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Journal of Spectroscopy
Volume 2013, Article ID 841738, 6 pages
http://dx.doi.org/10.1155/2013/841738
Research Article

A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and Brazil

1Instituto de Física de São Carlos, Universidade de São Paulo, Cx. Postal 369, 13560-970 São Carlos, SP, Brazil
2Citrus Research and Education Center, IFAS, University of Florida, 700 Experiment Station Road, Lake Alfred, FL 33850, USA
3Departamento Científico, Fundecitrus, Avenida Dr. Adhemar P. de Barros, 20114 807-040 Araraquara, SP, Brazil

Received 5 June 2012; Accepted 29 October 2012

Academic Editor: Luciano Bachmann

Copyright © 2013 Caio B. Wetterich 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 [11 citations]

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

  • Jan Behmann, Anne-Katrin Mahlein, Till Rumpf, Christoph Römer, and Lutz Plümer, “A review of advanced machine learning methods for the detection of biotic stress in precision crop protection,” Precision Agriculture, 2014. View at Publisher · View at Google Scholar
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  • Caio Bruno Wetterich, Ruan Felipe de Oliveira Neves, José Belasque, and Luis Gustavo Marcassa, “Detection of citrus canker and Huanglongbing using fluorescence imaging spectroscopy and support vector machine technique,” Applied Optics, vol. 55, no. 2, pp. 400, 2016. View at Publisher · View at Google Scholar
  • Haiyan Cen, Haiyong Weng, Jieni Yao, Mubin He, Jingwen Lv, Shijia Hua, Hongye Li, and Yong He, “Chlorophyll Fluorescence Imaging Uncovers Photosynthetic Fingerprint of Citrus Huanglongbing,” Frontiers in Plant Science, vol. 8, 2017. View at Publisher · View at Google Scholar
  • Alireza Pourreza, Won Lee, Eva Czarnecka, Lance Verner, and William Gurley, “Feasibility of Using the Optical Sensing Techniques for Early Detection of Huanglongbing in Citrus Seedlings,” Robotics, vol. 6, no. 2, pp. 11, 2017. View at Publisher · View at Google Scholar
  • Ruan Felipe De Oliveira Neves, José Belasque, Reza Ehsani, Luis Gustavo Marcassa, and Caio Bruno Wetterich, “Detection of Huanglongbing in Florida using fluorescence imaging spectroscopy and machine-learning methods,” Applied Optics, vol. 56, no. 1, pp. 15–23, 2017. View at Publisher · View at Google Scholar
  • Nikos Petrellis, “A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis,” Symmetry, vol. 10, no. 7, pp. 270, 2018. View at Publisher · View at Google Scholar
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  • Haiyong Weng, Jingwen Lv, Haiyan Cen, Mubin He, Yibing Zeng, Shijia Hua, Hongye Li, Youqing Meng, Hui Fang, and Yong He, “Hyperspectral reflectance imaging combined with carbohydrate metabolism analysis for diagnosis of citrus Huanglongbing in different seasons and cultivars,” Sensors and Actuators B: Chemical, vol. 275, pp. 50–60, 2018. View at Publisher · View at Google Scholar