Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola

Dustin Severtson,Christian Nansen,Nik Callow,Matt Olejnik,Andreas Neuhaus,Ken Flower

doi:10.1007/s11119-016-9442-0

Abstract

There is growing evidence that potassium deficiency in crop plants increases their susceptibility to herbivorous arthropods. The ability to remotely detect potassium deficiency in plants would be advantageous in targeting arthropod sampling and spatially optimizing potassium fertilizer to reduce yield loss due to the arthropod infestations. Four potassium fertilizer regimes were established in field plots of canola, with soil and plant nutrient concentrations tested on three occasions: 69 (seedling), 96 (stem elongation), and 113 (early flowering) days after sowing (DAS). On these dates, unmanned aerial vehicle (UAV) multi-spectral images of each plot were acquired at 15 and 120 m above ground achieving spatial (pixel) resolutions of 8.1 and 65 mm, respectively. At 69 and 96 DAS, field plants were transported to a laboratory with controlled lighting and imaged with a 240-band (390–890 nm) hyperspectral camera. At 113 DAS, all plots had become naturally infested with green peach aphids (Hemiptera: Aphididae), and intensive aphid counts were conducted. Potassium deficiency caused significant: (1) increase in concentrations of nitrogen in youngest mature leaves, (2) increase in green peach aphid density, (3) decrease in vegetation cover, (4) decrease in normalized difference vegetation indices (NDVI) and decrease in canola seed yield. UAV imagery with 65 mm spatial resolution showed higher classification accuracy (72–100 %) than airborne imagery with 8 mm resolution (69–94 %), and bench top hyperspectral imagery acquired from field plants in laboratory conditions (78–88 %). When non-leaf pixels were removed from the UAV data, classification accuracies increased for 8 mm and 65 mm resolution images acquired 96 and 113 DAS. The study supports findings that UAV-acquired imagery has potential to identify regions containing nutrient deficiency and likely increased arthropod performance.

Highlights

Arable soils in many broad-acre agricultural areas have become deficient in potassium (K) as a result of K removal in grain and hay (Brennan et al 2013; Zorb et al 2014)
This study, which was conducted over one growing season, demonstrated that field regions containing K-deficient canola plants were accurately classified relative to K-sufficient plants using canopy reflectance data acquired from a six-band multicamera array (MCA)
Significant decreases in classification accuracy when imaging dates were combined implies that spatial assessments for heterogeneity of K-deficiency are best achieved on individual imaging dates and temporal extrapolation is relatively inaccurate

Summary

Introduction

Arable soils in many broad-acre agricultural areas have become deficient in potassium (K) as a result of K removal in grain and hay (Brennan et al 2013; Zorb et al 2014). Information regarding the effects of K-deficiency on the performance of economically important arthropod pests would benefit agricultural production specialists by emphasizing the importance of K nutrition, not just for optimal growth and for plant defence against key arthropod pests and diseases. It is laborious, time consuming and costly to sample and test soils or plant material over large areas for concentrations of K in order to adequately map concentrations. There is a need to identify means other than direct soil and plant tissue sampling, in order to identify where nutrient deficiencies impose serious constraint to crop performance, and to investigate the links between K deficiency and arthropod infestation

Methods

Results

Discussion

Conclusion