Statistical Models for Prediction of Dry Weight and Nitrogen Accumulation Based on Visible and Near-Infrared Hyper-Spectral Reflectance of Rice Canopies

<p>Objective: To obtain the characteristics analysis results of aronia melanocarpa leaves under saline alkali stress state and improve yield and quality of aronia melanocarpa. Methods: Using hyper spectral imaging system to obtain hyper spectral images of aronia melanocarpa leaves under saline alkali stress. The clear hyper spectral image is obtained by the conversion of reflectance, spectral envelope removal, and spectral denoising, and final hyper spectral image is obtained by the normalization of a clearer hyper spectral image. Results: Spectral information of aronia melanocarpa leaves under slight saline alkali stress: in the visible band, leaf reflectance is lower than leaf nutrient rich stress condition; in the near infrared band, the nutrient rich leaves was significantly higher than that of stress leaves. Spectral information of leaves under moderate saline alkali stress: spectral reflectance of different lesion spots for a same leaf in 550-680nm band is: severe > moderate > slight > normal, in the near infrared band is on the contrary; spectral reflectance for different lesion grades in 550-680 nm, severe lesion leaves have highest reflectance, and normal leaves have the lowest reflectance. Under severe saline alkali stress, the leaf spectral information is: there is no significant difference in leaf spectral reflectance between the attachment aphid and the damaged leaf, but the difference is obvious for normal leaves in the band of 450-500 nm, 560-680 nm and 750-900 nm. Comparative results analysis for the three of saline alkali stress degree is: the near infrared band of 560-680 nm and the visible band of 780-900 nm is the sensitive band for the diagnosis of three kinds of stress; slight saline alkali stress has the most significant differences at 550 nm, and 780-900 nm; severe saline alkali stress has at 680 nm and 780-900 nm. Conclusion: The proposed method can analyze hyper spectral image characteristics of aronia melanocarpa leaves under different saline alkali stress the condition of is a kind of plant leaves, which is an efficient method for the analysis of characteristics of plant leaves under saline alkali stress.</p>

Analysis of the perpendicular structure of vegetation by spectral reflectance in the visible and near Infrared

Journal of Agricultural Meteorology ◽

10.2480/agrmet.52.567 ◽

1997 ◽

Vol 52 (5) ◽

pp. 567-570

Author(s):

Kengo ITO ◽

Kyoichi OTSUKI ◽

Makio KAMICHIKA

Keyword(s):

Spectral Reflectance ◽

Near Infrared

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Food Science and Technology Research ◽

10.3136/fstr.22.267 ◽

2016 ◽

Vol 22 (2) ◽

pp. 267-277

Author(s):

Baicheng Li ◽

Baolu Hou ◽

Yao Zhou ◽

Mantong Zhao ◽

Dawei Zhang ◽

...

Keyword(s):

Near Infrared ◽

Spectral Imaging ◽

Hyper Spectral ◽

Hyper Spectral Imaging

Horticultural Characteristics and Susceptibility of Table Beet Cultivars to Cercospora Leaf Spot in New York

HortTechnology ◽

10.21273/horttech03743-17 ◽

2017 ◽

Vol 27 (4) ◽

pp. 530-538 ◽

Cited By ~ 5

Author(s):

Sarah J. Pethybridge ◽

Niloofar Vaghefi ◽

Julie R. Kikkert

Keyword(s):

New York ◽

Leaf Spot ◽

Field Trial ◽

Near Infrared ◽

Value Added ◽

Dry Weight ◽

Disease Assessment ◽

Canopy Reflectance ◽

Cercospora Leaf Spot ◽

Disease Progress

Table beet (Beta vulgaris ssp. vulgaris) production in New York is increasing for direct sale, use in value-added products, or processing. One of the most important diseases affecting table beet is cercospora leaf spot (CLS) caused by the fungus Cercospora beticola. CLS causes lesions on leaves that coalesce and leads to premature defoliation. The presence of CLS may cause buyer rejection at fresh markets. Defoliation from CLS may also result in crop loss because of the inability to harvest with top-pulling machinery. The susceptibility of popular table beet cultivars (Boldor, Detroit, Falcon, Merlin, Rhonda, Ruby Queen, and Touchstone Gold) to CLS was tested using C. beticola isolates representative of the New York population. Two trials were conducted by inoculating 6-week-old plants in the misting chamber. A small-plot replicated field trial was also conducted to examine horticultural characteristics of the cultivars. In the misting chamber trials, disease progress measured by the area under the disease progress stairs (AUDPS) was not significantly different between the red cultivars, Detroit and Ruby Queen, and was significantly higher in ‘Boldor’ than the other yellow cultivar Touchstone Gold. In the field trial, the number of CLS lesions per leaf at the final disease assessment and AUDPS were significantly lower in cultivar Ruby Queen than others and not significantly different between the yellow cultivars. The dry weight of roots was not significantly different among cultivars at first harvest (77 days after planting). At 112 days after planting, the dry weight of roots was significantly higher in cultivar Detroit than Rhonda and Boldor. Leaf blade length and the length:width ratio were cultivar-dependent, which may facilitate selection for specific fresh markets. Significant associations between canopy reflectance in the near infrared (IR) (830 nm), dry weight of foliage, and number of CLS lesions per leaf suggested that this technique may have utility for remote assessment of these variables in table beet research. Implications of these findings for the management of CLS in table beet are discussed.

Deriving Hyper Spectral Reflectance Spectra from UAV Data Collected in Changeable Illumination Conditions to Assess Vegetationcondition

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8518049 ◽

2018 ◽

Author(s):

France Gerard ◽

Charles George ◽

Jakob Iglhaut ◽

Richard Broughton ◽

Cecilia Chavana-Bryant ◽

...

Keyword(s):

Spectral Reflectance ◽

Reflectance Spectra ◽

Hyper Spectral

Assessing Metal-Induced Changes in the Visible and Near-Infrared Spectral Reflectance of Leaves: A Pot Study with Sunflower (Helianthus annuus L.)

Journal of the Indian Society of Remote Sensing ◽

10.1007/s12524-018-0846-3 ◽

2018 ◽

Vol 46 (12) ◽

pp. 1925-1937 ◽

Cited By ~ 1

Author(s):

Paresh H. Rathod ◽

Carsten Brackhage ◽

Ingo Müller ◽

Freek D. Van der Meer ◽

Marleen F. Noomen

Keyword(s):

Helianthus Annuus ◽

Spectral Reflectance ◽

Near Infrared ◽

Helianthus Annuus L ◽

Infrared Spectral ◽

Induced Changes

Optical properties study of In.08Ga.92As/GaAs using spectral reflectance, photoreflectance and near-infrared Photoluminescence

Superlattices and Microstructures ◽

10.1016/j.spmi.2013.04.009 ◽

2013 ◽

Vol 59 ◽

pp. 133-143 ◽

Cited By ~ 9

Author(s):

N. Tounsi ◽

M.M. Habchi ◽

Z. Chine ◽

A. Rebey ◽

B. El Jani

Keyword(s):

Optical Properties ◽

Spectral Reflectance ◽

Near Infrared

Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves

Applied Sciences ◽

10.3390/app10072259 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2259 ◽

Cited By ~ 2

Author(s):

Haixia Qi ◽

Bingyu Zhu ◽

Lingxi Kong ◽

Weiguang Yang ◽

Jun Zou ◽

...

Keyword(s):

Chlorophyll Content ◽

Spectral Index ◽

Spectral Reflectance ◽

Near Infrared ◽

Plant Density ◽

Cell Structure ◽

Spectral Bands ◽

Study Results ◽

Leaf Spectral Reflectance ◽

Estimation Models

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.

Deep Learning Application for Predicting Soil Organic Matter Content by VIS-NIR Spectroscopy

Computational Intelligence and Neuroscience ◽

10.1155/2019/3563761 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Zhe Xu ◽

Xiaomin Zhao ◽

Xi Guo ◽

Jiaxin Guo

Keyword(s):

Organic Matter ◽

Deep Learning ◽

Infrared Spectroscopy ◽

Soil Organic Matter ◽

Near Infrared Spectroscopy ◽

Spectral Reflectance ◽

Near Infrared ◽

Spectral Variation ◽

Full Spectrum ◽

Reflectance Data

Deep learning is characterized by its strong ability of data feature extraction. This method can provide unique advantages when applying it to visible and near-infrared spectroscopy for predicting soil organic matter (SOM) content in those cases where the SOM content is negatively correlated with the spectral reflectance of soil. This study relied on the SOM content data of 248 red soil samples and their spectral reflectance data of 400–2450 nm in Fengxin County, Jiangxi Province (China) to meet three objectives. First, a multilayer perceptron and two convolutional neural networks (LeNet5 and DenseNet10) were used to predict the SOM content based on spectral variation and variable selection, and the outcomes were compared with that from the traditional back-propagation neural network (BPN). Second, the four methods were applied to full-spectrum modeling to test the difference to selected feature variables. Finally, the potential of direct modeling was evaluated using spectral reflectance data without any spectral variation. The results of prediction accuracy showed that deep learning performed better at predicting the SOM content than did the traditional BPN. Based on full-spectrum data, deep learning was able to obtain more feature information, thus achieving better and more stable results (i.e., similar average accuracy and far lower standard deviation) than those obtained through variable selection. DenseNet achieved the best prediction result, with a coefficient of determination (R2) = 0.892 ± 0.004 and a ratio of performance to deviation (RPD) = 3.053 ± 0.056 in validation. Based on DenseNet, the application of spectral reflectance data (without spectral variation) produced robust results for application-level purposes (validation R2 = 0.853 ± 0.007 and validation RPD = 2.639 ± 0.056). In conclusion, deep learning provides an effective approach to predict the SOM content by visible and near-infrared spectroscopy and DenseNet is a promising method for reducing the amount of data preprocessing.

Comparison of the Lunar Models Using the Hyper-Spectral Imager Observations in Lijiang, China

Remote Sensing ◽

10.3390/rs12111878 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1878

Author(s):

Yang Wang ◽

Xiuqing Hu ◽

Lin Chen ◽

Yu Huang ◽

Zhanfeng Li ◽

...

Keyword(s):

Near Infrared ◽

Spectral Ratio ◽

Spectral Irradiance ◽

Lunar Phase ◽

Absorption Bands ◽

Absorption Region ◽

Phase Dependence ◽

Imaging Spectrometer ◽

Lunar Phases ◽

Hyper Spectral

A lunar observation campaign was conducted using a hyper-spectral imaging spectrometer in Lijiang, China from December 2015 to February 2016. The lunar hyper-spectral images in the visible to near-infrared region (VNIR) have been obtained in different lunar phases with absolute scale established by the National Institute of Metrology (NIM), China using the lamp–plate calibration system. At the same time, the aerosol optical depth (AOD) is measured regularly by a lidar and a lunar CE318U for atmospheric characterization to provide nightly atmosphere extinction correction of lunar observations. This paper addressed the complicated data processing procedure in detail from raw images of the spectrometer into the spectral lunar irradiance in different lunar phases. The result of measurement shows that the imaging spectrometer can provide lunar irradiance with uncertainties less than 3.30% except for absorption bands. Except for strong atmosphere absorption region, the mean spectral irradiance difference between the measured irradiance and the ROLO (Robotic Lunar Observatory) model is 8.6 ± 2% over the course of the lunar observation mission. The ROLO model performs more reliable to clarify absolute and relative accuracy of lunar irradiance than that of the MT2009 model in different Sun–Moon–Earth geometry. The spectral ratio analysis of lunar irradiance shows that band-to-band variability in the ROLO model is consistent within 2%, and the consistency of the models in the lunar phase and spectrum is well analyzed and evaluated from phase dependence and phase reddening analysis respectively.