Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale

Qian, Yi; Huang, Jingfeng; Wang, Fumin; Wang, Xiuzhen

doi:10.1631/jzus.b0730019

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…In past efforts, remote estimation of canopy N content yielded the best results when applied to agricultural ecosystems of various types [35,[58][59][60]. This is perhaps to be expected.…”

Section: Discussionmentioning

confidence: 89%

Estimating Canopy Nitrogen Content in a Heterogeneous Grassland with Varying Fire and Grazing Treatments: Konza Prairie, Kansas, USA

et al. 2014

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Quantitative, spatially explicit estimates of canopy nitrogen are essential for understanding the structure and function of natural and managed ecosystems. Methods for extracting nitrogen estimates via hyperspectral remote sensing have been an active area of research. Much of this research has been conducted either in the laboratory, or in relatively uniform canopies such as crops. Efforts to assess the feasibility of the use of hyperspectral analysis in heterogeneous canopies with diverse plant species and canopy structures have been less extensive. In this study, we use in situ and aircraft hyperspectral data to assess several empirical methods for extracting canopy nitrogen from a tallgrass prairie with varying fire and grazing treatments. The remote sensing data were collected four times between May and September in 2011, and were then coupled with the field-measured leaf nitrogen levels for empirical modeling of canopy nitrogen content based on first derivatives, continuum-removed reflectance and ratio-based indices in the 562-600 nm range. Results indicated that the best-performing model type varied between in situ and aircraft data in different months. However, models from the pooled samples over the growing season with acceptable accuracy suggested that these methods are robust with respect to canopy heterogeneity across spatial and temporal scales.

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“…In past efforts, remote estimation of canopy N content yielded the best results when applied to agricultural ecosystems of various types [35,[58][59][60]. This is perhaps to be expected.…”

Section: Discussionmentioning

confidence: 89%

Estimating Canopy Nitrogen Content in a Heterogeneous Grassland with Varying Fire and Grazing Treatments: Konza Prairie, Kansas, USA

et al. 2014

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“…Analyzing the mean determination coefficient values, scenarios considering reflectance data obtained a mean determination coefficient of 0.49 and scenarios considering FDR data obtained a mean determination coefficient of 0.50, Analyzing these two groups of data with the two-sample Student’s T-test it can be said that there are not significant differences between the mean values of these two groups with a p-value of 0.78. There are studies in the literature in which the use of the FDR presents advantages [ 29 , 44 , 45 ]. However, other studies concluded that the use of the FDR presents advantages only in specific situations [ 46 , 47 ], and there are also a few studies in the literature in which the use of the FDR does not present advantages at all [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

Leaf and canopy reflectance spectrometry applied to the estimation of angular leaf spot disease severity of common bean crops

et al. 2018

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This study is aimed at (i) estimating the angular leaf spot (ALS) disease severity in common beans crops in Brazil, caused by the fungus Pseudocercospora griseola, employing leaf and canopy spectral reflectance data, (ii) evaluating the informative spectral regions in the detection, and (iii) comparing the estimation accuracy when the reflectance or the first derivative reflectance (FDR) is employed. Three data sets of useful spectral reflectance measurements in the 440 to 850 nm range were employed; measurements were taken over the leaves and canopy of bean crops with different levels of disease. A system based in Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) was developed to estimate the disease severity from leaf and canopy hyperspectral reflectance spectra. Levels of disease to be taken as true reference were determined from the proportion of the total leaf surface covered by necrotic lesions on RGB images. When estimating ALS disease severity in bean crops by using hyperspectral reflectance spectrometry, this study suggests that (i) successful estimations with coefficients of determination up to 0.87 can be achieved if the spectra is acquired by the spectroradiometer in contact with the leaves, (ii) unsuccessful estimations are obtained when the spectra are acquired by the spectroradiometer from one or more meters above the crop, (iii) the red to near-infrared spectral region (630–850 nm) offers the same precision in the estimation as the blue to near-infrared spectral region (440–850), and (iv) neither significant improvements nor significant detriments are achieved when the input data to the estimation processing system are the FDR spectra, instead of the reflectance spectra.

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“…Analyzing the mean correlation values for all experiments, experiments that used reflectance data obtained a 67,059% of mean correlation and experiments that used FDR data obtained a 67,417%, which is not a significant difference. There are studies in the literature where the use of the FDR present advantages [28,42,43] but there are other studies in the literature where the use of the FDR present advantages only in specific situation [44,45]. Our study show neither advantages nor disadvantages using the reflectance data and the FDR data, which concurs with the literature review as it was said in the previous sentence.…”

Section: Methodology Evaluation Resultssupporting

confidence: 90%

Artificial neural networks applied to the resolution of regression and classification multivariate analysis problems in the agricultural and the industrial fields

Martínez-Martínez¹

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Artificial Neural Networks (ANN) are processing models inspired in the human brain that have been widely employed to solve regression and classification problems because of the inputoutput mapping capability of the ANNs. Agriculture and industry are two examples of fields in which there are many processes affected by multiple variables that need to be modelled, and the input-output mapping capability of the ANNs makes a good alternative to be used to build these models.

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Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale

Cited by 11 publications

References 23 publications

Estimating Canopy Nitrogen Content in a Heterogeneous Grassland with Varying Fire and Grazing Treatments: Konza Prairie, Kansas, USA

Estimating Canopy Nitrogen Content in a Heterogeneous Grassland with Varying Fire and Grazing Treatments: Konza Prairie, Kansas, USA

Leaf and canopy reflectance spectrometry applied to the estimation of angular leaf spot disease severity of common bean crops

Artificial neural networks applied to the resolution of regression and classification multivariate analysis problems in the agricultural and the industrial fields

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