Abstract
The analysis of chlorophyll concentration based on spectroscopy has great importance for monitoring the growth state and guiding the precision nitrogen management of potato crops in the field. A suitable data processing and modeling method could improve the stability and accuracy of chlorophyll analysis. To develop such a method, we collected the modelling data by conducting field experiments at the tillering, tuber-formation, tuber-bulking, and tuber-maturity stages in 2018. A chlorophyll analysis model was established using the partial least-square (PLS) algorithm based on original reflectance, standard normal variate reflectance, and wavelet features (WFs) under different decomposition scales (21–210, Scales 1–10), which were optimized by the competitive adaptive reweighted sampling (CARS) algorithm. The performances of various models were compared. The WFs under Scale 3 had the strongest correlation with chlorophyll concentration with a correlation coefficient of −0.82. In the model calibration process, the optimal model was the Scale3-CARS-PLS, which was established based on the sensitive WFs under Scale 3 selected by CARS, with the largest coefficient of determination of calibration set (Rc2) of 0.93 and the smallest Rc2−Rcv2 value of 0.14. In the model validation process, the Scale3-CARS-PLS model had the largest coefficient of determination of validation set (Rv2) of 0.85 and the smallest root–mean–square error of cross-validation (RMSEV) value of 2.77 mg/L, demonstrating good prediction capability of chlorophyll concentration. Finally, the analysis performance of the Scale3-CARS-PLS model was measured using the testing data collected in 2020; the R2 and RMSE values were 0.69 and 3.36 mg/L, showing excellent applicability. Therefore, the Scale3-CARS-PLS model could be used to analyze chlorophyll concentration. This study indicated the best decomposition scale of continuous wavelet transform and provided an important support method for chlorophyll analysis in the potato crops.
Highlights
Potato (Solanum tuberosum) is the world’s fourth-largest food crop following rice, wheat, and maize [1,2]
Results are shown in Fig Chlorophyll concentrations were measured from S1 to S4
We presented an effective method for analyzing the chlorophyll concentration of potato plants through canopy spectroscopy
Summary
Potato (Solanum tuberosum) is the world’s fourth-largest food crop following rice, wheat, and maize [1,2]. The crop analysis method based on spectroscopy primarily includes proximal spectroscopy analysis and remote sensing [10]. The former has advantages of high resolution and accurate data sampling [11]. It is suitable for the spectroscopy mechanism studies (e.g., the characteristic absorption bands of some material components) and the development of analysis algorithms, thereby laying a foundation for methods of large-scale and large-area remote sensing [12]. The motivation of this study is to accurately analyze the chlorophyll concentration in potato crops based on proximal spectroscopy
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