Early detection of potato leafhopper damage on potatoes using drone imaging technology

The unmanned aerial system (UAS, known as drones) technology has been gaining an increasing attention in integrated pest management (IPM) applications. UASs can be employed as a new tool alongside traditional on-foot scouting techniques. The goal of this study is to investigate the practicality of small UASs outfitted with lightweight multispectral sensors as remote (digital) scouting instruments to detect plant stress caused by the potato leafhoppers (PLH) in Yukon Gold potato, Solanum tuberosum. Our study objectives are to; 1) identify specific and sensitive spectral wavelengths to PLH damage and to soil nitrogen, 2) model the relationship between the PLH damage and UAS imagery derived vegetation indices, and 3) evaluate the effectiveness of vegetation indices to discriminate PLH feeding damage from soil nitrogen stress. In field experiments, six different treatments were applied based on a randomized complete block design to a total of 36 plots measured at 6ft x 6ft each. The six treatments included no fertilizer, standard, low rates of nitrogen fertilizer with PLH infestations, low rates of nitrogen fertilizer without PLH infestations, and no fertilizer without PLH infestations. A handheld leaf spectrometer was used to acquire direct leaf reflectance concurrently with UAS imagery acquisitions. We also collected in-situ chlorophyll measurements. We will present the initial findings of spectral modelling of PLH damage based on two key vegetation indices: normalized difference vegetation index (NDVI) and normalized difference red-edge (NDRE) index. Based on our field experience, we will also discuss the opportunities and challenges associated with UASs in digital scouting.