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Laser flower thinning technology, as an emerging and promising technology in the field of smart orchard management, still faced critical challenges in optimizing laser parameters and achieving precise apple flower detection. Aiming at the optimization of key parameters in apple blossom laser flower thinning technology, a laser flower thinning test stand was designed, and the height of the test stand, the laser striking time and the PWM duty cycle were optimized by the orthogonal test method to obtain the optimal parameter combinations: a laser height of 20 cm,a striking time of 10 s and a laser power(PWM duty cycle) of 50% would achieve the best flower thinning effect. For apple flower identification and localization in laser flower thinning, the lightweight anl targeted-you only look once (LT-YOL0)apple flower detection model was proposed, the DPRVITBlock module based on ViTBlock and the DPRVBC2f module based on the C2f module were designed, and the ELA attention module of the DPRVBC2f module was added,which was applied in the feature extraction of the detection backbone and the detection head to enhance the apple blossom detection performance, validation focused on the accuracy,recall and average mean of the model were 83.16%,82.15% and 87.47%,respectively,compared with that of the YOLO v8 model it was improved by 5.04 percentage points,2.12 percentage points and 2.15 percentage poin1ts,respectively. The model size was 5.26 MB and the detection speed was 128/s,which met the accuracy and real-time requirements for use. The research result can provide a scientific basis for the further optimization and intelligent application of apple flower thinning technology.