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

doi:10.1109/IGARSS.2018.8519115

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

Electronic And Electrical Engineering

Research output: Contribution to conference › Paper › peer-review

14 Citations (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 language	English
Pages	1744-1747
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS.2018.8519115
Publication status	Published - 5 Nov 2018
Event	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018

Conference

Conference	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18

Keywords

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

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10.1109/IGARSS.2018.8519115

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title = "Banana disease detection by fusion of close range hyperspectral image and high-resolution RGB image",

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.",

keywords = "diseases, cameras, hyperspectral imaging, color, spatial resolution, image colour analysis, close range hyperspectral image, data fusion, banana diseases",

author = "Wenzhi Liao and Daniel Ochoa and Yongqiang Zhao and \{Villegas Rugel\}, \{Gladys Maria\} and Wilfried Philips",

year = "2018",

month = nov,

day = "5",

doi = "10.1109/IGARSS.2018.8519115",

language = "English",

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note = "38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",

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T1 - Banana disease detection by fusion of close range hyperspectral image and high-resolution RGB image

AU - Liao, Wenzhi

AU - Ochoa, Daniel

AU - Zhao, Yongqiang

AU - Villegas Rugel, Gladys Maria

AU - Philips, Wilfried

PY - 2018/11/5

Y1 - 2018/11/5

N2 - 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.

AB - 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.

KW - diseases

KW - cameras

KW - hyperspectral imaging

KW - color

KW - spatial resolution

KW - image colour analysis

KW - close range hyperspectral image

KW - data fusion

KW - banana diseases

UR - http://hdl.handle.net/1854/LU-8565500

U2 - 10.1109/IGARSS.2018.8519115

DO - 10.1109/IGARSS.2018.8519115

M3 - Paper

SP - 1744

EP - 1747

T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

Y2 - 22 July 2018 through 27 July 2018

ER -

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

Abstract

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Keywords

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