Drought Stress Affects Spectral Separation of Maize Infested by Western Corn Rootworm
2023
Raquel Peron-Danaher | Lorenzo Cotrozzi | Ali Masjedi | Laramy S. Enders | Christian H. Krupke | Michael V. Mickelbart | John J. Couture
Root-feeding herbivores present challenges for insect scouting due to the reliance on aboveground visual cues. These challenges intensify in multi-stress environments, where one stressor can mask another. Pre-visual identification of plant stress offers promise in addressing this issue. Hyperspectral data have emerged as a measurement able to identify plant stress before visible symptoms appear. The effectiveness of spectral data to identify belowground stressors using aboveground vegetative measurements, however, remains poorly understood, particularly in multi-stress environments. We investigated the potential of hyperspectral data to detect Western corn rootworm (WCR; <i>Diabrotica virgifera virgirefa</i>) infestations in resistant and susceptible maize genotypes in the presence and absence of drought. Under well-watered conditions, the spectral profiles separated between WCR treatments, but the presence of drought eliminated spectral separation. The foliar spectral profiles separated under drought conditions, irrespective of WCR presence. Spectral data did not classify WCR well; drought was well classified, and the presence of drought further reduced WCR classification accuracy. We found that multiple plant traits were not affected by WCR but were negatively affected by drought. Our study highlights the possibility of detecting WCR and drought stress in maize using hyperspectral data but highlights limitations of the approach for assessing plant health in multi-stress conditions.
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