Predicting copper content in chicory leaves using hyperspectral data with continuous wavelet transforms and partial least squares

Aiming to accurate and rapid prediction of leaf Cu content in Cichorium intybus L., a hydroponic experiment with three cultivars was performed from May to August 2020. Eight different Cu concentration treatments (0, 5, 10, 25, 50, 100, 200, and 300 mu mol/L) were established with four replications per treatment, and the in situ leaf hyperspectra (350-2500 nm) were taken on at four, six, eight and ten-leaf stages. Moreover, data from an independent experiment under 0, 25, 100, and 200 mu mol/L Cu stress conditions from July to August 2020 were also collected to test the transferability of the established optimal monitoring model for leaf Cu content prediction. Continuous wavelet transform (CWT) technique was applied to process the collected reflectance hyperspectra; partial least square (PLS) and lambda-lambda r(2) (LL r(2)) models were used to analyze the relationships between leaf Cu content and the hyperspectral reflectance. Results showed that CWT transformation technique can significantly improve the prediction accuracy of chicory leaf Cu content, and the best decomposition scales are CWT-3, CWT-4, and CWT-5. The PLS model for leaf Cu content prediction in the three decomposition scales yielded a relatively higher accuracy compared to the leaf raw spectra based on the full range hyperspectra, the residual prediction deviation (RPDval) was 5.386 for the CWT-3, 6.016 for the CWT-4, and 5.830 for the CWT-5, respectively. Finally, seven bands (735, 910, 1010, 1105, 1194, 1380, and 1590 nm) were identified as effective wavelengths for predicting the leaf Cu content of chicory. The newly-developed PLS model using the effective wavelengths also performed well for CWT-3 (r(val)(2) = 0.830; RPDval = 3.433), CWT-4 (r(va)(l)(2) = 0.848; RPDval = 3.583) and CWT-5 (r(va)(l)(2) = 0.804; RPDval = 3.136) leaf Cu content prediction. The overall results demonstrated the applicability and feasibility of the CWT and PLS algorithms for estimating the Cu status of chicory using in situ leaf hyperspectral reflectance data.

Automated summary

The study demonstrated that the continuous wavelet transform technique significantly improves the prediction accuracy of chicory leaf Cu content, while seven effective wavelengths were identified for leaf Cu content prediction. The developed PLS model showed good predictive performance at different decomposition scales.