The effective detection of leaf area of target canopy in the orchard is the basic for the online calculation of the pesticide application rate. A three-dimensional test platform for leaf area measurement and a light detection and ranging (LiDAR) detection mobile test platform were built. Tree targets of different thickness and density were constructed for the two canopy types of dense and sparse tree targets. Partial least squares regression (PLSR) algorithm and back propagation (BP) neural network algorithm were used for canopy leaf area detection model among the number of LiDAR point clouds data, canopy thickness and canopy leaf area. The experimental results showed that the coefficients of determination (R2) of the equations of dense thick canopy, sparse thick canopy, dense thin canopy and sparse thin canopy obtained by PLSR algorithm were 0.9626, 0.4130, 0.8896 and 0.2699, and the R2 obtained by BP neural network of the canopies were 0.9727, 0.5302, 0.8993 and 0.4290, respectively. Based on the LiDAR canopy leaf area detection model, the detection accuracy of the dense canopy was high, the value of R2 was not less than 0.8896, and the detection accuracy of the sparse canopy was relatively poor, which was not higher than 0.5302. Comparing the PLSR algorithm and the BP neural network algorithm, the latter can significantly improve the accuracy of the model, and the R2 value can be increased by 0.1591. The proposed three-dimensional space tree target canopy leaf area detection method can be used to calculate dense canopy leaf area online to guide orchard accurate variable spraying.