Chlorophyll content is a crucial indicator for characterizing vegetation growth.In this study,we utilized high-spectral technology to rapidly monitor the chlorophyll contents of cotton leaves.We collected 125 cotton leaf seedling samples from Xinjiang and measured their chlorophyll content and spectral data.To achieve this,we employed various spectral preprocessing techniques and used a combination of vegetation indices.Subsequently,we constructed a whale optimization algorithm/random forest regression(WOA-RFR)quantitative inversion model for cotton leaf chlorophyll content.Finally,we conducted a comparative analysis,contrasting the results of the WOA-RFR model with those obtained from the support vector regression(SVR)and RFR models.The results indicated that the spectral transformation methods(logarithm transformation,fractional order differentiation,and wavelet transformation)effectively improved the correlation between the vegetation indices and the chlorophyll content.We also found that the best inversion performance was achieved with the WOA-RFR model using a fractional order differentiation with a transformation order of 0.9 and the Vogelmann3,RVI,DVI,SR[675-700],Mndvi705,ND,VOG1,NVI,TVI,VOG2 combined vegetation indices.The model exhibited R2 values of 0.920 and 0.955 for the training set and validation set,respectively.The corresponding RMSE values were 0.987 and 0.986,while the MRE values were 0.013 and 0.014.Compared to the RFR and SVR models,the WOA-RFR model demonstrated higher predictive accuracy,and the optimization effect of the WOA algorithm was evident.As a result,this study provides valuable decision-making support for accurately quantifying cotton leaf chlorophyll content.
combination of vegetation indexcottonchlorophyll contentwhale optimization algorithm
国家科技期刊平台
NSTL
维普
万方数据
