GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Unmanned Aerial Vehicle (UAV)–Based Imaging Spectroscopy for Predicting Wheat Leaf Nitrogen
    American Society for Photogrammetry and Remote Sensing ; 2023
    In:  Photogrammetric Engineering & Remote Sensing Vol. 89, No. 2 ( 2023-02-01), p. 107-116
    Details
    In: Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, Vol. 89, No. 2 ( 2023-02-01), p. 107-116
    Abstract: Quantitative estimation of crop nitrogen is the key to site-specific management for enhanced nitrogen (N) use efficiency and a sustainable crop production system. As an alternate to the conventional approach through wet chemistry, sensor-based noninvasive, rapid, and near-real-time assessment of crop N at the field scale has been the need for precision agriculture. The present study attempts to predict leaf N of wheat crop through spectroscopy using a field portable spectroradiometer (spectral range of 400–2500 nm) on the ground in the crop field and an imaging spectrometer (spectral range of 400–1000 nm) from an unmanned aerial vehicle (UAV) with the objectives to evaluate (1) four multivariate spectral models (i.e., artificial neural network, extreme learning machine [ELM], least absolute shrinkage and selection operator, and support vector machine regression) and (2) two sets of hyperspectral data collected from two platforms and two different sensors. In the former part of the study, ELM outperforms the other methods with maximum calibration and validation R2 of 0.99 and 0.96, respectively. Furthermore, the image data set acquired from UAV gives higher performance compared to field spectral data. Also, significant bands are identified using stepwise multiple linear regression and used for modeling to generate a wheat leaf N map of the ex perimental field.
    Type of Medium: Online Resource
    ISSN: 0099-1112
    URL: Article
    DOI: 10.14358/PERS.22-00089R2
    RVK:
    RA 4132.4
    Language: English
    Publisher: American Society for Photogrammetry and Remote Sensing
    Publication Date: 2023
    detail.hit.zdb_id: 2317128-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Detection of engineered surfaces using deep learning approach in AVIRIS-NG hyperspectral data
    Informa UK Limited ; 2022
    In:  Geocarto International Vol. 37, No. 23 ( 2022-12-02), p. 6932-6952
    Details
    In: Geocarto International, Informa UK Limited, Vol. 37, No. 23 ( 2022-12-02), p. 6932-6952
    Type of Medium: Online Resource
    ISSN: 1010-6049 , 1752-0762
    URL: Article
    DOI: 10.1080/10106049.2021.1953616
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2109550-4
    SSG: 14
    SSG: 14,1
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Automatic Object Recognition from Satellite Images using Artificial Neural Network
    Foundation of Computer Science ; 2014
    In:  International Journal of Computer Applications Vol. 95, No. 10 ( 2014-06-18), p. 33-39
    Details
    In: International Journal of Computer Applications, Foundation of Computer Science, Vol. 95, No. 10 ( 2014-06-18), p. 33-39
    Type of Medium: Online Resource
    ISSN: 0975-8887
    URL: Article
    DOI: 10.5120/16633-6502
    Language: Unknown
    Publisher: Foundation of Computer Science
    Publication Date: 2014
    detail.hit.zdb_id: 2548770-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    Spectral – spatial urban target detection for hyperspectral remote sensing data using artificial neural network
    Elsevier BV ; 2021
    In:  The Egyptian Journal of Remote Sensing and Space Science Vol. 24, No. 2 ( 2021-08), p. 173-180
    Details
    In: The Egyptian Journal of Remote Sensing and Space Science, Elsevier BV, Vol. 24, No. 2 ( 2021-08), p. 173-180
    Type of Medium: Online Resource
    ISSN: 1110-9823
    URL: Article
    DOI: 10.1016/j.ejrs.2021.01.002
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 2583596-8
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models
    Frontiers Media SA ; 2023
    In:  Frontiers in Plant Science Vol. 14 ( 2023-2-23)
    Details
    In: Frontiers in Plant Science, Frontiers Media SA, Vol. 14 ( 2023-2-23)
    Abstract: Rice is the staple food of more than half of the population of the world and India as well. One of the major constraints in rice production is frequent occurrence of pests and diseases and one of them is rice blast which often causes yield loss varying from 10 to 30%. Conventional approaches for disease assessment are time-consuming, expensive, and not real-time; alternately, sensor-based approach is rapid, non-invasive and can be scaled up in large areas with minimum time and effort.  In the present study, hyperspectral remote sensing for the characterization and severity assessment of rice blast disease was exploited. Field experiments were conducted with 20 genotypes of rice having sensitive and resistant cultivars grown under upland and lowland conditions at Almora, Uttarakhand, India. The severity of the rice blast was graded from 0 to 9 in accordance to International Rice Research Institute (IRRI).  Spectral observations in field were taken using a hand-held portable spectroradiometer in range of 350-2500 nm followed by spectral discrimination of different disease severity levels using Jeffires–Matusita (J-M) distance. Then, evaluation of 26 existing spectral indices (r≥0.8) was done corresponding to blast severity levels and linear regression prediction models were also developed. Further, the proposed ratio blast index (RBI) and normalized difference blast index (NDBI) were developed using all possible combinations of their correlations with severity level followed by their quantification to identify the best indices. Thereafter, multivariate models like support vector machine regression (SVM), partial least squares (PLS), random forest (RF), and multivariate adaptive regression spline (MARS) were also used to estimate blast severity. Jeffires–Matusita distance was separating almost all severity levels having values & gt;1.92 except levels 4 and 5. The 26 prediction models were effective at predicting blast severity with R 2 values from 0.48 to 0.85. The best developed spectral indices for rice blast were RBI (R1148, R1301) and NDBI (R1148, R1301) with R 2 of 0.85 and 0.86, respectively. Among multivariate models, SVM was the best model with calibration R 2 =0.99; validation R 2 =0.94, RMSE=0.7, and RPD=4.10. The methodology developed paves way for early detection and large-scale monitoring and mapping using satellite remote sensors at farmers’ fields for developing better disease management options.
    Type of Medium: Online Resource
    ISSN: 1664-462X
    URL: Article
    DOI: 10.3389/fpls.2023.1067189
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2687947-5
    detail.hit.zdb_id: 2613694-6
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 6
    Online Resource
    Online Resource
    Comparative Assessment of Target-Detection Algorithms for Urban Targets Using Hyperspectral Data
    American Society for Photogrammetry and Remote Sensing ; 2021
    In:  Photogrammetric Engineering & Remote Sensing Vol. 87, No. 5 ( 2021-05-01), p. 349-362
    Details
    In: Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, Vol. 87, No. 5 ( 2021-05-01), p. 349-362
    Abstract: Hyperspectral data present better opportunities to exploit the treasure of spectral and spatial content that lies within their spectral bands. Hyperspectral data are increasingly being considered for exploring levels of urbanization, due to their capability to capture the spectral variability that a modern urban landscape offers. Data and algorithms are two sides of a coin: while the data capture the variations, the algorithms provide suitable methods to extract relevant information. The literature reports a variety of algorithms for extraction of urban information from any given data, with varying accuracies. This article aims to explore the binary-classifier approach to target detection to extract certain features. Roads and roofs are the most common features present in any urban scene. These experiments were conducted on a subset of AVIRIS-NG hyperspectral data from the Udaipur region of India, with roads and roofs as targets. Four categories of target-detection algorithms are identified from a literature survey and our previous experience—distance measures, angle-based measures, information measures, and machine-learning measures—followed by performance evaluation. The article also presents a brief taxonomy of algorithms; explores methods such as the Mahalanobis angle, which has been reported to be effective for extraction of urban targets; and explores newer machine-learning algorithms to increase accuracy. This work is likely to aid in city planning, sustainable development, and various other governmental and nongovernmental efforts related to urbanization.
    Type of Medium: Online Resource
    ISSN: 0099-1112
    URL: Article
    DOI: 10.14358/PERS.87.5.349
    RVK:
    RA 4132.4
    Language: English
    Publisher: American Society for Photogrammetry and Remote Sensing
    Publication Date: 2021
    detail.hit.zdb_id: 2317128-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 7
    Online Resource
    Online Resource
    Rapid prediction of soil available sulphur using visible near-infrared reflectance spectroscopy
    Indian Council of Agricultural Research, Directorate of Knowledge Management in Agriculture ; 2021
    In:  The Indian Journal of Agricultural Sciences Vol. 91, No. 9 ( 2021-09-27)
    Details
    In: The Indian Journal of Agricultural Sciences, Indian Council of Agricultural Research, Directorate of Knowledge Management in Agriculture, Vol. 91, No. 9 ( 2021-09-27)
    Abstract: Rapid and accurate prediction of soil available S, an important secondary nutrient, is crucial for its site-specific management in a cultivated region. Although traditional chemical analysis of any nutrient is an accurate method, but often costly, time-consuming and destructive in nature. Recently visible near-infrared (VIS-NIR) reflectance spectroscopic technique has gained its popularity for rapid, non-destructive and cost-effective assessment of soil nutrients. Hence, a study was carried out in an intensively cultivated region of Katol block of Nagpur, Maharashtra, during 2018-20 for rapid prediction of soil available S using spectroscopic technique. Both spectroscopic and chemical analyses were carried out using 132 georeferenced surface soil samples (0-15 cm depth). The descriptive statistical analysis showed that the available S content varied from 1.09 to 47.88 mg/kg. Multivariate models namely partial least square regression (PLSR) and random forest (RF) were applied to develop spectral models for S prediction from spectral dataset. Several statistical diagnostics like coefficient of determination (R2), root mean square error (RMSE), ratio of performance deviation (RPD) and ratio of performance to interquartile distance (RPIQ) were used to evaluate the performances of two models. The best prediction of S was achieved from nonlinear RF model (R2 = 0.71, RMSE = 8.86, RPD =1.18, RPIQ = 1.69) as compared to linear PLSR model (R2 = 0.53, RMSE = 9.04, RPD = 1.16, RPIQ = 1.66) datasets. Therefore, the result suggested applying non-linear multivariate model (RF) for obtaining best predictability for S from spectroscopic technique.
    Type of Medium: Online Resource
    ISSN: 0019-5022
    URL: Article
    DOI: 10.56093/ijas.v91i9.116080
    Language: Unknown
    Publisher: Indian Council of Agricultural Research, Directorate of Knowledge Management in Agriculture
    Publication Date: 2021
    detail.hit.zdb_id: 2553598-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...