Detection of stress induced by potato leafhopper (Hemiptera: Cicadellidae) in green beans using multispectral imagery from unmanned aerial vehicle

Potato leafhopper, Empoasca fabae (Harris) is one of the common pests of green beans. In view of its wide host range, geographical distribution, and damage potential, it has been emerging as a key pest in several countries. Current monitoring methods for potato leafhopper (PLH) damage are time consuming and not always accurate. Detection of crop stress is one of the major applications of remote sensing in agriculture. Some studies have demonstrated the capability of remote sensing using Unmanned Aerial Vehicle (UAV)-based multispectral imagery for detection of insect stress on plants but none so far on PLH feeding damage on beans. By identifying stress conditions in plants before physical damage occurs, appropriate measures can be taken to mitigate the stress. However, remote sensing has often detected stress after the visible damage symptoms have become apparent. A study was initiated to demonstrate if the multispectral sensor attached to a drone can detect PLH stress in beans before the damage is visible. Small-plot trials were conducted in summer of 2022 where cages were used to manipulate PLH infestation in green beans (Provider cultivar) at their first-trifoliate stage. 2 PLH per plant were introduced into half of the cages keeping the other half insect-free. Canopy reflectance was captured using a multispectral sensor attached to the UAV. Our findings indicate that near-infrared reflectance could differentiate healthy beans from infested ones. These findings provide first documentation of PLH-induced stress in beans being detected from UAV-based multispectral imagery.