Classifying cultivars of rice (Oryza sativa L.) based on corrected canopy reflectance spectra data using the orthogonal projections to latent structures (O-PLS) method

Lin, Wen-Shin; Yang, Chao; Kuo, Bo‐Jein

doi:10.1016/j.chemolab.2012.04.005

Cited by 29 publications

(25 citation statements)

References 62 publications

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“…Although some researchers reduced data dimension first [3], many used discriminant analysis [4,13], principal components analysis [4,8,26] and other classification methods such as partial least square regression [6], neural networks [27][28][29], and image-based data [21,30] to classify crop species. Many studies have determined that the visible and NIR portions of the EMS are significant in agricultural research, especially for estimating crop biophysical parameters such as leaf area index and chlorophyll content, in particular, for crop discrimination [3,8,13,30,31].…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing is a cost-effective, non-destructive and highly efficient method for conducting agricultural research including the estimation of biophysical parameters such as leaf area index (LAI) and chlorophyll content [2,3], identification of weed species [4,5], crop separation and classification [6][7][8], and yield estimation [9]. In the last few decades, numerous studies have demonstrated that remote sensing, in particular, hyperspectral remote sensing, is a more desirable option for conducting agricultural research as it provides contiguous reflectance data allowing the ability to monitor slight changes in crops using multiple bands [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Typically, studies that used handheld devices were conducted in the visible (350-700 nm) and near-infrared (NIR) portions of the electromagnetic spectrum (EMS). A number of studies have focused on discriminating weed species [5,7], different strands or cultivars of the same crop such as wheat, rice and cotton [6,23,24] and finally different species of crop altogether [8,22,25].…”

Section: Introductionmentioning

confidence: 99%

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Separating Crop Species in Northeastern Ontario Using Hyperspectral Data

2014

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Abstract:The purpose of this study was to examine the capability of hyperspectral narrow wavebands within the 400-900 nm range for distinguishing five cash crops commonly grown in Northeastern Ontario, Canada. Data were collected from ten different fields in the West Nipissing agricultural zone (46°24'N lat., 80°07'W long.) and included two of each of the following crop types; soybean (Glycine max), canola (Brassica napus L.), wheat (Triticum spp.), oat (Avena sativa), and barley (Hordeum vulgare). Stepwise discriminant analysis was used to assess the spectral separability of the various crop types under two scenarios; Scenario 1 involved testing separability of crops based on number of days after planting and Scenario 2 involved testing crop separability at specific dates across the growing season. The results indicate that select hyperspectral bands in the visual and near infrared (NIR) regions (400-900 nm) can be used to effectively distinguish the five crop species under investigation. These bands, which were used in a variety of combinations include B465, B485, B495, B515, B525, B535, B545, B625, B645, B665, B675, B695, B705, B715, B725, B735, B745, B755, B765, B815, B825, B885, and B895. In addition, although species classification could be achieved at any point during the growing season, the optimal time for satellite image acquisition was determined to be in late July or approximately 75-79 days after planting with the optimal wavebands located in the red-edge, green, and NIR regions of the spectrum.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Separating Crop Species in Northeastern Ontario Using Hyperspectral Data

2014

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“…The Y-predictive components are the components that contain the variation commonly correlated with X and Y. Moreover, O-PLS can effectively reduce the components in PLS and contribute to the analysis process [16]. O-PLS-DA analysis results between Group L and other three groups (Groups A, B, and C) are provided in Table 2.…”

Section: Analysis Results Of Pls-da and O-pls-damentioning

confidence: 99%

Dynamic analysis of phospholipid metabolism of mouse macrophages treated with common non-steroidal anti-inflammatory drugs

Peng

Zhao

et al. 2015

Mol Cell Biochem

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Through studying the changes of the total phospholipid components in mouse macrophages under the inflammatory status and the drug intervention status, we found the targets of non-steroidal anti-inflammatory drugs on phospholipids, thus providing the basis for the targets of in vitro anti-inflammatory effects of non-steroidal anti-inflammatory drugs. After RAW264.7 cells were pretreated with common non-steroidal anti-inflammatory drugs (aspirin and ibuprofen) and, respectively, stimulated with KLA for various periods (0.5, 4, 12, 16, and 24 h), the phospholipids were extracted. The dynamic changes of phospholipids in cells under various stimulations were analyzed with UPLC-Q-TOF-MS technique. Through the statistical analysis of Simca-P, we explored the potential targets of non-steroidal anti-inflammatory drugs on phospholipids. Through the dynamic analysis of phospholipids, we found two biomarkers (PC(17:1/18:1), PA(18:0/18:4)) which might be in vitro intervention inflammatory response targets of non-steroidal anti-inflammatory drugs. The analysis results show that in anti-inflammatory effects, non-steroidal anti-inflammatory drugs can inhibit COX, induce the cellular fatty acid desaturation and the changes of phospholipid components, stimulate free fatty acids, activate calcium ion channels of endoplasmic reticulum, and promote cell endocytosis, thus controlling inflammation and activating cells. Non-steroidal anti-inflammatory drugs can promote endocytosis, alter cell inflammatory response, and activate the process cells, thus realizing the anti-inflammatory effects.

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“…However, there were some disagreements about the advantages and disadvantages of their applicability. Lin et al [29] demonstrated the OPLS methods are simpler, easier to interpret, and more accurate than the PLS method, while Tapp and Kemsley [30] opposed this. In our study, models established by OPLS were marginally more accurate than the PLS models when MSC, SNV, DF1 or DF2 pre-processing were applied.…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral reflectance models for soil salt content by filtering methods and waveband selection

Zeng

Huang

et al. 2016

Ecological Chemistry and Engineering S

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Abstract:For improving the understanding of interactions between hyperspectral reflectance and soil salinity, in situ hyperspectral inversion of soil salt content at a depth of 0-10 cm was conducted in Hetao Irrigation District, Inner Mongolia, China. Six filtering methods were used to preprocess soil reflectance data, and waveband selection combined by VIP (variable importance in projection) and b-coefficients (regression coefficients of model) was also applied to simplify model. Then statistical methods of partial least square regression (PLS) and orthogonal projection to latent structures (OPLS) were processed to establish the inversion models. Our findings indicate that the selected sensitive wavebands for the 6 filtering methods are different, among which the multiplicative signal correction (MSC) and standard normal variate methods (SNV) have some similar sensitive wavebands with unfiltered data. Derivatives (DF1 and DF2) could characterize sensitive wavebands along the scale of VNIR (350-1100 nm), especially the second derivative (DF2). The sensitive wavebands for continuum-removed reflectance method (CR) have protruded many narrow absorption features. For orthogonal signal correction method (OSC), the selected wavebands are centralized in the range of 565-1013 nm. The calibration and evaluation processes have demonstrated the second order derivate filtering method (DF2) combined with waveband selection is superior to other processes, for it has high R 2 (larger than 0.7) both in PLS and OPLS models for calibration and evaluation, by choosing only 156 wavebands from the whole 700 wavebands. Meanwhile, OPLS method was considered to be more suitable for the analyzing than PLS in most of our situations.

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Classifying cultivars of rice (Oryza sativa L.) based on corrected canopy reflectance spectra data using the orthogonal projections to latent structures (O-PLS) method

Cited by 29 publications

References 62 publications

Separating Crop Species in Northeastern Ontario Using Hyperspectral Data

Separating Crop Species in Northeastern Ontario Using Hyperspectral Data

Dynamic analysis of phospholipid metabolism of mouse macrophages treated with common non-steroidal anti-inflammatory drugs

Hyperspectral reflectance models for soil salt content by filtering methods and waveband selection

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