Literature DB >> 30727449

Remote Sensing for Assessing Rhizoctonia Crown and Root Rot Severity in Sugar Beet.

Gregory J Reynolds1, Carol E Windels2, Ian V MacRae3, Soizik Laguette4.   

Abstract

Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani AG-2-2, is an increasingly important disease of sugar beet in Minnesota and North Dakota. Disease ratings are based on subjective, visual estimates of root rot severity (0-to-7 scale, where 0 = healthy and 7 = 100% rotted, foliage dead). Remote sensing was evaluated as an alternative method to assess RCRR. Field plots of sugar beet were inoculated with R. solani AG 2-2 IIIB at different inoculum densities at the 10-leaf stage in 2008 and 2009. Data were collected for (i) hyperspectral reflectance from the sugar beet canopy and (ii) visual ratings of RCRR in 2008 at 2, 4, 6, and 8 weeks after inoculation (WAI) and in 2009 at 2, 3, 5, and 9 WAI. Green, red, and near-infrared reflectance and several calculated narrowband and wideband vegetation indices (VIs) were correlated with visual RCRR ratings, and all resulted in strong nonlinear regressions. Values of VIs were constant until at least 26 to 50% of the root surface was rotted (RCRR = 4, wilting of foliage starting to develop) and then decreased significantly as RCRR ratings increased and plants began dying. RCRR also was detected using airborne, color-infrared imagery at 0.25- and 1-m resolution. Remote sensing can detect RCRR but not before initial appearance of foliar symptoms.

Entities:  

Year:  2012        PMID: 30727449     DOI: 10.1094/PDIS-11-10-0831

Source DB:  PubMed          Journal:  Plant Dis        ISSN: 0191-2917            Impact factor:   4.438


  2 in total

1.  Field Detection of Rhizoctonia Root Rot in Sugar Beet by Near Infrared Spectrometry.

Authors:  Leilane C Barreto; Rosa Martínez-Arias; Axel Schechert
Journal:  Sensors (Basel)       Date:  2021-12-02       Impact factor: 3.576

2.  Surveying soil-borne disease development on wild rocket salad crop by proximal sensing based on high-resolution hyperspectral features.

Authors:  Angelica Galieni; Nicola Nicastro; Alfonso Pentangelo; Cristiano Platani; Teodoro Cardi; Catello Pane
Journal:  Sci Rep       Date:  2022-03-24       Impact factor: 4.379

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.