@inproceedings{4103bd43ccd44934a0cbc3a6eb06d7a2,
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",
month = jan,
day = "1",
doi = "10.3920/978-90-8686-888-9_42",
language = "English",
series = "Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019",
publisher = "Wageningen Academic Publishers",
pages = "339--346",
editor = "Stafford, {John V.}",
booktitle = "Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019",
address = "Netherlands",
}
