Technical Note: Crop stress detection using AVIRIS hyperspectral imagery and artificial neural networks
The objectives of this study were to compare the results of artificial neural network (ANN) and standard vegetation algorithm processing to distinguish nutrient stress from in-field controls, and determine whether nutrient stress might be distinguished from water stress in the same test field. The test site was the US Department of Agriculture's Variable Rate Application (VRAT) site, Shelton, Nebraska. The VRAT field was planted in corn with test plots that were differentially treated with nitrogen (N). The field contained four replicates, each with N treatments ranging from 0 kg ha−1 to 200 kg ha−1 in 50 kg ha−1 increments. Low-altitude (3 m pixel) Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery (224 bands) was collected over the site. Ground data were collected to support image interpretation. An ANN was applied to the AVIRIS image data for detection of crop and water stress. Known vegetation indices were used as a baseline for comparison against ANN-based stress detection. The resulting comparison found that ANN methods provided a heightened capability to separate stressed crops from in-field, non-stressed controls and was sensitive to differences in nutrient- and water-stressed field regions.
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