Plant disease detection based on multispectral and hyperspectral image analysis using deep learning techniques

A. Chauhan; G. Polder; H.A.C. de Villiers

Plant disease detection based on multispectral and hyperspectral image analysis using deep learning techniques

A. Chauhan, G. Polder, H.A.C. de Villiers

Research output: Chapter in Book/Report/Conference proceeding › Abstract

Original language	English
Title of host publication	Book of Abstracts European Federation for Information Technology in Agriculture, Food and the Environment (EFITA) 2019
Publisher	EFITA
Pages	37-37
Number of pages	1
Publication status	Published - 27 Jul 2019
Event	European Federation for Information Technology in Agriculture, Food and the Environment (EFITA) - Rhodes Island, Greece Duration: 27 Jul 2019 → 29 Jul 2019 Conference number: 12

Conference

Conference	European Federation for Information Technology in Agriculture, Food and the Environment (EFITA)
Abbreviated title	12th EFITA International Conference
Country/Territory	Greece
City	Rhodes Island
Period	27/07/19 → 29/07/19

HT17222 Exploitation of high-tech plant phenotyping tools (BO-67-001-014, BO-52-002-005)
van de Zedde, R.
1/01/18 → 31/12/22
Project: EZproject

Cite this

@inbook{6b94a91f55a54c928daecf9cebdb9273,

title = "Plant disease detection based on multispectral and hyperspectral image analysis using deep learning techniques",

author = "A. Chauhan and G. Polder and {de Villiers}, H.A.C.",

year = "2019",

month = jul,

day = "27",

language = "English",

pages = "37--37",

booktitle = "Book of Abstracts European Federation for Information Technology in Agriculture, Food and the Environment (EFITA) 2019",

publisher = "EFITA",

note = "European Federation for Information Technology in Agriculture, Food and the Environment (EFITA) , 12th EFITA International Conference ; Conference date: 27-07-2019 Through 29-07-2019",

}

Chauhan, A , Polder, G & de Villiers, HAC 2019, Plant disease detection based on multispectral and hyperspectral image analysis using deep learning techniques. in Book of Abstracts European Federation for Information Technology in Agriculture, Food and the Environment (EFITA) 2019. EFITA, pp. 37-37, European Federation for Information Technology in Agriculture, Food and the Environment (EFITA) , Rhodes Island, Greece, 27/07/19.

Plant disease detection based on multispectral and hyperspectral image analysis using deep learning techniques. / Chauhan, A.; Polder, G.; de Villiers, H.A.C.
Book of Abstracts European Federation for Information Technology in Agriculture, Food and the Environment (EFITA) 2019. EFITA, 2019. p. 37-37.

Research output: Chapter in Book/Report/Conference proceeding › Abstract

TY - CHAP

T1 - Plant disease detection based on multispectral and hyperspectral image analysis using deep learning techniques

AU - Chauhan, A.

AU - Polder, G.

AU - de Villiers, H.A.C.

N1 - Conference code: 12

PY - 2019/7/27

Y1 - 2019/7/27

M3 - Abstract

SP - 37

EP - 37

BT - Book of Abstracts European Federation for Information Technology in Agriculture, Food and the Environment (EFITA) 2019

PB - EFITA

T2 - European Federation for Information Technology in Agriculture, Food and the Environment (EFITA)

Y2 - 27 July 2019 through 29 July 2019

ER -

Plant disease detection based on multispectral and hyperspectral image analysis using deep learning techniques

Conference

Projects

HT17222 Exploitation of high-tech plant phenotyping tools (BO-67-001-014, BO-52-002-005)

Cite this