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Digitalization and intelligence play a crucial role in facilitating the high-quality development of the kiwifruit industry. Unlike other fruit trees, kiwi trees are vine plants that require abundant mineral nutrients during their key growth period. Inadequate management can easily lead to nutrient deficiencies, which not only affect the health of the trees but also impact the yield and quality of kiwis. Therefore, real-time monitoring of tree growth health is essential. To achieve fast and large-scale monitoring of overall growth and health in kiwi orchards, the drone was used to capture multispectral images of orchards, and then Pix4Dmapper software was utilized to splice UAV multispectral images for orthophoto maps and radiation correction on canopy leaves. The segmented orthophoto images were used as samples from 420 regions. The maximum inter-class variance (Otsu) method was employed to segment canopy leaves from soil backgrounds in the sample images, enabling measurement of canopy SPAD values for constructing a multispectral dataset. Firstly, outliers within the dataset were detected by using box plot analysis and subsequently removed as abnormal samples. Next, based on data characteristics derived from multi-channel images, feature values such as change rates between adjacent channels and 23 kinds of common vegetation indices were extracted, as well as their combination, to serve as sample feature values. Then three feature screening algorithms, including CARS, LARS, and IRIV were applied to optimize these features accordingly. Eight modeling methods, partial least square regression (PLSR), support vector regression (SVR), ridge regression (RR), multiple linear regression (MLR), extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator regression (Lasso), random forest regression (RFR), and Gaussian process regression (GPR), were employed to construct models for identifying canopy chlorophyll content in macaque peach orchards. Finally, the performance of the 24 models constructed with different sample features was compared and analyzed. The experimental results showed that GPR model had the best performance among the models based on the change rate of adjacent channels, R2 and RMSE were 0.770 and 3.044, respectively. Among the models based on the combination of vegetation index and adjacent channel change rate, GPR model also had the best performance, R2 and RMSE were 0.783 and 2.957, respectively. The XGBoost model based on vegetation index was the best among all models, R2 and RMSE were 0.787 and 2.933, respectively. Consequently, the intelligent detection model utilizing UAV remote sensing enabled accurate assessment of orchard canopy chlorophyll content while facilitating analysis of orchard health status to provide decision support for subsequent intelligent orchard management.