Wavelet-based detection of crop zinc stress assessment using hyperspectral reflectance

Accurate detection of heavy metal-induced stress on the growth of crops is essential for agricultural ecological environment and food security. This study focuses on exploring singularity parameters as indicators for a crop's Zn stress level assessment by applying wavelet analysis to the hyperspectral reflectance. The field in which the experiment was conducted is located in the Changchun City, Jilin Province, China. The hyperspectral and biochemistry data from four crops growing in Zn contaminated soils: rice, maize, soybean and cabbage were collected. We performed a wavelet transform to the hyperspectral reflectance (350–1300 nm), and explored three categories of singularity parameters as indicators of crop Zn stress, including singularity range (SR), singularity amplitude (SA) and a Lipschitz exponent (α). The results indicated that (i) the wavelet coefficient of the fifth decomposition level by applying Daubechies 5 (db5) mother wavelets proved successful for identifying crop Zn stress; the SR of crop concentrated on the region was around 550–850 nm of the spectral signal under Zn stress; (ii) the SR stabilized, but SA and α had developed some variations at the growth stages of the crop; (iii) the SR, SA and α were found among four crop species differentially; and moreover the SA increased in relation to an increase in the SR of crop species; (iv) the α had a strong non-linear relationship with the Zn concentration (R2:0.7601–0.9451); the SA had a strong linear relationship with Zn concentration (R2:0.5141–0.8281). Singularity parameters can be used as indicators for a crop's Zn stress level as well as offer a quantitative analysis of the singularity of spectrum signal. The wavelet transform technique has been shown to be very promising in detecting crops with heavy metal stress.