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The Scientific World Journal
Volume 2014, Article ID 214674, 13 pages
Research Article

Integrating SOMs and a Bayesian Classifier for Segmenting Diseased Plants in Uncontrolled Environments

1ITESM, Autopista del Sol, 62790 Xochitepec, MOR, Mexico
2Centro de Investigación en Biotecnología/Laboratorio de Investigaciones Ambientales, Universidad Autónoma del Estado de Morelos, Avenida Universidad 1001, 62209 Cuernavaca, MOR, Mexico

Received 18 July 2014; Revised 5 October 2014; Accepted 6 October 2014; Published 4 November 2014

Academic Editor: Gonzalo Pajares

Copyright © 2014 Deny Lizbeth Hernández-Rabadán 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 [4 citations]

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

  • Arti Singh, Baskar Ganapathysubramanian, Asheesh Kumar Singh, and Soumik Sarkar, “Machine Learning for High-Throughput Stress Phenotyping in Plants,” Trends in Plant Science, 2015. View at Publisher · View at Google Scholar
  • Sharada P. Mohanty, David P. Hughes, and Marcel Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Frontiers in Plant Science, vol. 7, 2016. View at Publisher · View at Google Scholar
  • Guan Wang, Yu Sun, and Jianxin Wang, “Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning,” Computational Intelligence and Neuroscience, vol. 2017, pp. 1–8, 2017. View at Publisher · View at Google Scholar
  • Zachary C. Campbell, Lucia M. Acosta-Gamboa, Nirman Nepal, and Argelia Lorence, “Engineering plants for tomorrow: how high-throughput phenotyping is contributing to the development of better crops,” Phytochemistry Reviews, 2018. View at Publisher · View at Google Scholar