Banana disease detection by fusion of close range hyperspectral image and high-resolution RGB image

Wenzhi Liao, Daniel Ochoa, Yongqiang Zhao, Gladys Maria Villegas Rugel, Wilfried Philips

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

Early detection of banana disease can limit the spread of disease, as well as reduce the treatment costs. Current methods focus on either manually interpretation or calculation of spectral indices (e.g., the normalized difference vegetation index). In this paper, we exploit the fusion of close range hyperspectral (HS) image and high-resolution (HR) visible RGB image for potential disease detection in banana leaves. Our approach applies the joint bilateral filter to transfer the textural structures of HR RGB image to low-resolution HS image and obtain an enhanced HS image. Initial experimental results on Musa acuminata (banana) leaf images demonstrate the efficiency of our fusion approach, with significant improvements over either single data source or some conventional methods.
Original languageEnglish
Pages1744-1747
Number of pages4
DOIs
Publication statusPublished - 5 Nov 2018
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
CountrySpain
CityValencia
Period22/07/1827/07/18

Keywords

  • diseases
  • cameras
  • hyperspectral imaging
  • color
  • spatial resolution
  • image colour analysis
  • close range hyperspectral image
  • data fusion
  • banana diseases

Cite this

Liao, W., Ochoa, D., Zhao, Y., Villegas Rugel, G. M., & Philips, W. (2018). Banana disease detection by fusion of close range hyperspectral image and high-resolution RGB image. 1744-1747. Paper presented at 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain. https://doi.org/10.1109/IGARSS.2018.8519115