Fig. 1 Leaf moisture content of walnuts fruits at different growth stages The irrigation quotas for the control，W1，W2，W3，and W4 are 9 000，5 250，6 000，7 500，and 8 250 m3 · hm-2，respectively

Fig. 2 Removal of blade outliers

Fig. 3 Correlation coefficient between 21 original spectral preprocessing methods and leaf moisture content Each preprocessing method is detailed in Table 2

Fig. 4 Spectral data of walnut leaves with different pretreatments at‘Wen 185’walnut A：Raw spectrogram；B：First derivative pre-processing（FD）；C：Detrend preprocessing；D：Convolutional smoothing combined with first-order derivative preprocessing（SG + FD）；E：First derivative and standard normal variation combination preprocessing （SNV + FD）；F：Standard normal variation and detrend combination preprocessing（SNV + Detrend）

Fig. 5 The wavelength variable selection process of different preprocessing CARS algorithms a：Trend of changes in the number of variables；b：RMSECV value change trend；c：The trend of regression coefficient values for each variable

Fig. 6 Feature extraction results of different preprocessing CARS algorithms

Fig. 7 Principle of extracting feature wavelength from different preprocessed UVE A，C，E，G，I，K：During the screening process of each preprocessing band，the regression coefficients of each variable are obtained by adding white noise variables and using the PLS model cross leave one method，Two horizontal lines represent the upper and lower threshold lines filtered by UVE variables. B，D，F，H，J，L：Screening results of feature bands extracted by each preprocessing

Fig. 8 Feature wavelength results extracted from different preprocessed SPA A，C，E，G，I，and K are the screening processes for each preprocessing band，representing the number of variables selected by the SPA multiple linear regression model. The red square represents the number of variables，and the average standard deviation of the prediction is the smallest at this time；B，D，F，H，J，and L are the filtering results of each preprocessing band，where the red square marks the feature bands extracted through SPA（Continuous Projection Algorithm）

Fig. 9 Principle of extracting feature wavelength from different preprocessed BPSO A，C，E，G，I，K are the screening processes for each preprocessing band；B，D，F，H，J，L are the screening results of each preprocessing band； The change in fitness curve is directly proportional to the screening error. As the number of iterations increases，the fitness curve shows a downward trend，and the model error also decreases. When the error drops to the lowest value，the selected feature wavelength is the optimal feature wavelength

Fig. 10 Prediction results of different models constructed based on FD preprocessing and extraction of four feature bands