Abstract: To address the issue that the detection of the orchard canopy is easily interfered by environmental factors such as sunlight, rain, fog, and wind, and it is difficult to achieve precise extraction of canopy information throughout all-weather environment. This study conducts research on the extraction of orchard canopy information based on millimeter-wave radar. This study uses millimeter-wave radar to extract canopy information, builds a multi-module collaborative data acquisition platform based on millimeter-wave radar, and completes multi-source data fusion with the STM32F407ZGT6 microcontroller. At the same time, it is equipped with adjustable-speed diaphragm pumps, ring-shaped atomizing nozzles, axial fans to simulate the all-weather environment. Based on the point cloud parameter characteristics of millimeter-wave radar, through point cloud data fusion and preprocessing, a combined algorithm of adaptive DBSCAN and Alpha-shape based on the variable-axis ellipsoid model is proposed. Through adaptive clustering segmentation and three-dimensional reconstruction, it solves the problem that traditional algorithms require manual judgment and input of three global parameters, such as the neighborhood radius Eps, neighborhood density threshold Minpts, and rolling ball radius α. The measured values are the artificial measurement results of plant height, crown width, and canopy volume, and the extracted results of the algorithm are used as the extracted values. The accuracy of target canopy information extraction is studied, and the canopy information is extracted under all-weather climate conditions. The results show that compared with the traditional DBSCAN algorithm and Alpha-shape algorithm and the single improved adaptive algorithm, the three-dimensional reconstruction algorithm of orchard canopy based on millimeter-wave radar has stronger adaptability, and the three-dimensional reconstruction effect is the best. Compared with the artificial measurement results, the root mean square error RMSE of stem height, canopy width, and canopy volume extraction results is 2.99 cm, 2.44 cm, and 0.07 m³, respectively, and the average relative error MRE is 3.38%, 4.11%, and 12.82%, respectively, and the determination coefficient R² is 0.89, 0.91, and 0.57, the orchard canopy information extraction results are reliable. In all-weather environment, the extraction results of orchard canopy information under different illuminations, spray volumes have no significant effect. Different wind speeds have no significant effect on the extraction results of plant height, but have a significant effect on the extraction results of canopy width and canopy volume. This is mainly because the detection of dynamic targets by millimeter-wave radar is more sensitive than that of static targets. In the multi-frame data detected by millimeter-wave radar, due to the disturbance of the axial fan causing the branches and leaves to randomly sway, more canopy boundary point clouds are collected, resulting in an increase in the extraction values of fruit crown width and canopy volume. To accurately extract the canopy information under different wind speeds, the extraction results of crown width and canopy volume under different wind speeds are normalized and fitted to establish a two-stage function to eliminate the influence of wind speed on the extraction results. This study demonstrates that millimeter-wave radar can accurately extract canopy information under harsh orchard environmental conditions such as illumination, rain, fog, and wind, meeting the requirements for all-weather information acquisition in orchards and being of great significance for achieving precise management and operations in orchards.