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The Scientific World Journal
Volume 2012, Article ID 630390, 11 pages
http://dx.doi.org/10.1100/2012/630390
Research Article

Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops

Institute for Sustainable Agriculture (IAS), CSIC, P.O. Box 4084, 14080 Córdoba, Spain

Received 13 December 2011; Accepted 11 January 2012

Academic Editors: R. Sarkar and E. Tyystjarvi

Copyright © 2012 Ana-Isabel de Castro et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [17 citations]

The following is the list of published articles that have cited the current article.

  • Ana Isabel Castro, Francisca López-Granados, and Montserrat Jurado-Expósito, “Broad-scale cruciferous weed patch classification in winter wheat using QuickBird imagery for in-season site-specific control,” Precision Agriculture, 2013. View at Publisher · View at Google Scholar
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  • Jaafar Abdulridha, Reza Ehsani, and Ana De Castro, “Detection and differentiation between laurel wilt disease, phytophthora disease, and salinity damage using a hyperspectral sensing technique,” Agriculture (Switzerland), vol. 6, no. 4, 2016. View at Publisher · View at Google Scholar
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  • Mohamad Anuar Izzuddin, Abu Seman Idris, Mohd Noor Nisfariza, Abd Aziz Nordiana, Helmi Zulhaidi Mohd Shafri, and Bahrom Ezzati, “ The development of spectral indices for early detection of Ganoderma disease in oil palm seedlings ,” International Journal of Remote Sensing, pp. 1–23, 2017. View at Publisher · View at Google Scholar
  • Jie Chen, Lifu Zhang, Hongming Zhang, Taixia Wu, and Peng Zhang, “Development and preliminary results of a drilling core spectral imaging and cataloging system,” Optical Engineering, vol. 56, no. 8, 2017. View at Publisher · View at Google Scholar
  • Wei Feng, Shuangli Qi, Yarong Heng, Yi Zhou, Yapeng Wu, Wandai Liu, Li He, and Xiao Li, “Canopy Vegetation Indices from In situ Hyperspectral Data to Assess Plant Water Status of Winter Wheat under Powdery Mildew Stress,” Frontiers in Plant Science, vol. 8, 2017. View at Publisher · View at Google Scholar
  • M. Louargant, S. Villette, G. Jones, N. Vigneau, J. N. Paoli, and C. Gée, “Weed detection by UAV: simulation of the impact of spectral mixing in multispectral images,” Precision Agriculture, 2017. View at Publisher · View at Google Scholar
  • Reginald S. Fletcher, and Rickie B. Turley, “Employing Canopy Hyperspectral Narrowband Data and Random Forest Algorithm to Differentiate Palmer Amaranth from Colored Cotton,” American Journal of Plant Sciences, vol. 08, no. 12, pp. 3258–3271, 2017. View at Publisher · View at Google Scholar
  • Saman Akbarzadeh, Arie Paap, Selam Ahderom, Beniamin Apopei, and Kamal Alameh, “Plant discrimination by Support Vector Machine classifier based on spectral reflectance,” Computers and Electronics in Agriculture, vol. 148, pp. 250–258, 2018. View at Publisher · View at Google Scholar
  • C Fernández-Quintanilla, J M Peña, D Andújar, J Dorado, A Ribeiro, and F López-Granados, “Is the current state of the art of weed monitoring suitable for site-specific weed management in arable crops?,” Weed Research, 2018. View at Publisher · View at Google Scholar
  • Jaafar Abdulridha, Yiannis Ampatzidis, Reza Ehsani, and Ana I. de Castro, “Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado,” Computers and Electronics in Agriculture, vol. 155, pp. 203–211, 2018. View at Publisher · View at Google Scholar
  • Oscar Barrero, and Sammy A. Perdomo, “RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields,” Precision Agriculture, 2018. View at Publisher · View at Google Scholar