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Aiming at measuring the number of main stem nodes of soybean plants quickly and efficiently, a statistical method of soybean main stem nodes was proposed based on image processing and clustering algorithm. Firstly, multiple perspectives of the soybean plants were obtained by using a camera, the initial image collection interval and sampling step were set, and then some of the plants were extracted as the train set and validation set. Secondly, through the operations such as plant segmentation, skeleton extraction, and denoising of the main stem nodes, the soybean main stem nodes to be detected were obtained. Meanwhile, the multiple dimensional scaling (MDS) was used to convert the data into a space which was easy to cluster. Then, the hierarchical densitybased spatial clustering of applications with noise (HDBSCAN) clustering algorithm was used to cluster the soybean stem nodes to be detected from multiple perspectives, and the recognition accuracy of the number of main stem nodes were recorded. Finally, the optimal collection interval was used to determine the number of main stem nodes of the remaining sample plants and conduct statistical analysis. The experiments were carried out based on the above method by using 63 samples which variety was called Zhonghuang 30. 21 plants were selected as the training set, and it turned out that, under the condition of 90° interval, and four soybean images were captured and fused with the minimum cluster of 2, the node number recognition results were mostly distributed in the effective range. To identify and analyze the main stem node number of the remaining 42 sample plants, the corresponding soybean main stem node number recognition accuracy rate can reach 98.25%. The experiment results showed that this method can meet the needs of soybean plant test requirements.