Abstract: 【Objective】 This project aims to use quantitative estimation of chlorophyll content in cantaloupe canopy leaves by spectral technique to provide theoretical basis for water and fertilizer control and field management. 【Methods】 The first derivative was used to preprocess the visible and near infrared reflectance spectra of chlorophyll in the range of 400 to 1,100 nm. Firstly, competitive adaptive weighted sampling (CARS), genetic Algorithm (GA) and Monte Carlo information-free variable elimination (MC-UVE) were used in feature selection, and then they were fused with Principal Component Analysis (PCA) at the same time. Considering that different models might produce different prediction results, the limit learning machine (ELM), the support vector machine and the least square support vector machine (LSSVM) were established to predict SPAD of muskmelon leaves quantitatively. 【Results】 The results showed that the optimal prediction model was CARS+SVM, correlation coefficient of correction set was 0.903,5, correlation coefficient of prediction set was 0.893,1 under the single feature selection and fusion of feature selection and feature extraction. The optimal prediction model was GA+PCA+LSSVM, the correlation coefficient of calibration set was 0.955,8, and the correlation coefficient of prediction set was 0.939,7. 【Conclusion】 The optimized model can be used for the quantitative analysis to achieve the accurate determination of chlorophyll content in muskmelon leaves.