Preliminary Approach in Detecting Cotton Fleahopper Induced Damage Via Unmanned Aerial Systems and Normalized Difference Vegetation Indices

Abstract

The use of unmanned aircraft systems (UAS) delivering imaging technologies in agricultural settings has become more prevalent over the past five years and is growing in pest management programs. Here, spectral data from a three-band consumer-grade camera with a filter to obtain Near Infrared (NIR) data, mounted on a fixed-winged UAS, was used to assess the ability to detect cotton fleahopper, Pseudatomoscelis seriatus (Reuter) (Hemiptera: Miridae), injury to immature fruiting bodies on cotton. In a small plot experiment conducted two years and two planting periods each year, cotton fleahopper densities were manipulated with insecticide. Variable populations of cotton fleahopper across the plots were achieved in 2015, ranging between 0 and 3.5 cotton fleahopper-days over a five-week period when squares were forming. Derived from spectral data of multiple UAS flights, unexpected but inconsistent trends (by regression analysis) of increasing Normalized Difference Vegetation Index (NDVI), values with increasing cotton fleahopper days were detected in both plantings and years (five of 12 regressions were significant). Our preliminary data suggest that differences in cotton fleahopper activity on cotton may be reflected in NDVI values using a modified consumer-grade camera in-season. But the interpretation of NDVI may be complicated by the feeding site of cotton fleahopper, leading to unexpected and inconsistent regressions. Exploration of image resolution and bandwidth to define optical sensor needs appears important for cotton fleahopper, given its feeding habitat and injury to cotton. The application of UAS-derived remotely sensed data to detect insect-induced plant stress continues to have merit, but a merging of best suited UAS technology to the needs of detecting insect-induced cotton stress will be a research-intensive endeavor.

Early Detection of Potential Infestation by Capnodis tenebrionis (L.) (Coleoptera: Buprestidae), in Stone and Pome Fruit Orchards, Using Multispectral Data from a UAV

“…While there are existing studies exploring the correlation between remote sensing data and disease detection in crops, insufficient research has been conducted on the relationship between remote sensing data and the identification of pest damage in crops using machine-learning techniques. Moreover, in the majority of these studies, regression stands out as the predominant model employed [44][45][46]. The current study highlights XGBoost as a highly promising machine-learning model for predicting pest infestation in crops, but further investigation is warranted.…”