Potato leafhopper infestation and nitrogen deficiency detection in green beans using drone-based multispectral imaging

Remote sensing has emerged as an innovative method for detecting plant stress and is increasingly utilized in IPM applications. Numerous studies have confirmed the effectiveness of drone-based imaging in scouting agricultural crops for nutrient stress and pest infestations. However, its potential for detecting nitrogen (N) deficiency and potato leafhopper (PLH)-Empoasca fabae (Harris)- feeding stress in green beans has not yet been explored. PLH is a significant pest, known for its extensive host range and high damage potential. Current PLH monitoring methods and laboratory techniques for detecting nitrogen deficiency are both time-consuming and expensive. Remote sensing could offer an efficient alternative monitoring tool for these plant stress factors. To address this need, a study was initiated to assess the capability of a multispectral sensor attached to a drone in detecting PLH stress and N deficiency in beans Phaseolus vulgaris. Small-plot trials were set up, using cages to control PLH infestation in green beans- at their first-trifoliate stage. Half of the plots were infested with PLH, while the other half were kept insect-free. These two levels of PLH infestation were combined with two nitrogen treatments: commercial-level N applications and no N application. Canopy reflectance data was captured using a five-band multispectral sensor. Our findings demonstrate that drone-based imaging can effectively detect both N deficiency and PLH feeding damage in beans.