Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields

The characteristically clustered occurrence and low level of mobility of Heterodera schachtii and Rhizoctonia solani in the soil and the induction of stress symptoms in the sugar beet canopy make them ideal targets for site-specific arrangements with precision agriculture tools. A field site infested with H. schachtii and R. solani was investigated in 2009 with near-range and aerial hyperspectral sensors during the growing season. At 31 sample points ground truth data for incidence and severity of the two organisms were collected and geo-referenced. Spectral vegetation indices computed from reflectance measurements obtained from two flight campaigns (AISA, 17th of June; HyMap, 28th of August) and the near-range spectroradiometers were significantly correlated (P < 0.01) with symptoms caused by the nematode or Rhizoctonia crown and root rot. A supervised classification with Spectral Angle Mapper of leaf symptoms induced by the organisms resulted in a classification accuracy of 72 and 64% for the AISA and HyMap data, respectively. The results demonstrated that remote sensing in combination with geographic information system technologies can be used effectively for the detection and mapping of symptoms caused by beet cyst nematode and Rhizoctonia crown and root rot.

► Symptoms of R. solani and H. schachtii discriminated by leaf reflectance. ► Vegetation indices calculated from near-range and aerial hyperspectral reflectance. ► Vegetation indices significantly correlated to ground truth pathogen ratings. ► Supervised classification of symptoms resulted in high overall accuracy.