Abstract: An accurate and rapid detection of the orchard canopy is easily interfered by the environmental factors, such as the sunlight, rain, fog, and wind. It is often required to extract the canopy information under all-weather environment. This study was conducted on the extraction of the orchard canopy information using millimeter-wave radar. A multi-module collaborative data acquisition was also established to realize the multi-source data fusion. The STM32F407ZGT6 microcontroller was equipped with the adjustable-speed diaphragm pumps, ring-shaped atomizing nozzles, and axial fans, in order to simulate the all-weather environment. The point cloud data was performed on the fusion and preprocessing, according to the point cloud parameter of the millimeter-wave radar. A combined algorithm of the adaptive DBSCAN and Alpha-shape was proposed using the variable-axis ellipsoid model. The conventional algorithms were often required the manual judgment and the input of three global parameters, such as the neighborhood radius Eps, neighborhood density threshold Minpts, and rolling ball radius α. The plant height, crown width, and canopy volume were measured to extract the values after adaptive clustering segmentation and three-dimensional reconstruction. The accuracy of the extraction was evaluated on the target canopy information under all-weather climate conditions. The three-dimensional reconstruction of the orchard canopy shared the stronger adaptability, compared with the conventional DBSCAN, Alpha-shape and the single adaptive algorithm. The best performance was achieved in the three-dimensional reconstruction. Compared with the measurement, the root mean square error (RMSE) of the stem height, canopy width, and canopy volume extraction was 2.99 cm, 2.44 cm, and 0.07 m³, respectively. The average relative error (MRE) was 3.38%, 4.11%, and 12.82%, respectively, and the determination coefficient (R²) was 0.89, 0.91, and 0.57, respectively, indicating the reliable extraction of the orchard canopy information. There was no significance in the extraction between the spray volumes and the orchard canopy information under different illuminations in all-weather environment. Different wind speeds shared no significance on the extraction of the plant height. But there was the significance on the extraction of the canopy width and volume. The detection of the dynamic targets by millimeter-wave radar were more sensitive than that of the static ones. The multi-frame data was detected by the millimeter-wave radar, because the disturbance of the axial fan was caused the branches and leaves to randomly sway. More point clouds of the canopy boundary were collected to increase the extraction values of the fruit crown width and canopy volume. The extraction information of the crown width and canopy volume were normalized under different wind speeds. A two-stage function was established to remove the influence of the wind speed on the extraction. All-weather information acquisition was also realized to accurately extract the canopy parameters under harsh orchard environments, such as illumination, rain, fog, and wind. It is of great significance for the precise operations in orchards.