Using Hyperspectral Remote Sensing

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