Rapid detection of agricultural food crop contamination via hyperspectral remote sensing

In this study, the authors investigate the use of hyperspectral imaging for food crop monitoring and contamination detection and characterization. The authors investigate the use of a newly developed automated target recognition (ATR) system, that uses a combination of discrete wavelet transforms, multiclassifiers, and decision fusion, to effectively exploit the hyperspectral data to achieve high detection rates while maintaining low false alarm rates. The performance of the proposed hyperspectral ATR system is compared to ATR methods currently used in the remote sensing community, including those based on principal component analysis (PCA), discriminant analysis feature extraction (DAFE), and maximum-likelihood classifiers. The efficacy of both the proposed and conventional hyperspectral analysis methods are evaluated via an extensive 2-year field campaign, consisting of field-level experiments of corn and wheat exposed to highly controlled, varying levels of chemical contaminations. Both handheld and airborne hyperspectral data were collected at multiple times throughout the two growing seasons. The proposed ATR system provided very promising results, indicating the potential of hyperspectral remote sensing as an effective tool for detection and characterization of chemical contaminants in agricultural food crops.