Early detection of Fusarium infection in corn using spectral analysis

T. Sandovsky; Y. Edan; S. Gad; A. Etzioni; T. Nacson; V. Alchanatis

doi:10.3920/978-90-8686-888-9_42

Early detection of Fusarium infection in corn using spectral analysis

T. Sandovsky, Y. Edan, S. Gad, A. Etzioni, T. Nacson, V. Alchanatis

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This work presents a non-destructive methodology for early detection of Fusarium infection, by spectral analysis in the 350-2,500 nm range. Corn plants in greenhouse conditions were analysed using spectral analysis. The Lasso model was used to differentiate infected from non-infected plants based on the first derivative of leaf spectral reflectance. Fusarium infection was successfully recognized in plants at V2 growth stage with 74% success rate. This result enables infection detection at a stage which currently is not possible without destroying the plant, which can be further applied to map the disease in field scale.

Original language	American English
Title of host publication	Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019
Editors	John V. Stafford
Publisher	Wageningen Academic Publishers
Pages	339-346
Number of pages	8
ISBN (Electronic)	9789086863372
DOIs	https://doi.org/10.3920/978-90-8686-888-9_42
State	Published - 1 Jan 2019
Event	12th European Conference on Precision Agriculture, ECPA 2019 - Montpellier, France Duration: 8 Jul 2019 → 11 Jul 2019

Publication series

Name	Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019

Conference

Conference	12th European Conference on Precision Agriculture, ECPA 2019
Country/Territory	France
City	Montpellier
Period	8/07/19 → 11/07/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger

Keywords

Disease detection
Fusarium
Multispectral
Spectral analysis

All Science Journal Classification (ASJC) codes

Agronomy and Crop Science
Computer Science Applications

Access to Document

10.3920/978-90-8686-888-9_42

Cite this

Sandovsky, T., Edan, Y., Gad, S., Etzioni, A., Nacson, T., & Alchanatis, V. (2019). Early detection of Fusarium infection in corn using spectral analysis. In J. V. Stafford (Ed.), Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019 (pp. 339-346). (Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-888-9_42

Sandovsky, T. ; Edan, Y. ; Gad, S. et al. / Early detection of Fusarium infection in corn using spectral analysis. Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019. editor / John V. Stafford. Wageningen Academic Publishers, 2019. pp. 339-346 (Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019).

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title = "Early detection of Fusarium infection in corn using spectral analysis",

abstract = "This work presents a non-destructive methodology for early detection of Fusarium infection, by spectral analysis in the 350-2,500 nm range. Corn plants in greenhouse conditions were analysed using spectral analysis. The Lasso model was used to differentiate infected from non-infected plants based on the first derivative of leaf spectral reflectance. Fusarium infection was successfully recognized in plants at V2 growth stage with 74% success rate. This result enables infection detection at a stage which currently is not possible without destroying the plant, which can be further applied to map the disease in field scale.",

keywords = "Disease detection, Fusarium, Multispectral, Spectral analysis",

author = "T. Sandovsky and Y. Edan and S. Gad and A. Etzioni and T. Nacson and V. Alchanatis",

note = "Publisher Copyright: {\textcopyright} Wageningen Academic Publishers 2019; 12th European Conference on Precision Agriculture, ECPA 2019 ; Conference date: 08-07-2019 Through 11-07-2019",

year = "2019",

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Sandovsky, T, Edan, Y, Gad, S, Etzioni, A, Nacson, T & Alchanatis, V 2019, Early detection of Fusarium infection in corn using spectral analysis. in JV Stafford (ed.), Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019. Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019, Wageningen Academic Publishers, pp. 339-346, 12th European Conference on Precision Agriculture, ECPA 2019, Montpellier, France, 8/07/19. https://doi.org/10.3920/978-90-8686-888-9_42

Early detection of Fusarium infection in corn using spectral analysis. / Sandovsky, T.; Edan, Y.; Gad, S. et al.
Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019. ed. / John V. Stafford. Wageningen Academic Publishers, 2019. p. 339-346 (Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Early detection of Fusarium infection in corn using spectral analysis

AU - Sandovsky, T.

AU - Edan, Y.

AU - Gad, S.

AU - Etzioni, A.

AU - Nacson, T.

AU - Alchanatis, V.

PY - 2019/1/1

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N2 - This work presents a non-destructive methodology for early detection of Fusarium infection, by spectral analysis in the 350-2,500 nm range. Corn plants in greenhouse conditions were analysed using spectral analysis. The Lasso model was used to differentiate infected from non-infected plants based on the first derivative of leaf spectral reflectance. Fusarium infection was successfully recognized in plants at V2 growth stage with 74% success rate. This result enables infection detection at a stage which currently is not possible without destroying the plant, which can be further applied to map the disease in field scale.

AB - This work presents a non-destructive methodology for early detection of Fusarium infection, by spectral analysis in the 350-2,500 nm range. Corn plants in greenhouse conditions were analysed using spectral analysis. The Lasso model was used to differentiate infected from non-infected plants based on the first derivative of leaf spectral reflectance. Fusarium infection was successfully recognized in plants at V2 growth stage with 74% success rate. This result enables infection detection at a stage which currently is not possible without destroying the plant, which can be further applied to map the disease in field scale.

KW - Disease detection

KW - Fusarium

KW - Multispectral

KW - Spectral analysis

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M3 - Conference contribution

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SP - 339

EP - 346

BT - Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019

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T2 - 12th European Conference on Precision Agriculture, ECPA 2019

Y2 - 8 July 2019 through 11 July 2019

ER -

Sandovsky T, Edan Y, Gad S, Etzioni A, Nacson T, Alchanatis V. Early detection of Fusarium infection in corn using spectral analysis. In Stafford JV, editor, Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019. Wageningen Academic Publishers. 2019. p. 339-346. (Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019). doi: 10.3920/978-90-8686-888-9_42

Early detection of Fusarium infection in corn using spectral analysis

Abstract

Publication series

Conference

UN SDGs

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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Cite this